Hi readers,

First, the big personal news — I’m married! Emma and I had a very small ceremony in the desert town of Moab, Utah, where we enjoyed a week of hiking, UTVing, eating, and dancing with our immediate family, and we’re back for a few weeks before heading out to our honeymoon in Japan. Thanks to all my readers/colleagues/friends for your kind notes!

But before the honeymoon, I have some “behind the scenes” projects to write about at a16z :)

Behind the scenes at a16z
It’s been fun to build in public and share the behind-the-scenes on my work at Andreessen Horowitz. This past year, the primary focus has been to incubate and launch our new games fund (I recently shared the pitch deck we used to raise $660M). I’ve learned a ton in our first year of investing, and I wanted to follow up on all of this to talk about a new effort we’ve launched called SPEEDRUN, the first startup accelerator focused on the intersection of Games x Tech — where we invest $500k in startups focused on everything from AI, AR/VR, web3, infra, game studios, and more. We target startups at the very earliest stages of development, often teams without products or metrics.

If this sounds interesting to you, please join the program!

Apply to SPEEDRUN 2024. (deadline 9/30/2023)

But before I get ahead of myself, let’s start with a little history.

What are the biggest problems founders face?
Back in 2020 when my colleague Jon Lai and I were starting to invest in the intersection of Games and Tech, we would often sit in a16z’s offices in Menlo Park and talk about some of the structural challenges of building a startup in the industry. Although the sector is 100% software and fully digital, it had its own quirks as it was its own self-contained community.

And also, founders are founders. They want to connect with each other, learn the latest, and they want to raise money. These are universal, but for our sector of Games x Tech, we noted a few concrete problems:

• There’s not really a startup community within Games x Tech. I’ve been investing in the sector for the past few years, and although there are plenty of companies, and some big conferences (like the Game Developers Conference), there isn’t really a place focused on founders and new startups. A lot of it is for very large game developers. So it would be *amazing* to be able to create that, so that folks can learn from each other
• There’s a lot of unique knowledge, and founders have a lot to learn from each other. The reality is, most of the best new strategies and techniques aren’t written down. And things are always changing. Founders have to learn from each other. And if you are in a specific industry, like the intersection of games and tech, then the knowledge and relationships are often even more obscure — from picking game engines to launching on Twitch to Discord, to what’s cutting edge in AI — there’s just a lot. So there’s an opportunity to bring people together.
• It’s great to support a large, diverse, set of first-time founders. When we see 1000s of startup ideas each year (and yes, it really is that many), you inevitably end up trying to back the tried-and-true founders that are starting their second or third company. Or wait for traction. Yet so much innovation comes from people trying for the first time. They have unique challenges — in particular quitting their job :) — and also finding cofounders, picking the idea, etc. By doing an accelerator where there’s a larger batch of companies, you inevitably are able to build more.
• Raising money is hard, but particularly so folks building a new game. There’s often fewer angel investors and seed funds to be the first dollar into the Games x Tech intersection. And founders may not be as familiar either — although very valuable companies like Roblox, Supercell, Riot, etc have been started, the industry has mostly focused on game publishers doing revenue shares and minimum guarantees to finance development. (Kind of like music, or movies). Venture capital isn’t as well understood or known, and founders often have a tough time connecting with angels and seed firms. And for tech companies that intersect with games (like a next gen Twitch, Discord, Unity, or that kind of thing) you often found yourself educating investors, who might not have the relationships and network inside the industry.

From the founders’ perspective, the above challenges presented major obstacles to building new startups. I’ve often remarked that it feels a bit like 2005 where there aren’t hundreds of angel investors and dozens of seed investors to get these new companies off the ground. There aren’t games-specific GPs at every major fund (whereas there are for SaaS!). And although Games have incredible cultural impact, it’s not well understood as an investment area.

However, in 2020 gaming was just one sector of several that I covered in the a16z consumer venture fund, and we couldn’t do much. However, with the creation of GAMES FUND ONE last year, we were able to think about these problems from scratch, and with it, we decided to run the experiment to solve some of these issues, by building an accelerator that combines education, community, capital and more.

We rallied the team inside a16z to begin putting together the idea, then announced it earlier this year. The result: 1600+ companies applied. Whoa. I ended up ending an email to the whole team saying, hey, this is all we’re going to be working on now :) and it’s going to take so much work we might all suffer some burnout. But it’s going to be fun. And then we started cranking.

An accelerator overview
Let me take y’all through some slides on how we describe SPEEDRUN to our founders.

First, we talk about SPEEDRUN as an accelerator because we want to target startups at the moment of formation, really pre-seed or seed. This has a ton of advantages, starting with the fact that $500k goes a long way. And we can bundle it with education, a community, and mentorship, so that people have a great experience. It also allows us to share the companies with a wide set of angels and seed investors, to build the ecosystem.

When we ran the first SPEEDRUN batch, it looked like this:

The offer is kind of a slam dunk to new startups. It’s a good bit of capital for a new team — $500k — and you don’t need a product launched or even built. The value prop is as much about the capital as solving the other problems I mentioned earlier on connecting with a founder community, learning the cutting edge, etc. So that was great, and we were pleased by how many companies applied.

One of the big lessons from the first batch was — oh man, it is a lot of work. We ended up taking the entire team and we basically all worked on SPEEDRUN for the entire two months we were putting it on. If any of you have been involved in organizing individual marketing events, this was like organizing 20+ in a row. This included speaker talks, social events, office hours, a kickoff and closing program, a demo day with hundreds of attendees, and more. It was a lot of work, but also very rewarding.

Above: SPEEDRUN ended up targeting founders who had worked either at games companies or in adjacent tech companies. We had 1600+ apply for SPEEDRUN 1 (and we’re at 2000+ for SPEEDRUN 2) and we ended up selecting a few dozen — so the conversion rate is well below 2%, more selective than a top university. But it wasn’t all based on resume, we also had very interesting people who just built really cool products with great traction, and we funded them based on their progress.

The way it works is pretty simple — someone has to actually go through thousands of applications :) And then you select out a subset to interview (probably <5-10% of the overall), and then you select down again until you’re <2% of the total number. This obviously means a crazy number of 15 minute interviews, and you have to be very selective, but it’s also incredibly fun to hear so many ideas and meet great folks.

Above: One of the primary draws was the amazing speakers we were able to bring into the mix. This included interviews and lectures from the creators/founders of Zynga, Valorant, Twitch, Supercell, King (aka the creators of candy crush!), and much more. We also had a bunch of fantastic lunches with folks like Ben Horowitz and Marc Andreessen here at a16z, which was fun for all the founders.

Above: The program we designed emphasized a structured program. This included frequent lectures on every aspect of creating a new startup, from fundraising to picking an idea to everything else. We layered on social events and a ton of time to connect with other founders and office hours with the a16z team.

Above: The finale was a Demo Day in front of many of the most important and influential companies and VCs in the industry. Over 80% of the startups were able to raise additional money, and we helped coach them, gave them tips on the follow-on process, and we ended up with a ton of great co-investors.

End to end, the program was just under two months, and by the end of it, the startups in the program had more capital, had built a community, and learned a ton of new skills for the next phase of their adventure.

Above: Ultimately, we declared SPEEDRUN 1 a success and doubled down with a new program for 2024. We made a bunch of changes, including putting a full-time team to focus on it. We increased the length of the program, committed $75M of our games fund towards supporting the companies, and made a ton of changes both big and small to make SPEEDRUN 2 even more of a success.

The aftermath
It was a blast to take an idea from 2020 that came up during a brainstorm, and turn it into a real thing a few years later once we had the resources. The past few quarters have felt the most like launching a new product as I’ve had inside of venture capital. It reminds me of the good old days when I actually designed and launched products for a living :)

As you can tell from my writeup on this, overall we consider this whole effort a success and it’s been a blast to put together. We’ll be doing this many more times, and adding this as one of our major investing motions — in addition to our usual big checks into Series A and B companies, as well as later stage companies, we’re now going to be operating an accelerator! Kudos to the entire a16z games team for making it happen, it was a huge team effort.

Expect more updates on this (and other fun new initiatives at a16z) over time on this blog. I’d love to share, as much as I can, my thinking as we navigate the idea maze of starting a new fund inside the games industry. Super fun. And finally, if you are a founder and interested to apply to a future SPEEDRUN program, here is the link to do that.

Hi readers,

Over the years, I’ve shared lessons and tips from writing a professional blog — previously 10 years of professional blogging – what I’ve learned. Recently, I’ve had some major changes in my workflow, and wanted to share more.

Pre-AI blogging workflow
My pre-AI workflow for blogging — what I’ve been doing for over years — looks something like this:

• Have interesting conversations at work or with friends/colleagues
• Randomly grab my phone, and email myself the title of a blog post
  • These have punchy opinions/titles that expand easily to blog posts – some examples:
    • “what needs to happen for web3 gaming to work”
    • “lessons learned from launching games that apply to web and mobile apps”
    • “why startups should ignore social media haterade”
    • etc
  • Recently, I’ve been using this app, Email Me, which is opens to a text box and it’s a one-button send (also nice for articles to read later, todo list items, etc)
• Later, when I’m doing email, I do one of two things:
  • If I’m motivated, start a blog post right away with the title
  • Or put it into my Notes app where I collect a long list of 100+ titles that are on the backlog
• I generally write on the weekends and try to do a full essay in one sit – usually take 2-3 hours
• Very very rarely do I go back and proof read or rewrite them (don’t blame me! I’ve been writing for hours!)
• Usually the essays are scheduled to go out Monday/Tuesday morning at 9am
• The day the post goes out, I do a light proof-read and then once it goes out, I write a tweetstorm directing traffic

This whole process has served me well for years. In the end, there’s no real magic to it — 99% of the battle is carving out a few hours on Sundays to write when there’s a many dozens of emails that need to be returned, Netflix shows to be watched, and games I should be “researching” for my job as a games industry investor.

But in the past year, I’ve experimented with changing my workflow to incorporate a number of new generative AI tools — particularly ChatGPT. I wanted to share some experiences with what it seems to be good at, and what it’s not.

Post-AI blogging workflow
After ChatGPT was released, I began to experiment with it in various ways — here are a few ways where it’s been useful.

First, writing from a blank page is hard. Sometimes it’s easier to just generate a first draft of something, even if it’s not great, just to get your creative juices going. I might use a prompt like this:

I’m writing a blog post about how Gen Z consuming more video, playing games, and other forms of rich and interactive content will mean that they will read less long-form text like newspapers and books. Write me a first draft of a 5 paragraph essay that explores this topic, but only use 2-3 sentences for the opening and closing so that it’s terse and punchy. Be opinionated.

And ChatGPT returns something that sort of makes sense? But reading the results more closely, it lacks examples and story-telling, and statistics or anything else. I don’t like Paragraph 3 here, since it misses an opportunity to talk about how much of YouTube is education purposes, for example. The writing is a bit too stiff and formal, and doesn’t match my own personal style.

In general, it’s just not that good.

But after staring at a page like this and finding all the flaws, I can then iterate pretty easily. I can put in a followup prompt to change out Paragraph 3 and connect it to a different point. Or I can just start editing it and rewriting it completely, and even if at the end 90% of it is different, it’s still better than starting with a blank page.

Brainstorming outlines, lists of topics and questions, and more
If ChatGPT is weak at actual writing, where I find it excels is as a non-judgmental brainstorming partner that doesn’t care if you have stupid ideas or if your writing is boring. It’s supremely useful for generating outlines of blog posts, making lists of topics, and creating a bunch of questions you can answer as inspiration, etc. In this use case, the AI doesn’t have to have a 100% hit rate on its content. If it creates lists and lists of content, but 20% inspires you in your actual work, then that’s a success.

For example, let’s say that you know you want to write a blog post about opportunities to build new apps in VR. You can ask something like:

I’m writing a blog post about new app ideas in VR. What are 15 questions that someone might ask me about this topic? Make them spicy, and if helpful, cite statistics to make the questions more interesting

It comes back with a mishmash of questions, some which are dumb and uninteresting — like the first, one, which is just asking what VR apps will be useful. But it also has a bunch of interesting ones on esports, content creation, corporate training, etc.

You could also imagine using this as a pretty helpful tool if you are making a new podcast and are interviewing guests. Or if you want to write a new essay that critiques some existing paradigm. (“Give me 10 questions from someone who is skeptical of of the value of AI safety”). And once you have an interesting question, it becomes easy to ask it to outline an entire blog post about it.

For example, I often find it useful to write prompts for outlines specifying specific topics and ideas.

Take the concept of K-12 education and VR – can you write an outline expanding on the promise and also skepticism around this idea? In particular touch on the concept of blending entertaining and gaming and learning, as one of the points. Create 6 sections, and put 3 sub-bullets under each. Use strong opinions and ask tough questions.

I’ll leave it to an exercise for the reader, but the result is not bad. It’s a decent starting point.

ChatGPT works great for outlines for long form writing also. And as many of you know, I wrote my book 3 years ago without the benefit of AI. The book is on network effects, and I ended up making an outline that acts in various stages, and uses examples from iconic tech companies. I can prompt ChatGPT with something like this:

Create an outline for a book on network effects for mobile apps and websites. Stage out the outline so that it starts on launching a product from zero, and the issues there, versus the stages of scaling the product, then hitting market saturation. Make sure you are able to weave in the issues facing network effects-driven products like social networks, marketplaces, dating apps, and collaboration tools. Write a 10 section book, with 3-5 sub-bullets. The first sub-bullet in each section should always be an example of an iconic tech company, like Uber, Airbnb, Dropbox, Tinder, Slack, or others. Please make the comprehensive outline for a book focused on network effects targeted at professionals working in the tech industry

The result actually isn’t bad. It’s not the way that I wrote it, since it starts to dive into each category of product rather than expanding on the broad concept. But again, it’s not a bad starting point. I could easily have imagined using this for brainstorming and prototyping purposes, iterating through different versions in describing the idea. It might have helped me work at more of a conceptual level in the early months, without getting dragged into the minutiae of writing all the various sentences.

Cleanup — at the beginning, and at the end
Finally, one of the tips I have for folks who are struggling with writing is to simply talk out their ideas aloud. Some of us are often more verbally wired, and we will connect disparate ideas and make interesting connections when asked a question, but flop when we’re just staring at a blank text. The voice feature in ChatGPT’s mobile app — and also products like Oasis AI — are interesting in that you can go stream of consciousness on a topic, and these products can clean them up significantly. The output isn’t that usable, IMHO, but you can then go and add/edit the result and develop it further.

It’s powerful when you combine this with AI-generated outlines. Just give ChatGPT a topic or better yet, an opinion (“open source products have bad consumer UX!”) and have it come up with an outline of the argument. Then start talking through the argument, and record it. Process it in one of the new AI apps, and then get it down to text. Then edit from there.

The other place where AI-driven cleanup can help is at the end, if you want to make sure you sound professional, or like an expert, and not an idiot. You can have ChatGPT rewrite content and incorporate a different tone. Or feed it some examples to cite and build off of. The ability to clean up ideas and textual content during and then after the creation flow is all pretty interesting.

There’s a bunch of things ChatGPT can’t yet do
I’m excited for all the ways that generative AI will help the writing process. I find edges to its capabilities that will soon be addressed, I’m sure. But here are some obvious ones that I notice all the time:

• I want it to have all the data, up to today (or in real-time), because I want it to use examples and cite numbers that are as recent as possible
• Some genAI tools can generate images, which is useful, since I want to create charts and figures, sketches of concepts, and even the little lead image that sits on top of each essay
• It’d be ideal if it was connected to Twitter and other social media platforms, so that you could tweet out content easily — writing tweetstorms via AI would be great!
• I want to train it on a corpus of my own writing, of course, and also writers that I respect. Right now it takes some work to customize the tone of voice to what I want
• There’s data, documents, and other info that I want to feed into ChatGPT, and then have it utilize the content as part of its arguments and suggestions. Imagine a routine task like, “hey, take this 10,000 word chapter and summarize it into a 100 entry tweetstorm” would be hugely helpful. Right now it can’t do that

There’s a lot more to go here, but it’s incredibly promising.

When I’m blogging these days, no wonder I find myself with WordPress open in one window, and then ChatGPT in the other. And I find myself spinning up new chats, copying and pasting back and forth. It’s the first time in 10+ years that I’ve found my workflow significantly change, and I’m excited to see what happens next.

There’s been a big wave of Creator Economy startups over the past few years, as the rise of social media platforms has empowered content creators to become a focal point for consumer engagement. This wave of startups promised creators that they could help them better monetize their audience on social media if they only promoted their products. We’ve all seen these — creators promote a startup’s new offering via a link in bio, or mentions in video or via links — and drive their followers to a landing page that enables some new interaction or functionality involving the creator. Initially, these started almost as “tip jars” but over the years, many, many creative products have been tried, spanning e-commerce to newsletters to Q&A, and more. These products all promised a win/win with creators so that when their fans spent money, the company would only take a % of earnings, usually something like 10% plus or minus.

There have been big successes, with some of these Creator Economy companies hitting billions of earnings paid to creators, while others have struggled. The successful creator startups are much more defensible than previously thought, and new entrants (often with splashy celebrity backing) have struggled to launch. Now that a few years have passed, what have we learned about the dynamics of this sector? Why have some Creator Economy startups worked and why have others lagged?

I have a few theories of the dynamics at play:

• The creator power law: A small, concentrated number of creators have all the audience, which makes Creator Economy startups potentially fragile and dependent
• Battle for the bio link: Creator economy companies acquire their audience from larger social media platforms that often just have one spot — the link in bio — to promote a single company. It’s a zero-sum game to overpower other companies
• The graduation problem: Startups often charge a take rate — % of bookings — and if the creator is acquiring own their customers and also doing the underlying work, they want to pressure you towards reducing costs. The biggest creators often “graduate” from a platform, building their own, and taking their revenue with them
• Algorithmic feast and famine: Creator traffic is driven by social feed algos, which lends itself to big spikes in traffic that appear and then go away — the opposite of the steady, durable growth that startups seek

These are all concepts that I’ve learned from meeting dozens of creator companies over the past few years. And as the next generation of Creator Economy startups emerges, these are some of the dynamics they’ll have to figure out how to navigate. Let’s jump in.

The creator power law
So you want to start a Creator Economy company? The biggest dynamic you have to master is the power law of audience and earnings within the creator class itself.

Here’s a graph that shows % that the top creator earns on a platform like Patron, versus the 2nd and 3rd and 4th creators, all the way down (credit: Power Laws in Culture). You can see there’s quite a dropoff:

Imagine if you graph this all the way out, to the many millions of creators on these platforms on the x-axis. You’d see that it eventually flattens just one tick above 0%. There’s a lot of reasons why this is the case, starting with the idea that these creator platforms build themselves on top of social media which themselves have well-documented power law distributions for followers and content engagement. In turn, social media platforms have power law curves because of algorithmic discovery, but a small number of social butterflies just know a lot more people than that.

Thus any creator economy product that builds on a social platform inherits these power law curves. OnlyFans creators offer free content on many social platforms that then drive traffic to their private landing pages. Below is a graph of creator earnings, which show a similar curve – via API scraping in the essay The Economics of OnlyFans – showing that while some creators earn up to $100,000/month, the median is closer to $180/month. A familiar curve emerges:

While power laws naturally emerge in social media platforms, that can’t be the only other explanation. The reason is that creative work — including TV, films, music, and more — generally follows a power law pattern. Here’s an example from TV, from the essay Power Laws in Culture (worth reading in its entirety):

A few hit shows get all the viewers. And if you look at video games, movies, fiction, directors, authors, and more:

There are a lot of things going on here that might explain the universality of this phenomenon, but one core issue is the uneven distribution of creative skills in the world. A top writer or film director is really just that much better than the 100th. You can look at research output, the distribution of so-called “10x engineers,” and patent filings, for a parallel universe of power laws as well.

So what does this mean for Creator Economy companies? Well, it means a few things:

• When Creator Economy companies first launch, the long-tail creators they initially attract are too small to be meaningful
• To hit scale, they need to attract the largest creators — the ones who are most likely to be distracted with many other projects and products
• And even once you have large creators on your platform, revenue is often quite concentrated into a small group — so that if they churn, the financial impact can be big and negative

These dynamics all mean that the initial phase of a startup’s launch can be perilous. The best companies can aggregate so many small creators that the numbers start to matter, or organically attract large/mid-sized players. If a startup finds itself manually DMing/acquiring/handholding many creators (read: high cost of acquisition and ongoing service), then that’s a sign that the product might not solve a big enough problem for things to happen on their own.

The battle for the bio link

Social media platforms like Instagram and TikTok have advertising business models, and as a result, they don’t want to give people *too much* organic traffic. Better they make you pay to sponsor posts, creators, and ads. One way they’ve done this has been to offer a single link for driving organic traffic — the infamous “link in bio” — that appears at the top of a profile.

This is insanely valuable real estate for Creator Economy startups. If you can convince a creator to place your startup into this link, then organic traffic will appear in your product. With some monetization mechanics in place, the startup takes its cut. And initially, it worked. Early in the Creator Economy cycle, startups were competing with non-monetizing links — either links to other social media profiles or personal websites. But as time went on, people began to fill their bio links with highly monetizing links to Patreon, Substack, Twitch, and otherwise — this is much fiercer competition.

It’s now a zero-sum battle to displace another startup’s link in bio. The only way to gain organic traffic from creator profiles is by monetizing better than other older, more proven competition. If you simply match what an incumbent might make you, then that’s not enough – it has to be significantly more. Or you have to find a different piece of real estate, whether that’s inside the creator content itself – whether that’s video, text, or otherwise. Either way, new entrants will find this a major barrier, and while they might be tempted to subsidize earnings with investor money initially, that may not be enough to reach a meaningful scale.

The graduation problem
The graduation problem is what happens when your best customers get big, and eventually “graduate” — taking themselves and their customers off of your platform. Why does this happen? Creators provide obvious value to startups — driving traffic, creating content, and monetizing their users — and that makes the Creator Economy model attractive. But work with creators long enough, and they often think to start to think it’s *too* attractive. They start to think, they’re doing all this work, what gives you the right to charge XX%? Why isn’t this a $99/month WordPress subscription, why do I have to pay a %? This is particularly problematic because of power law curves, where a small number of whales often dominate top-line revenue. If a whale starts to ask, couldn’t they replicate your product by hiring an agency and paying them to build a custom website, then there’s a huge temptation to drop take rates to accommodate them. They eventually are tempted to “graduate” from the platform, reaching sufficient scale to build their own platform.

Contrast this to marketplaces startups and the on-demand wave to which the Creator Economy is often compared. In that sector, a company like Airbnb or Uber aggregates both the supply and demand sides of the network independently. These 2-sided marketplaces work best when each side is highly fragmented, which is why the biggest outcomes have been consumer-to-consumer or consumer-to-SMB marketplaces, versus B2B. (More on this from an essay of mine from a few years back, What’s Next in Marketplaces). In their initial formation, Creator Economy startups look more like B2B networks or maybe even SaaS platforms — their customer bases (the creators) are highly concentrated, and the creators bring their consumers. No wonder the frustration.

To overcome the graduation problem, Creator Economy startups have to provide a significant amount more value than the utility of payments and other commoditized tech. They need to have a moat, not just for external companies but also for their own customers who are tempted to graduate over time. The best version of this is to create network effects on their own — by acquiring and cross-pollinating customers and bringing them to each creator, a 2-sided network forms, with all of its usual advantages. (I describe all these dynamics more in my recent book, The Cold Start Problem). The additional functionality that the startup creates should ideally be proprietary on its own. If an AI-enabled creator economy company develops a very good foundational model that allows creators to monetize 10x more than before, it’s unlikely the creator will ever leave.

Algorithmic feast and famine
Creator economy startups often find themselves highly dependent on the whims of social media platforms and on the hits-driven nature of viral content. If a video goes viral on TikTok, a big spike in user acquisition might ensue. But startups are always trying to grow steadily month by month, and unlike SEO or referral programs, or paid marketing, it’s hard to create a consistent march of 20% MoM growth. Compare this to marketplace startups, which add value by doing the work to aggregate each side of the market — often spending billions of dollars to build buyers and sellers. When I was at Uber, during the hypergrowth years, the annual performance marketing budget to acquire Uber riders was a billion, and the driver side was close to $2B, and that was diversified across SEO, brand marketing, paid, referral programs, partnerships, and otherwise. This added a ton of value since the two sides couldn’t connect otherwise.

Creator economy startups are different in that they use creators to find their customers, but in doing so, they are highly dependent on a single channel. A dependency on a single marketing channel is always dangerous, as we’ve seen in prior years where changes to SEO algorithms obliterated multiple generations of SEO-dependent content sites. A dependency on social media is even more fragile since the content is naturally more ephemeral and delicate. I think this is also one of the reasons why subscription (with upgrades) has become the dominant business model for successful Creator Economy companies — allowing creators to build a long-term, durable revenue stream from each follower is just much more stable than a transactional model. It’s just much easier to stack revenue over time this way.

Algorithmic feeds also play into a competitive factor. In recent years, we’ve also seen YouTube, Twitch, Twitter, and other underlying platforms try to go after directly paying creators themselves and playing a more vertically integrated role in the Creator Economy. As this happens, you could imagine a multitude of platform shenanigans where they try to hoard creator relationships at the expense of new startups.

The best solution here, of course, is to layer on additional marketing channels to drive predictability. Combine a spiky social media channel with steady retention, an inflow of traffic from referral, SEO, mobile installs, and otherwise, and the growth curve becomes much more durable. But in the early days of a Creator Economy startup, they’re often going all-in on social, and it’s only with success that they can choose to invest in the other channels.

The upside and the future
Creator Economy companies are going through their second and third generations of startups. The bar has gotten higher. Instead of providing functionality akin to fancy tip jars, startups are building full-blown products — supporting multiple platforms, new forms of interaction, and providing new functionality for creators to interact with their followers. These products will have network effects of their own, sometimes becoming destinations of their own. And instead of launching a product anchored by one celebrity and expecting it to succeed, instead, startups are building real technology — often involving AI — combined with a broad go-to-market strategy.

The upside of this sector is that mobile use, and thus social media platforms, continue to grow incredibly fast, taking time away from the hours that people used to spend watching TV:

A lot of this movement is of course driven by younger generations:

(btw, can you believe that most 18+ people still watch 4-5 hours of TV a day?)

The point is, social media continues to play a huge role, and creators are ultimately a new class of participants in the economy that continue to gain power in both cultural and economy terms. And the products and tooling they use to fulfill their goals will continue being attractive. This is especially true because in the end, creators don’t want to be dependent on one social platform themselves — if they are strong in video, they want to go to podcasting, and to have a huge Instagram. And startups can always seek to be friendlier to the creators than the mega-social platforms.

Thus, I argue that the future of the Creator Economy continues to be promising, but the approach has significantly evolved and the bar has been raised. Startups will need to provide new functionality, create new forms of monetization, and adopt new technologies that make them more defensible to competition and in-house efforts by creators to replace them. Personally, I’m much more interested in Creator Economy startups that are AI- or video-first, and act more like marketplaces in providing a highly managed solution to both sides. I’m more bullish about startups that know how to collect $1000 from a smaller niche of users — thus creating more value — rather than a tip jar model that collects $2 from everyone. In coming years we will see many more variations that will work, and given the underlying consumer trends, I’m bullish this will remain a source of highly valuable startups.

A simple question
The last few years have been crazy, and no wonder there’s a ton of folks thinking about making job changes right now. I know this since I’ve been getting the calls. Often the conversations open with a laundry list of different companies, roles, and compensation packages. Every opportunity is completely different and hard to compare. There’s got to be a better way to organize your thinking about these opportunities.

Here’s my favorite question to ask:

“What do you want to be your next next job? And why can’t you get it right now?”

And then, of course, you work backward from that. This is the “Next Next Job” framework for thinking about career moves, particularly in the highly chaotic situations that we find ourselves in today where there are many many opportunities across different industries and company stages. This reflects the very natural flow of the recruiting process, where recruiters and colleagues often make referrals across a wide swath of companies that are making. It’s always fun to talk through the various roles, but also it feels chaotic.

I know how it feels because, of course, I’ve faced this exact situation before.

The Next Next Job is an evaluation framework that I used myself many years ago, to make an important decision: As an early 30-something-year-old, at the tail end of a startup adventure that had gone awry, I had a big decision to make. A few months after putting my startup team/myself on the market, I was choosing between several very strong acquisition offers at pre-IPO startups. Each had its idiosyncratic benefits — some of the team cultures were a better fit for me than others and in others, I had a stronger connection to the founder. The packages were also very different. It was an emotional rollercoaster to meet dozens of companies over several months, and then need to choose amongst them.

It was tempting to pick based on a gut reaction, but I felt like there must be a better way. I sought a more analytical approach to augment the rollercoaster. I have tremendous gratitude to my close friend Bubba Murarka who coached me through all the conversations. Once the offers came in, he challenged me to stack rank the opportunities based on my “next next job” — almost a throwaway comment — but something that’s stuck.

How to answer a simple question
Let’s go back to the question – “what’s your next next job, and why can’t you get it today?” — it’s straightforward to ask, of course, but surprisingly hard to answer. Often we don’t know what we don’t know.

The first is that we often don’t know what our next next job might be — after all, if it’s unclear what the next job is, speculating about the next next job seems even more nebulous. Yet there’s an advantage here because you can make a few pretty big buckets of next next jobs. Or you can at least start to, based on what you know today.

It might look something like this:

• 50% – Become a startup investor
• 30% – Start another company
• 10% – Join a high-growth startup as a C-level exec
• 10% – Random stuff? (Switch into a new cutting-edge industry, become a blogger/writer, etc)

For someone who’s earlier in their career and the product management function, the goals might be more focused on becoming a first-time manager of PMs, becoming “employee 1” of a high-potential startup, or getting accepted into YCombinator, or something like that. Others might be thinking about transitioning from a non-tech role into a tech job or maybe going from a non-product role into becoming a PM/designer/eng.

Of course, sometimes it’s not obvious what other roles might be interesting or appealing — this in itself can be a useful thing to focus on when meeting with mentors and colleagues in the industry. But assuming you have some pretty big buckets to think about, the next step I’d encourage you to do is to pick the top 2-3 of these and do your research. Meet as many people as you can who have your next next job. What were their career paths? What did they need to accomplish before they could get the job? And you can ask them straight out — “what are the gaps in my skills that I need to fill, to get your type of role?” Keep asking questions and meeting people until the answers start to sound pretty similar, and the delta of new information decreases substantially.

Sometimes there’s a shortcut (and sometimes there’s not)
A funny thing sometimes emerges, particularly for people who rank “start a new company” as their next next job — it turns out they’re already qualified. Some of these jobs have high degrees of emotional baggage, because of Imposter Syndrome and not feeling ready. But the reality is, sometimes people over-prepare for a future job out of a deep sense of risk aversion. These are folks who are getting as credentialed and qualified as possible, rather than jumping in. These are the “wantrepreneurs” who are wasting their time getting multiple advanced degrees, working at all the top companies, and who are often very smart — but just can’t bring themselves to actually do something on their own. Usually, when this is one of my friends/colleagues, I try to talk them into taking the largest degree of risk possible :)

On the other hand, often the next next job isn’t attainable and it’s for good reasons. Maybe you’ve only worked at a series of failed startups, and you need a “shiny” role or two that helps add some credentialing. Or perhaps you’re in marketing and interested in becoming a PM but aren’t yet close enough to the engineers and the technical details. Perhaps you’ve never managed anyone, and want a role to demonstrate strong managerial ability before jumping into a team lead role. Identifying these gaps can help form the basis for evaluating potential job opportunities — which ones help fill them better and faster.

Gaps might encompass a number of things — skills, but also network, experiences, mentors, and ideas:

• What new skills do I need for my next next job?
• Is there a new network of people that would help me?
• Are there experiences that I need to demonstrate to land the next next job?
• Which mentors do I need, and how would I meet them?
• How do I get exposed to the ideas that might inspire me in the future?

Understanding these gaps are great, but that’s just playing offense. A “superpower” is often important, and there are superpowers that are so important that they overcome an imperfect set of gaps. I sometimes talk to folks who are interested to get into investing, and in the end, you can check off every skill on the list, but unless you have a specific superpower I care about — getting in the flow of new startups we’d be interested to meet and invest in — it doesn’t matter how good your analytical skills are, or that you attended fancy schools. If you have an incredible network of founders who seek you out, you can learn some of the other skills. For your industry and specialization, figure out the superpower that might trump everything else. Think offense (building up a superpower), not just defense (filling in gaps).

I want to give an example. For someone interested in investing as their next next job, and have substantial internal-facing roles at successful startups, I often find the list looks something like this:

• The next next job: Become a professional investor
• Gap: Need to develop a personal brand for other external-facing networks
• Gap: Haven’t done any angel investing
• Gap: Need to develop opinions on cutting-edge spaces
• Potential superpower: Get in the dealflow of recent spinouts/alumni of my prev companies

Again, this is just a hypothetical example – you can run your analysis and figure out what you need to do to close some gaps and develop a superpower. Of course, if you are tracking 2-3 options for next next jobs, you might find that a few gaps appear and re-appear. That’s great! This means the next role that helps you develop against that should be weighted more heavily.

Evaluating the grab bag of jobs
Now we can go back to the original question — “what do you think of XYZ as a company, and should I take this ABC role there?” The approach then becomes more obvious. First, you need more than one option. Go run a process, meet enough people, and look broadly at enough companies that you have different options to compare. Building up these options — while the opposite of instant gratification — will mean that you can truly make a good decision.

Then work backward on your options. Which ones are best at filling your gaps, and what will help you develop a superpower? Or if none of the options do much, then be patient. Develop more options. I think a lot of the reason why we often see a long series of 12-month stints on resumes is that we live in a world of instant gratification — whether that’s short videos for instant entertainment, on-demand food, groceries, cars, or online dating. But when it comes to making a big career decision that might commit you to work somewhere for (ideally) multiple years, the quality of the decision is important. Taking your time is key.

Of course, in the end, this big decision is very emotional. I really believe that. There are a ton of little things that go into getting excited about a new role: You’ll need to vibe with your manager, and you’ll want to like the work. But at the same time, having a bit of an analytical framework behind your decision will help. It might ultimately be 80% emotional and 20%, but I think that’s still better than 100% emotional rollercoaster.

To wrap up my story, almost ten years ago, I decided to go to Uber rather than the other very good options on the table. The background was that I knew it was likely I’d want to become an investor one day, and that pointed toward Uber as the right choice. The theory was that Uber would be a great place to meet future founders, would have problems at scale, and would be a very interesting place. And boy, was I right about that latter point :) It did OK on the other things I cared about — the scope of my role, the compensation package, etc — but I chose not to optimize for that. I knew it wasn’t my forever job. It was a stepping stone to the forever job I’d try to gain in startup investing, many years later.

The crisis arrives slowly, then all at once
At first, everything seems rosy. The growth rate of a new product is spiking, and growing quickly, maybe even hundreds of percentage points a year. But weirdly, a year or two in, there’s some softness in the latest numbers. Maybe it’s seasonality, or maybe something else. But worryingly, it keeps slowing. First to 300% a year, then 200%. Then 100% – a mere doubling annually in a startup ecosystem that demands a much faster target. More features are planned, and some are even shipped. Eventually, there’s a back-to-back where things are completely flat. What starts as a slow boil – where the team has a well-planned roadmap and a big vision – becomes a sudden crisis. There are late evening phone calls and emergency sessions. Analytics dashboards are pulled and re-pulled, to figure out what’s going on. The team needs a new plan.

There’s a saying that no military plan survives first contact with the enemy, and similarly — no product roadmap survives first contact with stalled growth. Instead, a crisis ensues, and the entire roadmap has to be rewritten. Particularly for startups, where continual growth is life and death.

When this crisis hits, the question is, what to do about it? How do you come up with a plan?

For better or worse, I’ve had this conversation with product managers and entrepreneurs many times over the years. The easy answer that people generally want to hear either falls into the camp of:

• This next magic feature will fix all our growth problems –The PM
• We need to spend more money on marketing –The Marketer
• Have you considered adding more AI? –The Investor

Don’t listen to these people :)

Instead, I offer the idea that you can analyze growth stalls systematically. You can ask questions, gather data, and assess the stall to zero in on the problems that are driving the metrics downwards.

Assessing the stall – starting with retention
First off, let me explain what’s happening during a growth stall. Yes, of course, it’s when a top-line number (like revenue or active users, or otherwise) stops growing. But what’s happening under the covers? At its core, a product stalls when its churn catches up with its customer acquisition.

I encourage y’all reading the entire thing, but I’ve written about this in the past in the deck The Red Flags and Magic Numbers That Investors Look For, which shows this growth of the underlying dynamics:

That is, a stall occurs when a product is churning enough users that it overpowers the counterforce – the product attracting new users and reactivating users (though this latter term is less important for startups). This happens because typically churn happens to a % of the user base, as anyone who’s seen cohort retention curves knows. But unfortunately, new customer growth channels tend to be fairly linear — most marketing channels don’t scale up as the user base scales up, and even the channels that do, like viral marketing, eventually saturate and slow down. All while churn continues to creep up over time as a percentage.

Because of these dynamics, I start by asking questions about retention to establish a baseline.

These questions are just a starting point, because once you ask them, the question is — what do you do with the answers?

• What is the D1/D7/D30 of the product? (if consumer?) How does it compare to other products in its category?
• If it’s a workplace product, how many days per week does the typical user engage? (This is the Power User Curve)
• Are people as active and engaged as you expect them to be? If it’s a daily-use product, does your DAU/MAU ratio reflect that?

There are many benchmarks out there for all the product categories, but as a very rough guideline, you need a D1/D7/D30 of 60/30/15% to be at respectable numbers for a social app. You need DAU/MAU over 20%, and if subscription based, you want churn <5% if SMB (and free acquisition). There are equivalent numbers for net revenue retention, session lengths, and lots of other metrics too.

A marketplace company might look at a different set of metrics. Often the demand side can have heavy churn, but the supply side should retain well (>50% YoY). An enterprise SaaS product would have its own set of metrics. It’s important to benchmark, to see if there are successful products with similar metrics that have gotten to scale. If you have similar numbers, then probably these underlying retention metrics are not the problem.

Let’s look there first, but understand that you might find a devastating truth.

Admit it when people don’t want your product
There’s an ugly truth that when most products are put under a microscope, most of them simply don’t have the retention to sustain growth over time — this is “pouring water into a leaky bucket.” A slow growth rate is inevitable because products start at a mega disadvantage of needing to replace all their existing users who churn, in addition to building new marketing channels that grow the overall number significantly.

But “my product is not retaining” is also sometimes a fancy phrase for “people don’t want to use my product.” I say this because it’s a blunt way of stating what’s often true – that a new product is too experimental, too unpolished, or so poorly positioned, or underdeveloped, that no one wants to use it. I think this was especially a problem in the Web 2.0 days when folks would combine their favorite random set of product mechanics — disappearing text messages sent to strangers near you, but you can only reply with a video — and launch them as the latest app (Disappr! – gotta love those 2010 app names). When people don’t want your product, no amount of new customer acquisition is going to solve that. Yes, you can sometimes generate very fast growth rates for a few weeks or months, but eventually, it catches up to you. And then the product stalls, per the graph above.

Instead, when initial product/market fit is low (yes, another fancy way to say people don’t get it), I usually recommend the exercise of positioning more closely to existing product categories. As I argue in Zero to Product/Market Fit, any founder can instantly get to product/market fit by simply going after an existing category — of course, we all know how to build and design a coffee cup such that there’s product/market fit. You incur other problems, of course, such as competitive differentiation, but if you combine a well-known product category with innovation, and picking at the right time and place in the innovation cycle, it can work.

There are major questions to ask here:

• Does my product have a clear, successful competitor? Is there a there there? (and do I have strong differentiation?)
• When I ask people to describe my product back to me — without the jargon — what do they say?
• When I ask people during user tests what kind of people might use the product, and what they’d use instead, do the answers make sense?
• Do people actually like my product, or are they just being nice to me? And a famous question- is it a painkiller or a vitamin?
• Are there any well-known product categories I could position against? Is there a way for me to test that positioning in user testing or otherwise?
• Is my growth the fault of shitty retention? Or do I need better user acquisition?

When retention sucks, but you haven’t growth hacked yet
What if retention sucks, but you haven’t added email notifications yet? What if you can just do a big marketing push, and that might spike the numbers? I can tell you as someone who has seen many underlying metrics for a wide variety of products, moving the retention number is the very hardest thing to move. Usually, the initial numbers are a ceiling, and it only goes down from there. So if your numbers are bad, don’t think that adding notification emails will solve it.

There is a very very narrow set of situations where I will take this back:

First, long-term retention is often most improved by better initial user activation. A few years ago, in Losing 80% of mobile users is normal, and why the best apps do better, I show that the biggest difference in the retention curves of the best apps and mildly good apps wasn’t as much in their long-term retention curves, as much as their ability to get the numbers in the first 7 days up higher than others. So I often will ask the question to product leaders- what differentiates someone who’s activated versus not, in your product? What % of users become activated? And how do you make that 100%?

Second, there’s a narrow class of products that have network effects — social apps, workplace collaboration tools, dating apps, marketplaces, etc — and they will often have a “smile curve” when retention goes up as time passes, and the network fills in. I wrote a whole book about this so I won’t belabor the point, but the main point is, if a product is more useful when more of your friends (or colleagues) are using it, then retention will naturally float up as the product grows. Thus, a product that has poor retention in the early days might just need more network density. For these situations, I might suggest the team do a completely manual, hands-on build of a network — launching at a high school or a single office — and measure retention there. Sometimes it’s much higher, which means there’s a there there, and the product just needs to be launched in a network-by-network manner as some of the great companies have done via college campuses, cities, workplaces, or otherwise.

Whatever you do, don’t fall for the idea that you can fix your retention by simply adding features:

The Next Feature Fallacy: the fallacy that the next feature you add will suddenly make people want to use the entire product. -@bokardo

There’s a longer explanation of the idea here, but the TLDR is that when you add features that engage hardcore users, that’s going to be such a small % when in reality you need to stem the bleed in D1/D2/…D7. That is, in the activation step of the product. If you get 10% of your hardcore users to engage more deeply, the reality is that it won’t move the needle enough mathematically to lift your entire retention curve. This means that you need to listen to the “silent majority” of users who churn, rather than the core users who stay and are highly vocal.

Thus, I’d ask myself the following questions:

• How is my retention? Am I counting on the ability to move metrics far beyond what’s reasonable? (You can increase 20%, but probably not 100%)
• Am I betting the farm on some product magic that hardcore users want? Or am I working on things that cause more newbies to love the product more quickly?
• Is my product in the category where network effects might substantially grow retention? Is that reasonable to think?

Top of funnel
It makes me happy when I see strong retention numbers with a flat growth curve. Funny enough, I consider this a very good thing. The history of fixing these situations is much better, and the approach is usually quite simple: Find more marketing channels, and scale existing ones. And if you can, find a self-repeating growth loop where users sign up for your product, use it, and then help generate more signups over time.

Just avoid the random lightning strikes. This could be from tech news coverage, a viral TikTok video, or a one-time email blast. You feel good for a moment, and when the excitement (and growth curve) dies down, then the crisis begins. It might be a fine way to solve a cold start problem or to get your first few hundred users. But it’s not a real growth strategy and leads to a product that’s lurching from crisis to crisis. Instead, the focus needs to be on repeatability, particularly once retention is established.

The easiest way to find a repeatable strategy is by simply fast-following other companies in your space. Finding and scaling marketing channels is typically pretty easy. If they are doing paid marketing, then go into those channels and test for CAC and measure payback periods. If they are marketing via Twitch creators or Instagram influencers, try that too. This method of simply experimenting and copying the competition goes a long way and often leads to success.

Testing marketing channels, alongside ad creatives and call-to-actions, requires an entrepreneurial spirit. There’s a huge advantage to testing a lot of different ideas, creatives, and landing pages and experimenting with messaging.

Growth loops scale and scale

Figuring out a growth loop is even more powerful. The idea here is that the loop helps attract users, who take actions that attract even more users, and so on. Thus a product with 10,000 users will grow quickly, but when it hits 1M actives, it can go even faster. This means user acquisition is a function of the size of the user base, and thus, it will keep up with the churn curve that’s stalking just behind it.

I have a few examples in my Magic Numbers deck, where I illustrate these as some of the classic and ideal growth loops:

Above: Viral loops are important because they are extremely scalable, free, and don’t require a formal partnership. This is based on users directly or indirectly sharing a product with their friends/colleagues, and having that loop repeat itself.

Above: A product like Yelp or Houzz fundamentally is a UGC SEO driven loop. New users find content through Google, a small % of them generate more content, which then gets indexed by Google, and then the loop repeats. Reddit is also like this. So is Glassdoor. And so on.

The process of figuring out these growth loops is not an easy task- it’s a form of product-led growth that requires an understanding of marketing, product, and sometimes growth hacking the underlying platforms/APIs to get a leg up (as Zynga did on Facebook, and Paypal did on eBay). But it’s very powerful when done well.

Polish your the UX flows that matter to growth — signup, inviting, payment — and ignore your hardcore user features
For teams that are focused on growth, it’s uncomfortable but necessary to ignore your best users and instead focus on UX targeted at users who may not be vocal at all. If you can polish your new user flow, then you can often make 20-50% gains to conversion, which then fall straight into the bottom line (whether that’s revenue or an active users count). When you polish your friend invite flows or referral flows, then you might get 20% of users to invite 100% more of their friends. And then that larger group of invitees will invite each more friends, and so on, with a larger viral factor. This is why when I assess product UX, I tend to focus on the less sexy stuff: Signup flows, invitations/referrals, and payment. And even surface areas like the lost password flow, which is for larger products, often block engaged users from getting back into their accounts.

Unfortunately, this is a product surface area that isn’t considered particularly sexy. If you’re at a large company, you may not get promoted to the next level of PM for delivering this type of project. In these settings, PMs are often rewarded more often for coordinating massive cross-functional projects than to move the needle on growth, by simply testing dozens of variations of signup flows.

And yet, this is often what matters!

There are a couple of key things I’ll often assess when looking at these growth-critical user flows:

• Are the value props clear, the headlines crisp, and generating urgency for the user?
• Are all critical elements — buttons, form fields, etc — above the fold?
• Are extraneous links removed, to not divert the user, or otherwise moved to below the fold?
• Instead of asking users to scroll, can content turn into a video, animated GIF, or slideshow?
• How does it look on desktop versus mobile?
• If the signup process is multi-step, can some steps be skipped for now, and done later?
• Is the order of the signup right? Can you bring forward the magic moment, rather than asking people to fill out form after form?
• Are there critical asks — getting a credit card, asking people to invite friends — that should be baked into the first few steps of the signup flow?
• Does the signup flow activate people correctly? Should the user be “forced” to activate in any way, by adding required signup steps?
• … and on and on

For new user flows, I try to get more users that hit the landing page to ultimately become activated users. I use tons of A/B testing and experiments in messaging to make this happen. For invite flows, I often try to stick them to the end of sessions so that users repeatedly see them as they engage the product. Maybe they create content, and you ask them if they want to share their newly created content with friends/coworkers. Do that every time, and you’ll be generating viral factor as you go, rather than just at the beginning. Payment is similarly important for products that focus on paid marketing to grow. The earlier you harvest purchase intent — often in the signup flow — the more you can plow that money into growth programs.

There are these flows and more, and they are the unsexy product features that drive growth.

Some final thoughts
Even great products stall growth. Famously, Facebook grew in its early years to take over colleges, but then saw a stall as saturation effects took over, and the product needed to be expanded past universities. Then there was another period of flatness, just before they expanded internationally. And another, before mobile. The same was true for Dropbox in its early years, as it saw a spike on Digg and Hacker News, but it needed a referral system and shared folders to push it to the next level. And in recent years, TikTok stalled as a platform for dance videos before it was acquired, and a very large paid marketing effort helped push it over the top based on building out a massive library of content.

These stories are common because successful products inevitably saturate a market, or need to jump from one acquisition channel to another, or any number of problems. When this crisis happens, it’s easy and reflexive to simply try to spend more on marketing. Or to try to develop more features. Or some other simplistic rule like that, sometimes based on the natural ability and interests of the product team.

Keep yourself from doing that.

Instead, consider that every stalled growth curve has its idiosyncratic issues. Sometimes it’s poor activation. Sometimes the novelty has worn off. Or perhaps the product is seasonal, or a marketing channel has been saturated. For better or worse, finding the levers to correct the stall requires patience, analytical abilities, and deep customer empathy. It’s hard, and every stalled product has its own story. But to identify the problem, fix it, and see the graph return to its previous glory — well, that’s just an amazing thing.

Hi readers,

I wanted to share a fundraising deck for a16z’s GAMES FUND ONE that we put together a little over a year ago. Of course a lot of fundraising decks have been published for our favorite companies — in fact, I have a folder full of the v1 pitch decks for Dropbox, Coinbase, Uber, etc — but rarely do you see decks for venture capital funds. I wanted to share this deck with some voiceover so you have a sense for what’s involved. Obviously there are lots of redacted slides, but you’ll at least get a flavor.

Thanks, Andrew. (Venice, CA)

A16Z GAMES FUND ONE PITCH DECK

We’ve hit the 1 year mark!!!! 🥳🥳🥳

A year ago, we raised GAMES FUND ONE, our first $660M fund focused on games. 25 investments and 10 new hires later, we’ve learned so much.

To celebrate, I’m going to share some slides we used to raise the fund. Yes, even VCs have to pitch :)

We structured our pitch deck into a discussion and overview about the games industry – many folks outside the industry needed some context to catch up. And then our investment areas, and then how we approached the team. (I’ve redacted notes about returns/companies/etc, of course)

For the overview – Games are driving the GenAI Revolution and this games are having their “Marvel Moment” showcasing their cultural power as some of the top shows – The Last of Us, Movies – Super Mario Movie, and Games are expanding entertainment IP like Hogwarts Legacy.

And by the way, the industry has changed substantially from when people were buying cartridges one at a time to decade-plus long-running games where friends come together as a next gen social network.

Games are often the killer app for new technologies. 3D, GPUs, and virtual items all came from games, though we might re-label them as metaverse, or NFTs. Before freemium, there was shareware. Games helped bring computing to the home, with Atari and Nintendo.

Here’s the “why now” slide – and today we’d update this slide with AI AI AI AI AI AI :) – but there are some amazing tailwinds that the industy has seen over the past decade

Last year we were very focused on studios, web3, and infrastructure. This year we’ve continued to maintain that focus, but have really started to lean into AI. In Q1 alone, we met over 100 AI x Games companies. And in 2023, 80% of the Games Fund’s investments have had a major AI component – either reinventing core gameplay or creating tools

We need to balance our approach to include all key areas of games, but also adapt to new trends.

As we invest across the ecosystem – both game studios but also infra, next gen consumer, and other areas – we need to also help the companies succeed. We do that by building a team that’s “games native”

A major part of a16z’s differentiation has been our Operating Team that helps companies. In fact the vast majority a16z’s staff focuses on that, and it’s a small minority that focuses on investing Gaming startups want help here: – hiring – creators – launching – publishers

Last year, we hired operators like Doug McCracken from Supercell to lead Marketing, Lester Chen from Youtube to lead Creators, and Jordan Mazer from Riot to lead Talent. Everyone has a startup mindset we all act like owners and work without clear directives. (Many more folks to come!)

Given the economic downturn, we’ve spent innumerable hours working with our portfolio to maximize their probability of long term success. This is when the operating team is so powerful. But the games market is resilient!

As we round out the year, we’re continuing to build and innovate. Builders are needed to help builders. We’ve been building the team in year one and hired from Blizzard, Supercell, Youtube, Riot, Twitch, and Unity. Join us!

Thanks to everyone who’s supported us in Year One!

Kudos to the new team, our founders/co-investors, and our friends across the ecosystem

And of course, my close colleagues Jon Lai, Marc, Ben, and the entire team who have been building this initiative from the start!

PS. Finally, our lawyers made me add this at the end :)

Above: Dropbox’s innovative growth initiative — A referral program to give/get storage

Why a referral program?
Referral programs — the “give $5, get $5” offers you see in many apps — have become popular in recent years. They have big advantages over paid marketing channels, in that you give your CAC to your users, who then spend it within your product, as opposed to handing it over to Google or Facebook. Because they are a form of viral marketing — utilizing your network of users to bring in more users — they tap in to your product’s network effects, as I describe in The Cold Start Problem. This is particularly useful for products that target high acquisition cost niches, whether that’s crypto users or on-demand drivers, whose CAC are often >$200, since the users often know each other.

A successful referral program can be 20-30% of your acquisition mix, as one of several acquisition loops. It’s not a silver bullet, but it’s worth adding to complement other marketing efforts.

The history of the referral program
How did a structured form of customer referrals come into being? It’s said that the first documented referral program was created by Julius Caesar, who in 55 BC would paid his soldiers 300 sestertii (something like a third of their annual pay) to refer a friend to join the army. And thousands of years later, we still use, plus or minus, the same idea. It seems as though every consumer app has implemented some form of a referral program, though I argue it really kicked off in ~2008, which is when Dropbox’s innovative referral program was rolled out.

Yes, the most famous early implementation of referral programs came from Dropbox, which inspired a generation of startups — particularly YCombinator-backed startups — to experiment with similar ideas. Why did this make sense for them? CEO/cofounder Drew Houston’s made a very helpful presentation describing his journey towards referral programs, and the general trajectory was the following:

• First, do all the things you’re “supposed” to do
  • Big bang launch at a tech conference. Try some AdWords, hire a PR firm / VP of Marketing
  • Paid marketing programs created a CAC of $233-388 for a $99 product
  • Then trying affiliate programs, display ads, and many other things — which all failed
• … but then failing! And realizing none of it works that well
• Then realizing the key was to accelerate word-of-mouth and viral growth by offering a “give and get storage space” program
• Boom. 100,000 users to 4 million in just 15 months, with 35% of daily signups

The entire deck is wonderful — created roughly a decade ago, but still very relevant — and I highly encourage you to check it out here.

Referral programs work very well for certain kinds of products, particularly ones that are already spreading via word of mouth. In Dropbox’s case, there is a natural use case between friends and colleagues — shared folders — which naturally complement the referral channel. Referrals drive that forward, providing an economic incentive to tell friends. As another example, at Uber where I ran the referral programs for drivers and riders at various points (and spent >$300M/year on them), the program for driver referrals was naturally successful. Drivers were often from certain sub-communities, whether newly arrived immigrants or limo drivers, and people were naturally already talking about the earning opportunity. Referrals, sometimes as high as $500/signup, accelerated that in a big way.

And yet referral programs have their limits. Of course they don’t really work that well for products that have low LTV — that’s why we don’t see free social photo sharing apps reward their users for referrals. There’s no LTV to arbitrage against, and the referral amounts create a form of customer acquisition cost. They also tend to decline in importance over time. Years after the rollout of Dropbox’s referral program, I had the opportunity to join Dropbox as an advisor, where I got a first-hand look at the data. By then, the natural virality of their core product — just the process of people sharing their folders and files with others — had come to completely dominate user acquisition. This had become the primary method of spread, and the referral program became much less important. I’ll discuss why, later on, but this seems to be the natural pattern of things — referral programs are very helpful at the beginning of a market. Eventually it becomes less important, and that’s OK.

But we get ahead of ourselves. Let’s start first by looking at how a referral program is usually defined.

The structure of a referral program
We see the same rough patterns in referral programs that are implemented across the industry. Airbnb, Uber, Instacart have them, and so do Coinbase and Wealthfront. There are variations of course, as some focus on giving and getting dollars. Some ask you to share a code, or a link, or connect your addressbook to invite friends.

One way to organize all these variations is to divide them into the following — and you need to answer a series of questions for how you structure the program:

• Ask
  • When do you ask the user to refer?
  • Why do you refer? Is it tied to a holiday, or a particular promotion?
  • What’s the message?
• Target
  • Which users do you target? All of them?
  • How do you set referral amounts?
• Incentive
  • What’s the incentive, is it extrinsic ($) or intrinsic (points, storage, etc)?
  • Do you give the inviter or recipient the same reward?
• Payback
  • What is the success criteria for the program?
  • How do you think about cannibalization?

Let’s use an example to describe this.

For example, take Airbnb’s host referral program:

You could break this down into the following categories:

• Ask: Invite someone who can host their entire place or private room
• Target: All Airbnb users
• Incentive: Earn $200
• Payback: CAC is better/comparable to other marketing channels (just speculating!)

This is the basic structure, and now that we have this in place, it’s time to talk about a number of design considerations needed when creating a referral program.

The Ask
Product folks often start by agonizing over the ask. They wonder if it’s too trivial to create a “Get $5, Give $5” referral program, or if that’s too basic. But I think that’s the wrong place to focus — after all, you can always word smith and test many variations later once you have the program up and running.

The real question is, WHERE do you make the ask? And my answer is simple: Ask many times, in many places, with different messages, and in-context with whatever action you’re asking the user to take. What you find, after instrumenting all your referral UI, is that there’s just a certain conversion rate on this screen. And that most users, if you put the referral functionality on a banner somewhere random in the product, simply don’t interact with the referral features. Rather than trying to raise conversions, instead, show the screen more often — get more impressions!

Thus, make the referral ask part of the main flows. After the user is buying something within your app, ask them if they want $X cash back now, by inviting someone. Or if they interact with a friend within the app — assuming the product allows invitations of some sort — follow up by asking if they want to invite others. And add it to the onboarding flow, and at the end of key transactions when the user is otherwise done, and you might as well capture engagement. And for god’s sake, don’t make it look like “an ad” with big splash text and graphics — make it plan, like something that’s part of the normal UI where the user can interact.

One of my favorite ideas from Uber is the concept of “holidizing” a referral campaign. For drivers, as the holidays approached, you might tell them to earn extra money towards gifts and festivities, by participating in a referral program. Or for the run up to a major concert in town, you might run a special tiered campaign where referring 1 friend gets you X, but 5 gets you 5*X and a huge bonus on top. There’s something great about freshening up the messaging each month to align to major holidays, with new amounts, new imagery, and otherwise.

The Target
The headline best practice is that your referral program should target new users to refer their friends — this means prompting users during their initial onboarding flows, and adding emails as part of the onboarding, among other surface areas. This is in direct contradiction to folks who often argue to let users experience the product first, have a good experience, before they’re hit up to invite. Why focus on new users? First, mathematically, it’s easiest to make a big impact when you are hitting a cohort of 1000 new users when it’s as close to 1000 as possible, not in day 30 when the cohort will have churned and gotten down to 150. And in the math of the viral factor, you have a better chance to hit >1 when you have 1000 users invite 1000 users than to ask 150 to invite 1000. Second, new users generally have more friends who haven’t yet used the product, because they are new themselves. Once they have gone through the referral program a few times, then they will have naturally tapped out their networks.

And of course, the simplest thing to do is a “give $5, get $5” and give that offer to everyone, in an untargeted fashion. But a product leader soon realizes that this is inefficient — perhaps it’s best to give some users $15 and others $5, depending on their value. This is exactly what many marketplace companies have done, when it’s easy to segment their network into high-value cities like New York and SF versus, say, Memphis — you can set custom referral amounts in each place. But why stop at cities? Perhaps you do an analysis and figure out certain leading characteristics of high-value users as their account balance, or the types of other apps they use, or otherwise — once you think of this as personalizing an ephemeral offer to users, then you can run whatever promotions you want.

The Incentive
You’ll note in the original Dropbox offer, the incentive itself was storage space not dollars — this is the dilemma of intrinsic versus extrinsic rewards for users that participate in your program. Many referral programs for mobile games tend towards intrinsic rewards as well, earning you points if you invite friends. The advantage of intrinsic rewards is that it’s particularly cost effective when the incentive is something you can control, like points. The problem with intrinsic rewards, of course, is that external users — people who have never heard of your product — are the least responsive to points or otherwise. Dropbox’s storage offer is maybe somewhere in the middle, since it’s at least a concrete form of value. As a result, most referral programs have tended towards dollars over time, though I think the important idea is to prioritize new, outside users, and think about how to make the incentive as concrete as possible.

There’s the basic question of how to set the incentive amount. Typically this is based on a basic calculation of CAC/LTV, which has major weaknesses as it doesn’t take into account cannibalization (which we’ll discuss later). Instead, the focus is often to pick a simple number — if you know that the average user who signs up spends $20, then you can create a referral program that rewards a $5 give/get with some margin of safety. But the big lever on the incentive, of course, is to increase the amount — and the largest amount generally comes from tiered offers that have some form of breakage. An example of this is to say, “$100 when you sign up and buy 5 things” rather than “$5 when you sign up.” Given that the difference between a signup and a repeat conversion rate might be 100x, you might be able to safely raise the amount 20x. At Uber, this went so far as to combine two distinct numbers: A headline number that combined both the initial signup conversion as well as the first month’s earnings (again, as long as you drove X trips in the first few weeks). This resulted in a $3000+ number, a huge upgrade from the initial $200 numbers we started with. These larger headline numbers always tested much better on A/B tests, whether in email marketing or banner form, and while it might feel like the reward becomes unattainable, it’s possible to create a second or third or fourth tier to go along with the big headline number. You could say, earn $X when you fulfill all the requirements, but then a smaller number, $Y, when you only fulfill a few. That way you get the marketing impact of the big number but still have a fallback for users who don’t hit all the milestones.

The last aspect of the incentive structure I’ll discuss is a symmetric versus asymmetric offer — that is, should it be a “give $20, get $5” or “give $5, get $20.” Which one sounds better to you? This is anecdotal, but in testing I’ve seen, the inviter-centric amount generally works better — that is, catering to their self interest. However, I’ve also seen B2B contexts where in a professional setting, people tend towards inviting more if they are perceived as altruistic, giving out a large $ discount to others. In the end, probably just worth A/B testing to see what works best.

The Payback
You’ll need some kind of ROI metric to drive the strategy of the referral program. Are you spending the right amounts, or should you increase the numbers? How much product effort should be put into implementing new surface areas? Etc. Is it working? These fundamental questions are often answered with a classic CAC/LTV analysis, and there’s a reason to doing that.

After all, if the lifetime value of these users exceeds the cost of acquiring them, shouldn’t you just go full steam ahead? Well, maybe. What if you can get much cheaper acquisition via another channel, like TikTok ads. Then any dollars that go to this might be better spent on ads. Or what if improving a referral program takes engineering team away from critical features? So yes, of course look at the CAC/LTV of your referral initiative, but think about how you might compare the tradeoffs against everything else.

The trickier ROI question is cannibalization: How many of the users that you bring in via referrals would have come in through word of mouth anyway? If you spike the referral amounts, going from $5 to $20, are you just creating a “pull forward” effect where users that would have arrived for free a few months from now are coming in suddenly, but at a cost, and to the detriment of a later month?

Cannibalization is a hard effect to tease out, but generally the goal is to measure something like “Cost Per Incremental Customer” by A/B testing offers to a control group that gets the standard number, and a test group that gets the elevated number. Because you’re trying to capture organic users, you often have to do this as a “twin cities” experiment, where we run one set of offers in Phoenix and another in Dallas — this is the Uber approach, and in B2B you might do it via one set of companies versus another. And then you measure what the uptick actually looks like. If there is a lot of cannibalization, then the CPIC number will be large — this is the true CAC, cannibalization aside.

The other, simpler form, is simply to do an “On/Off test.” If you turn off all your referrals for a few days, do you notice a big drop in new users? If yes, then your referral program is working. If not, then you are potentially paying a lot of customer acquisition cost for something that would be happening anyway.

The weaknesses of a referral program
As I mentioned at the intro of this essay, Dropbox’s eventually became less dependent on their referral program. There’s a natural trajectory here, because as the market matures and more users have already adopted the product, the fewer friends there are to invite. You only need a few “I already have that” responses to stop participating in referrals altogether. For products that have a true network effect, as Dropbox does, the acquisition will eventually be taken over by intrinsic use cases like folder sharing rather than something as extrinsic as a referral reward.

And in a way, I find myself mostly skeptical when teams approach me to build a referral program. The first thing I ask is, are you sure you wouldn’t rather build a viral growth engine? Building viral features and a referral program are similar problems — trying to get users to invite friends — but truly viral features around sharing and communicating are evergreen and create lasting value. They help users engage and retain, and as a secondary effect, generate new users as well. And it’s a huge benefit to get these new users onto your platform for free. For Dropbox, that means investing in product features like inviting teammates into projects, or file sharing, or otherwise, rather than creating ever more complex referral structures. At Uber, this might mean building virality into features like “Share ETA” or bill splitting or group food ordering.

In that way, I find myself a reluctant fan of referral programs — they can work, and can become a 10%+ acquisition channel for products — but they will always take a back seat for me, compared to building great viral functionality.

Dear readers,

This blog is officially back! I’m excited to restart my writing here, after a long hiatus. It took more than three years to write my book, The Cold Start Problem and all my energy went into that. But as of last December, it’s finally out in the world, and I want to thank all of you for making it a success. I’ll be sharing more about what I learned in my journey in writing and publishing the book in a future essay — and all my random life hacks for clearing out a few hours every week to put it together.

In the meantime, I have some big life (and work) updates:

1) LA Tech Week 2022
First, putting this up front, so the link doesn’t get lost — join me at LA Tech Week 2022 in mid-August! I’ll be co-hosting a number of events, along with my a16z colleague Katia Ameri. Just register on the site to see all the events. I’m involved in the organization of the conference (more on this in a second) and will be hosting a number of events related to gaming, the metaverse, consumer apps, etc. I’ll be tweeting the events I’ll be attending as the dates approach, and would love to see y’all there.

2) I’ve moved to LA (well, specifically, Venice Calfornia!)
Related is the the actual personal news: I’ve moved to LA after nearly 15 years in San Francisco. Big change for me. This is the result of a very random series of events where the COVID lockdown convinced me to spontaneously try #vanlife, where on a whim I purchased and lived out of a sprinter van for months at a time. This became my life in the back half of 2020, where I drove from California to Nevada, Utah, and Arizona, working full-time on the week days and exploring national parks on the weekends. Surprisingly, it worked well! Throughout, and particularly during the colder months, I used LA as my home base between longer trips.

Although I had visited many times before, I hadn’t actually tried to actually live there, so I started to try different neighborhoods, and began to Airbnb all throughout the city — Malibu, Silver Lake, WeHo, Santa Monica, and otherwise — before eventually deciding to settle down in one spot. Today, I call Venice, CA my home. It’s walkable, hip, artsy, and has pockets of amazing beauty. If you visit, make sure you check out Abbott Kinney Blvd, the Venice Canals, the walk streets, and Gjelina’s! And don’t worry, my SF friends and coworkers — I’ll be back to the Bay Area often, maybe every month or two. (I’m keeping my place there)

Since arriving in LA, I’ve been working with the startup/VC community to help boost the already vibrant tech ecosystem here. We’re committed to have a major presence here — a16z recently signed an office in Santa Monica (opening soon, it’s a whole building with multiple floors etc) and already has several dozen employees here, as part of our move to the cloud. I’ve set up numerous Whatsapp groups, including for the several hundred investors based here, and also maintain several rotating dinner series for games industry founders and executives.

3) A new role at a16z: GAMES FUND ONE
Earlier this year, we launched GAMES FUND ONE, a16z’s first investment fund focused on investing in the metaverse, AR/VR, games studios, social platforms for gamers, and so on. Check out the launch video!

I’ve been dabbling in the industry, backing a number of top startups spun out of Riot Games, Epic, and otherwise. But as part of this launch, I joined the fund full-time, where I’ll be focused on the convergence of consumer apps and games along with my colleagues Jon and James who will cover content and infrastructure, respectively. Of course, I maintain a number of my board seats and a broad portfolio for consumer startups that I’ll continue to support, but am excited to focus my new investments within Games.

I’ll be writing more about why I’m excited for games (and the “metaverse,” whatever that is!) to define the next generation of consumer tech.

More on that soon.

Finally, I just want to thank everyone who has been a subscriber to this blog (and newsletter) over the past few years. I started writing it in 2007 upon arriving to SF as a mid-20s noob aspiring founder, and over a decade later, it taught me that writing is one of my favorite activities. And I’ve met so many great people from doing it, too. I’m glad to be back!

best,
Andrew

(Btw, for LA history fans, check out this fun video from Lost LA on the history of Venice CA and why it shares a name with an Italian city. It has some great old photos and tidbits)

Why you’ll need to rethink your user growth strategy
Downturns fundamentally rewrite the industry’s strategy (and expectations) for user growth. In a bull market, the focus is on top line growth. You often want 2-3x YoY for a a new product in its first few years, and even faster when its right out the gate. High growth and high burn are fine. Because if you need to spend a lot of money to get there, whether through paid marketing or partnerships, you do it… after all, you can just raise more money, right?

But in a bear market, the answer changes: No. It turns out, you won’t be able to just raise more money to keep going. No, you can’t just expect to hire dozens of engineers, regardless of progress — particularly when hiring freezes are coming into effect. For startups, the bar for raising the next round just went way up, as many investors are waiting out the turbulent market. This means the strategy for user growth just went from “as much as possible” to “efficient, profitable, productive” in just a few quarters.

What are some ways you should be rethinking your growth strategy? Here’s some things every team should be thinking about:

• Embrace the new normal
• Cut your marketing spend
• Laser focus on your engaged, high LTV users
• Live to fight another day

I’ll unpack some of these as we go.

The new normal
Efficient growth is now the key focus for product teams. During a bull market, the primary metric that people talk about is just top-line growth — what’s your year-over-year growth rate. Some of the truly eye-popping growth rates might exceed 10x YoY, often subsidized with investor money — as has been in the case with on-demand services.

However, the new normal is focused on efficient growth. Although there’s a floor for how fast a product has to grow to be interesting — probably something like 2.5x — there’s a much bigger emphasis on efficiency. What’s the best way to measure this? One metric that’s been recently popularized by David Sacks is the “Burn Multiple” — he defines it below:

Burn Multiple = Net Burn / Net New ARR

This puts the focus squarely on burn by evaluating it as a multiple of revenue growth. In other words, how much is the startup burning in order to generate each incremental dollar of ARR?

In other words, if you spend $10M and gain $5M more in annual recurring revenue, that’s a 2x burn multiple — which he grades as “Suspect.” The Burn Multiple metric is simple, but it’s precisely useful because it’s so simple. A lot of times, unit economics are hand-waved by product teams because some costs are excluded from the contribution margin or net revenue calculations that maybe shouldn’t be — like headquarters costs, real estate, and so on.

Burn multiple cuts through all that, since it’s just aggregate cash versus revenue, and it’s hard to hide anything with a metric so simple. And with this simple metric, it allows you also compare different companies, and potentially different scenarios for a given company, to figure out how to best reduce it.

To provide some benchmarks, my colleagues at a16z, Justin Kahl and David George, recently wrote an article on navigating the downturn where they collected some empirical data:

As you can see, the bar for what constitutes a good burn multiple goes up as revenue goes up. Naturally, you burn more upfront during the product development phase, and then get more efficient as the business gets scale.

From a product growth lens, the shift from topline growth to efficient growth means that you should be thinking about how much burn, how many engineers, and how much marketing is required to hit the milestones you want to reach. And the first questions to ask are often around marketing.

Cut your marketing spend
The first and simplest thing to do is to cut your marketing spend. And in a particular order:

• Keep the high ROI channels, cut the low ROI ones, even if they provide volume
• Focus on accountable spend, and reduce ones have a long/fluffy payback?
• Rethink brand marketing spend — do you really need it?

On the first point, every marketing/growth effort is built from layers of channels built on top of each other. The highest ROI tends to be channels like SEO, word of mouth, and other organic efforts. The next might be paid channels like newsletters, which are hard to scale but highly productive. Then there’s highly targeted paid marketing. Usually the lowest ROI tends to be broad targeting — particularly display ads — on large advertising networks.

Usually these layers are built over time, one by one, by growth marketing folks who keep investing and arbitraging 10:1 LTV/CAC ratios down to 3:1, then 1.5:1, before they slow down. There might also be ongoing marketing experiments to try new short-format video or otherwise. It’s time to unwind that. Usually each incremental channel might add more volume, but is rarely as efficient as the preceding efforts. There’s a diminishing returns — Law of Shitty Clickthroughs strikes again — as each layer is built. Instead, go back to the core.

The other vector to think about this is direct response versus brand marketing. Brand efforts are a great way to spend money without understanding its actual effectiveness to impact metrics, and it’s time to dial those down. Whether it’s large scale events, brand marketing, PR/comms, splashy videos, or otherwise — unless you can justify the costs, it’s time to reduce.

Either way, it’s time to retrench and focus on high intent, low CAC channels.

Laser focus on your engaged, high LTV users
In a hot market, there’s often a land grab to acquire as many users as possible. If there’s a goal to grow 10% in a time period, the pressure is often to grow 10% by acquiring a mass of new users — most of which will burn off from lower usage — when the better option might be to grow 10% by incentivizing higher engagement from existing, core users. At Uber, it was often noted that it was much faster to get drivers to spend 10% more time on the platform, so that there’d be more “supply hours” to react to demand — than to acquire 10% more drivers in a market. The latter would require a big marketing push, and might take weeks for the drivers to ramp up to the same level of engagement.

The reason why this dynamic exists — where the core users outperform new ones — is that there’s often a central segment of where the product is really working, and then an “Adjacent User” where it only kind of works. For example, early Instagram was working well with high-tech, urban users but not well at all within older demographics. Later, it wasn’t working well for Android users in emerging markets. But there’s often pressure to grow by capturing new segments of users, rather than improving on the core, which means that the users that come in through marketing channels are worse quality, lower intent, and less engaged than the core.

This can become a tradeoff between Marketing versus Product-Led Growth, where the former drives CAC, whereas the latter is built on product development costs. The advantage of growth driven within the product — whether that’s better user onboarding, high impact features, or otherwise — is that they impact a wide swath of users within the product. You can invest once and get benefits over a long period of time, and amortize costs across a large segment of users. I’d lean towards product, when possible, when the roadmap is clear on what to do. Obviously marketing spend and engineering time isn’t interchangeable, but reducing marketing budgets while maintaining/increasing product teams feels like a good trade.

Live to fight another day
The milestones required to unlock additional funding/headcount has almost certainly gone up, and specifically for startups, the bar needed to raise more venture capital money has gone up as well. Understanding these milestones will allow teams to fight another day, and coming up with a realistic plan is a key step.

The best way to understand how the bar has moved is can be shown by a chart in the aforementioned article shared by my a16z colleagues David and Justin, showing how the forward revenue multiple for public companies are down significantly. Meaning, you need much more revenue to justify the same valuation — what used to be a 15x multiple is now 7x, meaning valuations are down half even given the same revenue numbers.

Or said another way, when the valuation of public software companies gets halved, then the amount of revenue needed to justify the a valuation goes up double. (For early products that think more about Active Users or DAUs or otherwise, you can recreate these graphs based on $/DAU or otherwise — and yes, those are way down)

This is causing a domino effect in the industry. When you see a $2B public company cut down to $1B, then a $500M privately held startup is cut down to $250M, and so on. The tricky part is that for a public company, of course you have a real-time stock quote to see these valuation changes. For a tech startup, you raise new funding rounds every year or two. That means for much of the industry, the next round of a startup just became much, much harder, but we potentially won’t know for a year+ how much the bar has moved. Either way, this means fewer resources to hit the same hard growth goals.

The easiest way to flex to hit these elevated targets is to take more time, with higher efficiency. Teams have to buy more runway, focusing on better ROI and not a “high growth, high burn” mindset to hit the growth metrics. For startups who have recently raised, they’ll need to “catch up” on their most recent valuation, and additionally progress to justify the customary 2-3x jump in valuation between rounds. That’s the new bar.

Conclusion
The next few years are going to see a lot of change in the tech landscape, particularly for how teams think of growing their products. Much of the last decade has been focused on growth by any means — and investor subsidies, chasing volume via high CACs, have all played a key role. But in the next phase, efficiency means that we’ll need to retrench within the industry and talk about quality and efficiency.

There’s a myriad of complex trends intersecting at the same time: The new Apple privacy changes to ad networks, the potentially stingy venture capital landscape, the hiring freezes that are happening, how web3 plays out over the next few years, and so on. Just as product leaders had to reinvent their thinking to take advantage of the mobile boom, we’ll see them do the same in the coming years for the new environment that’s rapidly taking shape. In the meantime, it’s critical for teams to take a pause, figure out a new approach, and build towards the next boom.

Hi readers,

I’m so, so excited that my upcoming book is dropping in one week, on December 7!! I’ve been working on it for years, and am thrilled that it’ll finally be out.

The Cold Start Problem, as the book is called, is about the secret that drives many of tech’s most successful products. It’s the story of how messaging apps, marketplaces, workplace collaboration tools, multiplayer games, all share a common thread of being products that connect people with each other. And launching and scaling these products requires a mastery of “network effects,” one of the most-used but misunderstood jargon terms in the industry. My book aims to change that, systematically laying out concepts for startups and folks launching new products to consider.

One of the crucial concepts to understand this is the concept of an “Easy Side” and a “Hard Side” of a network.

To describe this more detail, below, I’ve included a full-length chapter called “Solve a Hard Problem” focusing on the idea that every network has an “easy side” and a “hard side.” Here’s how I define each side:

There are usually a minority of users that will create disproportionate value and as a result, they will have disproportionate power. This the “Hard Side” of your network. They do more work, contribute more to your network, but are that much harder to acquire and retain. For social networks, these are often the content creators that generate the media everyone consumes. For app stores, these are the developers that actually create the products. For workplace apps, these are the managers that author and create documents and projects, and who invite coworkers to participate. For marketplaces, these are usually the sellers and providers who spend their entire day attracting users with their products and services.

In order to make your product and its network launch successfully, it’s not enough to build a killer set of features. It’s also important to solve something crucial for the “hard side” of your network. Cheekily, I call this, “Solving a hard problem” — and I use the example of Tinder to illustrate this concept.

Hope you enjoy!

Regards,
Andrew

Chapter 8: Solve a Hard Problem (Tinder)
The hardest problem to solve in creating the first atomic network is, well, the hard side. Focus on attracting content creators to a new video platform, or sellers to a new marketplace, or the project managers inside a company to a new workplace app. The other side of the network will follow. The question is, how?

The answer is by building a product that solves an important need for the hard side of the network. Let’s look at online dating, which evolved over time to better solve the matchmaking problem that has bedeviled humanity since the beginning of time. Dating apps are network effects-driven products that grow city-by-city, and the more folks that join the network, the better the chances that people will find matches. But at the beginning of the product category, the experience was terrible, especially for the hard side of the network.

The problem of too many love letters
Online dating was invented at the beginning of the web, in the early 1990s. They were designed like newspaper classifieds, where men and women browse large databases of profiles, and could message each other if they were interested. Match.com and JDate were successful pioneers in this category, which worked despite its flaws. The classifieds-based design created a poor product experience since the popular members — particularly women — would become overwhelmed with a large number of messages, and they would struggle to reply. At a bar or club, potential suitors might be dissuaded if they saw a line of people waiting to talk to an attractive man or woman, but online, there was no such signal. So in turn, the experience for everyone else also ended up poor, because it seemed like no one would write them back.

The lesson is, unsurprisingly, that attractive people — particularly women — are the Hard Side of the online dating network. A few years later, the next generation of online dating would emerge, led by products like eHarmony and OKCupid. These products used quizzes and matching algorithms so that the system could decide who got which matches, and how often. This ensured women got fewer messages, and hopefully more of the right ones. And the men got more replies too, so that it didn’t feel like it was devolving into a copy-paste messaging exercise.

It wasn’t until 2012, at the beginning of the explosion in mobile apps, that yet another generation of dating apps would emerge. These apps, exemplified by Tinder, would innovate even further for the hard side of the network. I had a chat with Tinder’s co-founder, Sean Rad, about how Tinder innovated on the previous generation of products. He described the combination of new ideas:

The older dating sites made it feel like you were doing work, like you were inside the office. You’d go and do work emails during the day, then go home and write more messages at night. Only to prospective dates rather than work colleagues. Tinder was different — it made dating fun. You could sign up without filling in a bunch of forms. It’s visual, you just swipe back and forth, and you could take 5 minutes to do it while you were waiting in line or something like that. It’s a form of entertainment.[^1]

The other problem was how to wade through all the replies. In real life, you’re often introduced to potential romantic partners through friends, or you had a shared context — like work or school — that helped filter. For online dating, the most attractive members of a network needed some additional signals to help sort through their matches. Tinder did this by integrating with Facebook, and Sean also explained how the app was able to build trust:

Tinder started by making everyone connect their Facebook, so that we could show the number of mutual friends you had, which built trust. We also made it so that you could only be matched with people who lived around you — we used the GPS location from your phone, which was new. These were people with mutual friends living around you, the sort of person you might meet in real life! Connecting with Facebook also made sure you would never be shown to friends, or vice versa, if you were worried about that. This all created trust. Tinder also had built-in messaging so that you didn’t have to give out your number. If the conversation didn’t go anywhere, you could just unmatch without worrying about getting harassed.

And of course, the mechanic of swiping itself is a way to make sure people don’t feel overwhelmed. Whereas men tend to swipe right (that is, to indicate interest) on about half of women’s profiles — about 45% to be exact — the ladies in the product swipe on only 5% of profiles they see. As a result, women mostly match with the guys they select. However, if they feel like they are in too many conversations, they can stop swiping for a while and just focus on the messaging their existing matches. All of these insights made Tinder a much better experience for most important side of their network, solving one of the most important obstacles in the Cold Start Problem.

The Hard Side for marketplaces is usually the supply side
Marketplaces tend to revolve around its sellers. I’ve seen the difficulty of managing the hard side for rideshare first-hand, where drivers are the ones selling their time and effort in the market. For Uber, in any given market, so-called “Power Drivers” constitute 20% of the supply but create 60% of the trips. These are some of the most valuable users on the planet, as they are the core of Uber’s business.

Uber’s drivers are just one example of a broader set of workers that drive most marketplace companies. For marketplaces, the hard side is usually the “supply” side of the network, which refers to the workers and small businesses who provide the time, products, and effort and are trying to generate income on the platform. They use digital marketplaces as a side hustle, selling collectibles or coaching sessions, or otherwise. They do this often as an alternative to hourly jobs, of which there are nearly 80 million in the US. These are folks often living in the middle of the country, who work in hourly retail jobs that turn over 100% year over year, and are struggling for additional income. Marketplace startups often provide these opportunities to this group.

To solve the Cold Start Problem for marketplaces, often the first move — as it was for Uber — is to bring a critical mass of supply onto the marketplace. For a marketplace like eBay, you start with sellers of collectibles. For a marketplace like Airbnb, you might start with people with a few extra rooms in their place. For a social platform like YouTube, it might be video creators. For a more esoteric category, like Github, it’s helpful to bring on some prominent Open Source projects and key developers. But once the supply has arrived onto the network, it’s time to bring in demand — the buyers and users that will form the bulk of the network. Once that’s working though, it becomes all about supply again. Thus the order of operations, at least for most consumer-facing marketplaces, is “supply, demand, supply, supply, supply.” While supply might be easy to get onto the network early on through subsidies, eventually it will become the bottleneck. The Hard Side of a network is, by definition, hard to scale.

Uber had to get creative to unlock its Hard Side. Initially, Uber’s focus was on black car and limo services, which were licensed and relatively uncontroversial. However, a seismic shift soon occurred when rival app Sidecar innovated in recruiting unlicensed, normal people as drivers on their platform. This was called the “peer-to-peer” model that created millions of new rideshare drivers, and was quickly copied and popularized by Lyft and then Uber. Jahan Khanna, cofounder/CTO of Sidecar spoke of its origin:

It was obvious that letting anyone sign up to a driver would be a big deal. With more drivers, rides would get cheaper and the wait terms would get shorter. This came up in many brainstorms at Sidecar, but the question was always, what was the regulatory framework that allows this to operate? What were the prior examples that weren’t immediately shut down? After doing a ton of research, we came onto a model that had been active for years in San Francisco run by someone named Lynn Breedlove called Homobiles that answered our question.[^2]

It’s a surprising fact, but the earliest version of the rideshare idea came not from an investor-backed startup, but rather from a nonprofit called Homobiles, run by a prominent member of the LGBTQ community in the Bay Area named Lynn Breedlove. The service was aimed at protecting and serving the LGBTQ community while providing them transportation — to conferences, bars and entertainment, and also to get healthcare — while emphasizing safety and community.

Homobiles had built its own niche, and had figured out the basics: Breedlove had recruited, over time, 100 volunteer drivers, who would respond to text messages. Money would be exchanged, but in the form of donations, so that drivers could be compensated for their time. The company had operated for several years, starting in 2010 — several years before Uber X — and provided the template for what would become a $100 biillion+ gross revenue industry. Sidecar learned from Homobiles, implementing their offering nearly verbatim, albeit in digital form: Donations based, where the rider and driver would sit together in the front, like a friend giving you a ride. With that, the rideshare market was kicked off.

Nights and weekends
The key insight in the stories of Homobiles or Tinder is — how do you find a problem where the Hard Side a network is engaged, but their needs are unaddressed? The answer is to look at hobbies and side hustles.

There are millions of content creators, app developers, marketplace sellers, and part-time drivers that power the hard side of networks. They are smart, motivated, early adopters who are finding opportunities to make themselves useful. They are the developers behind the Open Source movement who have built Linux, WordPress, MySQL, and many of the other technologies that underpin the modern internet. They are the millions of eBay sellers that have created jobs and companies by buying and selling goods that people want. For photo sharing and messaging products like Instagram and YouTube, they stem from the countless amateur photographers and videographers that like to record travel, special occasions, architecture, beautiful people, and everything else.

What people are doing on their nights and weekends represents all the underutilized time and energy in the world that if put to good use, can become the basis of the hard side of an atomic network. Sometimes the army is built on people with excess time, but sometimes it is built on people with underutilized assets as well. Rideshare networks, for example, fundamentally depend on the underutilization of cars, which generally sit idle most of the time besides the daily commute and the occasional errand. Airbnb is built on the underutilization of guest bedrooms, second homes, combined with the time and effort of the hosts. Craigslist and eBay are built on letting people sell their “junk” – the stuff that people don’t value anymore – to new owners who might value them more.

Usually the Hard Side will continue to use Airbnb or TikTok because that’s where the demand is, and thus, are locked into the positive network effects on those platforms. However, the trick is to look closer — it is better to segment the Hard Side of the network and figure out who is being underserved. Sometimes this is a niche, like a passionate sub-community of content creators for makeup or unboxing that might be better served with additional commerce features. It could be a low-production quality, amateur part of the community, like those who are doing #whateverchallenge of the week, who would benefit from basic video editing tools. For networks that are derived from underutilized assets, it might be the niche of who like having new side-hustles every weekend to make money online. Or perhaps there is a new platform shift coming soon that feels niche, but might upend the entire ecosystem.

The idea is to start with these underserved segments — which may not be very attractive customers on their own, and to apply Clayton Christensen’s disruption theory. New products often disrupt markets by starting on the low-end of the market, providing “good enough” functionality, and growing them there. They use a different technology foundation that allows them to eventually roll up the market from low-end into the medium, and eventually into the core market of the incumbents. Or, there has been a recent trend in the opposite — products like Uber and email company Superhuman, where you start at the top of the market as a luxury product, and work your way down.

When we combine disruption theory with that of network effects, it makes even more sense – atomic networks often start at the low end in terms of functionality, in a niche market. But once they establish an atomic network, then often the Hard Side of the network is willing to extend their offerings and services to go into the next vertical — attracting an incrementally higher-end opposite side, which in turn, spurs it even further. Airbnb may have started with airbeds, but the same hosts that might be willing to rent out an airbed might be willing to rent out their room, or their entire apartment. This changes the potential nature of the supply in the marketplace, attracting a higher end demand-side, which in turn attracts higher-end inventory. No wonder today, Airbnb hosts a wide variety of high-end offerings, from luxury penthouses to boutique hotel rooms. In that way, network effects can play a key role in disrupting new industries — creating the momentum for a low-end atomic network to slowly build out into higher-end offerings over time.

The Hard Side of dating apps
Let’s go back to online dating for a moment — when viewed as a networked products, the apps bring together two sides in a romantic context. In that way, Tinder, Bumble, Match, eHarmony, HotOrNot, and the line of dating apps reflects something that existed as a human behavior for eons. It’s long been a hobby of amateur matchmakers to introduce their single friends to each other, both demonstrating a deep need for this service as well as the skills needed to make it successful. In the the modern age we have digitized dating, using algorithms to match people, dating profiles so that thousands of profiles can be swiped through, and real-time messaging to make communication easier.

All of these improvements are great for any product, but most importantly, they help attract and maintain the most desirable members of a dating network — the Hard Side. The matchmaking algorithms need to find them equally attractive matches, and the profiles they browse through must help them decide between princes and frogs. The in-app messaging experience has to cater to their needs, with an option to get out of conversations quickly if needed. Without these types of features, desirable people will churn from the product, degrading the network and worsening the experience for everyone else. The best products have to solve a problem for the Hard Side of the network.

While dating apps — and really, all networked products — need to find a value proposition for the Hard Side of the network, what about all the other users? Well, it’s a high bar, but you need to nail the experience for the rest of the network. You need to build a “Killer Product” that sits at the heart of any network.

Hi readers,

My first book — The Cold Start Problem — is out next week!!! The countdown begins. You can preorder it here as hard cover, kindle, or audiobook. And yes, I recorded the audiobook myself.

I have a bit more more audio today to talk about as well… When I first moved to the San Francisco Bay Area in 2007, I was introduced to Tim Ferriss at a dinner — before he wrote 4 Hour Work Week, before his podcast, when we were both getting started. We lived not too far from each other — I was in an apartment with roommates in Bernal Hill. I remember visiting his place and checking out these weird cannonballs with handles on them — kettlebells — and talking about how he was going to launch his book.

Years later, it’s been awesome to follow his success and today, something new:

I’m on his podcast — The Tim Ferriss Show — for the first time. The episode was just released this morning. You can listen to it on Apple Podcasts, Spotify, Overcast, Podcast Addict, Pocket Casts, Stitcher, Castbox, Google Podcasts, Amazon Music, or on your favorite podcast platform.

A couple of the topics we talk about:

• “Growth hacking” and how it became popular
• The history of marketing — coupons, direct marketing versus brand, and more
• Games and the future of the Metaverse
• The magic of Bay Area tech companies
• The creator economy
• Web3 and how it’ll impact consumer startups
• The Cold Start Problem and what it was like to write it

I hope you enjoy!

Thanks,
Andrew
(Back from a long road trip from Zion, Bryce, Sedona, etc over Thanksgiving)

Hi readers,

In just a few weeks, I will be dropping new book, The Cold Start Problem!! If you haven’t gotten your copy — if you are in the US, here are the relevant links:

• Amazon · Bookshop (support your local bookstore!)
• Or if you are international, go to the coldstart.com website to find all the pages for Europe, Asia, and more.

Thanks to all of you for the tens of thousands of emails, tweets, and preorders in support of my work. If you’re reading about this book for the first time, you might be asking yourself — what is the Cold Start Problem about?

Let me set it up in a few bullets:

• Many of tech industry’s most valuable products — Slack, Zoom, Instagram, Twitch, YouTube, and others — are at their core products that connect people to each other. People are connected for commerce, communication, collaboration, and more
• I’ve come to believe this is the secret of much of Silicon Valley’s success. I saw it first-hand at Uber, which scaled to billions in revenue, and also within startups at Andreessen Horowitz, which has funded companies from Github, Coinbase, and Figma to Clubhouse and Airbnb
• These types of products benefit from “network effects” — that they become more useful as more users engage. This is why they grow to be so powerful, and valuable. But also why they are impossible to get off the ground, because people won’t use products where their friends/colleagues aren’t already engaged
• The Cold Start Problem is a book a collection of case studies, from Tinder, Twitch, credit cards, Dropbox, and others — about the lifecycle of these networked products. How to get them started, how to scale them, and what it’s like to compete
• These frameworks are targeted at teams building new products, first and foremost. But it’s also for people who work in the business of commerce, travel, publishing, and many other industries that are getting reinvented by tech.

I hope that’s a good teaser :)

In previous posts about the book, I’ve hinted at its contents. But over the next few weeks, I’ll be more substance and previews of the content.

Today, for the first time, I’m going to provide a preview of the opening chapter of The Cold Start Problem — which is partially about my experience at Uber and Andreessen Horowitz, but also about why I began writing in the first place.

I hope you enjoy it!

Thanks,
Andrew
writing from Venice, CA

The Cold Start Problem

Introduction
It was 2015 in December, and on a Friday evening, the office was buzzing. Amid the vast, monochromatic corridors of Uber’s San Francisco headquarters at 1455 Market Street – two football fields worth of gleaming LED lights, light woods, concrete, and steel – the office was still mostly occupied at 8PM. Some sat at their desks quietly typing email while others debated energetically with colleagues over videoconference. Others were drawing on whiteboards, hosting impromptu jam sessions to tackle the tricky operational problems facing who-knows-what. And a few pairs of employees were walking up and down the main flow in 1-on-1 meetings, some in intense discussion and others just catching up.

Everywhere you looked, there were reminders of the global scale of Uber’s business as well as the international heritage of the team driving it. Colorful flags from every country hung from the ceiling. Conference room screens hosted videoconferences with colleagues from faraway offices in Jakarta, San Paulo, and Dubai – sometimes simultaneously! Flat screen TVs were scattered throughout the floor showing metrics, broken down by mega-region, country, and city, so that teams could monitor progress. The global culture seeped all into the naming conventions for conference rooms: Near the entrance, the names started with Abu Dhabi and Amsterdam, and at the far other end of the floor, ended with Vienna, Washington, and Zurich.

At first glance Uber might just look like a simple app — after all, the premise was always to hit a button and get a ride. But underneath its deceptively basic user interface was a complex, global operation required to sustain the business. The app sat on a vast worldwide network of smaller networks, each one representing cities and countries. Each of these networks had to be started, scaled, and defended against competitors, at all hours of the day.

It was in my role at Uber that I really came to viscerally understand networks, supply and demand, network effects, and their immense power to shape the industry. As you might imagine, the Uber experience had its ups and downs – it was a rocketship and a rollercoaster, rolled into one. I’ve come to call it a “rocketcoaster” experience, which is an appropriate description for a company that had went from an idea to a tiny startup to a massive global company with over 20,000 of employees in less than a decade.

The worldwide operations of the company was complex and intense, and much of the command and control radiated from the center of the of Uber’s San Francisco headquarters. In the middle of the main floor, built from gleaming surfaces of glass and metal, stood the War Room.

To many, it was a big mystery – the War Room didn’t share the normal naming convention of city names where Uber operated. It couldn’t be booked for meetings as the others could, and was sometimes attended to by security guards. That’s because it wasn’t a normal meeting room. Many companies (inside and outside of tech) have the notion of “war rooms” but they are typically conference rooms converted temporarily to dedicated use by a product team that works intensely to tackle an emergency project, and after the situation is resolved, is quickly converted back into normal use. For Uber, perhaps appropriate to its unique needs, this War Room was not temporary at all – it was built to operate 24 hours, around the clock. It was built as a huge, permanent room with dark wood walls, multiple flatscreen TVs, a large conference table that could fit a dozen people, with additional sofa seating. Red digital clocks gave the current times in Singapore, Dubai, London, New York, and San Francisco. Given the company’s global footprint, there was almost always some kind of emergency situation somewhere in the world that needed attention, and this was often the room where it was dealt with.

That December, the emergency was in San Francisco, the company’s hometown.

Scheduled to start at 7pm and run into the night, the urgent meeting was booked on everyone’s calendar as “NACS” – which stood for the North American Championship Series, an oblique reference to its agenda focusing on operations, product roadmap, and competitive strategy in the top markets in the US and Canada. This meeting was a key mechanism for the CEO of Uber, Travis Kalanick – called “TK” within the company – to review the entire business, city by city.

A small group of about a dozen executives and leaders attended the meeting, including myself and the heads of finance, product, and critically, the RGMs — short for “Regional General Managers.” The RGMs ran the largest teams at Uber, constituting the on-the-ground Operations city teams that engaged with drivers and riders. The RGMs were thought of as the CEOs of their markets, holding responsibility for revenues and losses, the efforts of thousands of Ops folks, and were always closest to the trickiest problems in the business. I was there to represent the Driver Growth Team — a critical team responsible for recruiting the scarcest asset in the entire business, Uber drivers. It was a big effort for Uber — we spent hundreds of millions just on driver referrals programs, and nearly a billion in paid marketing. Adding more drivers to the Uber network was one of the most important levers we had to grow the business.

The weekly NACS meeting opened with a familiar slide: A grid of cities and their key metrics — tracking the top two dozen markets. Each row represented a different city, with columns for revenue, total trips, and their week-over-week change. It also included operational ratios like the percentage of trips that hit “surge pricing,” where riders had to pay extra because there weren’t enough drivers. Too much surge, and riders would switch to competitors. Uber’s largest markets, New York, Los Angeles, San Francisco were always near the top as the list, representing billions of annual gross revenue each, with smaller cities like San Diego and Phoenix near the bottom.

TK sat closest to screen, dressed casually in a gray t-shirt, jeans, and red sneakers. At the sight of the numbers, he sprung up from his chair and walked up close to the screen. He squinted, staring intensely at the numbers. “Okay, okay…” he said, pausing. “So why did surge increase in San Francisco so much? And why is it up even more in LA?” He began to pace up and down the side of the War Room, the intensity of the questions increasing. “Have we seen referral signups dip in the last week? How’s the conversion rate in the funnel going? Were there a big events this week? Concerts?” Folks in the room began to chime in, answering questions and raising their own.

A network of networks
It was my first year at the company, and although many companies have weekly reviews, Uber’s were different. First, in the discussion about each city, the level of detail surprised me. For San Francisco, the group began to discuss the surge percentages in the city’s seven-by-seven versus East Bay, versus the Peninsula. This was a senior group of executives, but the granularity and level of detail was incredible. But this was a requirement to run a complex, hyperlocal network like Uber where supply and demand went down to popular neighborhoods and frequent “lanes” — like Marina and the Financial District — which tended to be poorly served by other transportation options.

In the weekly dashboard, each row represented a city — yes — but more importantly each city was an individual network in Uber’s global network of networks that needed to be nurtured, protected, and grown. It was deeply and uniquely ingrained in Uber’s DNA to talk about metrics at the hyperlocal network level. In my several years there, it was unusual to ever hear about an aggregate number — like total trips or total active riders — except as a big vanity milestone at a company all-hands. Those aggregate metrics were regarded as mostly meaningless. Instead, the discussion was always centered on the dynamics of each individual network, which could be nudged up or down independently of each other, with increased marketing budget, incentive spend for either drivers or riders, product improvements, or on-the-ground operational efforts.

The NACS meetings were used to evaluate the health of each of the networks and the global network as a whole — a central means of accounting for the 20 or so cities that represented the majority of revenue to the company. Furthermore, it was important to go even further in granularity and break the network into the two sides, both the rider side (demand) or the driver side (supply), to make sure each side was healthy but also that they were in balance with each other. Too much surge, and riders stop taking trips. Too little surge, and drivers start to go offline and head home after a long night.

The slides continued. Several of us on the NACS team, including myself, had been working on a hypothesis over the past few days. Ops teams had reported seeing large increases in driver referrals by our primary US competitor, Lyft, over the past few weeks, which was causing drivers to switch over in droves. Driver referrals typically structured as a give/get incentive — give $250 and get $250 when your friend signs up to drive. In conjunction with a dramatic rise in demand during the holiday season, it was causing a big undersupply of drivers in the key competitive markets on the West Coast, primarily SF, LA, and San Diego. For riders, this resulted in a terrible experience — if you request a ride, it would take far longer than usual, sometimes twenty minutes, which meant more riders were canceling their requests. They might even decide to check our competitor’s pricing and service level, and book there instead. These cancels were frustrating for Uber’s drivers, who might have already driven for a few minutes. Piss them off too many times, and it might cause a chain reaction as they’d have even more incentive to stop for the night, switch off to a competitor’s network.

TK grew more intense and agitated as the hypothesis was presented. “This is not good, guys. Not good.” He exhaled deeply. What was the right solution? With the years of experience from operating these networks, it was likely that one solution would be in quickly rebalance the sides of the market. The right solution would need to start on the supply side, to grow our base of drivers quickly and lower ETAs and the cancel rate, and that meant a driver incentive. “What if… we did a $750 / $750 referral bonus here in SF, LA, and San Diego?”

This would be a big move, a far bigger number than had ever been thrown out. But SF, LA, and San Diego needed the help. These were some of the most competitive markets that would need to be quickly rebalanced with more supply. TK looked around the room, pausing, and then answered his own question. “Yeah. That would get their attention. That’ll wake them up!” he said, smiling and nodding.

Others were not so quick to jump to incentives as the solution. The past year had been good for Uber in the US, turning it into a cashflow positive area as the competition in the new China business simultaneously generated both incredible trips growth as well as severe losses. Uber was in a vicious fight with Didi — its Chinese rideshare competitor — burning on the order of a billion dollars a year primarily because of incentive spend. We started to bat around other ideas, from improving how to display ETA estimates as well as ways to discourage riders from canceling. There were other ways to rebalance the various networks without using incentives, which is a powerful tool but not the only one. The conversation went in circles, and TK grew visibly frustrated.

TK paced around the room again. “No, no! Look, guys. Our network is collapsing. We need to stop the bleeding… now!“ He chopped his hand into the other. “Let’s do the other stuff and get it on the roadmap, but let’s get this email out over the weekend. Who can help me put it together?” This decisiveness was informed by years of fierce in-the-trenches competition — companies like Flywheel, Sidecar, Hailo, and many others that were vanquished — driven by lightning fast responses in situations like this. The Uber team monitored and responded to the health of their local city networks with speed and precision. And with that, the next step was clear.

The RGMs agreed to own it, and I would work with my team — which was accountable the product/engineering side of driver referrals — to make changes to the structure and amounts. We committed to ship the changes before Monday. We took note of a number of other follow-ups due from the meeting, and we all decided to reconvene the group again next week. It was Friday and almost 10pm, and many of us had been working since early morning to prep for this meeting. I walked home, just a few short blocks away in the Hayes Valley neighborhood of San Francisco, and started my “Netflix and email” routine to close out the day.

This was my first experience with the North American Championship Series, and it turned into a weekly briefing, usually Friday mid-morning. But sometimes it got scheduled at Tuesdays at 9pm, or Sundays at 2pm, when that was the only way to get everyone together. Although NACS was just one part of my role at Uber, it quickly became one of the most educational in how to think about starting and scaling network effects. For a multiyear span, I was lucky to embedded in this critical team that operated Uber’s biggest markets. Each week was different. At the NACS meetings, we shifted our attention nimbly each session from network rebalancing on the West Coast, to prioritizing product features to increase revenues, to launching new regions, and everything in between.

Uber was already hitting its stride when I joined but I had a front row seat to the team that took grew the business to 100 million active riders in 800+ markets worldwide, and $50B in gross revenue. It was an incredible experience, and am proud of the work that we did there. It didn’t happen automatically — there were tens of thousands of people working hard to deal with network dynamics in hundreds of markets around the world, and we learned all the hard lessons from competing with fearsome local competitors who have their own strong network effects too. I’m lucky to have been at Uber during a hypergrowth period, where I joined just at the base of the hockey stick curve when it was well under a billion trips, but saw it 10x over the next few years:

My time at Uber was an unforgettable experience. I got to see a startup scale to tens of thousands of employees, millions of customers, and billions in revenue. I saw new products start at zero and then rapidly scale up to dominate the market. It was a deeply educational journey, one that created many lifelong friendships — including people I still talk to every week. But by 2018, it was time for me to move on. The company had a tumultuous few years, a complete changing of the guard, and a new set of priorities that were less entrepreneurial than in the past. I wanted the opposite of that, and for my next chapter, I decided to go back to my roots: Working with entrepreneurs to build the next new thing, but this time, as a venture capitalist.

Foundational questions
In 2018, I began a new career after Uber, as a startup investor at Andreessen Horowitz. Started a decade earlier by entrepreneurs Ben Horowitz and Marc Andreessen, the firm made a splash when it launched, quickly making a series of notable investments in startups including Airbnb, Coinbase, Facebook, Github, Okta, Reddit, Stripe, Pinterest, Instagram, and others. The firm was built around a philosophy of hands-on operating expertise — this fit me perfectly as I would parlay my lessons from Uber into picking and building the next great technology startups.

Rejoining the startup world, this time as an investor, let me tap into a network of relationships and knowledge built over a dozen years in the San Francisco Bay Area. Pre-dating Uber, I had been writing and publishing nearly a thousands essays on topics like user growth, metrics, viral marketing — along the way, popularizing tech industry jargon like “growth hacking” and “viral loops.” My blog was would be read by hundreds of thousands, and due to this as well as the natural serendipity of the startup ecosystem, I came to become acquainted with a broad community of entrepreneurs and builders. I would come to serve as an advisor and angel investor to dozens of startups, including Dropbox, Tinder, Front, AngelList, and many others. All of this, combined with my expertise from Uber, would be the foundation to launch my career in venture capital.

Everything was different in the new role. Rather than commuting to Uber’s offices in the chaotic center of San Francisco, instead I headed to the firm’s idyllic offices near Stanford University. The a16z offices combine culture and invention — its hallways lined with artwork from Rauschenberg, Lichtenstein, and contemporary artists, while its conference rooms are named after great inventors and entrepreneurs like Steve Jobs, Grace Hopper, Ada Lovelace, and William Hewlett. The work was very different from Uber’s day to day as well — rather than going very deep into one sector, like rideshare, instead my purview was extremely broad.

Every day I was meeting with entrepreneurs to talk about their new ideas. In a given year, the firm might see thousands of startup ideas, many of which are new kinds of social networks, collaboration tools, marketplaces, and other new products — relevant to the examples to this book. Conversations with startups begin with a “first pitch” meeting, where the entrepreneurs introduce themselves, show the product, and talk through their strategy. These are pivotal meetings, because when they go well, the startup could eventually receive an investment in the millions or even hundreds of millions of dollars. It’s high stakes.

Jargon thrives in these presentations: “Network effects.” “Flywheel.” “Viral loops.” “Economies of scale.” “Chicken and egg.” “First mover advantage.” These are some of the buzzwords and jargon that get thrown around in pitch meetings. And they are often accompanied with diagrams full of arrows and charts going up and to the right. The term “network effect” has almost become a cliché. It’s a punchline to difficult questions, like “What if your competition comes after you?” Network effects. “Why will this keep growing as quickly as it has?” Network effects. “Why fund this instead of company X?” Network effects. Every startup claims to have it, and it’s become a standard explanation for why successful companies break out.

But with all of these discussions and pitches, I realized I was getting confused, and I wasn’t the only one. While “network effects” and its related concepts were often invoked, there was no depth to the idea. No metrics that could prove if it was really happening or not.

In my work with startups, and after a decade and a half of living in the San Francisco Bay Area, I’ve heard “the network effect” used a zillion of times in conversation. Sometimes over coffee, in meetings, or in investor discussions, but the concept was always discussed at a superficial level.

So how do you hear something thousands of times and still not quite understand it?

I argue that we don’t understand network effects well because if it were a straightforward concept to understand, we would be in strong agreement on which companies have network effects, and which ones don’t. We would know what numbers to look at to validate it was really happening. And we’d have a step-by-step understanding of how to create and build up network effects. And yet we don’t. And it bothers me to a great degree, because it is has become a critical topic in today’s technology landscape. This is the journey that brought me to writing this book.

I began to research and to write THE COLD START PROBLEM because I found my own understanding of the dynamics of networks to be unforgivably shallow for something so core to the technology industry. The network effect is something I’ve seen firsthand at Uber, and yet I lack the vocabulary and the frameworks to articulate the deep nuances.

There’s a gap between the practitioners and the rest of the business world. For practitioners who work on specific networked products, the focus is on improving the mechanics within their very particular domains. Within rideshare, the discussion revolved around riders and drivers, reducing pickup times, surge pricing, and an accumulated set of specialized vocabulary and concepts that only apply to on-demand transportation. For a workplace chat tool, it’s about channels and discovery and notifications and plug-ins. They feel unrelated, even though both product categories have deep network effects and are both ways to connect people. There should be a set of universal concepts and theories to talk about network effects, regardless of their product category.

We need to be able to answer the basics:

What are network effects, really? How do they apply to your business? How do you know if your product has them — and which other products don’t? Why are they so hard to create, and how do you create them? Can you add a network to your product after the fact? How do they impact your business metrics, at the tactical level? Is Metcalfe’s Law actually right, or should you apply something else to your strategy? Will your network fail and will it succeed? Does your competitor have network effects, and if so, what is the best way to compete with them?

Startup advice says, all that matters is to build a great product — after all, that’s what Apple does. But why has it also been so critical to launch products in the right way? To get your product in the hands of influencers, or high school students, or aspirational technology companies — if B2B — if all that matters is the product? What’s the right way to launch, and what’s the sequence of ways to expand?

How do you build network effects in your product? How do you know when network effects are kicking in, and if they are strong enough to create defensibility? How do you pick the right metrics to optimize to achieve viral growth, re-engagement, defensibility, and other desired effects? What product features do you build to amplify network effects?

When fraudsters, spammers, and trolls inevitably show up, what’s the proper recourse? What have we seen other networks do in the past to combat the negative effects of a large, thriving network? And more generally, how do you keep scaling a network that’s already working, especially in the face of saturation, competition, and other negative dynamics?

What happens when two networked products compete — what makes one player win over another? Why did we see big networks often succumb to smaller ones? How do you launch new networks across new geographies and product lines, particularly in competitive markets?

These are the most fundamental questions we can ask about network effects, and when you search for the answers — whether in books or online — there are only smatterings of actionable, pragmatic insights though there was plenty of high-level strategy. The best thoughts came from operators, at startups and bigger companies, who have done been in the trenches and so that’s where I started the process of writing my book.

I began by conducting more than a hundred interviews with the founders and teams that built Dropbox, Slack, Zoom, LinkedIn, Airbnb, Tinder, Twitch, Instagram, Uber, and many others. I asked them questions to learn about the earliest days, when it was just the co-founders and a handful of other people trying to take on the world. I also researched historical examples spanning hundreds of years — going back to chain letters, credit cards, and telegraph networks, and tying their success to modern innovations in Bitcoin, livestreaming, and workplace collaboration tools. All of this exposed a rich set of qualitative and quantitative data which forms the foundation of this book.

I found that people were repeating the same ideas and concepts, and observed that they were recurring throughout multiple sectors. You could talk to someone who spent their career working on social networks, and find that they had ideas that were equally applicable to marketplaces. Similarly, my time at Uber made me understand the dynamics in a network of riders and drivers, which informs my view of products like YouTube and its two-sided network of creators and viewers. Or Zoom, with its meeting organizers and attendees. Dozens of these recurring themes echo throughout the industry, whether we’re thinking about B2B or consumer products.

The definitive guide to network effects
THE COLD START PROBLEM is the culmination of hundreds of interviews, two years of research and synthesis, and nearly two decades of experience as an investor and operator. It takes much of the knowledge and core concepts swirling inside the technology industry and frames them in the context of the beginning, middle, and end of a network’s lifecycle. This is the core framework I’ll describe via the major sections of this book, along with examples and hopefully inspiring you to take actionable can apply to your own product.

This is a critical topic. I’ve come to see network effects — how to start them, and how to scale them — as one of the key secrets of Silicon Valley. There are just a few dozen software products with a billion active users on the planet, and many of them share lineages of founders, executives, and investors who have unique expertise. This knowledge, in turn has been developed in the tech community over decades of building social networks, developer platforms, payment networks, marketplaces, workplace apps, and so on. This community of elite talent collaborates and cross-pollinates, switching from one product category into another, bringing all of this knowledge together. I have seen this first hand, and my interviews with founders and experts in writing THE COLD START PROBLEM further illustrated the interconnectedness of these concepts.

Based on the foundational theories of network effects, I’ve taken these lessons and put skin in the game, focusing my venture capital investing at a16z towards products that have networks at their core. I find myself most captivated by new startups where connecting people lay at the heart of the product, whether for communication, socializing, work, or commerce. I’m now 3 years into the industry, and have invested over $400M into over two dozen startups in marketplaces, social apps, video and audio, and more. I’ve found my learnings about network effects to apply widely across the industry — everything from Clubhouse, which seeks to build a new audio social app, or Substack, which lets writers publish and monetize premium newsletters for their readers. And even video games, food pickup, or edtech.

My goal is to write the definitive book on network effects — one that was practical enough, and specific enough, to apply to your own product. You should be able to use its core framework to figure out where your product is on the journey, and what product efforts are needed to drive it forward. I’ve tried to lay out the entire lifecycle — from the underlying mechanics of how to create network effects, how to scale them, and the best way to harness them — all from a practitioner’s point of view, diving deep far beyond the buzzwords and high-level case studies that have been written.

The first phase of the core framework, naturally, is called The Cold Start Problem, which every product faces at its inception, when there’s no users. I’m borrowing a term here for something many of us have experienced during freezing temperatures — it’s extra hard to get your car started! In the same way, there’s a Cold Start Problem when a network is first launched. If there aren’t enough users on a social network and no one to interact with, everyone will leave. If a workplace chat product doesn’t have all your colleagues on it, it won’t be adopted at the office. A marketplace without enough buyers and sellers will have products listed for months without being sold. This is the Cold Start Problem, and if it’s not overcome quickly, a new product will die.

This is all in the service of helping you, the reader, whether you are a software engineer, designer, entrepreneur, or an investor. Perhaps you partner with one of these companies I reference throughout the book, or are seeing technology reinvent your industry in the form of networks. Network effects are a powerful and critical force in the technology sector — as the entire economy is increasingly reinvented, it will become even more important to understand.

But let’s not get ahead of ourselves — first, what’s a network effect, anyway?

Above: Prosperity Club, one of the first chain letters. This is the type of spikey, bad viral growth you don’t want!

Hello readers,
Viral growth — specifically the type baked into product features, in the form of invites, sharing, and collaboration — is a topic I’ve been endlessly fascinated by. It’s the central force that has propelled many of the world’s most popular products to billions of users. I’ve been talking about viral loops, k-factors, and invite mechanisms for almost ten years now — it’s a favorite topic.

However, having implemented many of the ideas in my own startup products (back in the day) and then in viral referral programs at Uber, I have several new interpretations of these classic ideas that I want to share. Importantly, I have a chapter of my upcoming book, The Cold Start Problem, where I argue that viral growth is a flavor of network effect (more on that later) — which has important implications on how to amplify its effect.

(And by the way, you can order my upcoming book here! I appreciate your support)

I plan to write a few essays on this topic — it’s relevant, given the new trend of “Product-Led Growth” in B2B, and the resurgence of new social apps in the wave of Creator Economy startups. It’s time to revisit this topic in more detail.

thanks,
Andrew

Viral growth and retention are inextricably linked
Most people think of viral features in their app — like sharing, collaboration, or invites — in the context of a single session. How do you get your users, upon installing an app, to invite at least a few of their colleagues and friends? You might focus on small growth hacks, like leveraging their addressbooks for inviting, pumping up the dollar amounts in referral codes, or radically optimizing a sharing flow. But this is a simple minded way to look at viral growth.

The better way to understand virality is that it is inextricably linked to retention. After all, if a user retains well and is likely to use the product almost every day through the course of a month, you’ll have 30x the number of visits as a product that doesn’t retain well and generates a lot of one-and-done users. Each visit is a small opportunity to prompt them to share, invite, or otherwise bring a friend in. You’ll have 30 shots a month, and each shot can add up through the course of time to represent a lot of invites sent, or content shared. Whereas a poorly retaining product has be aggressive, making most of the one session where the user will engage, a sticky product can take its time, building viral growth over weeks and months. When you think about this mathematically, it makes sense — a (nearly) infinite sum of invites generated by a sticky product will eventually surpass a single, static number generated in one or two sessions.

But I get ahead of myself. First, what do I mean by viral growth anyway?

Viral growth is often misunderstood
Let’s start with all the confusion with the term “viral growth” in the context of new products. Let me excerpt a passage from The Cold Start Problem where I describe how it’s often misunderstood:

Viral growth is deeply misunderstood — you might read the phrase and think, is this the same thing that happens when a funny video “goes viral?” Or maybe it makes you think of an ad agency organizing a clever stunt, like a flash mob, where dozens of people simultaneously begin to dance, and then sharing it on social media?

No, what I’m referring to is completely different. What ad agencies have called “viral marketing” is usually to take consumer goods or services — usually with no network effects — and build an advertising campaign around it, usually with a bit of shareable content. What I’m referring to — network-driven viral growth — is much more powerful.

[Products with network effects] are unique because they can embed their viral growth into the product experience itself. When a product like Dropbox has a built-in feature like folder sharing, it can spread on its own. PayPal’s badges and core user-to-user payments also accomplishes the same. This is the Product/Network Duo at work again, where the product can have features to attract people to the network, while the network brings more value to the product. Workplace collaboration products like Slack ask you to invite your colleagues into your chat, and photo sharing apps like Instagram make it easy to invite and connect to your Facebook friends. They can tap into your phone’s contacts, integrate with your company’s internal employee directory, or tap into the sharing widgets built into your phone. This is software, not just building a buzzy, shareable video.

(From Chapter 20 of The Cold Start Problem: The Acquisition Effect — PayPal)

This is an important distinction because it’s the product-led versions of viral growth that has propelled products like Facebook, Slack, LinkedIn, Dropbox, Zoom, and others to hundreds of millions of users, or billions, of users. In fact, these the userbases for these products are so huge that it would have simply been impossible to build them through traditional marketing techniques that emphasize customer acquisition spend. It’d take tens of billions of marketing dollars to build a product with that type of scale — it’s simply not possible. Viral growth is the only way to get there, because the primary business benefit of virality is that the customer acquisition cost is zero.

Instead, the right way to think about viral growth — one of the most powerful forces in user growth — is the following provocation:

How does your product grow itself, based on its network of users to attracting their friends/colleagues?

In other words, viral growth is the ability for a product to tap into its network of users to bring in even more users. Sometimes this is via sharing, collaboration, or invitations. There are dozens of mechanisms to help drive this forward, and you can write software to optimize the virality to be more effective over time. In this way, I’ve come to believe that viral growth is actually a flavor of a network effect — it’s the ability for a product to leverage its network of users to acquire other users. (Alongside the Engagement Effect, and Economic Effect, I refer to this as the Acquisition network effect — it forms the trio of forces that underpin the broad term “network effects” that we use in the industry)

But the most mind-blowing part of this idea is that you can actually quantify viral growth — using what’s commonly referred to as viral factor.

The viral factor
We’re going to take a nerdy detour and do a little simple math, and cover the single most important metric at the heart of viral growth. In its simplest and most naive retelling, the viral factor is a single metric of how well the existing network of users in your product ends up inviting the next batch of users. For example, let’s say your new workplace chat app has 1000 active users, and over the next few months, this group invites 500 of their coworkers. This means there’s a viral factor of 0.5, which 500/1000=0.5. But after you get the 500 new users, they eventually also invite 250, then 125, and so on.

This is a powerful metric, because it adds up. Here’s how viral factor helps you multiple your userbase — a simple equation:

1/(1-v) where v=viral factor

Thus, a viral factor of 0.5 = 1/(1-v0.5) = 2x. So if you start with 1000 active users, with a viral factor of 0.5, you’ll end up with 2000 actives at the end.

With a tremendous amount of difficulty — honestly, far more than is worth it — it’s possible to directly measure the viral factor of a product and then instrument each step of the signup funnel and invite flows. This then gives you the ability to make changes to specific parts of your flow and see its effect on viral factor. Optimize a single page, and see your viral factor grow +10%. Add a prompt to share, and grow your viral factor even more.

The magical thing that can sometimes happen — for short periods — is when viral factor exceeds >1.0. This means 1000 users brings in 1100 users, which brings in 1210, and so on. (For the nerds, the equation is: users*v_factor^(1+time_period), which gives you the new users each time period, which you can then sum up in a spreadsheet). Of course, >1.0 means exponential growth. Back in the day, when asking people to invite their friends was fresh and new, you might have 20% of users invite 10 people during their onboarding flow — it meant crazy growth right off the bat.

When I first grokked the viral factor concept, it blew my mind. And if you’re like me, it makes you jump immediately to a key question:

How do you build a product with viral factor over 1.0!!???

… but funny enough, this is the wrong question to ask. It’s the tail wagging the dog.

And let me explain why.

How viral factor can nudge you towards spammy thinking
In the early social network days — think of this as around 2005-2010 — the model for increasing viral factor was simple. For any social network, you’d view it something like this:

X = % of users who invite friends
Y = # of friends they invite

X*Y = viral factor (albeit the most naive understanding of it)

At the time, Web 2.0 was just starting, and the concept of an invite was considered novel. The goal at this point was to get as many people to invite friends as possible, and to use techniques like email addressbooks to maximize the # of people you invite. And if you get these two to increase, you could dramatically increase the amount of users brought in through viral growth. A lot of innovation happened here. It became obvious that asking people to invite as early as possible in their signup flow was better, since there was less of a natural dropoff of users then. If you asked people to put in emails of their friends, they might only put in 2 or 3. But if you asked them to “Find their Friends” by connecting their addressbooks, you could sneakily ask them to email 200+ of their contacts. With enough work optimizing email invite subject lines, deliverability, and the various signup flows, you could get a viral factor over 1.0.

There was a problem with this strategy though — it pushes you towards spammy thinking. It incentivized product designers to aggressively get people to invite their friends — sometimes aggressively, leading to accidental sends of 100s of email invites. Eventually the term “dark patterns” was coined to describe these kinds of user interfaces. As users got used to them, they learned to skip prompts, ignore email invites, and response rates went down. For a short time window, this all worked, leading to spammy apps gathering millions of users overnight. But years later, very few of this generation of startups endured — we rarely talk about social networks and Facebook apps like Tagged, Hi5, BranchOut, and RockYou, who grew quickly and mastered this style of viral growth. Many of these products would also quickly collapse. In an essay I wrote over a decade ago, called Facebook viral marketing: When and why do apps “jump the shark?” I built a series of simple Excel models showing how poor retention eventually cancels out viral acquisition. And as the app saturates its market, it quickly falls back down to from the peak, thus a “shark fin.”

The predecessor to viral invites
The funny thing is that history repeats itself. If you go back 100 years ago, you actually see this entire phenomenon of highly viral mechanics followed by a quick collapse in the late 1800s, in the form of chain letters. And this story precisely tells you exactly why you don’t want to build viral loops like this. Here again is an excerpt from my upcoming book, The Cold Start Problem.

Chain letters — yes, the type you still occasionally get via email, or see on social media — have their roots in snail mail, first becoming popularized in the late 1800s. One of the most successful ones, “The Prosperity Club,” originated in Denver in the post-Depression 1930s and asked people to send a dime to a list of others who were part of the club. Of course, you would also add yourself to the list as well, as to participate. The next set of people would return the favor, sending dimes back, and so on and so forth — the promise was that it would eventually generate $1562.50. This is about $29k in 2019 dollars — not bad! The last line says it all: “Is this worth a dime to you?”

It might surprise you that in a world before email, social media, and everything digital, The Prosperity Club chain letter spread incredibly well — so well, in fact, that it reached hundreds of thousands of people within months, within Denver and beyond. There are historical anecdotes of local mail offices being overwhelmed by the sheer volume of letters, and not surprisingly, eventually the US Post Office would make chain letters like Prosperity Club illegal, to stop their spread. It clearly tapped into a post-Depression zeitgeist of the time, promising “Faith! Hope! Charity!”

However, even if chain letters have network effects, they suffer from the dynamic that they are heavily oriented around viral acquisition and lack strong retention mechanics. Ultimately the value of these networks are primarily driven by novelty and require a constant inflow of new people into the chain. Yes, in this way, it’s just like multi-level marketing campaigns, Ponzi schemes, and the like. And of course, what happens with both chain letters and Ponzi schemes is that they collapse when the mathematical limits of the expansion mean it runs out of new, novelty-seeking recipients, and as a result, existing participants stop getting paid. This in turn, causes churn, which then unravels the network entirely. A network needs retention to thrive, it can’t just continually add new users.

If you want to see an example of this chain letter, I included a screenshot of it at the top of this essay. It’s a fun historical example of something that has the same dynamics as viral invites on email and in other forms. And yes, if you did the math, you could model out the viral factor of the Prosperity Club chain letter — you just have to ask yourself, what % of people who receive this follow the instructions to mail it out to 5 new people? If it’s over 20%, then it will be viral factor >1.0 and grow exponentially.

The myth of viral factor and why retention matters
So back to the original point of this essay. If these aggressive viral loops aren’t in fact the right way to think about viral growth, what is the better approach instead?

In the aftermath of the social network wars and the eventual shutdown of the Facebook apps platform, I looked at the products that grew with viral loops and succeeded, and they shared a couple interesting traits. First, unlike my original assumption, they weren’t very aggressive about viral growth. I remember thinking through Dropbox and Slack’s viral loops, and realizing their viral hooks just felt natural, and user aligned. How was it at these products which were growing the fastest were also not aggressive? I realized they had the benefit of a secret weapon: Retention. This changed their math entirely.

To update my mental model, I realized that a modern theory of viral growth has to incorporate a number of nuances:

• Viral factor isn’t actually a single, static number. In reality, people can share and invite in sessions over time. So 100 users can invite 10 friends in the first week, then another 10 in the next, and so on. The more sessions you have with a user — meaning, higher engagement and retention — the more you can build up the viral factor over time. It can be 0.2 on the first session, then 0.3 on the 5th session. And 1.2 on the 50th session. The better the retention, the more “shots on goal” you get to ask the user to take the viral action
• Asking people to invite 100s of others isn’t realistic. There was a brief period of time where consumers were naive and might accidentally invite everyone in their email addressbook in one click. Not so, anymore. Spam scores will punish you for sending tons of random email. Users will complain when this happens. Gmail’s spam filters filter out all this “bacon.” And also, people are building mobile apps, and generally are more suspicious about sharing contacts (<20% of people shared contacts with the Uber app, at least when I was there), and they might only text an invite link to a handful of users. At one point, people tried to use mobile contacts and Twilio to deliver invite links, but that didn’t work very well — this technique fizzled. Products now grow by having users invite small batches of other users, in natural use cases
• Sharing > invites.  The advantage of a simple viral mechanism like invites is that you can generally apply it to every product. On the other hand, the problem is that consumers are tired of this mechanic. Same with “Give $10, get $10” which was novel 5 years ago, but no longer fresh. And less fresh means lower response rates. Instead, the best viral features are “native” to the product. Dropbox’s folder sharing functionality is both a really useful feature, completely consistent with the goal of the product, and also generates millions of organic, virally acquired customers for free. Same with Instagram, whose early content sharing features helped it grow without paid marketing.
• Virality amplifies other channels, rather than being a core driver. Based on the above, it’s clear that getting >1.0 viral factor is in fact unrealistic. Instead, it’s more likely to get something like 0.5, which is a 2x multiplier on everything else that’s happening in the product. Or to slowly build up to 1.0 over a long period as a result of very strong retention. But it’s most often the case that other activities — organic word of mouth, SEO, content marketing, and so on, generates a stream of users, which are then amplified by viral growth

In this new model of viral growth, instead of the simple X*Y dynamic, instead you get an equation like this:

X = % of users who engage in some viral feature (ideally content sharing, collaboration, or other user-aligned feature)
Y = # of people who are invited each time (probably a small group, a handful at a time)
Z = % of users that retain between each visit

Viral factor = X*Y*Z + X*Y*Z^2 + X*Y*Z^3 …

In other words, it’s likely that X and Y are actually very small numbers — few users inviting a small set of other users. But if retention is strong (high Z) then this term gets added up over and over again. Eventually over time, sometimes weeks or even months, the viral factor can summed into something real.

This might sound hard. And I think it is, because it requires the product to be extremely high retention. But I think it’s a more accurate reflection of reality — that in fact, the highest retention products have empirically shown to be the most viral. And when you optimize a product that’s already working well with high engagement and retention, then it is able to grow even faster. And at the same time, it matches the empirical observation that low retention products eventually die, even when they grow really fast at first.

Lessons for the next generation of social apps and product-led GTMs
The core lesson of all of this is that — for better or worse — you can’t force viral growth to happen in an era where small batches of invites are sent to small numbers of people. Virality is still a powerful force, but it has to happen through retention and natural sharing flows, not forced invites. The underlying math of viral factors is still relevant, but needs to be adjusted to think of the metric as an ongoing number that builds up over time.

And finally, the medium of viral growth has shifted over time — early on, of course, it was chain letters! Kidding, I won’t go that far. Originally, it was more centered around email invites, then there were other forms of virality like sharing content or embedding playable widgets and so on. But today, that’s all moved towards sharing on social media — how do you make apps that naturally generate shareable content as part of using it? This is particularly interesting in the world of video and other visual platforms. Similarly, B2B products are interesting because a lot of these growth ideas are still counterintuitive in an industry that is still mostly dominated by sales-driven go-to-markets. Adding viral features throughout your product will naturally allow it to expand after landing in a new account — these underpin some of the huge successes, in recent years, of products like Notion, Airtable, Figma, and so on. I think we’ll continue to see more of this here.

There was a period of time that I was convinced that you could “engineer” a viral product into existence. And from a lot of work that I did pre-Uber, at my own startups, it turns out you can. But it doesn’t mean that you can keep them. And thus, the most powerful force in the tech industry isn’t viral growth, as I once believed. It’s actually retention, and strong product/market fit. That’s the magic.

The most frequent question
One of the most frequent questions that founders ask is – I’ve read all about the importance of user growth, so now, how do I hire a Head of Growth? It’s asked often for good reason. Growing your startup’s users and revenue is so critical that it makes sense to hire someone to run it, and to potentially add a team underneath them to support this goal. It’s been a decade since “Growth Teams” became popular in the industry, sprouting up at places from Facebook and LinkedIn to Uber, Slack, Dropbox and others — and it’s natural that startups want to replicate the strategy.

However, it’s rarely easy to hire a Head of Growth. Aside from the historically tight labor market for these skills, it’s also tricky to have a simple answer to the question. “It depends” is the right place to start.

First, you have to define the correct job requirements, and what you’re looking for ultimately depends on the context of your startup — Are you trying to get your startup off the ground? Or are you trying to scale its success? Or are you focused on fending off competitors? Or growing yourself out of a saturation point. Depending on your goals, you should be looking for different things. The initial step is to define your needs.

In this essay, I’ll aim to answer this critical question — how do you hire a Head of Growth — using parts of framework from my upcoming first book, The Cold Start Problem. In the book, I describe stage-by-stage how to successfully start and scale the central forces that power tech’s most successful companies — network effects. Network effects emerge when products, like marketplaces, social apps, B2B collaboration tools, and so on, get more useful the more users that are on them.In the book, I present 5 distinct stages of a product’s lifecycle that require different goals and skillset, and understanding these stages will help you answer the more important question:

How do I hire a Head of Growth for my stage in the framework?

Let me start by spending time on each stage.

Which stage of the startup journey are you on?
I want to excerpt a passage from The Cold Start Problem that describes each of the stages, but please have some important things in mind: Not every product has network effects. After all, some are simply utilities for individual users. However, for the ones that do — and many consumer apps and consumerized workplace apps do — it’s an incredibly powerful idea that fundamentally changes the dynamic of the journey. Many startups fall in this category, and there are unique growth challenges when getting these types of products off the ground.

At this point, I’ll take a few sentences to describe “The Cold Start Problem” — one of the central ideas in the book. The idea is that for every network effects-driven product, there’s an existential problem at the beginning where users won’t find a product valuable if there’s no other users on it. Imagine a dating app with no profiles on it, or a new video app with no users and no content. Or a workplace chat app where none of your coworkers are active. These products are useless, and it’s kind of a circular problem, since to get users you need users. That’s the Cold Start Problem.

And as you might imagine, if you want to hire a Head of Growth while you’re in this first phase, that is very different than someone who is taking you from tens of millions of users to hundreds of millions of users, which is primarily the phase that I experienced at Uber.

Here’s an excerpt from The Cold Start Problem on each of the stages in the book’s central framework:

The central framework described in this book is a new way to think about network effects — split into stages, each with its own distinct challenges, goals, and best practices. My goal is to not simply to describe what happens as a network grows and evolves, but actually how to take action and propel a product from one stage to the other.

There are 5 primary stages:

1. The Cold Start Problem
2. Tipping Point
3. Escape Velocity
4. Hitting the Ceiling
5. The Moat

1. The Cold Start Problem
The framework for the book starts with an empirical insight: Most networks fail at the beginning. If a new video-sharing app launches and doesn’t have a wide selection of content early on, users won’t stick around if they can’t find what they want. The same is true for marketplaces, social networks, and all the other variations – if users don’t find who or what they want, they’ll churn, but this leads to a self-reinforcing destructive loop. In other words, it is the average case that the network effects that startups love so much actually hurt them. I call these “anti-network effects” because these dynamics are downright destructive – especially in the early stage as it’s getting off the round. Solving the Cold Start Problem requires getting all the right users and content to be on the same network at the same time — making for a difficult launch plan.

This is the Cold Start Problem, and to solve it, I look at a series of examples — examining Wikipedia’s most prolific content creators, the invention of the credit card, and how Zoom launched a killer product. From these case studies, I describe an approach that focuses on building an “Atomic Network” — that is, the smallest possible stable network that is stable and can grow on its own. For example, Zoom’s videoconferencing network can work with just two people, whereas Airbnb’s requires hundreds of active rental listings in a market to become stable. I also look at the product idea at the heart of every network effect, and the similarities many startups have used to pick the product and its features. I also ask, what are the first, most important users to get onto a nascent network, and why? And how do you seed the initial network so that it grows in the way you want?

2. Tipping Point
It takes an enormous amount of effort to build the first atomic network, but it’s obviously not enough to just have one. To win a market, it’s important to build many, many more networks to expand into the market — but how does this happen, at scale? Luckily, an important dynamic kicks in: As a network grows, each new network starts to tip faster and faster, so that the entire market is most easily captured. This is the second phase of the framework, the Tipping Point. I use Tinder as an example here, showing their initial launch at USC, but then how their success then unlocked other colleges in, then larger cities like Los Angeles, and then the broader market — including India, Europe, and other markets.

Imagine network launch as tipping over a row of dominos. Each launch makes the next set of adjacent networks easier, and easier, and easier, until the momentum becomes unstoppable — but all radiating from a small win at the very start. This is why we so often see the most successful network effects grow city-by-city, company-by-company, or campus-by-campus as rideshare, workplace apps, and social networks have done. SaaS products often grow inside of companies — landing and expanding — which also jumping between companies as employees share products with partner firms and consultants. This is when a market hits its Tipping Point.

3. Escape Velocity
When a company like Dropbox, Slack, or Uber hit scale, it might seem like network effects kick in, and the next phase is easy. But it’s not — to the contrary, this is when technology companies start to hire thousands of people, launch a series of ambitious new projects, and try to continue the product’s rapid trajectory. The Escape Velocity stage is all about working furiously to strengthen network effects and to sustain growth.

This is where the classical definition of a “network effect” is wrong. Instead, I redefine so that it’s not one singular effect, but rather, 3 distinct, underlying forces: The Acquisition Effect, which lets products tap into the network to drive low-cost, highly efficient user acquisition via viral growth. The Engagement Effect, which increases interaction between users as networks fill in. And finally, the Economic Effect, which improves monetization levels and conversion rates as the network grows.

By understanding how these forces work, we can accelerate the systems that power them. For example, the Acquisition Effect is powered by viral growth and the user experience that compels one set of users to invite their others into the network. I describe examples like PayPal and their viral referral programs, or LinkedIn’s recommendations for connecting as tactics that increase the power behind the acquisition force. The Engagement Effect manifests itself by increased engagement as the network grows — this can be developed further by conceptually moving users up the “engagement ladder.” This is done by introducing people to new use cases via incentives, marketing/communications, as well as new product features — as Uber did as it leveled users up from airport trips to dining out to daily commutes. And finally, the Economic Effect — which directly affects a product’s business model — can be improved over time as well, by increasing conversions in key monetization flows and ramping up revenue per user, as the network grows.

Stitched together, all of these combine into a flywheel that can power networks into the billions of users.

4. Hitting the Ceiling
In many narratives about network effects, by the time a product has hit the Tipping Point, that’s the fairy tale ending of the company — it’s won. Ask the operators inside a company though, and you’ll hear a different story: A rapidly growing network wants to both grow as well as tear itself apart, and there are enormous forces in both directions.

This is when a network “hits the ceiling,” and growth stalls. This is driven by a variety of forces, starting with customer acquisition costs that often spike due to market saturation, and as viral growth to slows down. Similarly, there’s the Law of Shitty Clickthroughs, which drives down the performance of acquisition and engagement loops over time, as users tune out of stale marketing channels. There’s fraudsters, overcrowding, and context collapse — all natural outcomes of a network that grows and matures. And many other negative forces that grow as the network grows.

In the real world, products tend to grow rapidly, then hit a ceiling, then as the team addresses the problems, another growth spurt emerges. Then followed by another ceiling. Then another cycle after that, each one often successively getting more complex to address over time as the problems become more fundamental.

I look at a series of case studies as major products hit periods of slowing growth: The implosion of Usenet discussion groups from the early days of the internet, eBay’s slowing US business, to the origins of Nigerian prince scams. In each of these examples, sometimes they are easily dealt with and sometimes they destroy the network over time. The solutions are difficult — a successful product inherently comes with various degrees of spam and trolls. These are problems to be managed, not fully solved.

5. The Moat
The final stage of the framework focuses on using network effects to fend off competitors, which is often the focus as the network and product matures. While it is not the only moat — brand, technology, partnerships, and others can help — it is one of the most important ones in the technology sector.

However, there’s a problem — using network effects to compete with competitors is tricky when everyone in the same product category are able to take advantage of the same dynamics. Every workplace collaboration is able to leverage network-driven viral growth, higher stickiness, and strong monetization as more users arrive. Same for marketplaces, messaging apps, and so on.

This dynamic drives a unique forms of rivalry — “network-based competition” — that isn’t just about better features or execution, but about how one product’s ecosystem might challenge another’s. Airbnb faced this problem in Europe when a strong, local competitor called Wimdu emerged with a boatload in funding, hundreds of employees, and on paper, had more traction in its home market. Airbnb had to fight off its European competitor by competing on the quality of the network, and scaling its network effects — not via traditional competitive vectors like pricing or features.

Because all products in a category likely have the same type of network effects, competition ends up being asymmetrical while leveraging the same forces. A larger network and the smaller network in any given market have distinctly different strategies — think of it as a David strategy versus a Goliath strategy. The upstart has to use its smaller size to pick off niche segments within a larger network, and build atomic networks that are highly defensible with key product features and when applicable, better economics and engagement. The incumbent strategy, on the other hand, using its larger size to drive higher monetization and value for its top users, and fast-following any niches that seem to be growing quickly. I will also examine Uber and Lyft, eBay China and Alibaba, and Microsoft’s strategy of bundling new products, to go deeper on how networks compete.

By now, hopefully each of these stages are getting your wheels turning! Obviously this excerpt hints at a lot of different ideas that I cover in the book, and you can read more expanded versions of the ideas when it’s released!

Different skills for different stages
Back to the original question on hiring a Head of Growth. Since this essay is targeting at startups, I’m going to mostly focus on the first few stages of the framework and the skills that it implies for your new hire. If you look at each stage, they roughly imply a different set of problems you are trying to face – first, finding product/market fit and building the initial “atomic network” — a stable set of users who retain. Then finding the playbook for repeatable growth, and third, scaling growth into millions of users. The first few stages are very different:

1. The Cold Start Problem = Getting a critical mass of users to prove product/market fit
2. Tipping Point = Finding a playbook to find repeatable growth
3. Escape Velocity = Scaling up growth across many efforts and channels

And if you start to think about how this maps to the types of growth projects, some stark differences emerge. I’ll look at each stage and discuss, starting from the first stage.

Context, goals, and assessment for The Cold Start Problem
In many ways, the skills needed to succeed for the first stage are the hardest to define, because it’s the most generalist and entrepreneurial:

• The Cold Start Problem
  • Goal: Getting a critical mass of users to prove product/market fit
  • Roadmap: A series of “Hustle”-driven efforts — often idiosyncratic, entrepreneurial, and surprising
  • Outcomes: A stable “atomic network” of users who retain, are engaged, and provide a launching pad for the next phase

The honest truth it, this stage is generally usually pretty random. If you read the stories of companies like Slack, Reddit, Dropbox, YouTube, Uber, and so on, you’ll realize that what worked for one company probably won’t work for you. Passing out discount codes at the train station works for a rideshare app, and a funny viral video works for YouTube, but that may not be relevant at all. So instead, the early stage usually requires a lot of elbow grease. It requires the founders to be very involved onboarding users, sometimes one-by-one, into the product. It’s usually a 100% focus on acquisition — and not much on retention at all, so it’s more hustle and less notifications/email-led.

As a result, the “Head of Growth” that makes sense here may not be one at all. Either there’s nothing to “head” — it’s such a small startup team that it’s probably an individual contributor role — or secondarily, the people heading the growth efforts here should actually be one of the founders.

That might be disheartening for a group of early founders to hear. They may want to focus on a superpower on product rather than learning a new skill on growth. Yet it’s critical to develop an intuition around go-to-market and validate hypotheses on a new product’s growth strategy. Often, the founder has to come up with a novel launch strategy, since hiring someone leads to more of the same. In today’s zeitgeist, creator partnerships for “creator economy” companies might be the cutting edge, or using NFTs as a way to attract new users. I’m just not sure that you can hire employees to do this kind of work. Sometimes the really entrepreneurial projects — in product, but also in growth too — just have to be done by the founders.

Sometimes this opinion makes founders grumble. So what if you really, really want someone to lead growth at this stage? If you really want one, I might try to find an entrepreneurial generalist and have them mostly execute a playbook that’s already worked in the past. If a team is building a new social app, I might suggest growing from high school to high school, or college to college. Or if it’s a workplace app, to use social media to drive beta users into a wait list. Things we’ve all seen work, rather than asking them to innovate totally new growth tactics. With luck, you might be able to hire someone who’s entrepreneurial and can execute those strategies. If they’re paired with advisors and operators who are experts on the playbook, that can work well. But I still like to push one of the founders to ideally drive or be the primary sponsor of the work.

What’s needed to succeed at the Tipping Point
The next stage starts once there’s a small group of users, often only a few hundred or thousand who seem to be coming back and where the network is stable. I often use benchmarks like D30 >20% or projecting out M12 to be >30% to try to assess this. But more importantly, it should be qualitative. It should feel like it’s working.

After that, it’s time to stamp out these networks in a more repeatable fashion — this is when the market begins to “tip” in your direction, and in the book, I refer to this stage as The Tipping Point.

• Tipping Point
  1. Goal: Finding a playbook to find repeatable growth
  2. Roadmap: Testing a series of more scalable growth channels, showing that if one network can be built, then multiple networks might be launched as well
  3. Outcomes: At least one scalable growth strategy. Typically organic “pull” from the market combined with a scalable channel with reasonable metrics (CAC/LTV, or viral factor, or otherwise) going into in-app acquisition funnels

In the second phase, the Tipping Point, usually the growth efforts to focus on a set expert skills to scale rather than explore. If you are working on a product in real estate that competes with Redfin, the expert skill is probably figuring out how to capture high-intent traffic — like SEO/SEM. If you are working on social apps, it’s probably viral growth. If it’s a marketplace product, then maybe referral codes and paid marketing. Being able to successfully guess a startup’s most probably growth channels allows you to then recruit for a “Head of Growth” who can supercharge the strategy. At this point, it’s important to note that the focus is still on customer acquisition, although efforts to improve engagement might also start.

The “Head of Growth” at this stage is probably someone who is a doer. They should be able to create a detailed plan for how their first 6 months might look like, and the handful of people they might hire. This is someone who knows what they want, which is made easier since the company they’re growing has product/market fit. They are hands-on, and has maybe led a small team before, but I’d prioritize tactical know how over management skills. A hard requirement is that they’ve stood up a new growth channel from scratch before. It’s important to find a hire who hasn’t just maintained the strategy of bigger companies for years and years — they must know how to instantiate it out of nothing. I like to see people who are still very entrepreneurial, is potentially less specialized on a primary channel but maybe has done a few related growth areas — like a smattering of projects that cross through product-led growth, email marketing, and paid. (And yes, I get scared if they talk too much about hiring lots of agencies, big budgets for TV and radio, or if they’re pushing back hard against metrics — there’s a time for all this, but not right now)

During the interview, I tend to look for clear spikes in the specific channel that the team thinks is the most likely to succeed. While I’ll ensure they have a strong foundation for understanding growth, I also want them to be an expert in at least one growth strategy. In fact, I usually call this out — “usually people in growth are great at one channel, and know the other channels fine but less. What’s yours?” And I’ll follow that up with detailed questions about on their superpower channel, making sure — if they claim to understand email marketing — that they really understand how to improve deliverability, all the various tools in the market, technical details like setting up DKIM/SPF, etc. Or if they are focused on paid marketing, that they understand retargeting down to the level of understanding why tracking “pixels” aren’t really pixels, how cookies actually work, how to calculate CAC/LTV when conversions happen months later, and so on.

Does this sound hard to hire for? It is! Because the labor market is so tight for people with the right skills, sometimes the only choice is to cross-train someone from an adjacent category. I often like people who are former founders, or from product management or marketing who started their careers in banking/consulting. It helps if they show a deep quantitative interest in business. Similarly, product folks from highly quantitative categories like travel, games, and marketplaces tend to do well. I want them to be spikey in their skillset, and be strong at execution, even if they need to go to advisors and peers to figure out the actual growth strategy. If they can pick it up quickly, can attract and retain talented employees for the various specializations, it can sometimes be the Plan B. I tend to lean away from people from the brand world, or from agency work, or who have only worked at larger companies. Speed of execution is just so important here.

The biggest mistake I see here happens here is when the founders try to hedge their bets. They have a slide on “growth strategy” in their investor deck with 20 different ideas, when the key is to just iterate their way into 2-3 core things that work. In the end, the largest properties on the Internet are driven off of just a few growth loops — SEO and inviting for Linkedin. Direct sales and product-led viral growth for most B2B collaboration apps. A lack of focus means that the startup is perpetually finding the next quick growth hack, rather than starting to build a system for repeatable growth.

Next is Escape Velocity
At this point, the product is ready for Escape Velocity if it’s consistently growing >3-5x year-over-year growth rate with at least one (if not two) channels that are obvious levers. One oft-used metaphor is that the product becomes a “coin operated machine” where $1 of spend into the growth loops will spit out $5. Or if you just put X engineers on Y product optimizations, then the growth rate increases by Z. This is when growth becomes predictable, repeatable, and forecastable.

And with Escape Velocity, we see the emergence of the classic “Growth Team” that the big companies have.

• Escape Velocity
  1. Goal: Scaling up growth across many efforts and channels
  2. Roadmap: Multiple growth strategies — primarily 2 or 3 — that work in concert to grow the product to millions or tens of millions of users
  3. Outcomes: Multiple scalable channels, combined with optimizing the in-app acquisition and engagement loops

The “Head of Growth” in this configuration is really more of an executive than a tactician, though domain-specific knowledge is needed. This role starts to emerge with small growth team of >10 people, in a wider company of >50-100 employees. The Head of Growth becomes more of a people manager, can set strategy and recruit people onto the team with the needed skills, but also work horizontally across teams at the company.

The complexity happens across multiple dimensions. Both acquisition and retention both become priorities. On the acquisition side, it’s not just one channel but pushing into multiple new ones while maintaining the core. By this point, there’s a few growth channels that are working, but there’s a big push towards testing and adding new channels. Or at least variations of the channels. If TikTok ads are working, then maybe try Instagram and YouTube and other visual formats. If viral invites are working, then maybe add in referral programs and more sharing prompts. So often there is a big effort to just grow the core. At the same time, there should be an effort to try a few big new channels. And then finally, there is the matter of integration — making the acquisition channels work alongside the activation/re-engagement products that happen over email and push notifications. And optimizing the product funnels and sharing flows to amplify all the marketing efforts.

To give an example, at Uber, all of these growth efforts eventually got complex — very complex. There were teams working referral programs, landing pages, adtech integrations, funnel optimizations, activation sequences over email, retention and loyalty programs, as well as all the underlying infrastructure. There were projects to help with A/B testing (Morpheus!), communication platforms to streamline notifications to users, and much more. Then on top of all of that, add in several hundred people working on international growth, customizing the product to work better in China, developing countries, and otherwise.

For most startups, getting to this scale of a growth effort is a fancy problem to have — the team might be 100+ within a larger startup numbering thousands of people. The “Head of Growth” in this case should probably just be recruited from a larger incumbent who already knows how to do this. But if this is a smaller team than that, then you can similarly downgrade and find a growth leader from inside an incumbent that’s a first line manager.

The most common pitfall
What’s the biggest mistake that founders make in hiring their Head of Growth? In my opinion, it’s when the founders are trying to abdicate responsibility for their growth efforts, and hoping that a magical hire solves everything. The problem is, a startup’s growth is so fundamental that the founders have to take it on themselves. The buck stops at the top. If the founders pick the wrong growth channel to double down, even if they hire an expert on the channel, most likely the journey will end in failure. If the founders hire some amazing growth folks but don’t give them the engineering and product support to get their loops to work, then it won’t work either. The founders need to be there to supervise the growth, because ultimately they are directly responsible.

The second biggest mistake is probably to hire the “executive” Head of Growth too soon. If you are under 100 employees, you’ll only have a half dozen or dozen people working on growth, and in reality, you need a hands-on leader. Someone who’s primarily a people manager — particularly one that starts their tenure by hiring a bunch of agencies to build and execute their plans — will lead you down a path that simply won’t be concrete enough, at a moment when that’s what the startup needs. Early startups need to move the needle each week, growing 5 or 10% week-over-week, and no amount of PowerPoint can do that. The executive is helpful when you start needing to manage 3 or more teams focused on building a repeatable system — this is probably a 20+ team in a broader team of hundreds.

In the end, the  “Head of Growth” title is probably too vague. Each of these stages requires such different skills and are judged so differently that a single title feels like it doesn’t capture the nuance. Instead, founders to incorporate the context of their startup — what stage are they in? How much conviction do they have on their growth channels? How many people are going to constitute the growth team, and thus, how much expertise versus management ability is needed? What are the most similar companies — from a growth perspective — that they can recruit from? These are the questions to ask, and by answering them, the Head of Growth role will be more clearly defined.

Hi readers,

I’m back writing! Well, tweeting mostly, but then developing my points more thoroughly and cross-posting them to my blog. Hope you enjoy this one, which was inspired by a convo I had with Gagan Biyani at Maven about when to scale versus not.

Also, a brief announcement: I’ve released the Table of Contents for my upcoming book, The Cold Start Problem. I put it on the landing page here so you can look at it, but the book is chock full of examples from my time at Uber, but also looking at coupons, credit bureaus, the original Internet, banner ads, and so on.

Here’s the Table of Contents and preorder link.

The book release is in a few months and am excited to share more. In the meantime, here’s a new concept I want to share, the Traction Treadmill.

thanks,
Andrew
writing while visiting Venice Beach, CA!

Questionable product/market fit meets premature scaling
Here’s why it’s so dangerous to scale a product with questionable product/market fit: The Traction Treadmill.

It’ll kill your company. Let me explain why it happens.

Sometimes you build a product that’s just doing okay. Not great, but not bad either. Users show up, and some of them stick around – but not many. You can quantify this with cohort retention curves. For a social app, this might be when your D30 is 10%, not 20%+. For a subscription product, this might be when your annual retention is <20%. It sort of works, but not really.

So what do you do if it’s going OK, but not quite working? Some teams look at this and are overly optimistic – it’s time to scale. Just add users, instead of increasing stickiness. And this works, for a time. You can grow fast just by doubling ad spend. Or tripling ad spend.

The blessing of a small user base
In the early days this works. Let’s use a real example — let’s say you start with 1,000 active users, and then you want to 3x. The good news is, 3x with 1,000 users just means you need to buy 2,000 users. To grow it further, you just repeat again, right?

Well, not quite. Even if you’ve bought 2,000 users, you might only retain 500 of them longer term. (So that you have 1000 actives + 500 new actives). The problem is that a large % of these users burn off. That’s fine, maybe just replace them, by buying more users? To 3x again, you have to replace:

• The % you lost
• Plus, buy another whopping amount just to grow

If you’re starting with 1,000 and you buy a bunch to get to 3,000, and then again to 9,000, that works. Your marketing budget might allow for a $10 CAC, for instance, and your outlay is <$100k to triple each time. Always worth it. That might work, if you’re still dealing with 1,000s or 10,000 users

The treadmill arrives
The traction treadmill eventually arrives once the numbers get big. This is when you lose a % of users fast, but then just have the budget and funding to replace them- but then can’t keep grow on top. Eventually on a base of millions of users, you might churn a million users, and need to replace them — and buy more, to grow. So then you’re starting to talk about very large outlays, just to stay afloat, much less to consistently grow.

You start running hard and just staying in one place.

You might increase budgets. Pay up on CAC/LTV. Optimize and run experiments. And that works a little.. until the treadmill reappears.

The problem with the treadmill is that your team and scale makes it hard to iterate substantially on the product and business. You’re locked in. If things flatten for months, or a year, eventually morale becomes a problem. Options start to narrow. And the real solution – to increase stickiness – becomes too slow and complex to execute.

Whenever I see a high-flying startup that’s raised $100s of millions of dollars with a high paid marketing dependency which then flatlines, it’s usually some variation of this story. The problem ultimately is that the early days of growth are less about the intrinsic quality of the product, and more about the ability for the team to ramp up spend. And as CAC’s float up as volume increases – due to the Law of Shitty Clickthroughs – it gets harder and hard.

Then you fall off the traction treadmill. Game over.

Conclusion
In the end, this story isn’t about polishing your product forever. I’m certainly not encouraging that. But I do think you need to understand where your product stands relative to other successes (and failures in the market). The benchmarking is important. And if you try to scale and fix at the same time, be prepared for the treadmill to show up. By then, your nimble speedboat of a product will have grown into an aircraft carrier, and it’ll be hard to turn things around. This is when scale becomes the enemy to iteration.

(acquisition vs retention image above – hat tip: @mattwensing)

Hi readers,

Boom! I’m back with another — I told you, I’m really back, after 3 years of hiatus focused on the new book. Anywyay, first, I want to thank you for all the outpouring of support and kind notes on my return to writing. And of course, the preorders for my upcoming book THE COLD START PROBLEM which is all about user growth in the context of network effects — relevant for social apps, marketplaces, workplace collaboration apps, and so on.

For today, I’m cross-posting and combining two tweetstorms I recently did on the future generation of social apps, and what we can learn from the Web 2.0 era. I’m toying with the idea of doing a series on all of these “forgotten lessons” with the idea that they will be relevant for people coming up. More soon!

thanks,
Andrew
(writing from San Francisco, but headed back to LA shortly!)

Before and after — social network edition

When you examine the ideas that are percolating now for social apps, you see a clear pattern.

The past generation of social apps won the market through a playbook:

• building big networks with feeds for discovery
• creating a followers/status competition for engagement
• bringing together creators and audiences
• monetizing with ads
• supporting photo, text, video

The next generation of social is necessarily a reactionary movement:

• small networks or algo-driven big ones
• creating real connection with people
• letting creators own their audiences
• monetizing directly with subs, NFTs, etc
• evolving media format towards interactivity

The most interesting things about these “new” ideas is that they simply didn’t make sense back in the original emergence of the social network category.

Why didn’t these reactionary ideas make sense?
Back then, building large networks with invites and solving discovery with feeds was the right move when it was novel to simply see what your friends were up to. Today, too many connections is bad. Maintaining which of 1000s of high school friends and employers have access to which content, is quite simply a chore. Small chat groups simplify this. Algo-driven feeds also simplify by just showing you the best stuff. More will be invented to solve the problem of maintaining connections.

Similarly, the era of edited, posed travel, food, and concert photos showing the top 1% of peoples’ lives is creating a fake reality that is repetitive. This worked at first since it was a spectacle. A new app can focus on what’s real, or who has talent, or something in reaction to this overly repetitive 1%.

Originally, creators didn’t exist. They weren’t a thing. So bringing them together with their audience — even if they didn’t own that audience — was amazing since it provided a degree of ads-based monetization. Today, creators are looking for more, and new apps can cater. A lot of the Creator Economy, of course, is a reaction to pre-existing platforms that get between a creator and their audience. And brands sponsorships who inevitably nudge creators into compromising their integrity. New apps can target this and align creators with their audience. NFTs, subscription, ecommerce, and other biz models now exist for creators. More will come – lots of innovation here. Furthermore, the “back office” will become an important battleground. Instead of coffee shops and retail, online will be the next generation of small business.

Finally, the last decade of social has been about simply putting videos, photos, and text up. And making this easy for anyone to publish. This has unlocked hundreds of billions of dollars of business value. I think it may not be enough to simply do more photos, text, and video. Instead, new platforms will emerge that let people author 3D content more easily — and maybe put it into a game. Or interactive content. Or NFTs. And of course audio. All of these new forms of media will probably look like toys at first, but they may catch on in a big way.

All of these ideas weren’t the first set to tackle during the Web 2.0 days. In a world where uploading a video was hard, it made sense that Youtube was the first type of app to be built. Social status and competition is an easy engagement hook.

It is the success of the incumbent social networks that have established dominant norms that then allows new startups to try new ideas that might work. However, the new is built from the old, and there’s an important set of knowledge and skillset from 15 years (!!!) back that needs to be considered. Yes, let’s talk about Web 2.0.

The lost art of Web 2.0
There was a generation of founders trying to add social to everything. Social and music. Social fitness. Social news. Social social social. There were new kinds of creation, profiles, etc. No, I’m not talking about today. I mean 15 years ago!

There’s a lost art of Web 2.0.

This was the era of Flickr, Tumblr, Blogger, Hi5, Digg, Slide, Imeem, Flixster, Odeo, and dozens of other companies that just the grizzled old tech veterans now remember. Web 2.0 was about a lot of things. There was big UX breakthroughs, like tagging, profiles, feeds, photos, and everything else in user generated content. This was built on AJAX (lol!), Rails, early cloud platforms, and so on. New apps were being built in weeks – but most failed.

A key lesson from Web 2.0 — growth matters
After all, one of the hardest lessons we learned from that period: It wasn’t enough to build the app, you needed to also grow a critical mass of users. Without the right users there at the beginning, a user-generated content product would inherently be too shallow on content. Low engagement would lead to more low engagement.

People solve this with ideas like wait list hacks, A/B tested invite flows, contact importers, “people you may know” algos. Embedded video widgets, Facebook platform API hacks, viral factor tracking, and so on. The details of many of these ideas are intricate — to this day, there are teams of dozens of people working on “people you may know” recommendations at companies like LinkedIn and Facebook. Building the density of connections is incredibly important. Yet these fundamental tactics are getting swept under the rug while fun, new concepts like creator houses, memes, etc take all the spotlight.

15 years later, social is back but much of the hard won lessons of web 2.0 have been lost. The folks of that era are in their 40s now – more likely to be investors or execs at big tech cos (you know who you are! Lol) – than the new gen founders trying to build the new social app. Of course history will not repeat itself – it’ll all be reinvented

Yet many of the same core strategies still hold. It’s still better to focus on a single community, gain saturation, before adding adjacent networks. Viral loops are still a thing that can be constructed, measured, and optimized. The core stickiness of an app is all about p/m fit

The next generation of social
Web 2.0 was a magical time with 1000s of new social apps. Today’s resurgence is comparable in scope, but with a larger maret, built on the supercomputer in our pockets, and with the full knowledge of how big it can be when everything goes right.

It’s gonna be magic again.

Hi readers,

I’ve been MIA! But ready to be back and writing more now that I’m finished with my book (more on that in a second).

Two things today:

First, I recently posted a tweetstorm about my adventures in the LA tech ecosystem, and how it compares to SF, and the upcoming changes. I have it below, with some updates and edits

And second, I’m happy to announce that I just turned in a final version of my upcoming book, THE COLD START PROBLEM, which has its final book cover and is ready for pre-order.

Pre-order here!  (Not in the US? Canada · UK · Australia · France · Germany · Japan · India)

I’ll talk a bit more about the book and the process there shortly, but wanted to drop those links in too.

Thanks,
Andrew

The clash between SF and LA, and how it’s thawing
Something big is happening at the intersection of tech and entertainment – of SF and LA. From LA, you see celebs angel investing, and promoting tech. And SF startups are actively courting creators, both mainstream and digital native, as the anchors in their new social products

This was not always the case. For a long time, the SF world found the LA ecosystem to be byzantine, full of gatekeepers and copyright owners. The most common interaction was music labels and movie studios suing early startups – Napster, YouTube, BitTorrent – not collaboration.

From the LA POV, the user generated platforms – Web 2.0 – were building products built on the backs of content creators and owners, but without giving much back. Whereas the entertainment ecosystem is driven by cash and % profits, the startup ecosystem is built on equity

And it went on for years.

How the thawing happened
But in the recent past, some big things have happened:

• First, you had Dr Dre making $700M+ on selling a company like Beats to Apple. Tech can be big when you engage
• The social apps also got so big that gaining followers became obviously valuable
• For creators who weren’t yet successful in the traditional mold — with an agent, TV show deal, or otherwise — the openness of the social media ecosystem became a huge plus. This created a new type of talent
• Also, Netflix/Amazon/others became simply too big to ignore or reject
• The social media platforms became a major force for brand and advertising dollars. And a way to launch new consumer brands. If talent embraced this, it could be a new way to build an empire

The view of startups started to change also — and not just bc of the “creator economy.” Startups began to realize – in a crowded growth ecosystem with tapped out paid mkting, etc – that influencers could be a new effective channel. It helps with PR – and for recruiting – when celebs (both mainstream and B2B influencers) invest in your company. Every edge counts.

Creator go-to-market
The “creator economy” thesis leverages celebs/influencers/creators both as an end customer but also as a distribution method. When creators use your product – like a Substack or Shopify – they naturally promote it as part of its use. Yet there are limits to how well this SF x LA intersection will work

It’s not yet clear that celebs can “make” a product happen. Yes, sometimes they join early, as they did with Twitter/Clubhouse, and can accelerate growth

There’s still a culture clash between the two worlds. Celebs/creators/influencers often expect to get advisory shares, or to get the last round’s valuation, simply for being involved. The gatekeepers also often ask for their own — it’s annoying! Tech is not building beverage cos here!

The folks in movies/TV/music are still somewhat curmudgeonly. Yes, they are engaging with tech – not just suing them – but it’s still early. New young startups can’t afford licensing, upfront fees, and so on

What’s the solution?

The next wave
The opportunity might be to simply to engage with new sectors. Rather than working with the classic A-list stars, instead, the opportunity is to engage TikTokers and Twitch streamers. Or bloggers and Substackers, rather than published authors. These digitally native content creators got big by adopting technology early, and might be willing to work with startups earlier. They don’t work with the traditional gatekeepers in entertainment, and thus might be easier to connect with — startup founder to digital creator — to quickly work out deals.

The truth is, well-established talent in the entertainment world often face a kind of innovator’s dilemma in adopting new products. If they jump in too soon, they might expend energy on an app or technology that doesn’t go anywhere. They might also end up creating a lot of value that is hard to create. By not charging to participate, they might undermine their other mainstream revenue streams, where they charge for commercials, appearances, and otherwise. Digital creators often don’t have any of this to worry about.

The other thought, even more radically, to invest in game studios and new interactive experiences – not film/movies. As we’ve articulated in the past, we think games will be the next social network, and the next huge entertainment medium. I have a Venturebeat interview here that spells out the thesis. Importantly, games are software. They launch like apps, buy Facebook ads, engage social networks, and have many of the motions that look like the software companies we’re used to in SF. Rather than focusing on film/TV/music, it might be that games becomes the focal point that brings the two groups together.

This is a long way of saying, while LA x SF is trending up – more cross-pollination, more people spending time in both, more collaboration – it’s still early. But more is happening and it will unlock new approaches to engage consumers that could never of happened before

My next moves
I’ve committed to spending more time in LA over the next year – something like 60/30/10 in SF/LA/other will be the split. In recent visits, I’ve found a fascinating ecosystem of people. I would summarize these constituents like:

• “new to LA” founders — this is the new gen of startups
• fancy SF CEOs who are now in LA — lots of folks have moved down, on the down low!
• SF/LA investors — especially seed funds, and SF folks who have relocated
• digitally native creators — tiktokers, streamers, etc
• big co tech platforms — YouTube and Netflix folks who have a big presence in LA
• incumbent entertainment folks — this is a big umbrella, but includes agencies, studios, talent, and much more
• DTC startups — lots of these!
• OG LA tech — the folks who started it all

There’s many other groups of course, and it’s quite diverse, but fascinating to see a much more complex ecosystem — in many ways — than SF which constitute maybe a third of this. SF is more clustered into various “mafias” than LA, since it’s more tech than not.

Anyway, I plan to be between LA and SF in the coming quarters. If you’d like to meet up, send me a DM!

Above: One of the final book cover designs I’m considering!

Dear readers,

So, you may have noticed that I’ve been away from writing for a bit. Actually for almost a full year. But I have an excuse! After nearly three years and many late nights and long weekends, I have an announcement.

My first book is dropping in late 2021, called THE COLD START PROBLEM, published by Harper Business.

You can pre-order it here on Amazon »

😎😎😎

I’ll have a lot more to announce about the book soon, including bonus material, add-ons, and more.

This is my first book, and… wow. My tldr; on the experience of writing a book is: OMG IT IS SO MUCH WORK. It started out benign — I thought it would be fun to do a little research to explore doing a book, and interviewed friends from Uber, Airbnb, Slack, Zoom, Dropbox, Tinder, and many more interesting companies. 20 interviews eventually turned into nearly two hundred.

I became obsessed with a topic that emerged. The products that most intrigued me in the tech industry are marketplaces, social networks, messaging apps, workplace collab tools, etc. — that can grow and grow. These products have network effects, but are unusual for how you start them. There’s a “cold start problem” when a social app launches and no one’s on it! You need a critical mass to make it functional. I started to organize all the stories I was hearing and organize them into a framework. It was an attempt to understand and process my own experience at Uber, and how it fit into the rest of the industry.

Eventually, I wrote an outline of what I wanted to put together as a potential book. Just the outline was 30 pages… gulp. Then came the writing. A lot of writing. Then even more writing. I did some of the writing in warm, sunny places like Miami and Cabo. But a lot of it was done on my sofa. It was a lot. Then COVID. Then writing from a van, driving across the country, while avoiding people, but still writing. And in fact, I’m still in the middle of fixing sentences and polishing what’s left, but it’s nearly done at over 300 pages. I’m starting to look at potential book covers (one of the candidates above) and I’m very excited for y’all to read it — much more on this soon.

This isn’t all that I’ve been doing in the last year. In other news, I recently led a16z’s investment in Clubhouse and joined the board of directors. Clubhouse is a new audio-first social network — definitely worth trying out. I mention it because I’ve been learning a ton from being involved — about growth, metrics, viral loops, and much more. From a growth perspective, it’s an incredible expression of network effects. It grows explosively through viral loops — it’s been a top app in Europe for the past week, and recently just landed in Asia, growing quickly — and also has increased its engagement as the network fills in with more diverse content creators. I’m learning a ton and am lucky to be working with Paul and Rohan, Clubhouse’s founders. More on this in the future.

Besides the new book and Clubhouse, I’ve also been sheltering at home in the Bay Area like most of you. OK, not quite true. I did take a long trip around the US recently in a van — that was fun. Check out some pics over here. I drove, wrote, and did Zoom calls for almost 3 months.

And finally, I just wanted to say: I’m back! Now that I’m starting to wrap up on the book, I’m going to return to a much more frequent writing cadence. Thanks for your patience, and I hope to begin writing a lot more coming up.

Thanks for reading,

Andrew

San Francisco, CA

Guest Post by Bangaly Kaba (EIR @ Reforge, Former VP Growth @ Instacart, Instagram)

The following was written by Bangaly with contributions by other Reforge EIR’s Elena Verna (Miro, MongoDB, SurveyMonkey) and Fareed Mosavat (Slack, Instacart, Zynga). Reforge is a community for those leading and growing tech companies. To learn more from Bangaly, Elena, and Fareed check out the upcoming Career Accelerator Programs like Product Strategy, Marketing Strategy, and Retention + Engagement Deep Dive.

When I joined Instagram in 2016, the product had over 400 million users, but the growth rate had slowed. We were growing linearly, not exponentially. For many products that would be viewed as an amazing success, but for a viral social product like Instagram, linear growth doesn’t cut it. My job was to help the team accelerate and get back to exponential growth. Over the next 3 years, the growth team and I discovered why Instagram had slowed, developed a methodology to diagnose our issues, and solved a series of problems that reignited growth and helped us get to over a billion users by the time I left.

Our success was anchored on what I now call The Adjacent User Theory. The Adjacent Users are aware of a product and possibly tried using the it, but are not able to successfully become an engaged user. This is typically because the current product positioning or experience has too many barriers to adoption for them.

While Instagram had product-market fit for 400+ million people, we discovered new groups of users who didn’t quite understand Instagram and how it fit into their lives. Our insight was that it is critical for growth teams to be continually defining who the adjacent user is, to understand why they are struggling, to build empathy for the adjacent user, and ultimately to solve their problems. And Adjacent User Theory doesn’t just apply to hyper-growth machines like Instagram, I’ve seen the dynamic play out again and again at plenty of other product-driven companies.

The Importance Of The Adjacent User

Solving for the Adjacent user is critical for a few reasons.

Solving For The Adjacent User Captures The Potential Of Current Product Market Fit

When you have Product-Market Fit, you have healthy retention curves (they flatten out). But this isn’t the end goal. Your current retention doesn’t represent the true potential of your current product-market fit. There is a hypothetical retention curve that sits above that represents this true potential.

What creates this gap? There are a set of users who show intent for your product but are not quite able to get over the hump. Those are your Adjacent Users. Solving for the Adjacent User through growth and scaling work helps your product realize its true product-market fit potential.

The Impact Of Solving For The Adjacent User Compounds Over time

Every year there are massive efforts to getting voters to register and get to the polls. Those voters not only impact the outcome of one election, but can change the engagement of future elections and generations of politics.

In a similar way, when you enable adjacent users to successfully experience the core value proposition, it not only changes the engagement of near term cohorts but flows through to creating impact for all future cohorts of users. This doesn’t just impact retention, but flows through your growth loops to impact acquisition and monetization as well.

The Adjacent User Is A Different Way To Focus Product Efforts

Most product teams know their existing users pretty well. But your future audience is always evolving. The challenges that these potential users face in adopting the product increase over time. Without a team dedicated to understanding, advocating, and building for your next set of users, you end up never expanding your audience. This stalls growth, and the product never reaches the level you aspire it to.

Going Deeper On The Adjacent User

You can think about your product as a series of circles. Each of these circles is defined by the primary user states that someone could be in. For example Power, Core, Casual, Signed Up, Visitor. Each one of these circles have users that are “in orbit” around it. These users have an equal or greater chance they drift off into space rather than crossing the threshold to the next state. There is something preventing them from getting over the hump and transitioning into the next state. These are your adjacent users and the goal is to identify who they are and understand their reasons struggling to adopt. As you solve for them, you push the edge of the circle out to capture more of that audience and grow.

Instagram Example Of The Adjacent User

Lets go through a couple of examples, starting with Instagram. The primary thresholds that a user has to cross to becoming a core user:

• Not Signed Up → Signed Up
• Signed Up → Activated
• Casual → Core Usage

At each one of these thresholds, there are users that are circling around them, that have an equal or greater chance of not crossing the threshold.

Bangaly: “At Instagram, if a user had more than 10 followers in the first 7 days after signing up there was over a 65% chance that the user would become activated. There was always a group of users on that margin that would struggle to build their audience. But the reasons they struggled varied across different sets of user and changed over time.”

Slack Example Of The Adjacent User

Lets go through an example for Slack. The primary thresholds that user has to cross through are:

• Not Signed Up → Signed Up (Acquisition Teams)
• Signed Up → Casual (Activation Teams)
• Casual → Core Free (Engagement Teams)
• Core Free → Monetized (Monetization Teams)

At each one of these thresholds, there are users that are circling around them, that once again have an equal or greater chance of just drifting off into space.

Fareed: “At Slack, we found that if a user was active 3 days out of the last 7 (3d7), they were right on the edge and had a roughly 50/50 chance of churning or retaining the next week.”

Why Teams Don’t Focus On The Adjacent User

There are a few things that tend to lead teams away from focusing on the adjacent user:

• Focusing On The Power User
• Personas Are The Wrong Tool
• Trying To Hit A Home Run On Every Swing

The Gravity Of The Power User

Product teams by nature are power users of their own product. The parts of the product that the product team uses, tend to automatically get improved as the pain is right in front of them. But this leads to building for yourself (or your friends). While that can feed the ego, if you are only building for yourself or power users, you won’t grow. You need to constantly be building for that next user that doesn’t have the same level of knowledge, intent, or needs that you, your team, and your power users already have.

Working on the adjacent user requires you to cross a cognitive threshold. You have to specifically seek out the definition to “see” them and understand their experience, which is likely to be dramatically different from what you see as an employee. Once you see them, you can build empathy for them and their struggles, which in turn informs what you build.

Andrew: “At Uber, a lot of employees were power users of the Uber product. This led to a lot of voices thinking they knew what we needed to grow just because they used the product a lot. But these were rarely the things that pushed growth of Uber into new audiences.”

Personas Tend To Be The Wrong Tool

When trying to answer the question of who they are trying to solve for, product teams often use their stated personas as the answer. But personas, as they are typically defined, have one or more of the following issues:

1. Current User vs Next User – Product personas tend to describe who your current users are. The adjacent user is a forecast of who your next user is so that you can enable the things to make them successful.
2. Too Static – A company will often come up with their personas and anchor on them for years, never evolving and updating them. The definition of your adjacent user should be evolving and changing more frequently as you solve for one and move to another.
3. Too Broad – Personas tend to be too broad to be actionable. Living beneath a lot of persona definitions are many sub-segments. The adjacent user is about having a view on those sub-segments living at the edge of who the product is working for today.
4. Not Based on Usage – Companies often build personas based on demographic factors and emotional needs. While these are valuable for many applications, they won’t help you solve adoption issues for adjacent users unless you also include their usage barriers in your definitions.

Trying To Hit A Home Run Every Time

Product teams overvalue hitting home runs vs hitting 100 singles back to back. This leads them to take bigger swings by going after bigger markets of new users. They get bogged down by trying to establish product-market fit for a new set of users and never fulfill the potential of their current product-market fit.

Remember, adjacent users are the users who are struggling to adopt your product today. Non-adjacent users could literally be everyone else in the entire world. Sure, non-adjacent users might be a larger market, but the barriers to their adoption are also dramatically higher. Companies that try to go too big too soon and often, skip the next obvious steps and fail to solve their current adoption problems.

Solving for the adjacent user is often seen as “optimization”, which in some organizations is viewed poorly because they represent short-term thinking. Solving for your adjacent user is not short term thinking; it is this disciplined sequential execution that will enable your longer-term roadmap and faster growth. It is short turns on a longer term path, not short term.

How To Know The Adjacent User Is Here

Until you recognize that they are adjacent users and commit to helping them, they will remain adjacent. They aren’t going to get there on their own. You have to be passionate about them and learn to view the product from their eyes. If you don’t focus on them, growth slows and your cohorts decay.

Cohort Decay Is Your Signal

At a high enough volume of users, you will start to see the effect of the adjacent user show up in your cohorts. Sitting at the edge of each user state is a quantitative metric that indicates conversion from one state to the next. For example, free to paid conversion or signed up to activated.

When you look at these variables on a cohorted basis, you will almost always see a decline from cohort to cohort over time. This is because there is some segment of adjacent users that are entering that state and struggling to convert to the next.

Elena: “When you start investing into new channels (especially paid) it is typically a signal to the product org that adjacent users are coming. New channels bring in users that will be different on some vector. Lower intent, less solution aware, less brand aware, pain point not completely formulated, or something else.”

Slack Example of Cohort Decay

Fareed: “A common thing I see across freemium SaaS companies I advise, is free to paid conversion decline from cohort to cohort. This is your signal that there are a set of adjacent users on the edge of this state that you need to start solving for.”

What Fareed’s story is pointing out is that at the edge of Core Free → Paying User, the metric that monitors that edge is free to paid conversion. Over time, if you look at that metric on a cohorted basis, you will start to see the metric go down from cohort to cohort. This can happen at the edge of any user state (signup to activated, activated to core, etc).

Discovering and Defining Your Adjacent Users

The first step to seeing the product through the eyes of the Adjacent User is to build a hypothesis of who they are and why they are struggling. How do we do that?

The Goal Is To Get Visibility, But Not Perfect Visibility

The goal of defining your adjacent users is to get visibility, but not perfect visibility. You need to define the landscape in front of you to understand all your options and figure out which type of adjacent user to focus on. Knowing just one adjacent user segment isn’t enough because you often have several to choose from.

But there are equal problems trying to get perfect visibility. You will never have perfect visibility and perfect definitions. If you seek out understanding perfect visibility you will never get started.

The process is to lay out multiple hypotheses of who the adjacent users are, choose which one to focus on strategically, force your team to look at the product through their lens, experiment and talk to customers to validate and learn, then update the landscape to make your next choice. I like to think about it as a snowball. You know very little at first, but as the snowball turns you collect more information about the adjacent user, which helps you collect more snow (users).

Knowing Who Is Successful Today and Why

To understand your adjacent users, it is helpful to understand the attributes of who is successful today and why they are successful. The reason this is helpful is that your adjacent user is different on one or more of these attributes (but not all). These attributes create vectors of expansion. Lets go through an example.

At Instacart, we knew that over 75% of our core, healthy users were:

• Women
• Urban
• Located In Certain Cities
• Head of Household
• Had one or more kids
• Were more affluent and less price sensitive
• Willing to spend an hour filling up their Instacart Order

Some of these things we knew from data. Some of these thing we knew from customer conversations. Some of them we knew by inference. Each one of these attributes creates vectors of expansion

• Women → Men
• Urban → Suburban
• Cities → Other Cities
• Head of Household → Members of households
• 1 Kid → Smaller Families, Couples, Singles
• More Affluent + Less Price Sensitive → More Price Sensitive
• Willingness to put effort in the cart → Less willingness to spend that time

The more granular you can get, typically the better. But there are a set of common categories for attributes. Which categories are relevant and most impactful depend on the product:

• Gender
• Age
• Income
• Geo
• Language
• Price Sensitivity
• Tech Enablement
• Customer Maturity
• Device Capability
• Use Case for The Product
• Role
• Company

Who Is The Adjacent User?

Once you have hypotheses for who is successful and why they are successful, you can hypothesize possible adjacent users segments. This will involve changing one or more of the vectors that you identified.

I typically recommend starting with a bottoms-up analysis of your data. You do not need to spend weeks talking to users to get a sense for who your adjacent user is. Look at what is happening on the edges of these states in the data. The data will help you identify places in the product that people are dropping off. This is the starting point to help you develop hypotheses about why different segments of users are dropping off.

At Instacart, when we initially looked at the data, we found that it took a really long time for current successful users to create their first order. As we looked through that flow, it became an understandable problem. For someone that had never placed an order, there were tens of thousands of products, and this person wanted to find a specific product quickly. Our hypothesis was that current users were very high intent users who were willing to spend an hour filling up their cart versus driving to the store. It led us to start focusing on the discoverability of products within first use to capture users with less intent.

At Instagram, when we first looked at the data, we started to see an enormous amount of organic web traffic showing up, but they weren’t converting to sign-ups and healthy users. We didn’t have any idea why. But through a lot of data exploration, we started to figure out where those users were coming from, why they were coming via web traffic, and other reasons that helped us define the adjacent user.

When you have an early hypothesis of who the adjacent user is from the data, use that to inform who you recruit for user research. Those customer conversations help you do two things: One, validate and fill in your hypotheses on who the adjacent user is; and two, start to build empathy for the adjacent user and understand why they are struggling.

Why Are They The Adjacent User?

It is not enough to know who the adjacent user is, but you need to know why they are struggling. To do that, you have to build empathy with the adjacent user. This is the most important part.

Building empathy for the adjacent user is hard because by definition your team is not living the experience of the adjacent user. Your team are power users of the product. They know the product in and out. To build empathy with the adjacent user and create hypotheses of why they are struggling, I recommend four techniques:

1. Be The Adjacent User
2. Watch The Adjacent User
3. Talk To The Adjacent User
4. Visit The Adjacent User

Lets talk about each of these individually.

Be The Adjacent User

You need to force the team to be the adjacent user by experiencing the product in the conditions and settings that the adjacent user is experiencing. This is commonly referred to as dog-fooding. This starts by making sure the team is constantly experiencing new user flows, empty states, and product states that require a certain amount of usage before they become valuable.

This eventually progresses to building tools to be able to simulate the experience of your adjacent users. For example, at Instagram as our adjacent users increasingly became more international, we needed to find a way to experience the product across many permutations of devices, network speeds, languages, and much more. Facebook built something called Air Traffic Control, which simulated elements of these permutations like network speed so the team could experience the product through the eyes of the adjacent user.

At Instacart, we had to find ways to experience the product through the eyes of someone in an expansion market like Overland, Kansas. There the store options, delivery windows, and other factors were completely different than what a PM or engineer on the team in San Francisco would be experiencing.

Living every day as the adjacent user uncovers hidden connections and dependencies in the product that impact the experience for the adjacent user that would have otherwise gone unnoticed.

Watch The Adjacent User

The second technique is to watch the adjacent user using your product through usability tests. Ideally this is done with trained researchers when possible. Watch the adjacent user try to sign up, activate, see what they struggle on, have them talk about why they are having challenges and what their expectations of the experience are.

Do not help them until they get stuck so you can observer what kind of workarounds and hacks people create to get the outcomes they want. This is how you start uncovering behavior that explains aberrant data, or behavior for which data doesn’t exist.

Talk To The Adjacent User

The third technique is to talk to the adjacent user about why they are trying to use your product, what jobs they are trying to solve, and which alternatives they are considering or have already tried. Surveys are fastest to deploy to get signal on where you should spend more time and focus. But surveys alone are not sufficient. You need to talk to users in person to go deeper.

At the beginning of the post I talked about the example at Instagram where we started to see a large increase in access churn (users logging out, then failing to log back in successfully). Two directions emerged. We could either make it harder for people to log out, or easier to log back in. But to determine which path was best, we needed to understand why people were logging out in increasing volumes.

We decided to talk to a lot of users who were intentionally logging out. What we found were two things:

1. People had a real use case for logging out. They logged out either because they had a prepaid phone plan and were worried about background data usage, or they were sharing the phone with a family member.
2. These users also commonly used fake email addresses. Email addresses are more of a western paradigm, and new people to the internet internationally don’t use email, they just text.

Once we understood these two things, it was clear the right strategic direction was to work on making it easier to log back in vs harder to log out and we were able to come up with some creative solutions for the use cases.

Visit The Adjacent User

The last technique is to visit the adjacent user in their environment. Seeing how your adjacent user uses a product in their environment expands your understanding of their workflow, constraints, and needs. Are B2B customers constantly sharing screens with colleagues for a product that you previously thought of as a personal tool? Are users having performance or usability issues in the real world that you otherwise wouldn’t have considered? Users tend to employ their authentic workarounds and habits in their own environment, which you won’t see in a lab or other manufactured setting.

Sequencing Your Adjacent Users

One of the biggest failure points is sequencing your adjacent users incorrectly. You want to pick the right adjacent users to go after so that you are building towards longer term value over time.

If you are familiar with Geoffrey Moore Crossing The Chasm, he referred to something similar called the bowling alley strategy. Find one niche audience that if you solve for them, helps you get to the next audience.

The center of Moore’s framework was Customer Maturity: Innovators, Early Adopters, Early Majority, Late Majority, and Laggards. Moore theorized that by solving for the problems of next set of likely adopters you enable the following segments. Adjacent User theory is similar. By enabling your immediate next set of adjacent users, you create the conditions that enable future segments. You can push the boundaries of the core user outcomes down to a lot of vectors, customer maturity being just one.

How To Think About Sequencing

There are a few keys to sequencing segments of Adjacent Users.

1. Adjacent User Should Only Be Different On One or Two Attributes

Let’s say you have 5 different vectors you can expand on. If your adjacent user definition is different on all 5 of those vectors, or even the majority, choosing that segment is a bad choice. That is like trying to hit a home run on every swing. It is probably going to take too many changes that are too large to enable that segment.

You need the conviction that you can build something to validate or invalidate the adjacent user definition pretty quickly. The adjacent user is not about capturing one large definition at once, it is about layering on micro definition after micro definition.

Elena: “A segment that is different on multiple attributes typically requires enabling a new value prop to bring them into the product. That’s a very big swing. But it’s not an “if” you should be going after them, it’s a question of “when.” If you can first add smaller features for adjacent users that only differ on one attribute you can maintain momentum and growth velocity while working up to a new value prop for those multi-attribute users.”

2. Not All Adjacent Users Are Opportunities

As you explore your adjacent users, you are going to find a lot of possible segments. But just because they exist, does not mean you should choose to serve them. The key here is that the segment still needs to align with the strategic direction of where the product is going.

Sometimes you will have a lot of insight that an adjacent user exists, but you are unsure if serving them is meaningful and aligns with the strategic direction. This happened while working on Instagram. As we solved access churn (people churning because they had trouble logging back in) we noticed feed posts were going up. At first, we didn’t know why, but we eventually discovered people had 2nd and 3rd accounts that they were logging back into. One account was public-facing, and one was more private for friends. So the question came up, should we be more intentional about making it easier to create and navigate across multiple accounts? Is that meaningful? Does it align with the strategic direction of the product? We didn’t have a clear answer and therefore punted on that adjacent user until we had further validation that the 2nd account was an additive opportunity within our strategic direction.

3. Solve In-House Problems First

When choosing your adjacent users, it is typically better to solve “in-house” problems first. These are users that are already showing up in your funnel and product vs brand new users who aren’t there yet. Those that are already showing up are displaying intent, but having trouble finishing. Solving for them typically creates more short term impact.

Elena: *”For B2B products, the way I like to think about sequencing is:

1. First solve for those in the existing user base that can drive additional monetization.
2. Second, solve for those in the existing user base that drive additional value in indirect ways. For example, a user might not monetize well but drive tons of viral contribution.
3. Third, solve for the brand new adjacent user. These are people not showing up in the user base, but still share traits with the existing user base.”*

Part of the prioritization should be the impact that you think the adjacent user segment can drive if solved for. The impact is partially driven by the size of the segment today. But one mistake when thinking about impact is to not think about the impact on a longer time horizon. Often times one segment might be larger but not growing, while another could be smaller but have a much larger growth trajectory. When taking that trajectory into account, the second segment may be the better choice.

Fareed: “When we were looking at international growth opportunities at Slack, we found that both users in France and India had far worse monetization. A lot of teams would have probably chosen to solve for French users since they are a higher income audience.

But users in France weren’t growing and we didn’t have a clear hypothesis of why they weren’t monetizing. On the other hand, India was growing way faster and had a clear hypothesis as to why they weren’t paying. When looking at it on a slightly longer term horizon, solving for users in India was clearly the higher ROI opportunity.”

The Evolving Adjacent User Landscape

The landscape and understanding of your adjacent users are always evolving. When I started at Instagram, the Adjacent User was women 35 – 45 in the US who had a Facebook account but didn’t see the value of Instagram. By the time I left Instagram, the Adjacent User was women in Jakarta, on an older 3G Android phone with a prepaid mobile plan. There were probably 8 different types of Adjacent Users that we solved for in-between those two points.

Your Adjacent User is constantly changing for a few different reasons:

1. New InformationAs you experiment and solve for one adjacent user, you are constantly gaining new information. This can happen in a few ways:
   • Unexpected Result – You run an experiment and get a result you did not expect.
   • New Instrumentation – New data instrumentation creates visibility into what is happening in areas you didn’t have visibility before.
   • New Research – Through the course of user research to validate hypotheses, you uncover new hypotheses you hadn’t thought about before.

   This new information informs updates to your current adjacent user hypotheses and possibly creates entirely new adjacent user segments.

2. New Users Showing UpAs you unlock value for one type of adjacent user, they often start bringing in a new type of adjacent user into your orbit. All healthy products have some acquisition through word of mouth. So when you solve for one adjacent user, their word of mouth brings in their friends who just might be your next type of adjacent user.Unlocking the 35-45yo women in US and Europe on IG brought WOM growth through families. This is an important age group for mothers who would create private IG accounts to share family and kid photos. This influenced their friends to do the same, inspired other relatives to sign up just to see these photos, and their partners ended up joining as well
3. New Value PropsAs the product team enables new value props in the product, it can fundamentally change what the experience needs to be at the edges of user states for adjacent users to experience the product.At Instagram, this occurred when we launched Stories. Stories didn’t help activate new users. It actually made it harder. It was hard for new users to have available Stories content because it disappeared after 24hours. Users needed to follow a lot more accounts to have enough content on any given day. The bar was higher and it changed the experience of how we created activation and engagement.

Knowing that the adjacent user is constantly changing, we would reevaluate our understanding of the adjacent user every quarterly planning cycle based on learnings from experimentation and research. In addition, we would tend to have one off insights a couple of times per year from various events like unexplainable experiment results, press, surveys, or something else. This evolving landscapes highlights the importance of a few different things:

1. Taking Time To Understand The WhyToo often teams move on from the positive/negative result of an experiment without understanding why the experiment generated that result. You must take the time to understand “the why’ behind your experimental results. The why helps you understand the next adjacent user. If you don’t do this you can miss incredibly important shifts in user mindsets as you move from adjacent segment to segment. If you don’t take the time to understand the why behind your results, you miss the opportunity to build empathy and solve your adjacent user’s real problems.
2. Constantly Working On the Fundamentals Of Registration, Activation, Engagement and MonetizationToo often teams treat key flows in their product as projects. But the adjacent user highlights why you need to be constantly working on the fundamentals of registration, activation, engagement, and monetization. It’s a continuously evolving user challenge that you need to be constantly re-evaluating. The work never stops.Fareed: “In the early days of Instacart, the best performing landing page was an all white page, that said “Instacart, Groceries delivered to your door, put your zip code in”. Nothing else performed better. The user at that time was very high intent, tech savvy, urban millennial who knew what Instacart was because they heard about it from a friend through the press. As Instacart grew over time, you had to explain what Instacart was, why does it matter, who it is for, what stores we have. The same experiment on the white page a year later had dramatically different results because the adjacent user had changed.”
3. Continually Cross The Cognitive Threshold Of Your Adjacent UserAt the beginning of this post, we talked about how working on the adjacent user requires you cross a cognitive threshold of seeing the product experience through their eyes. As the landscape of the adjacent user evolves, you and the team need cross this threshold over and over. It is easy to get sucked back into the gravity of your existing user base. But your job is to constantly expand the definition of who is successful with your product.

All successful products must eventually shift their focus from core to adjacent users in order to maintain growth rates. Rapid success in your first audience will inherently lead to saturation and declining growth in that segment. While this is certainly an enviable problem to have, solving it is one of the most complex challenges in technology. The most successful companies are the ones that can continuously evolve to serve more adjacent users. The art is selecting the right groups of adjacent users to go after next. If you try to solve all problems for everyone, you’ll drown across too many issues and waste time acquiring users who have no chance of being successful today.

Adjacent User Theory demands an entirely different approach to being ‘user centric’. Static personas are OUT. Dynamic evolving personas that incorporate product adoption behavior are the standard to strive for. Every 3-6 months during hyper growth, you have to reorient your team around the next adjacent user, what they care about, and what problems you are solving for them.

As you are succeed, you’ll see improving cohort retention, engagement, and monetization in your target adjacent users. You’ll maintain your growth rate on larger and larger install bases. And you’ll continuously discover the next adjacent user who could use your product with just a little more help.

—

If you want to learn more from Bangaly and other leaders from top tech companies, check out Reforge’s upcoming Career Accelerator Programs such as Growth Series, Experimentation and Testing, and Monetization Deep Dive. Reforge programs distill invaluable knowledge from top leaders and deliver decades of career experience in an intensive 6-week part-time format.

Dear readers,

I’ve only been writing sporadically recently on here — mostly because I’m working on a new book project (more to come soon on that!!) which has been taking all my time. In the interim, I’ve stayed pretty active on Twitter, writing tweetstorms that sometimes turn themselves into essays on here.

For a quick summary of the essays that did make it onto here, including a couple guest collaborations, here’s an easy set of links:

• The Passion Economy (guest essay by Li Jin, a16z). How the work of the future will come from new tools, new media formats, and new monetization formats — and how the Passion Economy is different than the Gig Economy. Complementary, but different.
• 10 lessons from a serial entrepreneur – Justin Kan, Atrium, YC, and Twitch. I interview Justin Kan, co-founder of Justin.tv which turned into Twitch many years later. Fascinating discussion about what it’s like for a serial entrepreneur to raise money, operate a startup, lessons learned, etc.
• 28 ways to grow supply in a marketplace (guest essay by Lenny Rachitsky, ex-Airbnb) I’ve been brainstorming and talking to Lenny, who spent time at Airbnb doing much of the same kind of work that I did at Uber. He put together this wonderful piece on growing supply
• Why startups are hard – the math of venture capital tells the story. My partner at a16z, Scott Kupor, wrote a book recently and I excerpted it here, and talked a bit about why startups are a hard industry, looking at the numbers. Tldr; half of startups fail, and a small portion – 6% – make all of the money.
• The podcast ecosystem in 2019. A really detailed 68 page analysis of the podcast market. Yes, this is the type of industry deep dives we generate at work! It’s super fun. Really interesting report if you’re interested in podcasting/audio and the emerging industry around it
• The dumb idea paradox. Why some of the best ideas start their life by sounding dumb. Started as a tweetstorm.
• What do you look for an investment? How long should a founder be without salary? And other Q&A. I did a Q&A on Quora about investing, growth channels, metrics, and life as a startup CEO — it was fun, and some of the answers ended up getting a million views!

OK, and now for the tweetstorms. Here are all the tweetstorms I’ve written in the last year or so, all in one place. Hope you enjoy!

Thanks,
Andrew
Lower Pac, San Francisco, CA

1. American kids want to be youtubers, and the Chinese kids want to be astronauts.

More from the article here.

2. Is your startup idea already taken? And why we love X for Y startups

I turned this from a tweetstorm into a much longer discussion, and wanted to share it there! Here’s the link.

3. Cameras versus smartphones.

The iPhone comes out in 2007 and changes the camera industry. Amazing to see a 90% decline in just 10 years after growth for decades

Amazing that something can go from peak to trough in exact 10

Makes you ask- What’s the next product where this will happen?

4. 2019 state of tech investing:

Pre-seed- Bet on the entrepreneur 👨‍💻
Seed- Bet on the team 👨‍👩‍👦‍👦
Series A- Bet on the traction 🏒
Series B- Bet on the revenue 💸
Series C- Bet on the unit economics 💰

5. Did you know: 61% of all food delivery is pizza 🍕.

The average American eats 23 pounds of pizza per year. 93% of americans eat pizza every month. Omg right?

6. Dashboard clutter

Dashboard clutter – the addition of more KPIs over time – leads to strategy clutter.

The more you add, the less you (and your team!) understands your business. Then people go back to making decisions on intuition not data.

Via this 😂 comic by @tomfishburne below!

7. Fascinating infographic: Top grossing media franchises of all time.

Pokémon, Hello Kitty, Mickey Mouse, Star Wars, etc

Observations:
– most of the money has been made in merchandise and video games
– so many Japanese brands! 5 out of the top 10
– many created in the past decade

8. The internet culture supply chain

The internet culture supply chain works like this:
Asia ➡️ US teens ➡️ Adults ➡️ B2B.

Multiple data points on this already: Emojis, video streaming, esports…

Emojis are a classic example. First it was big in Japan, then teens. Now we all use it. Then it was baked into Slack and everything else. (Btw, this is an amazing read of pre-smart phone Japanese mobile: https://en.wikipedia.org/wiki/Japanese_mobile_phone_culture …)

Want to know what B2B communication/collaboration will look like in 5 years? It’s inevitable that livestreaming, virtual goods, asynch video, etc all eventually end up in the enterprise. There’s a 3-5 year lag, but it definitely happens

TikTok is a great example that’s mid-phase. Crossing from Asia into US teens, and we’ll see if it’ll be the way we do status updates at work in a few years :)

9. Magic metrics indicating a startup probably has product/market fit

1) cohort retention curves that flatten (stickiness)
2) actives/reg > 25% (validates TAM)
3) power user curve showing a smile — with a big concentration of engaged users (you grow out from this strong core)

3) viral factor >0.5 (enough to amplify other channels)
4) dau/mau > 50% (it’s part of a daily habit)
5) market-by-market (or logo-by-logo, if SaaS) comparison where denser/older networks have higher engagement over time (network effects)

6) D1/D7/D30 that exceeds 60/30/15 (daily frequency)
7) revenue or activity expansion on a *per user* basis over time — indicates deeper engagement / habit formation
8) >60% organic acquisition — CAC doesn’t even matter!

Having even one is impressive — it’d make me sit up!

10. What’s your biggest miss so far in tech?

In terms of a totally wrong / bad prediction. This was mine from years back — not getting Facebook and how big it was going to be: https://andrewchen.co/why-i-doubted-facebook-could-build-a-billion-dollar-business-and-what-i-learned-from-being-horribly-wrong/

Also for the first few years, I thought Uber was a weird niche service for super rich people to get limos. I managed to fix that bad prediction 😎

11. What is your least popular but deeply held opinion on tech/startups? Lively discussion here

12. The Law of Shitty Cohorts.

It’s not unusual for a startup to have “meh” retention. But then usually, team says it will improve retention via better activation, lifecycle marketing, improving product features, etc.

But the law of shitty cohorts says this is unlikely to happen.

The reason is that the early cohorts of users — let’s say the first couple million or so — are usually your best cohorts. They found you via word of mouth, they are the early adopters of tech, and you have no market saturation yet.

However, as you scale, each cohort gets worse

In rideshare, all the urban dwelling power users who don’t have cars, and use apps every day – they’ve all signed up. Now we’re acquiring rural/suburban users who have cars and only use it to get to the airport. Huge difference.

When you buy users with paid ads, it’s even worse

So even while teams improve their product, activation, and lifecycle, there’s an opposite (and sometimes even stronger force!) of worse cohorts joining your product over time.

(Inspired by the Law of Shitty Clickthroughs)

13. My order of operations when I sit down with a startup to figure out how to grow their new product.

1) first, is it working?
Usually the answer is no :) I look at retention rates, DAU/MAU, session lengths, how many visits are driven via push notifs, etc etc. Lots of benchmarking

2) if it is working, then how do we scale it?
I look at the acquisition mix — how are new users finding the product? Are they using all the channels that other similar products are already doing?

If there’s something that’s working, can we scale it to be much, much larger?

3) what can we do in the product to amplify all of the above?

Since product is expensive to build, let’s focus on top of funnel and work all the way down. Optimizing acquisition, then activation, then retention/churn, then reactivation (for later stage)

Tbh, #1 is the hardest!

Longer discussion on this here.

14. The Head/Heart/Hands framework

Or in emoji form, 👩/❤️/✋- for company cultures and personalities at work. The idea is that every work culture can be described as a pie chart of these three factors. (credit to my friends/coworkers at Uber who first described to me)

Not only does each company have this breakdown, so does each individual on the team. And the more their individual profile matches that of the overall company, the more in sync they are, the easier it is to get things done. Or that’s the theory.

👩 Head = how much of the culture emphasizes analytical ability, strategy, planning, etc. Cultures that are strong at this do a lot of analysis, information gathering, etc to try and make the right choices, but sometimes at the cost of moving quickly or bringing everyone along

❤️ Heart = how much the culture emphasizes team cohesion + happiness. Teams that do this invest a lot on internal values, having a clear mission, making decisions that consider the team’s views, not just business outcomes. Lots of obvious downsides when this goes too far, too

✋Hands = how much the culture emphasizes action, and getting things done. Cultures that do this can move quickly, are iterative, and are agile in the market. But they break things, can have a “fire first, aim later” mentality where a lot of energy is wasted

People said Uber 1.0 was a 30% head, 5% heart, 65% hands kind of place. Ridiculously indexed on action. Often doing the wrong thing for the first few iterations, but with so much activity, things would get figured out later. Needed more love for drivers and team though

Another startup that I’m close with, which will be unnamed, is more like 30% head, 50% heart, 20% hands. Great culture, people were close friends, didn’t get much done. Yet another is 70% head, 20% heart, 10% hands. Incredibly intelligent but doesn’t ship.

I like this framework in that it says, hey, there’s no right tradeoff – it’s just different. Some industries require hardcore orientation in one way, and others in another way. The VC industry doesn’t need 75% hands, for instance

Similarly, if someone’s not working out in one culture- they might in another culture, where things resonate. Perhaps they are too action-oriented in a place that requires a lot more deep thinking because decisions are hard to reverse. Again, there’s no “best” working style

As with Myers-Briggs, this exercise is more for fun, than science. However, you’d be surprised by how interesting of a conversation it generates. Ask someone to break down their company’s head/heart/hands, and press for examples. You’ll learn a ton

When you’re interviewing at a company, this can be a fun thing to ask. Otherwise if you ask “what’s the company culture like?” you’ll often just get generic stuff like, “oh people are are so smart and nice.”

A related question is: “What’s something that happens in this company culture that doesn’t at other places?” Or, “who’s the type of person who’s successful here who might not be at other places?” (or the reverse). Interesting to understand the contrasts

15. the LA consumer startup ecosystem

the LA consumer startup ecosystem is coming into its own — Honey, Snap, Riot Games, Tinder, Bird, Dollar Shave Club.

The most $1B+ consumer startups outside of the Bay Area?

A few years back, I might have guessed that NYC would be the emerging leader. But pretty clear it’s LA.

16. “The One That Didn’t Work Out.”

Startup founders, you know what I mean: We spend years on a product – starting it from scratch, recruiting friends, getting it off the ground. We think we’ll spend years on this. This is the one. We tell that to ourselves, investors, and friends

We celebrate all the milestones we’re supposed to. The first office. First check in. The product launch. Fun emails from the first users. An important hire. Team dinners. These are wonderful, great memories!

When it’s time to raise money, we tell potential investors that this is it. We’re gonna work on this for years, because we believe. And we do! But that’s not what happens…

There’s a messy second year. Traction’s not as good as what we want. Or maybe new users are showing up, but retention sucks. Some of the key hires leave. Fundraising isn’t as easy as it should be. Monetization is slow. It’s tough

When things get hard, it’s easy to go into hermit mode. Don’t go to tech events, because people will ask how things are going, and you don’t want to pretend it’s great. Because it’s not. Easier to stay at home and watch Netflix

You know the end of this story: A few years in, the once shiny new startup acquired by a larger company. Or it’s shut down. People maybe even make a ton of money. But the team splits up. The product that you stared at, every day, for years, gets shut down. It’s time to move on

But it’s hard to move on. It feels weird to walk past your old office. You don’t talk to your team anymore. You move your old photos, old decks, old prototypes into a folder deep in your Dropbox drive. Better to not think about it!

Yes, this is a story of my own journey for a startup I had years ago that didn’t work out. But I know it’s not just me. It’s many of my friends, and many of you, who are on their new startup, or a new big tech job, but still remember the one that didn’t work

You may have seen the wonderful tweetstorm by @dflieb about Bump from 10 years ago. You can see how much he grew from his journey. Even though Bump didn’t thrive, it’s now part of Google Photos and the ideas impact hundreds of millions of people. He should be proud! https://twitter.com/dflieb/status/1050990035892199424

The recent @andrewmason interview on Groupon is the same. You can tell how much he both cherished his experience and also how rough it was. Worth reading: https://twitter.com/andrewchen/status/1051576009454116867

There’s a wonderful journey that happens in the creation and ending of new products. The majority of startup journeys look like this – even in the success case – and we all learn a ton from building them. It’s an amazing experience, but also, it can be rough.

If you have the same Dropbox folder I do, it’s time to open it up. Scroll through the old photos, open up the old decks. It may be the startup that didn’t work out, but it’s also the one that made you stronger and smarter.

17. Uber alumni and the next generation of founders

There’s a TON of new startups coming from Uber alumni – I know of a half dozen in stealth, and Bird is already a breakout. It’s obv that the creativity and hustle required to make Uber work in its early years has trained hundreds of entrepreneurs. Very bullish on this group

As y’all know, Uber had a very decentralized mode of operation with each city being run as its own company. Each GM owned their P&L, hired their own people, and in the early years, would just put Facebook ads and other expenses on their credit cards! Great background

The product teams looked like this too. We had a “Programs and Platforms” model courtesy of Amazon / @jeffholden where each program was full stack, and the PMs ran hard again their mission/KPIs without introducing interdependencies

For everyone who joined in the early years – 2010 to 2014 – they’ve already hit their 4 year mark and many are spinning out. The really early folks are investing. Folks like @williampbarnes @joshmohrer have formed angel groups supporting alumni spinouts (and other startups too!)

From an investor standpoint, myself, @fffabulous @akad have all joined venture firms ready to invest in the next generation

The ATG / Otto folks are making moves as well. My good friend @drewjgray joined as CTO of the autonomous startup @voyage with @olivercameron. Also Kodiak, Kache, and many others.

Let’s talk about my fav topic, 🛴. @limebike has folks like my good friend @uber_ed_baker as an advisor, and is slowly collecting ex-Uber alumni (and Lyft! And other on-demand folks). Bird’s exec team consists of Uber’s prev “Supply growth” team – @travisv, RF, Schnell, others

Not everyone is doing startups, of course. Lots of folks on “sabbataquit” – Uber’s policy of allowing sabbaticals after 3 years of work means that people often do this before leaving. And then they keep traveling, sometimes for a year+. Many folks doing that

Whether they are starting, joining, or on sabbatical, it’s clear that this group knows a lot of important, venture-fundable markets well. There’s now 10,000s of focus who are experts on transportation, marketplaces, mapping, autonomous floating out there. This is the next gen.

Very excited about my ex-uber colleagues! Looking forward to what y’all do 😎

Finally…

Of course, if you want more of these as they come in real-time, follow me at @andrewchen! More in 2020.

☝️Above: Michelle Rial (follow her at @TheRialMichelle), then working at Buzzfeed, posted this hilarious infographic with all the “X for Y” ideas. Here’s the original article.

I had a quick laugh, of course. But then seeing this infographic made me think through some deeper things:

• What are “X for Y” companies and why do they sound compelling?
• Are “X for Y” companies actually a good idea?
• When do they work? When do they not?
• .. and finally, what happens when my “X for Y” startup idea is taken?

And so this is an attempt to provide some serious ideas for this otherwise funny question.

People love describing startups as “X for Y” — why?
A few years back, “Facebook for X” companies were all the rage. And then it was “Uber for X.” And now I’m hearing about “Stripe for X,” “Superhuman for X” and “Twitch for X.”

In fact, Marketwatch did an analysis of startup company descriptions and found that startups often compared themselves to other companies in their descriptions. Here’s the list of the most common startup comparisons:

“X for Y” comparisons are popular! This is a really common format to describe startup ideas because it accomplishes a couple things all at once: First, it positions you against something successful. Unless it’s intended as an insult (ha!), no one ever describes their startup as the”[Failed startup] for X.” Second, it both conveys a lot of information and also doesn’t — when someone who hears the idea, it’s like a short puzzle to solve to try and understand what it might mean. And yet, as a one-liner, it begs for interactivity, so that people will ask more.

Third, it makes it easy for the people passionate about what you’re doing — your employees, investors, and customers — to spread the news about what you’re doing. Nivi, half of the Venture Hacks blog, wrote back in 2008:

The pitch is the perfect tool for fans who are spreading the word about your company. Investors use the pitch when they tell their partners about your startup. Customers use the pitch when they rave about your product. The press uses the pitch when they cover the company.

In other words, short, pithy descriptions tend to travel further, and you want to arm your proponents with a blurb to spread!

But the real question is, do “X for Y” companies actually work? Is this a good strategy?

The “X for Y” companies that have worked
Interestingly, although you’d think that this strategy would lead to derivative/uninspired ideas, in practice they have worked.

I asked Twitter this question and did some googling, and there were a number of compelling examples:

• YouTube was originally “Flickr for video”
• Glassdoor was “TripAdvisor for jobs”
• Airbnb was “eBay for space”
• Baidu was “Google for China”

(I’m sure if you search around, you’d find even more examples — tweet at me at @andrewchen if you have others in mind!)

So which “X for Y” companies will work in the future?
Now that I’ve talked through all this and you want to go back up to look at the Buzzfeed infographic, you might be asking yourself, which of these ideas are actually good?

In broad strokes, all the “X for Y” ideas end up falling on a spectrum of:

• [Successful product] for [vertical segment] on side…
• … to [Successful product] for [new category] on the other

An example of the former might be something like, “YouTube for Kids” — which is a segment of the existing product. This has the advantage that there’s a lot of pre-existing behaviors to work off of, and if you go deep enough on the functionality for this vertical, there might be a way to create a differentiated experience. On the other hand, you are also more likely to end up building a sustaining innovation, where an industry incumbent sees it as part of their turf, and they can extend into the category quickly. So what you gain in minimizing execution risk you trade off in terms of increased competitive risk.

On the other hand, something like “YouTube for Amazon Echo” sounds kind of weird and foreign, since it doesn’t yet exist — yet it could still possibly make sense as an idea. It might be a social platform to create and play back audio clips from other users, like a UGC podcasting platform. I don’t know. At this end of the spectrum, you’re talking about a new category of products and new user behaviors that might make sense. In that way, you take a ton of market risk — but if it works, you might dominate the whole category.

And interestingly, in the examples above for YouTube, Glassdoor, Airbnb, etc. — I’d argue that they fell more into the new category creation side of the spectrum rather than a segment. At the time the products were created, Flickr didn’t have much video capability and it wasn’t a popular format for users. Tripadvisor didn’t let you review jobs (nor does it today). eBay didn’t support reserving homes and space. And Google wasn’t in China. And so these “X for Y” concepts, once they worked, had a higher ceiling since it wasn’t constrained by a giant competitor running them down quickly. The geographical aspect of Baidu was probably, in many ways, the smallest moat in terms of product, but we know that getting into China is special and most of the largest tech giants never made it happen.

Watch out for broken metaphors
I’ve written in the past on why almost all of the “Uber for X” startups failed — you can read that here — and ultimately, even if the idea sounds cool to your and your startup friends/investors, the value proposition must still be really strong to all the customers and users involved.

Something like “Uber for cleaning” sounds great until you ask if the cleaners actually want to work this way, if consistent/high-quality service can be delivered, and if the unit economics make sense? But it can be catchy for investors. That’s not enough. Broken metaphors happen when something that’s meant for an investor pitch becomes ingrained in the product itself. Rarely does the end user care about your startup’s desire to position itself against another successful startup.

So go work on “Tinder for doctors.” Or “Birchbox for pizza.” Use it for the Linkedin blurb describing your company, and on your 5-min accelerator pitch. But don’t forget, there’s a reason why “Uber for X” startups have mostly failed — you need to lead with the customer value, not with what is easily described within the startup community.

Hi readers,

Consumer startups have gone through many phases: Web 2.0, Facebook apps, Mobile (remember SoLoMo?), and in recent years, some of the best opportunities have been happening in the real world. In recent years, the Gig Economy has taken over. Startups like Uber, Airbnb, Instacart, and others have been able to find product/market fit and scale their businesses.

But what’s the next? The essay below argues: “The Passion Economy.” And my a16z colleague Li Jin unpacks this idea more thoroughly.

(And quick plug, if you want to read more essays like this — this article was originally posted on a16z.com and you can subscribe to the newsletter here)

The Passion Economy theme unifies a number of themes that we at a16z have been working on:

• Reinventing the service economy. We’ve written about this here. There will be many new software platforms that allow creators/influencers/service providers to work on what they love and earn income from that work
• Easy-to-use tools. Within the platform, there are deep SaaS tools that help people in the Passion Economy actually do their work — particularly key when the work is purely digital in nature, like video streaming or audio broadcasting
• Massive distribution. People need to get found — both by their customers and their audience — and we now can plug into platforms with billions of consumers. Platforms will need to provide marketplace-like features that lean transactional, or more like an ongoing subscription relationship
• Pragmatic education/training. College aren’t built to teach the skills for the Passion Economy, and people will instead turn to online schools/programs/training for lifelong learning in the ever-changing ecosystem
• Every startup is a Fintech. Fintech services that help facilitate both business and personal needs — whether that’s creative financing options, solopreneur banking services, or working capital

If this resonates, it’s because this is a list of the massive trends that are reinventing the way we work. As a society, we are right in the thick of this movement — but it’s helpful to give it a distinct name, although you might argue startups like Udemy, Patreon, Shopify, Substack, and many others have already been blazing this path.

I love this trend as someone who has worked deeply both in the passion economy — writing this blog combined with investing/advising startups — and also in a more traditional role in the gig economy at Uber. We’re seeing a lot of activity across the entire market and it feels like a meaningful addition to add both opportunities for Gig Economy and the Passion Economy together.

Thanks,
Andrew

The Passion Economy and the Future of Work, by Li Jin

The top-earning writer on the paid newsletter platform Substack earns more than $500,000 a year from reader subscriptions. The top content creator on Podia, a platform for video courses and digital memberships, makes more than $100,000 a month. And teachers across the US are bringing in thousands of dollars a month teaching live, virtual classes on Outschool and Juni Learning.

These stories are indicative of a larger trend: call it the “creator stack” or the “enterprization of consumer.” Whereas previously, the biggest online labor marketplaces flattened the individuality of workers, new platforms allow anyone to monetize unique skills. Gig work isn’t going anywhere—but there are now more ways to capitalize on creativity. Users can now build audiences at scale and turn their passions into livelihoods, whether that’s playing video games or producing video content. This has huge implications for entrepreneurship and what we’ll think of as a “job” in the future.

The Evolution of The Passion Economy

In the past decade, on-demand marketplaces in the “Uber for X” era established turnkey ways for people to make money. Workers could easily monetize their time in specific, narrow services like food delivery, parking, or transportation. These marketplaces automated the matching of supply and demand, as well as pricing, to enhance liquidity. The platforms were convenient for both the user and the provider: since they took care of traditional business hurdles like customer acquisition and pricing, they allowed the worker to focus solely on the service rendered.

But though these platforms provided a path to self-employment for millions of people, they also homogenized the variety between service workers, prioritizing consistency and efficiency. While the promise was “Be your own boss,” the work was often one-dimensional.

Monetizing Individuality

New digital platforms enable people to earn a livelihood in a way that highlights their individuality. These platforms give providers greater ability to build customer relationships, increased support in growing their businesses, and better tools for differentiating themselves from the competition. In the process, they’re fueling a new model of internet-powered entrepreneurship.

It’s akin to the dynamic between Amazon—the standardized, mass-produced monolith—and the indie-focused Shopify, which allows users to form direct relationships with customers. That shift is already evident in marketplaces for physical products; it’s now extending into services.

These new platforms share a few commonalities:

1. They’re accessible to everyone, not only existing businesses and professionals
2. They view individuality as a feature, not a bug
3. They focus on digital products and virtual services
4. They provide holistic tools to grow and operate a business
5. They open doors to new forms of work

1. They’re accessible to everyone, not only existing businesses and professionals

New consumer products are making it easy for anyone to become an entrepreneur. In the mid-2010s, the rise of the influencer industry allowed top-tier creators to monetize through advertising. These platforms expanded to support a broader range of money-making activities, from manufacturing physical products (e.g. Vybes, Hipdot, Genflow) to creating personalized videos (Cameo, VIPVR, Celeb VM).

Now the ability to make a living off creative skills has trickled down to individuals at scale, helping everyday people to launch and grow businesses. Previously, only established businesses could access software engineering talent to build websites or apps; now, no-code website and app builders like Webflow and Glide have democratized that ability. Startups are also building mobile-first, lightweight versions of incumbent desktop software: Kapwing, for instance, is a web and mobile editor for videos, GIFs, and images that aims to displace legacy creative software.

Companies have the opportunity to engage entrepreneurs in the early stages, then capture economic value as they grow. They might start with a very basic offering and add product capabilities as their customers earn revenue and develop new needs.

2. They view individuality as a feature, not a bug

Whereas previous services marketplaces were rigidly built for standardized jobs, new platforms highlight variation among workers in categories that can benefit from more diversity in user choice.

Take Outschool, an online marketplace for live video classes in which teachers are predominantly former school teachers and stay-at-home parents. On the platform, instructors can develop their own curricula or browse lists of courses requested by users. Beyond the subject matter, the marketplace’s UI emphasizes each teacher’s background, experience, and self-description. Parents and students can message instructors directly.

Above: Outschool lets teachers sign up to teach engaging video sessions to kids

For new platforms, this model can pose a sizeable risk: once consumers are able to work directly with a preferred provider on an ongoing basis, they may take that relationship offline. Marketplaces can combat disintermediation by offering workflow tools, like scheduling and invoicing, and by building in additional incentives that make it worthwhile for providers and users to remain on-platform. Marketplaces that cultivate these direct relationships can also succeed by focusing on areas—like education and tutoring—in which consumers might have repeated matching needs with a variety of different providers over time.

3. They focus on digital products and virtual services

Whereas past generations of entrepreneurship-enabling platforms typically focused on selling physical products (e.g. Amazon, Etsy, Ebay, Shopify) or in-person services (e.g. Taskrabbit, Care.com, Uber), new creator platforms are focused on digital products. A platform built specifically for packaging and selling digital products looks different than a platform that is built for tangible goods.

Podia, Teachable, and Thinkific are all SaaS platforms that allow creators to make and sell video courses and digital memberships. Previously, these types of “knowledge influencers” had to either conduct classes in-person (restricting them to local customers); jerry-rig platforms meant for physical products, like Shopify; or customize sites like Wix and Squarespace. New platforms capitalize on the idea that expertise has economic value beyond a local, in-person audience.

Above: Thinkific lets you create, market, and sell online courses

On the interior design marketplace Havenly, designers work remotely and interact with clients entirely online. For designers, the benefit is a steady stream of clients without the heavy lifting—since Havenly takes care of marketing—and the flexibility to work whenever, wherever. For clients, the benefit is access to a service that would otherwise be expensive or inaccessible.

4. They provide holistic tools to grow and operate a business

Unlike discovery-focused marketplaces, which monetize through advertising, membership fees, or cost-per-lead, new platforms in the creator stack are often monetized through SaaS fees that increase as customers grow. Others take a percentage of the creator’s earnings. This means that platforms are incentivized to help creators succeed and grow, rather than driving discrete, one-time transactions.

Some platforms offer marketing tools like custom landing pages, coupons, and affiliate programs. Others provide behind-the-scenes support: Walden, for instance, connects new entrepreneurs with coaches for strategy and accountability.

Sometimes, support may be bundled into platforms that help providers start a business. For instance, Prenda—a managed marketplace of K-8 microschools—provides teachers (called Guides) with curricula, computers, software, supplies, and assistance in navigating the necessary regulatory requirements and insurance.

5. They open doors to new forms of work entirely

New digital platforms enable forms of work we’ve never seen before:

For a more extensive model of how human capital can give rise to new industries, look to China. On the microblogging site Weibo, for instance, users sell content such as Q&As, exclusive chat groups, and invite-only live streams through memberships or a la carte purchases. This has spawned a wave of non-traditional influencers—financial advisors, bloggers, and professors—beyond typical beauty and fashion tastemakers.

Factors to consider: Marketplaces vs. SaaS

When building a company in this space, it’s important to consider the needs of the creators you’re targeting, as well as their desired audience. There are tradeoffs between marketplaces and SaaS platforms. What’s the difference?

Marketplaces are entirely plug and play, meaning providers can sign up and start earning revenue with minimal set-up. The strength of a marketplace’s two-sided network effect is directly correlated to the value it provides as an intermediary between supply and demand. One example of this model is Medium, which charges readers a subscription fee to access stories across the entire platform. The amount of money a writer makes is proportionate to the amount of time readers spend engaging with their stories.

By contrast, SaaS platforms require creators to work independently to acquire customers. Such platforms might help with distribution—providing tools for marketing, managing customer relationships, and attribution—but users are largely responsible for growing their own businesses. On Substack, for example, features include a writer homepage, mailing list, payments, analytics, and a variety of different subscription offerings. Substack collects a portion of the creator’s subscription revenue.

In the marketplace model, writers count on Medium to drive reader traffic and subscriptions. On the SaaS platform (Substack), writers drive their own direct traffic and subscriptions; they can export their subscriber list at any time.

Marketplaces bring value for creators looking to be discovered and attract customers over time. SaaS tools often make sense for more established creators who already have a customer base. In response to this dynamic, many startups are building SaaS platforms that aim to poach large creators from existing marketplaces.

Looking Ahead

New integrated platforms empower entrepreneurs to monetize individuality and creativity. In the coming years, the passion economy will to continue to grow. We envision a future in which the value of unique skills and knowledge can be unlocked, augmented, and surfaced to consumers.

If you’re building a company in this space, we’d love to chat!

This was originally posted to the a16z blog, which you can subscribe to here.

Dear readers,

The Serial Entrepreneur — investors want to back them, newbies want to learn from them, and people want to work on their teams. It turns out that, yes, being a repeat entrepreneur comes with big advantages, but there are difficulties too! And when you go through a tough experience like launching a new product, it’s often the case that you want a “do over” on many aspects. Of course, you try to fix them in your next attempt. What does a serial entrepreneur want to do better on their second, or third, or fourth try?

Well, let’s ask Justin Kan, who has started many, many companies and has a point of view. In fact, 10 points of view. As many of you know, Justin is a repeat entrepreneur who co-founded Kiko Software (a Web 2.0 calendar that pre-dated Google Calendar by 4 years); Justin.tv (a lifecasting platform); Twitch.tv (a live streaming platform for esports, music, and other creatives now part of Amazon); Socialcam. He was also a partner at YC, and will be a dad soon! (congrats Justin!)

His new company, Atrium, is one of my first investments at Andreessen Horowitz. It’s a tech-enabled law firm serving startups — learn more here.

Justin reflects on his journey and shares 10 + 1 lesson he’s learned along the way.

Here’s the video.

I wanted to add a quick summary of some of his points, as they are super interesting, and share with all of you. And for the lazy who don’t have time to watch a full interview, I added some notes below. Enjoy!

Thanks,
Andrew

The paradox of choice: choosing a focus

Justin says:

“Once you see some success … The world opens up. They want you to be a VC, they want you to work on projects with them, you can start any company that you want, which is great… but it’s a paradox of choice, and focus can be a huge problem”

This is the biggest, surprising thing about being a repeat entrepreneur, which is how easy it is to get pulled in a ton of directions. And also that you might not be as patient and let something develop, since your perceived opportunity cost is high. Justin ended up trying many different options — including as a partner of YC — and didn’t feel like he was learning/growing and the feedback cycle is too slow. Justin ended up picking a new startup because it’s the #1 vehicle for personal growth.

Tradeoffs between B2B versus B2C companies

Justin says:

“When we started Kiko, we had no skills. I never had a full-time job in my life … We were not good. When you have nothing going for you except that you are willing to put in long hours, and blood sweat and tears, you should focus on market risk… Now as someone with abilities and skills, you should focus on execution risk”

I often spend my time in the intersection of pure consumer startups and also consumerized enterprise, and notice that there are huge differences. One of the biggest ones is that B2B startups have relatively stable go-to-market motions — you have sales, marketing, and sell into buyers that you understand. Because of this, it’s mostly about execution and if the market size is big enough. Consumer is fascinating because the distribution channels are constantly changing — 15 years ago, SEO and email viral growth was the big thing. Then 10 years ago, it was mobile and Facebook platform. Right now you are seeing a lot that’s just word of mouth or touching the IRL channel.

Market risk vs execution risk

Justin says:

“When Justin.tv pivoted to Twitch, no one believed there was a market. Even Emmett was skeptical. The good part was that competition was low. ”

And also:

“It takes a lot of people with nothing to lose to discover [hit startups].”

I’ve written about how random consumer products seem to be — the past decade’s hits were: An app that lets you get into strangers’ cars. An app that lets you stay at random peoples’ houses. Disappearing photos. A site that doesn’t let you play video games, but you can watch other people play. Seriously? This is the Dumb Idea Paradox.

Fundraising strategy: go big or stay lean?

Justin says:

“I’ve not convinced that raising a ton of money out of the gate is the right strategy. When you have a ton of money you spend a lot of money.”

Nearly 10 years ago, Ben Horowitz wrote The Case for the Fat Startup — the idea that sometimes you need to raise a boatload of money in order to get your company off the ground. In Atrium’s case, that’s exactly aligned, because the market wants a stable legal provider, and as an execution risk with clear competition, real capital has to go in to prove out the model.

Managing the stress of being a startup CEO (again!)

Justin says:

“I never really worked on self-improvement stuff outside of being a better programmer. But I never worked on anything to make myself smarter, or harder working, or alert more hours of the day. Everything was kind of accidental. If there was a problem in the company, I would be really emotionally avoidant to it.”

The topic of mental health within a startup community has turned into a big deal — for good reason. Doing a startup is one of the most stressful things you can do in the age of cushy, white-collar jobs, and there haven’t been great ways to cope. Justin talks about his newfound focus on self-improvement, working with coaches, and speaking with his peers.

Seeking out mentors, coaches, and peers for help

Justin says:

“The best part of Silicon Valley is that there are people here who’ve done it before, who are willing to help you.”

And:

“I learn from [Emmett, his Twitch co-founder, his brother who’s cofounder of Cruise, and his friend Steve who runs Reddit]. The problems are actually all the same: I don’t have the right alignment among my team and I don’t have the right executive team. And it’s always some variation of those things.”

I mention in the interview that IMHO this is one of the best things about the Bay Area — it’s a place focused on long-term relationships, and people help each other over the years. I met Justin over a decade ago and haven’t gotten the chance to work directly until now. And no matter where you are in the ecosystem there are always quite a few folks a step ahead, or a young up-and-comer who has a fresher take on things.

Intentionally designing a culture to avoid the pitfalls of “culture eating strategy”

Justin says:

“I had never asked myself, what is the kind of company I want to show up to work?”

There’s a saying that initially, a startup is about building a product that works — that’s the machine. But eventually you have to transition into building the machine that builds the machine — meaning company building, as opposed to product building. Culture is the core glue that holds everything together, and sometimes a startup idea is so strong that it works regardless of the culture. But it can be even more effective when it works.

Things he’s still doing in his latest startup—and things he’s doing very differently

Justin says:

“Iterating quickly. Speed. Being helpful in the community.”

There’s some things that worked out great in a startup that are worth repeating. This is true when you’ve seen some success, in particular. And there are some things that you want to change completely. Justin talks about the “YC ethos” of iterating quickly and leaning on speed. But as both a16z and his previous companies have done, he argues that it’s important to help the community.

Managing higher expectations

Justin says:

“It’s always a battle with the devil on your shoulder that says you’ll never be good enough. And the way to win that battle is to internalize the idea that whatever happens, you’re gonna be fine. You’re probably going to be the same — not happier or less happy”

When you read the academic research on happiness, one of the intriguing ideas is that people have a “set point” for their level of happiness, and in general it doesn’t change much. If you get that, then it helps level out the ups and downs of something stressful. This is important to startups, of course, but also to many other things in life!

What he’s reading and listening to

Justin says:

“I’m reading a lot, because I deleted all the entertainment apps off my phone including the browser and I locked it so I can’t install new apps because I was a total phone addict.”

He goes on to list:

• The Untethered Soul
• Leadership and Self-Deception
• The 15 Commitments of Conscious Leadership
• Joe Rogan Experience

Bonus: advice he’d give his 20-year old self

Justin says:

“Join Facebook. Self-improvement is a thing. Stop eating pizza. Things take time.”

I, on the other hand, would encourage my 20 year old self to eat more pizza. Hope you enjoy the interview!

Hi readers,

The growth teams at Uber and Airbnb occasionally met over the years to share best practices, brainstorm ideas, and share observations on the startup world. I’ve had folks over to 1455 Market St, the headquarters of Uber, and I’ve reciprocated with visits to the Airbnb offices too. I’ve learned a ton from these conversations, and met awesome people along the way!

While both consumer marketplaces are very different — one is a city-by-city transportation service, the other a global network of homes — they also share a lot of similarities too: Both were founded within a year of each other, quickly found network effects, made major design innovations that made the consumer experience 10X better, and much more. Importantly, both companies are tremendous growth stories, and have needed to grow both demand but especially supply in all of their markets globally.

Today, I have a wonderful guest essay to share by Lenny Rachitsky (@lennysan) — he’s recently left Airbnb after 7 years, much of his recent years as the product leader on Supply Growth. We’re all lucky that he’s now sharing his wisdom more widely!

This discussion is critical because the supply side — homes/hosts for Airbnb and drivers for Uber — are the most important aspect of most consumer marketplace startups, which I’ve written about this in my previous essay, “Why Uber for X Startups Failed: The Supply Side is King.” Lenny’s essay below discusses a comprehensive list of tactics and ideas around growing this critical side of the market. It’s fantastic, and I hope you enjoy it.

Thanks,
Andrew

Twenty Eight Ways to Grow Supply in a Marketplace

By Lenny Rachitsky

Airbnb now seems like an unstoppable juggernaut, but early on it was so fragile that about 30 days of going out and engaging in person with users made the difference between success and failure.
— Paul Graham, founder of YC

Deciding to open your home to strangers is a complex decision. Over the course of the seven years that I spent at Airbnb, my work centered around helping people all over the world make this decision. As the number of homes on Airbnb scaled from around 100,000 in 2012 to over 6 million today, I led teams tackling everything from supply growth, to guest booking conversion, to marketplace quality. As a result of this experience, I’m often asked what I’d recommend startups do to grow supply in their marketplace. The truth is that at the root of Airbnb’s success was a very good idea — affordable and unique travel experiences for guests, and great income for hosts. That being said, an idea is nothing without execution.

Below is an overview of every tactic and strategy I’ve seen used to bootstrap and accelerate supply growth, both at Airbnb and other successful marketplaces. Though some of these worked at Airbnb, and some didn’t, every marketplace has different challenges — my advice is to pick a few tactics that resonate, experiment with them, learn, and adjust.

Tactic #1: Nail the value prop on your site/app 👌

Understanding the context and expectations of your audience is vital to engaging them. At Airbnb we tailored the value props all the way from ad creative to landing pages.
— Dan Hill, ex-Airbnb Growth Lead, CEO of Alma

What: You need to convince visitors why they should become “hosts” on your platform when they visit your site (and then deliver on that promise). This may be obvious, but this is a foundational piece that enhances every other tactic below. It’s especially impactful for marketplaces that primarily grow organically because you’ll end up converting a significantly larger portion of your traffic.

Stage: Start on day 1, and continue iterating

Cost: Small

Impact on Airbnb supply growth: X-Large

Examples:

At Airbnb, the earnings estimate was an order of magnitude more effective than any other value prop. Any time we hid or obscured it, growth dipped. However, it’s critical that this estimate is realistic, both for legal reasons and to set the right expectations for your users.

Tips:

• Take your best shot at the pitch at first, and keep iterating as you learn more about your users.
• Help the user understand how they would benefit from hosting with you, and address all of their concerns. In most cases, it’s primarily going to be about the income they can make.
• Deliver on the promise. Or at least get close. This will lead to word-of-mouth growth, which is key.

Question: What has worked when pitching your existing “hosts”? Make sure your site/app says the same thing boldly and directly.

Tactic #2: Add entry points to the value prop 👉

What: Drive your site visitors towards your “host” pitch. This includes call-outs in the top level nav, in the footer of every screen, and sprinkled throughout the user experience. Do not assume your visitors know this exists, or why they should ever consider it.

Stage: Start on day 1, and continue iterating

Cost: Small

Impact on Airbnb supply growth: Large

Examples:

Tips:

• It’s hard to have too many entry points into your “host” pitch. Users will ignore it if it doesn’t apply to them. Resist the urge to be shy about this, especially if you are supply constrained.
• Every time your user has a good experience, encourage them to become a “host”, to provide this experience to others (while making money).
• Be more aggressive with this than you’re comfortable.

Question: Where else can you include a call-out to consider becoming a “host”?

Tactic #3: Offer a referrals program 👭

If your product requires word-of-mouth to convince most people to start using it, you can engineer more growth by building an incentivized referrals program. The incentive will be fuel that pushes people over the edge to tell their friends about something they love using
— Gustaf Alströmer, ex-Airbnb Growth Lead, Partner at YC

What: Incentivize word-of-mouth by paying existing members for every new member they refer to the platform.

Stage: Start early with a scrappy version, and get smarter over time

Cost: Medium

Impact on Airbnb supply growth: Large

Examples:

At Airbnb, the host referral program became the single most efficient and effective growth lever for consumer supply, cost efficiently driving both the largest share of attributable supply AND the highest quality supply

Tips:

• If most of your supply growth is coming from word-of-mouth, and especially if your users have large social graphs, then a referrals program is going to be huge for you.
• Once you have this, don’t hide it. Promote it throughout the user experience.
• Once you reach scale, fraud becomes a real issue. Watch for it as you grow, and invest in addressing it.

Question: What’s the simplest way you can test a referrals offering?

Tactic #4: Run direct sales ☎️

After calling and activating 100 listings in a market, we then drove demand there to see what converted. We then called these hosts to help them convert their requests into bookings. This was all manual, but super effective, all the while we were getting direct market feedback.
— Georg Bauser, ex-International Expansion at Airbnb

What: Call, email, or go door-to-door to pitch potential “hosts” on joining your platform. Sometimes this includes convincing them to switch from a different platform, sometimes it includes teaching them how to do it in the first place. This tactic is one of the more complex and operationally heavy, but also often the most effective at bootstrapping a marketplace.

Stage: Early-stage for B2C, an evergreen lever for B2B

Cost: Medium

Impact on Airbnb supply growth: Medium-Large

Examples:

• Lyft: https://www.saastr.com/build-sales-team-1-100-video-transcript/
• DoorDash: https://medium.com/@DoorDash/doordashs-inside-sales-team-is-driving-the-growth-of-key-merchant-relationships-b1f926819426

Tips:

• Hand-hold your early members. Help each new early member becomes successful in order to seed the platform with the type of supply you want. As a bonus, these early hosts will become loyal and unlikely to switch to a competitor, because you are building the business together.
• This tactic is particularly effective if your supply is un-commoditized and has high LTV, and if you’re creating a new market or behavior.
• Be creative in how you figure out contact information.

Question: What’s stopping you from cold-calling potential “hosts” today?

Tactic #5: Piggy-back off of existing networks 🔌

Initially, the Etsy team were freelance web designers and one of their clients was a craft forum called getcrafty.com. Throughout the redesign process, the Etsy team interacted with the website’s 10,000 users to best understand their needs. They began to notice that there was a large number of users who were looking for a platform to sell their handmade wares. While they were building Etsy, they also found out about Crafster.org, this time a message board with 100,000 users and were able to tap into another willing market. We extended an accommodating bridge to a preexisting online community, and they jumped aboard happily.
— Chris Maguire, co-founder of Etsy

What: Go to the place your existing supply is distributed and convince them to switch.

Stage: Early-stage

Cost: Small

Impact on Airbnb supply growth: Large

Examples:

• Airbnb: https://davegooden.com/2011/05/how-airbnb-became-a-billion-dollar-company/
• Omni: https://www.reforge.com/blog/omni-bootstrapped-marketplace-liquidity-growth
• General: https://platformed.info/whatsapp-instagram-marketing/

Tips:

• To be successful here, you’ll have to be “creative” in how you do said piggy-backing. Airbnb piggy-backed off of Craigslist for both supply and demand.
• Make it super easy to switch from them to you.
• You will only win if you can drive more demand or profit to that same supply. Otherwise, why would they choose you?

Question: Where do people currently find what you are offering, and is there a way you can piggy-back off of that channel?

Tactic #6: Hold meetups 👋

To launch a city, we’d travel there and hold a meetup. Here in San Francisco, it’s not a big deal to meet a founder. In other places, that’s pretty novel. They would get so excited that they met us that they’d tell their friends. The markets started turning on, and we religiously focused on making sure customers loved us.
— Brian Chesky, CEO of Airbnb

What: Bring “hosts” and anyone considering become a host together in person. This can be small intimate gatherings or large’ish events.

Stage: Early-stage

Cost: Small

Impact on Airbnb supply growth: Medium early-stage, Small late-stage

Example:

• Read the first part of this interview with Reid and Brian.
• Lessons in Building Movements through Community Action from Douglas Atkin, Global Director of Community at Airbnb

Tips:

• The highest value of the meetups is for employees to listen to users, to answer questions, and simply to give people a chance to meet and share. Resist the urge to fill the time with presentations and speeches.
• You don’t need to spend a bunch of money on each meetup. They can be scrappy and simple.
• Don’t expect to be able to quantify the impact of hosting meetups. We tried this many times and we never saw any measurable impact. But, looking back, it’s clear that it was important early on.

Question: Where is your early community most concentrated? Why aren’t you there right now?

Tactic #7: Leverage events and PR 🎪

In the early days we targeted a lot of events: the DNC, the Presidential Inauguration, music festivals, the World Cup, Olympics, etc. Events and PR were the main way we bootstrapped the network in the early days.
— Brian Chesky, CEO of Airbnb

What: Leverage a specific event to pitch potential “hosts” and seed PR stories about how your service is helping people.

Stage: Early-stage

Cost: Small

Impact on Airbnb supply growth: Medium

Examples:

• Airbnb: https://www.theguardian.com/travel/2015/may/18/airbnb-float-a-house-on-the-thames-in-london
• Uber: https://www.annexcloud.com/blog/ubers-marketing-strategy-in-7-steps-revisited/
• TaskRabbit: https://www.networkworld.com/article/2223168/-1-500-to-be-first-in-line-for-an-iphone——or-a-publicity-stunt-.html

Tips:

• Find ways to be creative and stand out during the event.
• Use that same creativity to develop PR pitches. What would give the press an interesting angle on the event?
• Get on the ground. You need to be there, hustling.

Question: Are there punctuating moments where your offering is most beneficial for your supply?

Tactic #8: Run performance marketing 💰

Performance marketing is about reaching people where they are, inspiring them to take action and doing so cost effectively. You quickly learn that it’s a unique blend of art and science, and we were most effective at scaling this powerful lever when we had a dedicated cross-functional team sitting together — product, marketing, engineering, data science, design, content, and finance.
— Fatima Husain, ex-Airbnb host paid growth lead, Principal at Comcast Ventures

What: Run Facebook, Google, Twitter, etc. ads.

Stage: Early-stage for some businesses, late-stage for others

Cost: Medium/Large

Impact on Airbnb supply growth: Medium

Examples:

Tips:

• Knowing your supply LTV is key, so that you know how much you can spend.
• You can figure out the basics yourself, but try to hire people that have done this before.
• This lever works, but can easily become addicting.

Question: Where do your potential “hosts” spend time online?

Tactic #9: Convert demand to supply 🙃

Converting travelers to hosts definitely moved the needle when we launched a new market. Plus, these new hosts were the most empathetic as they remembered the guest’s needs.
— Kati Schmidt, ex-Head of B.D., Airbnb Germany

What: Convince users to become “hosts” on the platform. This builds on Tactic #2 — go deeper into the user experience and find moments when it makes sense to pitch “hosting” (e.g. after a great experience).

Stage: Start early, and continue iterating

Cost: Small

Impact on Airbnb supply growth: Medium early-on, Small later-stage

Examples:

Tips:

• This can be manual/ops based at first, and productized over time.
• Think about a clever pitch you can make to your users, e.g. “Pay for your trip by hosting your home”.
• See how Uber, Lyft, and Airbnb do this throughout the demand-side experience.

Question: What percentage of your demand could potentially be the supply? If it’s in double digits, see if you can suggest or even incentivize this behavior.

Tactic #10: Invest in SEO ⛓

What: Drive organic search traffic to your site.

Stage: Early for some businesses, late-stage for others

Cost: Small

Impact on Airbnb supply growth: Small

Tips:

• SEO is generally more effective for demand. I haven’t seen SEO be a major driver of growth on the supply side, including at Airbnb.
• The best SEO content is user-generated-content that is created as a part of the user experience.
• Find an SEO expert to help you figure this out.

Question: What job are you solving for potential “hosts”, and what does that translate to when they search for solutions?

Tactic #11: Acquire supply 🤑

We needed to move fast and acquiring companies with inventory in these competitive markets provided an immediate benefit. Often times the expense of the acquiring the company that had the inventory was cheaper than the cost to go about acquisition in an organic way. The key is to understand the migration percentage that would come through. We aimed for 60–80% to migrate over.
— Jonathan Golden, first PM at Airbnb, partner at NEA

What: Acquire companies that currently have the supply you want.

Stage: Always

Cost: Large (but strategic)

Impact on Airbnb supply growth: Small globally, Large in key markets

Examples:

• Airbnb: Crashpadder, Statthotel
• Rover: DogBuddy, DogVacay
• Eventbrite: Ticketfly, Picatic

Tips:

• Since you are betting on network effects, the value of that supply to your network should be much higher to you than to a small local company. Thus, it may be worth paying a premium.
• Make sure the supply you are buying is actually good.
• Don’t underestimate the work it’ll take to migrate the supply, both technically and interpersonally.

Question: Are there small players with a strategic foothold you can acquire or merge with?

Tactic #12: Partner with supply aggregators 👋

What: Plug-in a partner’s supply into your marketplace, through partnerships, licensing, or even scraping.

Stage: Always

Cost: Small

Impact on Airbnb supply growth: Small

Examples:

• GrubHub: https://table.skift.com/2018/05/01/grubhubs-aggressive-growth-fueled-by-chain-partnerships/
• DoorDash: https://medium.com/@DoorDash/the-doordash-outside-sales-team-builds-partnerships-and-empathy-with-business-owners-across-the-3fbdf1f0723b

Tips:

• Be strategic and thoughtful about the type and quality of supply you bring on. The quality and experience with that supply will reflect directly on your brand, not theirs.
• Make sure you are clear on your long-term competitive advantage, and not only growing this partner’s business. What’s your differentiator, and how will you maintain it?
• Make sure the user experience is smooth and feels native.

Question: What would be the biggest upside of adding 3rd party supply to your marketplace?

Tactic #13: Build your own supply 💪

One of the smartest things we did in the early days of Udemy was produce our own courses. Production (i.e. filming & editing video content) is a huge friction point in our supply-side process. So, we produced a few of our own courses in the beginning and then marketed the heck out of them. This wasn’t scalable, but it did allow us to build powerful social proof points which were critical to our long-term success.
— Dinesh Thiru, VP of Marketing at Udemy

What: In some marketplaces, you can either bootstrap supply by creating it yourself (e.g. videos), pay early users to become supply (e.g. Uber/Lyft paying drivers a salary), or your build your entire business on your own supply (e.g. Sonder).

Stage: Depends on business

Cost: Medium/Large

Impact on Airbnb supply growth: Small

Examples:

• At Airbnb, read Brian Chesky’s answer to the first question in this interview. His and Joe’s apartment was the first unit of supply on Airbnb.
• See example #1 in this post about Udemy.

Tips:

• Set the norms through the type of supply you create.
• Often not possible due to the business model.
• Be careful about the legal implications.

Question: What would it cost to build your own supply, and how does that compare to customer acquisition costs?

Tactic #14: Run broadcast and out-of-home ads 📺

What: TV commercials, podcast ads, movie ads, etc.

Stage: Always

Cost: X-Large

Impact on Airbnb supply growth: Small

Examples:

Tips:

• Don’t expect to ever be able to measure the impact. Because you won’t.
• The main benefit is generally brand-building.
• Do something unique and noteworthy.

Question: Are your potential “hosts” watching the same (ideally not super-popular) media or passing through the same physical parts of town?

Tactic #15: Run affiliate marketing 📝

What: Incentivize content producers to send you traffic by paying them for every member they refer.

Stage: Mid/Late

Cost: Small/Medium

Impact on Airbnb supply growth: Small

Examples:

Tips:

• Takes very few people to operate, should pay for itself from day 1.
• There are companies out there that do a lot of the heavy lifting for you.
• Make sure to have brand guidelines that content creators must follow, otherwise the content ends up being bad.

Question: Are your competitors doing affiliate marketing?

Tactic #16: Send direct mail 📬

What: Send potential “hosts” physical mail, pitching them on your platform.

Stage: Always

Cost: Medium

Impact on Airbnb supply growth: Small

Example:

• Uber: https://www.linkedin.com/pulse/ubers-print-marketing-so-crazy-seth-engel/
• GrubHub: https://blog.sendwithscout.com/grubhub-knows-how-to-design-a-postcard/

Tips:

• This is an under-appreciated channel.
• Tricky to measure, but possible.
• Check out Lob to make this super easy.

Question: Is this a channel your competition hasn’t tried yet?

Tactic #17: Optimize conversion 📈

All conversion optimization should start with user research. The biggest gains in optimization don’t come from brute-force A/B tests, but from trying to understand the real barriers to people using your product. For example, early at Airbnb we realized that the biggest hurdle for new hosts was knowing how much to charge for their space. So we built price guideance into the flow.
— Dan Hill, ex-Airbnb Growth Lead, CEO of Alma

What: Improving the percentage of people that start publishing that actually finish it.

Stage: Start early, and continue iterating

Cost: Small

Impact on Airbnb supply growth: Medium

Tips:

• Top-of-funnel levers will generally be orders-of-magnitude higher impact than any mid-funnel levers like this one, but early on are often low-risk big-wins. Later-stage, you can continue to squeeze % point’s of growth for a while.
• Figure out which part of the conversion funnel is the biggest issue, and focus all of your efforts there. Be careful spreading yourself too thinly across the entire funnel.
• In my experience, a big redesign of the flow often ends up hurting conversion.

Question: Which part of the funnel is most important to improve, and what are three things you do to improve this?

Tactic #18: Send re-engagement emails/pushes 📩

What: Emailing users that didn’t complete publishing, encouraging them to finish.

Stage: Always

Cost: Small

Impact on Airbnb supply growth: Small/Medium

Tips:

• You’ll get most of the win by simply having an email, and then quickly hit diminishing returns once optimizing it a few times.
• Don’t be afraid to send a few reminders.
• Make the CTA extremely clear.

Question: What’s one helpful thing you can suggest to bounced users to re-inspire them?

Tactic #19: Make re-engagement calls 📞

What: Calling users that didn’t complete publishing, encouraging them to finish.

Stage: Early-stage for B2C, an evergreen lever for B2B

Cost: Medium

Impact on Airbnb supply growth: Small

Tips:

• Always ask users why they didn’t finish the process — this should inform your roadmap and processes.
• Early on you can use this as a customer development opportunity.
• Long-term, measure the ROI to make sure it’s worth the time.

Question: Which bounced users appear to be the most valuable and worth a call?

Tactic #20: Optimize activation

When I think about the ROI of things that you can do in a business, make certain that your customer is safely handed from acquisition to the activation. Make certain that they are activated and you have done everything in your power in order to make certain they have found their “Aha” moment and they have began habit forming.
— Shaun Clowes, CPO at Metromile

What: Getting new users to a key milestone that you believe is important for long-term retention. This is sometimes called the “aha” moment.

Stage: Early/Mid-stage

Cost: Small

Impact on Airbnb supply growth: Small/Medium

Examples:

• At Airbnb, an activation was getting a new listing to its first booking within the first month.
• At Uber, an activation was completing the first ride.
• Inside the 6 Hypotheses that Doubled Patreon’s Activation Success

Tips:

• First, you need to figure out what milestone is key to a new “hosts” long-term success. It’s rarely an exact science.
• Goal your supply teams on reaching this point, vs. simply when they go live.
• Adjust this milestone if you learn something new down the road.

Question: What’s the one most impactful thing a “host” can do to improve their chances of getting booked?

Tactic #21: Optimize retention 🔐

Retention is the core of your growth model and influences every other input to your model. This is important because if you improve retention, you’ll also improve the rest of your funnel.
— Brian Balfour, Founder/CEO of Reforge

What: Increasing the percentage of new users that stick around for at least X months.

Stage: Always

Cost: Small

Impact on Airbnb supply growth: Medium early-stage, Small late-stage

Examples:

• At Airbnb, we didn’t spend a lot of time focused directly on retention. When we did, the majority of our efforts centered around helping new hosts get booked and have a great first stay.
• Excellent retention reading over at Reforge, including Why Retention Is The Silent Killer and Retention is Hard, and Getting Harder — Here’s Why.
• Retention metrics roundup of articles and links by Andrew Chen.
• Casey Winters on how to create long-term growth

Tips:

• In my experience, it’s difficult to significantly impact retention head-on. The key is that your product/service needs to continue to be genuinely useful. Make it more useful and retention will grow.
• Track retention by cohort, vs. globally.
• Make sure to capture data on why people leave, to inform future work.

Question: What is the single most common theme in why “hosts” leave, and what can you do about it?

Tactic #22: Expand existing supply 🤲

What: Convince existing successful “hosts” to increase the number of units they offer.

Stage: Always

Cost: Small

Impact on Airbnb supply growth: Small

Examples:

• An Airbnb host buying additional properties to manage.
• A Hipcamp host adding additional campsites.
• An Outschool teacher offering additional classes.

Tips:

• This can be one of the biggest growth drivers for certain businesses, don’t underestimate the potential here.
• In some cases, this is going to be easy (e.g. online classes), in some very hard (e.g. homes).
• Educate users on what kind of new supply would be most successful. Channel their excitement and point them in the right direction.

Question: Have you actually talked to your successful “hosts” about adding additional supply?

Strategy #1: Increase benefits, reduce costs ⚖️

What: Potential “hosts” will do mental calculus when considering signing up: is the cost (e.g. work, risk) worth the benefits (e.g. money, status). Make a list of ways to increase the benefits and reduce the costs, do them, and share this clearly.

Stage: Start on day 1, and continue iterating

Examples:

• Airbnb — Reduce costs: Host Guarantee, free photography
• Airbnb — Increase benefits: Guaranteed revenue, online payments
• Uber — Reduce costs: Car leasing, guaranteed income
• Uber — Increase benefits: Choose when to get paid, flexible hours

Tips:

Building it won’t be enough, make sure to make it clear what you do for your users to increase benefits and reduce cost.

Strategy #2: Single-player mode

OpenTable sold software to restaurants that created value for them without requiring any diners on the “buyer” side of the marketplace. They built a unique table management and CRM product (the “Electronic Reservation Book”) and charged a subscription fee for the service. The initial benefit to restaurant customers was the software. Once OpenTable acquired hundreds of restaurants in a city, they started to have a compelling diner value proposition.
— Eli Chait, ex-Director of PM at OpenTable

What: Make the platform to “hosts” useful even when there is no demand.

Stage: Early-stage

Examples:

• OpenTable: Simplified reservation management for restaurants
• Eventbrite: Created free web pages for your event
• More: https://blog.elichait.com/2018/04/09/how-the-100-largest-marketplaces-solve-the-chicken-and-egg-problem/

Tips:

• In most marketplaces, supply is king, so your single-player-mode should generally be focused on the supply side.
• This won’t be possible for every marketplace.

Strategy #3: Get to critical mass 💥

Our co-founder Nate Blecharczyk is highly quantitative and had determined that 300 listings, with 100 reviewed listings, was the magic number to see growth take off in a market. Observing New York, Paris, and a few other top markets, we saw a step-function change in the rate of bookings growth at 300 listings, the point at which guests had enough options to find a listing that matched their tastes and their travel dates.
— Jonathan Golden, first PM at Airbnb, partner at NEA

What: There is a point at which you have enough supply that you see an inflection point in demand conversion — estimate it and get your supply to that number.

Stage: Early-stage

Examples

• Airbnb: https://medium.com/@jgolden/lessons-learned-scaling-airbnb-100x-b862364fb3a7
• Airbnb and others: https://medium.com/immutable/network-effects-1b975c7a6859
• Facebook: https://medium.com/geckoboard-under-the-hood/how-facebooks-7-friends-in-10-days-got-everyone-confused-about-correlation-and-causation-25da4bb8220e

Tips:

• Don’t overthink it. Figure out a milestone enough people believe within the org, and use it until you can figure out something better. It’s more important to have something good-enough than to have nothing.

Strategy #4: Bootstrap trust 🤝

We looked at people’s willingness to trust someone, based on how similar they are in age, location, and geography. The research showed, not surprisingly, we prefer people who are like us. The more different somebody is, the less we trust them. That’s a natural social bias. What’s interesting is what happens when we add reputation to the mix — in our case with reviews. When you have less than three reviews, nothing changes. But if you’ve got more than ten, everything changes. High reputation beats high similarity. The right design can actually help us overcome one of our most deeply rooted biases.
— Joe Gebbia, Co-Founder of Airbnb and CPO

What: At first all you have is new supply, but users have no reason to trust it. Give them reasons to trust.

Stage: Early-stage

Examples:

Airbnb: Early employees were given credit to travel for free as long as they left reviews for new hosts.
Airbnb: Make the supply look great, e.g. free photography, structured data with limited customization.
Airbnb: Reduce risk, e.g. Handle payments online, Host Guarantee, 24/7 support.
Uber and Airbnb: https://firstround.com/review/How-Modern-Marketplaces-Like-Uber-Airbnb-Build-Trust-to-Hit-Liquidity/

Strategy #5: Internationalization ⛩

Internationalization is a challenge and risky. But tech companies need to be global to win. It’s about the right strategy and answering the right questions at the right time. Not going international is a wasted growth opportunity.
— Georg Bauser, ex-International Expansion at Airbnb

What: Make your site and experience work outside of initial native language/culture. This includes building out translations, local payment types, customer support in those languages, and often people on the ground getting things rolling.

Stage: Mid-stage

Examples:

• Launching airbnb.jp in record time
• Who did what in international expansion in 2018
• How Netflix Expanded to 190 Countries in 7 Years

Tips:

• International expansion is often one of the major inflection points for growth accelerating, so time it wisely.
• Pick the markets you want to win and go big there, vs. going broad immediately. It’s hard enough to win one market.
• This often requires doing things that don’t scale at first.

Strategy #6: Segment supply 👩‍🌾 👩‍✈️👩‍🔬

Early on Airbnb presented only one image of itself to hosts. Many types of hosts deemed the platform unsuitable, but opening ourselves and marketing to business travel hosts and luxury home hosts gave those hosts a message they wanted to hear. They could host the types of guest they felt were suitable for their property. This allowed us to onboard inventory we previously couldn’t, and gave existing hosts more optionality in how they marketed to and serviced those guests.
— Marc McCabe, ex-Head of Airbnb for Business

What: Determine what categories of supply you have and/or want, and dedicate teams to growing that type of supply. Generally, different categories require very different tactics, skills sets, and operating cadences.

Stage: Mid/Late-stage

Examples:

• Airbnb: Private homes, vacation rentals, luxury home.
• Uber: Black cars, individual car owner, scooter.
• eBay: Individual sellers, brick and mortar stores, wholesalers.

Tips:

• When introducing a new class of supply, there are many important considerations, including making sure you have a team whose ass is on the line for making this supply successful, making sure the demand side is set up to convert this new supply well, and avoiding overly diluting your marketplace differentiation.
• New segments often appear organically, and it’s up to you to decide if (and when) you want to double down on segment, or squash it.
• For the more professional segments, you’ll likely need to build advanced tools and integrations in order to fit into their existing workflows.

Final thoughts

Looking back at my time at Airbnb, a few things become clear. One, there were no silver bullets — success came from many wins building on each other. Two, most things we tried didn’t have an impact — but enough did. Three, it all only made sense in hindsight. My advice to you as you navigate scaling your marketplace is above all else, stay focused on providing value to your users. Their success will make or break you. Beyond that, avoid spreading your team too thinly across many tactics (aka focus), double down on the things that show promise (aka focus), and never lose sight of your north star (aka focus). Also, focus.

For more writings about growth, product, management, and related topics, make sure to subscribe to my new newsletter and hit me up on twitter.

Sincerely,

Lenny

Thank you Gustaf Alströmer, Andrew Chen, Jonathan Golden, Fatima Husain, Marc McCabe, Dan Hill, Kati Schmidt, and Georg Bauser for reviewing early drafts of this post and contributing great ideas. 🙏

Dear readers,

I’m happy to announce I’ve completed my first year in my new role at a16z, and it’s been a blast! I will write more about it coming up, but in the meantime, it’s very timely that my colleague Scott Kupor has written a new book, Secrets of Sand Hill Road, with the fun subtitle “Venture capital and how to get it.” I’ve had the pleasure of reading ahead of its release, and as expected, it’s excellent, and provides a detailed guide and fantastic in depth info on everything you’d want to know about venture capital. As an author, Scott could not have more street cred — he joined and built a16z from the very early years, and is our go-to on all the nitty gritty of the industry for the whole team.

You can (and should!) pre-order the book here »

There’s a bunch of great topics, including:

• Why the skill you need most when raising venture capital is the ability to tell a compelling story.
• What to do when VCs get too entangled in the day-to-day operations of the business.
• Why you need to build relationships with potential acquirers long before you decide to sell.
• Why most VCs typically invest in only one startup in a given business category. 

The math of startups and venture capital
Of all the topics of the book, one of my favorites has to be the math of startups and venture capital, because it gives us a perspective on the life and death of startups as a whole. Because venture capital is an index of the broader startup ecosystem, it can tell us a lot — everything from how often the Ubers, Dropboxes, Facebooks, and Googles emerge as startups, to how quickly doomed startups typically fail.

All of these tell you why many venture capitalists ultimately end up being interested in companies that want (and can!) get big — and it’s not the right way to finance the vast majority of new companies, many of whom are more focused on smaller markets or slower growth business models. I want to share a couple slides that Benedict Evans from a16z presented a few years back to make this point:

The above tells an amazing story: Over the past few decades, a small number of startups — 6% — end up driving 60% of the returns.

And I suspect if we were to dig into the 6%, we see that just a small number, probably a dozen or so per year, that drive a substantial amount. In other words, the startups that end up big end up really big. These startups aren’t just unicorns, they are another order of magnitude more successful than that.

It also tells you why, as an entrepreneur, that investors are so focused on network effects, high margins, technology differentiation, a 10X product experience, etc. — these are the foundational drivers that help create this super huge outcomes.

Above: Here’s another surprise from the data, which is that the best investors don’t seem to be better at avoiding startups that fail. It’s not about the downside. Instead, the data says that a “good” 2-3x fund and a fantastic >5x fund lose money about the same % of the time.

However, for a fantastic fund, its winners are much, much bigger than everyone else’s. For these top funds, the biggest startups end up generating 90% of the returns. It’s all about upside! For startups that ask why investors seem so obsessed with market size and say that few ideas are big enough, here’s the data that explains why.

The J-Curve
Finally, there is the concept of the J-curve in venture capital investing in which you look at a basket of startups over a long period — say, 10 years — and see how the returns look. And it often resembles a J, where the early years look pretty bad! And then eventually the big winners get bigger and bigger, picking up momentum to ultimately drive returns for the fund.

It looks like this:

This graph demonstrates the phrase that “lemons ripen early” — as Secrets of Sand Hill Road discusses. A portfolio of startups will often have early losses as the teams without product/market fit run out of money early. The successful ones that will become the winners take time to emerge. These days, it can take 3-5+ years from the company’s inception to see its true growth trajectory. As a result, there’s a J-curve that shows early losses followed by the successful startups making up the different in the later years.

If you are as fascinated as I am about all of this, I know you’ll enjoy Scott’s book. I want to leave you with an excerpt below. In the section, he discusses the J-curve in detail and why it behaves why it does. Hope you enjoy it!

Thanks for reading, and more from me soon.

Andrew

Secrets of Sand Hill Road: Venture Capital and How to Get It
by Scott Kupor

“Carried Interest”

The heart of compensation for GPs (at least for those who are successful investors) is carried interest. It’s rumored that the term “carried interest” derives from medieval traders who carried cargo on their ships that belonged to others. As financial compensation for the journey, the traders were entitled to 20 percent of the profits on the cargo. That sounds very civilized, if not rich. I’ve also heard—although my Google search is failing me now—that the carry portion of carried interest referred to the fact that the traders were allowed to keep as profit whatever portion of the cargo they could literally “carry” off the ship of their own volition. I prefer that story.

Regardless of its historical origins, carried interest in the VC context refers to the portion of the profits that the GP generates on her investments and that she is entitled to keep. As with the management fee, the actual amount of carried interest varies among venture funds but often ranges between 20 and 30 percent of the profits.

As it turns out, how we define “profits” and how and when the GP decides to distribute those profits to herself and her LPs is a matter for negotiation in the LPA.

Let’s use a simple example to illustrate.

Go back to that $100 million venture fund we talked about before, and assume that we are in year three of the fund’s existence. The GP invested $10 million in a portfolio company earlier in the fund’s life, and now the company is sold for $60 million. So, on paper at least, the GP has generated a tidy profit of $50 million for that investment. She’s also invested the rest of the $90 million in other companies, but none of those has yet been sold or gone public. Ah, she can taste the carry check already!

But how does the money get divvied up between the LPs and the GP? Let’s assume that the GP has a 20 percent carried interest; in simple terms that means that when the fund earns a profit, 20 percent of that goes to the GP.

So, in our example, the GP is sitting on a $60 million check, of which $50 million represents profit, and wants to give 80 percent of the profit (or $40 million) to the fund’s LPs and keep 20 percent (or $10 million) for herself. The other $10 million in this example will go back to the LPs as a return of their original capital. We’ll come back to this later in this chapter and add some additional complexity to this.

But wait a second. Is there really a profit on which the GP is entitled to take her 20 percent? The answer is maybe. We need to take a little detour to introduce two other important concepts before we can conclusively answer the question.

As with fine wine, VC funds should get better with age. In fact, that’s why people in the industry refer to funds by their “vintage year” (or birth year), just as winemakers date mark their wines based on the year of the grape harvest.

As we discussed earlier, in the early years of a fund, VCs are calling capital from LPs and investing that capital in companies. This is a decidedly negative cash flow motion—money is going out with (likely) no near-term prospect of money coming in. That’s an expected effect, but eventually a VC must harvest some of those investments in the form of those companies going public or being sold.
The effect of calling capital from LPs in the early years coupled with the long gestation cycles for companies to grow and ultimately exit—in many cases it takes ten or more years for companies to be sold or go public—creates what is known as the “J curve.”

As you see in the above picture, the LP has negative cash flow (from the capital it’s giving to the venture firm for investment) in the early years of a fund and (hopefully) positive cash flow in the later years of the fund, a combination both of the capital having already been called and invested and the portfolio companies being sold or going public.

Venture capital is truly a long-term game. But, as explained in our discussion of the Yale endowment in chapter 4, cash does eventually need to come out the other end. Successful GPs will manage their portfolios to drive to this outcome, which can affect how they interact with entrepreneurs on this topic.

One phrase you often hear in the hallowed halls of VC firms is “lemons ripen early.” That is, the non-performing companies tend to manifest themselves close in time to the initial investment. Interestingly, this exacerbates the J-curve problem in that not only are VCs investing cash in the early years of a fund, but the non-performing assets are certainly not helping the GP return money to the LPs.

Reprinted with permission of Portfolio Books

Dear readers,

Podcasting has been a slow burn, and has turned into a movement. 90 million Americans now listen to podcasts, and if your behavior is anything like mine, it’s turned into a multi-hour per week habit. I reach for my podcasts whenever I’m commuting, whenever I’m doing a long walk between offices, or if I’m doing random stuff around the house. No wonder the consumer investment team ended up digging into this trend by doing a market map report — the analysis led by Li Jin, Avery Segal and Bennett Carroccio, including work from myself, Connie Chan, and others.

See below for a long-form analysis everything we’ve observed in the podcasting industry. It was originally published on a16z.com. There’s a lot to read here, but I wanted to highlight a couple of my takeaways and what I’m looking for now:

• Podcasting is big, mainstream, but severely undermonetized — and some of the biggest opportunities in the podcasting space lay in pivoting the business model from ads into some kind of direct payment. I’m looking for startups that can change the game there.
• The bigger idea is actually “audio”, not specifically podcasting. And in fact, the combined revenue of Headspace and Calm are more than half of the entire podcasting market. Whoa! I’d be interested in other products that tap into the trends around AirPods, Alexa, voice assistants, etc., but may not directly sound like podcasting
• After this analysis, I’m looking for really differentiated verticals of audio. Meditation is one, but what about something that’s at a much higher price point? For example, something that’s very business-focused and can be put on a corporate credit card? It could create a strong advantage around paid marketing if a product has high subscription retention or ARPU, allowing them to make higher bids in the various ad networks.

The report covers things in more detail at the end, but that’s the tldr; from an investment standpoint.

The other quick plug I want to give — get a copy of the PDF version of the deck by joining the a16z newsletter:

Subscribe to the a16z newsletter here.

The a16z team uses the newsletter to circulate resources, including podcasts, op-eds, presentations, and more. You can subscribe to get more updates.

Without further adieu, here’s the a16z consumer team’s definitive analysis of the podcast ecosystem in 2019. Enjoy!

Thanks,
Andrew

The Podcast Ecosystem in 2019

By Li Jin, Avery Segal, and Bennett Carroccio

In the world of podcasting, the flywheel is spinning: new technologies including AirPods, connected cars, and smart speakers have made it much easier for consumers to listen to audio content, which in turn creates more revenue and financial opportunity for creators, which further encourages high-quality audio content to flow into the space. There are now over 700K free podcasts available and thousands more launching each week.

As new tech platforms hit scale, we on the consumer team have been closely watching the future of media and the technology driving it — in all forms. We’re interested in investing in the next wave of consumer products and startups coming into the ecosystem, and that includes the audio ecosystem.

Our investment philosophy is to not be too prescriptive, so we do the kind of “market map” overview below to help us have a “prepared mind” when we see new startups in the space. The below deck and commentary (with some sections redacted, of course) was presented to the extended consumer team, including general partners Connie Chan and Andrew Chen, who are investing in this space. If you’re working on anything interesting in this area, we’d love to hear from you!

From niche internet community to one-third of Americans

Over the course of the last 10 years, podcasts have steadily grown from a niche community of audiobloggers distributing files over the internet, to one-third of Americans now listening monthly and a quarter listening weekly.

Americans listening weekly to podcasts grew from 7% in 2013 to 22% in 2019. 65% of monthly podcast listeners have been listening for less than 3 years.

People are already spending a lot of time on podcasts, and it’s growing: listeners are consuming 6+ hours per week and consuming more content every year.

Among weekly podcast listeners, there’s high consumption: 7 episodes per week and nearly 1 hour per day.

The demographic of podcast listeners is not your average American. Roughly half of podcast listeners make $75,000 or more in annual income; a majority have a post-secondary degree; and almost one-third have a graduate degree [source]. There’s also a gender gap with podcast listeners skewing mostly male, mirroring the gap among podcast creators as well. However, the gender gap has narrowed from a 25% gap in 2008 to 9% today.

Podcast listeners are not your typical American: they’re affluent, highly educated, and skew male.

In the years following the release of Apple’s podcast app in 2012, smartphones pulled ahead of computers for podcast consumption and have grown to become the dominant way that consumers listen to podcasts. The green line includes smart speakers, which have grown 70% year over year in terms of listening.

Since Apple launched its Podcasts app in 2012, smartphones have quickly grown to become the most common device for podcast consumption.

What may surprise people living in heavy commuter markets is that listening primarily happens at home, which represents almost half of all podcast consumption.

We would also anticipate that more recent technologies like Bluetooth-enabled cars and smart speakers — now owned by 53M Americans or 21% of the population — could change the mix of where podcast listening happens.

The lion’s share of podcast listening happens at home, followed by taking place in a vehicle.

A brief history of podcasting

Simply put, podcasts are digital audio files that users can download — or in some applications, stream — and listen to. While podcasts differ widely in terms of content, format, production value, style, and length, they’re all distributed through RSS, or Really Simple Syndication, a standardized web feed format that is used to publish content. For podcasts, the RSS feed contains all the metadata, artwork, and content of a show.

To listen to a podcast, a user adds the RSS feed to their podcast client (such as Apple Podcasts, Spotify, etc.), and the client then accesses this feed, checks for updates, and downloads any new files. Podcasts can be accessed from computers, mobile apps, or other media players. On the podcast creator side, creators host the RSS feed as well as the show’s content and media on a hosting provider, and submit the shows to various directories, such as Apple’s podcast directory.

Podcast content is typically available for free, though creators can choose to set up private RSS feeds that require payment to access.

Current headlines about podcasts today hail them as the next major content medium, describing them as “suddenly hot”, as the next battlefield for content, and as an “antidote” for our current news environment:

How did this “suddenly” happen? As with all tech trends, it had a longer and slower start before going more mainstream. Let’s time travel back 15 years ago, when there were no smartphones and the internet was accessed only through desktop computers.

In February 2004, journalist Ben Hammersley wrote about the emergent behavior of automatically downloading audio content in a February article in The Guardian:

“MP3 players, like Apple’s iPod, in many pockets, audio production software cheap or free, and weblogging an established part of the internet; all the ingredients are there for a new boom in amateur radio. But what to call it? Audioblogging? Podcasting? GuerillaMedia?”

In doing so, Hammersley accidentally invented the term we still use today, “podcasting” — a portmanteau of “iPod” and “broadcast” — for this kind of content. The word was added to the Oxford English Library later that year.

In 2005, podcasts were added to the iTunes store, with Steve Jobs saying, “Podcasting is the next generation of radio, and users can now subscribe to over 3,000 free Podcasts and have each new episode automatically delivered over the Internet to their computer and iPod.”

In 2007, the first iPhone was introduced, but it wouldn’t be until 2012 that Apple created the Podcasts app. The release of this app is widely considered an inflection point for the industry, as it put podcasts a single tap away for hundreds of millions of users around the world. Ironically, a few months later, Google discontinued its own podcast app called Google Listen.

In 2014, the first season of Serial aired, considered to be the first breakout podcast, with its narrative audio journalism drawing in 5M downloads in the first month.

In the past 5 years, there’s been an explosion of listening behavior and innovative content. New devices made it easier to listen: Alexa launched in 2015, Google Home and AirPods in 2016. And an explosion of new content — ranging from daily news to narrative to talks shows — met the growing listener appetite. In tandem, ad spend has been growing steadily each year, from $69 to $220M in 2017 [source].

The app landscape

Many apps for listening to podcasts, but little differentiation or loyalty

Apple Podcasts played a pivotal role in the development of the industry and remains the dominant app for listening. However, its market share has fallen in the last few years, from over 80% to 63%. The corollary to this stat is that historically, podcasting has been predominantly an iOS user behavior, given that Google didn’t have its own native application, something that changed last summer with the launch of Google Podcasts.

Apple’s share of the podcasting market has slipped from over 80% to 63%, while Spotify has quickly grown to almost 10% of the market.

Spotify — which has made a big push into podcasts in just the past couple years — now accounts for almost 10% of listening.

Beyond these two large companies, there’s a long tail of listening apps from smaller companies. Most of these apps all have roughly the same content, given widely open directories of podcast RSS feeds. And there’s hundreds more listening apps out there. The barriers to entry for creating a new podcast app are quite low, since content is all distributed via RSS feeds and anyone can access them. There are also tools for creators to create their own podcast app from their own RSS feed.

A note on comparing listening apps: metrics between apps are not entirely an apples-to-apple comparison, as some apps (like Apple Podcasts, Overcast, and Stitcher) auto-download shows that users subscribe to, whereas others (e.g. Spotify, Castbox) don’t continuously download new episodes. This affects comparisons between apps and may overstate the traction of listening apps that auto-download shows. The industry has not standardized around what defines a download or listen.

A taxonomy of consumer podcast apps

From our research, users seldom feel passionately — either positively or negatively — about the podcast app they’re using. This suggests that the audio content itself is the core element users are engaging with, and since the content is the same on all apps, users don’t feel particular affinity to any one listening app.

Three major categories of consumer podcast listening apps: the incumbent, large existing audience and new podcast focus, and long tail listening apps.

I categorized consumer podcast listening apps into three major categories:

• The incumbent: Apple Podcasts
• Companies with large, existing audiences who are newly focusing on podcasts
• Long-tail listening apps

The major feature of Apple Podcasts is that — despite its shortcomings in user-facing features and monetization — it’s pre-installed on all iPhones, making it a tap away for 900M people worldwide. We estimate that Apple Podcasts has 27M monthly active users in the U.S., based on App Annie, so a sizeable absolute number but relatively small compared to the total install base. Though Apple accounts for the majority of podcast listening, the company currently doesn’t monetize podcasting at all — all ads that you hear on podcasts are a result of advertisers and podcasters connecting off-platform.

For some users, the app is a basic, functional listening app, as compared to other media apps and products, with rudimentary categorization and discovery features. For some creators, the features it currently lacks include native monetization capabilities, in-depth analytics, demographic information for listeners, or any attribution for where listeners come from. Since Apple Podcasts launched in 2012, the app itself has changed very little. The New York Times wrote in 2016 that “the iTunes podcasting hub that Mr. Jobs introduced remains strikingly unchanged,” and beyond adding more analytics features in 2017, the same still holds true today.

In the second category, there’s a number of media and technology companies that have large existing audiences making a big push into podcasts, including Spotify, Pandora, and iHeartRadio. The strategies for these companies are mostly centered around leveraging their existing audiences to cross-promote podcasts; using listener data to personalize listening experiences or to help surface relevant podcasts; and leveraging their reach and existing monetization mechanisms to help creators earn more revenue. Google, which launched a standalone Podcasts app last year, has talked about making podcasts a first-class citizen in terms of surfacing podcast content in search results, as well as the growth opportunity that Google users worldwide represent in terms of potential podcast listeners.

Finally, there’s the long tail of podcast apps. These are comprised of startups and a fair number of non-VC funded companies. These apps are predominantly competing on the basis of better user-facing features such as improved discovery, search, and social capabilities, as well as creator monetization including their own ad networks or direct user monetization features. Increasingly, startups in this last category are also looking for other ways to distinguish themselves outside of listening experience — including experiments with exclusive, sometimes paid, content.

A discussion about shifting user behavior around consuming podcasts would be incomplete without calling out Spotify. In just the past few years, Spotify has burst onto the podcast landscape, moving from being music-centered to “audio-first”, and becoming the second largest platform for listening after Apple Podcasts.

Spotify’s market share in podcasting has grown to 9% in a few short years based on data from Libsyn, a podcast hosting provider, and the company has laid out plans to become a destination for all types of audio content.

Interestingly, Spotify may be growing the market of podcast listeners: the data below from Megaphone (formerly Panoply Media) shows that downloads of podcasts from Spotify happen in geographies that historically had fewer podcast downloads.

Downloads data suggests that Spotify is growing the audience of podcasting.

Spotify also accounts for two of the largest podcast acquisitions in industry history — Gimlet and Anchor — which occurred earlier this year. The company has committed to spending hundreds of millions of dollars more on acquisitions, and has also stated that podcasts are strategically important for driving increased user engagement, lower churn, faster revenue growth, and higher margins than the core music business.

Spotify CEO Daniel Ek’s letter about their “audio-first” strategy is worth a read. He predicts that over time, more than 20% of listening on Spotify will be non-music content, and that the Anchor and Gimlet acquisitions position Spotify to be a leading platform for creators, as well as the leading producer of podcasts.

Podcast creator and listener activity

Extreme power curve among podcast creators

If traction among consumer listening apps appears highly concentrated among a small number of apps, the same can be said of podcast creators. The creator landscape reflects a power-law type curve, with most of the podcasts consumed in the top 1% of all content.

According to Libsyn, one of the oldest podcast hosting providers, the median podcast only has 124 downloads per episode — but the top 1% has 35K downloads per episode.

A taxonomy of podcast creators

I created a taxonomy of the podcast creator ecosystem as a rough framework for thinking about the various types of creators, roughly split across five categories: media companies with internal podcast efforts; standalone podcast-only studios; large indies (including what our editor-in-chief Sonal Chokshi calls “cult-of-personality” shows); non-media businesses and nonprofits; and the long tail of hobbyist creators.

In order of descending audience sizes, these categories are:

• Media companies that have internal podcast departments, whose goals in podcasting can range from audience development to diversifying revenue. Examples of companies in this category include traditional media companies like the New York Times, where audio was treated as an experiment before The Daily became a major hit in 2017; radio platforms like iHeartRadio, which bought Stuff Media to double down on podcasting; and digital media companies like Barstool Sports, a sports and pop culture blog which produces a number of podcasts. These companies can leverage their existing user base to drive listenership for the podcast — and if the podcast becomes popular, vice versa.
• Podcast production companies focused mainly — if not exclusively — on podcasting, which necessitates building a viable business from podcasting alone. Their revenue primarily comes from advertising, which means those podcasts need to amass large, repeatedly engaged listener bases. Examples include Gimlet (the creator of Reply All, StartUp, Crimetown, and others), acquired by Spotify in early 2019; and Wondery (Over My Dead Body, Generation Why, Dr. Death).
• Large indies and personality-driven talk shows primarily hosted by one or two personalities. These podcasts monetize mostly through ads, donations, and sometimes merchandise or live events. Examples include Tim Ferriss, Sam Harris, Rachel Hollis, Karen Kilgariff and Georgia Hardstark (My Favorite Murder), Roman Mars (99% Invisible), Joe Rogan, and many others.
• Non-media businesses and nonprofits that also produce podcasts. The primary goal behind these podcasting initiatives is mostly brand-building and marketing, rather than driving revenue. Mailchimp and HBS podcasts fall into this category.
• Lastly, there are the individual hobbyists creating and posting content — often un-monetized and with very limited audiences. Podcasting tools like Anchor and others are democratizing the ability to launch a podcast, which will lead to more and more hobbyist creators.

Note that these categories serve as a rough segmentation of the creator landscape, because there is a lot of overlap and blurriness between some of them.

For instance, NPR — the #1 podcast publisher in terms of downloads — produces many hit podcasts including Hidden Brain, How I Built This, Planet Money, and others, and is considered by some as having raised the profile of the medium overall. NPR sells ads on its podcasts and has teams of designers, planners, and strategists, but is technically a non-profit media organization. While podcasting has deep roots in public radio — This American Life, for instance, launched in 1995 under WBEZ (Chicago Public Radio) — the non-profit aspect of these organizations has implications on the business. Alex Blumberg, the CEO of Gimlet and a cofounder and producer of Planet Money, was reportedly frustrated with NPR’s slow decision-making and strict rules around advertising, which led him to found Gimlet: “‘We should be making more; people want more… There should be the Planet Money of technology! Of cars!’”

Rich variety of content

The top iTunes podcasts chart from May 2019 is interesting for its glimpse into the tastes of Americans who have iPhones. A small number of publishers account for multiple top shows, including Wondery and NPR. We can also see how much Americans love crime/mystery content, as well as talk shows!

While NPR and iHeartRadio have roughly the same number of monthly downloads, NPR is able to accomplish this with just 48 shows vs. iHeartRadio’s 170. (Shows with blue check marks have gone through Podtrac’s podcast measurement verification process.)

Making money from podcasting

The current state of monetization in podcasting mirrors the early internet: revenue lags behind attention. Despite double-digit percent growth in podcast advertising over the last few years, podcasts are still in a very nascent, disjointed stage of monetization today.

Today, podcasts primarily monetize via ads and listener donations. Though we’ve heard anecdotally from advertisers that podcast ads are effective — and are unique in their ability to reach a hard-to-access, attractive demographic — the ad buying experience is manual and tedious. Especially compared to purchasing other forms of digital advertising, since the dominant listening platform (Apple) doesn’t offer a way for hosts and brands to connect.

As a result, you’ll see price sheets floating around online for major shows, with set rates to sponsor episodes, based on historic downloads figures. Ad networks in the podcasting space like Midroll Media and AdvertiseCast aim to make this process easier, while more new listening platforms are also enabling easier advertising, for instance by selling ads on behalf of shows in its network.

But advertising doesn’t always cover the entire cost of producing a show, even for hit shows. Serial is one of the most successful podcasts ever — and the first ever podcast to reach 5 million downloads — and asked for donations in order to fund the production of the second season. This American Life also publishes requests for donations, including these blogposts detailing the high costs of producing the show, with Ira Glass writing, “People sometimes ask me if it’s frustrating, having to request donations directly from listeners. It’s not. It’s the fairest way to fund anything: the people who like these stories and want them to exist, we pitch in a few bucks.”

Donations to podcasters primarily happen off-platform today, via third-party tools such as Patreon, PayPal, and Venmo. The top podcaster on Patreon, Chapo Trap House, a political humor podcast, earns over $131K per month from almost 30K patrons (link). Himalaya, the U.S. podcasting app backed by the Chinese company Ximalaya, has a donations feature. And some other listening apps also have introduced one-off tipping capability or patronage features.

Another monetization mechanism that companies are experimenting with is branded content. As opposed to advertising — which first start with the content and then sell ads to monetize — branded shows create a podcast in collaboration with a company, for a fee. Examples include The Mission, which is selling to enterprises to create branded podcasts — for instance, a podcast called The Future of Cities, sponsored by Katerra; and Gimlet, which has collaborated on shows like The Venture with Virgin Atlantic. By removing dependence on ads for monetization, branded shows like these are able to go deeper into a subject matter and create more niche content that doesn’t rely on listening volume to generate revenue.

There’s also a lot of activity happening right now in the subscription and membership space. Recently-launched podcasting app Luminary Media (which bills itself as the “Netflix for podcasts”) charges $8 a month for access to a slate of more than 40 exclusive podcasts, and the app also has a free listening experience. The launch has been bumpy, with issues ranging from podcasters taking offense at their tweet that “Podcasters don’t need ads”; to controversy about removing links in show notes, including donation and affiliate links that help podcasters monetize; to using a proxy server to serve podcasts, which made it challenging for podcasters to receive accurate analytics. Luminary’s launch serves to signal a few things — that the golden age of investing in podcasting is underway in terms of dollars flowing in, but also that getting the buy-in of creators is just as important as winning over consumers in building a new platform.

The model of subscription premium audio content is popular in China, where Ximalaya, a unicorn consumer audio platform, has a subscription feature for $3 monthly that enables users to access over 4000 e-books and over 300 premium audio courses or podcasts. Audio content is also available a la carte starting at $0.03 per short, serialized book chapter, or anywhere from $10 to $45 for paid audio courses.

Other monetization models we’ve seen include grants or foundation support, ticket sales for live events, and merchandise sales. There’s also licensing deals happening with the likes of HBO, Amazon, Fox, and other content companies who view podcasts as a source of intellectual property and want to adapt them into movies and TV shows. For instance, Gimlet’s scripted podcast Homecoming debuted as an Amazon Original Series in November 2018. The directionality of influence goes both ways: some podcasts are offshoots of other content — such as HBO’s Chernobyl podcast which discusses each episode of the mini-series — or written content — like Binge Mode’s deep dive into Harry Potter.

Podcast ad revenue is growing but is still tiny compared to other content formats

In 2019, the podcast industry ad revenue is estimated to hit over $500 million dollars, having doubled each year for the past few years. However, overall industry revenue is still tiny compared to that of other content mediums.

In particular, based on average revenue per active user per hour, podcasts monetize at a fraction of other content types.

Though podcast ad revenue is growing, the medium monetizes at a fraction of the rates of other content types (source: Nielsen via Hacker Noon)

Limitations of podcast advertising

Based on our conversations, lag in monetization isn’t due to lack of efficacy of ads. Various studies, including by Nielsen and Midroll Media, have found that podcast ads meaningfully increase purchase intent.

Why is podcasting monetization so low? Reasons include:

• Inability to monetize directly on the dominant platform, Apple Podcasts.
• The long tail of podcasters not being able to monetize because advertisers only want to work with podcasts that have a high level of listenership. Given the lack of advertising inside major listening apps, advertisers need to connect off-platform with podcasters — whether directly or through an ad network. This manual process means that for most advertisers, the long tail of podcasts requires too much time and effort to find and work with.
• Lack of clarity around actual listens. For a long time, downloads were used to proxy delivered ads, but a “download” doesn’t necessarily mean a “play”.
• Detailed listener data is also not available. There’s also a lack of sophisticated targeting tools on par with what Facebook and other digital platforms offer advertisers.

Today, podcast ads are primarily direct response, with ads read by hosts. You’re probably familiar with ads on podcasts with hosts talking about a product and verbally sharing a discount code. Podcast ad attribution is very rudimentary: the common methods of attribution are vanity URLs (for instance, http://www.ecommercewebsite.com/<podcastname>); promo codes entered at checkout; and surveys asking users, “How did you hear about us?”

Despite all these issues and barriers to monetization, podcasts are still able to command a premium CPM of $25 to $50, based on downloads, due to their efficacy. And the highest performing shows can cost even more.

How much are podcasters making?

While the majority of shows don’t monetize at all, the most successful ones can earn substantial revenue through advertising. A couple of data points: in July 2018, The New York Times’ The Daily podcast was projected to book in the low eight-figures revenue in 2018 from ads, and had 5 million listeners monthly and 1 million listeners daily, or about $2 to $10 revenue per monthly listener. For context, The Daily was only started in January 2017. For comparison, in 2018, Spotify earned $605M from 111M monthly ad-supported listeners, or $5.45 per free listener.

The New York Times as a whole had $709 million in digital revenue in 2018, so podcasting is still small relative to their entire business, but has an outsized impact on brand awareness. Michael Barbaro, the host of The Daily, shared in Vanity Fair that “When we started the show, we had many goals. We didn’t realize we were going to make money that was actually going to get pumped back into the company.”

Blogger and podcaster Tim Ferriss has written that if he wanted to fully monetize the show at his current rates, he could make between $2-$4 million per year depending on how many episodes and spots he offers.

Some back-of-the-envelope calculations around how much podcasters are making: Assuming CPMs of $25-50, if a podcast is in the top 1% in terms of downloads episode, or has 35,000 downloads per episode, each episode could generate about $4,000 per episode with two ad slots.

Audio trends and lessons from China

Over the past five years, dedicated audio apps in China have been growing quickly. In fact, online audio market users grew by over 22% in China in 2018, a faster rate than either mobile video or reading. Looking at China can illustrate potential business models — partly through adopting an audio-centric approach rather than adhering to a strict definition of podcasting.

Ximalaya FM, which last raised $580 million in August 2018 with a $3.6 billion valuation, is an audio platform with over 530 million total users and 80 million monthly active users. Ximalaya’s product is audio content in every form — from podcasts and audiobooks to courses, live audio streaming, singing, and even film dubbing. The monetization models are just as diverse: there’s advertising, subscriptions, a la carte purchases, and donations / tipping. Interestingly, not all paid content is included in their subscription membership (similar to Amazon Prime Video’s mix of free and paid content), but members get an additional 5% discount on any exclusive content.

China’s unicorn audio platform Ximalaya helps illustrates creativity in product and business models.

As a result of the platform’s diverse purchasing models, the discover leaderboard filters not only by content category, but also according to monetization method, top hosts, most subscribers, and what’s trending on that very day.

Ximalaya leaderboards can be sorted by top grossing content, top hosts, highest number of subscribers, and also by category of content.

The app contains many different tabs with categorized content to allow users to optimize their listening experience. As the below screenshots indicate, users with children can get their feeds custom-curated for family-friendly listening; and users interested in learning English can get daily custom curated playlists with lessons, techniques, or even testing advice. In total, there are over 50 interest-based feeds available for users to choose from.

Ximalaya features customizable feeds of audio content.

Ximalaya places a large emphasis on social interaction and community, which also has its own monetization model. One of the app’s most popular features is live audio broadcasting — which resembles live video, but through voice only — where users can host their own channel, invite other broadcasters, and earn money through virtual gifts from their listeners. Popular live streaming categories include music (singing songs or talking with music in the background); chatting about relationships; or discussing anime. Meanwhile, the Discover tab curates audio content into a custom social network so users can see not just the most popular content, but also what people are saying about it.

Ximalaya social features include live audio broadcasting — monetized via virtual gifting — and a social feed of other users’ activity.

Ximalaya illustrates a potential path for the development of audio platforms in the U.S., through its wide range of content types, monetization strategies, and interactivity. Examining the product may also hint at experiments it could run with Himalaya, its U.S. podcasting startup.

Beyond Ximalaya, social audio is a growing category in China, with apps like Hello (live audio broadcasting); KilaKila (an anime community with live audio and video broadcasting); and WeSing (a social karaoke app), all of which monetize through virtual gifts. Other apps such as Soul, Zhiya, and Bixin leverage audio for making friends, dating, and even video game companionship. These apps showcase the potential of audio to serve as a platform for social interactivity — voices act as a core component of users’ identity and are the medium through which individuals interact.

Startup trends, challenges, and opportunities

Biggest outcomes: no large standalone companies yet

Early 2019 saw the two largest ever exits in the podcasting industry — but against the larger backdrop of venture-backed companies, the exits were still small. The industry hasn’t yet seen a “Facebook buys Instagram” moment — or a large independent company emerge.

Most acquisitions have been for listening apps or podcast production studios. Early 2019 saw the two largest exits ever for the podcasting industry, which were both to Spotify.

In early 2019, Spotify acquired Gimlet Media, the studio behind top podcasts including Startup, Crime Town, and Reply All, for over $200 million; and Anchor FM, a podcast creation and distribution platform that aims to make podcasting extremely simple and enable anyone to start a podcast using only their smartphone, for about $100M.

Beyond these two companies, there have been a number of smaller acquisitions in the space. Most of these exits have been “acquihires” of small listening apps that were subsequently shut down post-acquisition. More recently, podcast studios with expertise producing popular content have also been a target of acquisition, including Stuff Media (to iHeartRadio) and Parcast (to Spotify).

Startup trends: new apps, monetization experiments, production experiments

There’s been a flurry of funding activity in podcasting — so much so that some publications are wondering if we are in a “podcast bubble” (see for example this, this, and this). Here are some of the major trends we’ve seen.

2018 saw a record number of venture capital investments and capital raised for podcasting startups.

Startups are building new listening apps, verticalized audio platforms, and producing podcast content.

1. Consumer listening apps for general podcast content

A lot of startup activity is happening on the consumer side of listening apps: Many startups are capitalizing on the opportunity to create a better listener experience, given that Apple Podcasts is relatively simple and bare-bones, and until recently, there has been no default listening app for Android users. Issues these apps are addressing include better discovery of podcasts through algorithms, curation, or social signals; more effective ways to search for relevant content (e.g. by automatically transcribing podcasts so as to be able to search within them); or improved social features.

We on the consumer team tend to believe that better podcast discovery, recommendations, and other user-facing features alone aren’t sufficient to draw a large listener base. The core of what users are interacting with on a listening app is the content itself — after all, it’s normal for listeners to start playing audio content, then to background the app or put their phones away, so the listening app becomes secondary to the content. As a result, many podcasting startups have expressed interest in offering some flavor of exclusive content, as well as monetization options for creators, in order to further differentiate themselves.

Here’s a small sample of the approaches some of these new listening apps are taking:

• Charging consumers directly for podcasts — these apps’ exclusive podcasts account for a relatively small share of all of the content available in these apps. Examples include Luminary and Brew, both of which have subscription models for access to exclusive content, in addition to allowing users to listen to widely available free podcasts.
• Adding a social layer onto podcasts — to help with discovery and/or to capture the conversation happening around podcasts. Some early companies in this space include Breaker, Swoot, and others.
• Offering translation and transcription — essentially enabling episode-level rather than show-level discovery. Castbox, for instance, offers podcasts in multiple languages, as well as the ability to search within podcasts by transcribing content. The app also recently launched live audio broadcasting that allows anchors to interact with listeners via voice, text, and call-in and to earn tips from followers.
• Adding context — Since podcasts expose listeners to so much new information and prompt questions, these could be more seamlessly explored without disrupting the listening experience. Entale, for example, is a “visual podcast app” that uses AI to showcase relevant information to users as the podcast is playing — this could be displaying the Amazon link to a book that someone mentions, or linking to the Wikipedia page about a speaker’s biography.
• Specializing by vertical — For parents growing increasingly cognizant of exposing kids to screen time, having a curated selection of audio content targeted towards kids, suitable for entertainment and learning, can be valuable. Leela Kids, for example, is a children’s podcast app that curates kids-safe content.

2. Vertical consumer audio apps

Beyond general and for-kids podcasts, there’s also a number of adjacent audio apps with more focused content, including those targeting education, audio books, fiction, health and wellness and fitness. By focusing on a specific subject matter and going very deep, these apps aim to create full-stack listening experiences that combine original content around that particular vertical; user monetization mechanisms; and other value-added features that enhance the user experience and help users achieve their goals.

To give a few examples, Calm and Headspace are both guided audio meditation apps, which offer both free and subscription-only content that’s exclusive to their own platforms. Both have features beyond just the content itself that help users with mindfulness — for instance, daily reminders, streaks, visualizations and videos, etc. In the ASMR (autonomous sensory meridian response) vertical, Tingles is an app where fans can watch or listen to videos of ASMR content, filter by specific categories, and support creators through subscriptions. In the fitness category, Aaptiv, ClassPass Go, and MoveWith are examples of companies offering audio fitness classes across a variety of exercise types.

3. Podcast production companies

Lastly, there’s a surge of venture-backed podcast production companies creating podcast content and distributing it through third-party listening platforms. Examples of these include Wondery, the studio behind a number of hit shows including Dirty John, Dr. Death, and American History Tellers; and WaitWhat, the content incubator that developed Masters of Scale with Reid Hoffman and Should This Exist.

Most podcast producers are creating entertainment-focused, general interest content that appeals to a wide audience, likely because of their monetization model, which is primarily ad-supported. Since these content studios distribute through other platforms and don’t have direct relationships with end users, they need to monetize through advertising, which necessitates content that appeals to a wide audience and promotes lengthier consumption times and ongoing listening.

Successful production studios could be prime acquisition targets for media companies as efficient sources of IP, or for consumer listening apps as a way to differentiate based on content — and a number of startups in this space have already been acquired. Another possibility is that once these content companies generate enough listener traction, they could create distribution platforms of their own, and use these as a way to deepen listener relationships and diversify revenue, for instance by charging users for, say, early access to content, back catalogs, exclusive content, or other features.

So what are we interested in investing in?

Given the challenges with monetization, how can startups create a path to becoming a sustainable business? With the distribution and capital advantages that incumbents have — coupled with the fact that Apple and Google own the end mobile platforms, where are the opportunities for startups? And how do we evaluate these opportunities?

To understand startup opportunities, it’s important to consider where the incumbents and large audio companies like Spotify, Pandora, and iHeartRadio are uniquely advantaged:

• Consumer traction and awareness, and a large audience to which podcasts can be cross-promoted
• Large budgets for content production and acquisition
• User data on preferences and existing media consumption
• Existing monetization mechanisms, such as through ads, subscription

So how to navigate creating a large opportunity, given the above advantages?

We think the most promising players will combine the following aspects:

• Focus on audio content broadly, rather than exclusively podcasts. Just as the lines between blogs, articles, and other written content online have blurred, the same is happening with all audio content, and so we are interested in all types of content delivered via listening. As outlined above, podcasting was historically synonymous with audio distributed via RSS — now, with the rise of exclusive, paid podcasts, the distinction between podcasting and other audio content is becoming less meaningful.
• The potential for network effects. We’ve written extensively about network effects and how to measure them; the consumer team loves businesses with network effects! Network effects in audio could take different forms. Like many content platforms, there’s a two-sided marketplace network effect, where more high-quality content makes the platform more valuable to consumers, and more users makes it more appealing for content producers to distribute their content there. All things being equal, most users would prefer to use the platform that has the largest, best inventory of audio content. A social audio app could also have direct network effects, where the experience becomes better with more users/friends.
• While we prefer full-stack startups that own the experience end to end (positive feedback loops from listening app to content to monetization), we wouldn’t rule out breakout apps that are strong on any one aspect.
• High-quality differentiated and deeper content vs. broad, free libraries of shallower content. Since the large incumbents seek content that appeals to their large user bases, they’re less focused on seemingly niche, in-depth content. We also believe that for certain high-value content verticals, there’s potential to shift the burden of payment to businesses, schools, or other organizations — rather than on to end consumers.
• Consumption experience that enhances the experience of the audio content. This could be through live, social audio, or other features that increase stickiness and engagement. For instance, Headspace’s meditation streaks, animations, multi-level categorization, and session length options differentiate and enhance the experience, compared to listening to meditation audio content on general podcast listening apps.
• Alternative monetization beyond solely ads (Connie Chan has written a lot about this). Given the dominance of existing large platforms like Google and Facebook for ad targeting, it’s becoming increasingly challenging to build a new large company based on advertising alone. We are bullish about audio companies that are aiming to monetize users directly — this could be accomplished through charging for content that has higher perceived ROI, or by introducing payments as a way to alter the content experience (e.g. social recognition after tipping in live streams). More importantly, it’s also a way to align incentives of consumers with content creators.

What could some examples of these startups look like?

1. Vertical audio platforms

We’re excited about startups that are going deep within a particular vertical and building a full-stack audio experience tailored to that vertical. There’s less chance of incumbents competing directly here, given the more niche focus and fundamental differences in feature sets needed to enhance the user experience. We also see greater willingness for users to pay for content that has higher perceived ROI — for instance, various fitness and meditation/wellness audio apps have already gained high levels of traction in usage and monetization.

2. Interactive, social audio… finally

While people have been talking about it for years, we think there’s still an opportunity to finally have truly interactive, social audio. Without being too prescriptive on what this looks like (we want founders to tell us!), the fact is, audio content today is still largely broadcast in nature, with a one-directional flow of information from creator to listener. While there are some conversations happening around audio content (including on Twitter, Reddit, and other forums), they happen in a fragmented, isolated way, and on platforms that aren’t designed for that purpose.

Call-ins to radio and live talk shows are two current forms of interactive audio, with the social element fundamentally contributing to the content itself. Twitch also has podcasters who use the platform to live stream themselves while recording, sometimes responding to user comments which become part of the show’s content. There’s a number of startups enabling users to comment on static podcast content, but the social experience needs to become even more interactive to attract a wider audience and pull users off existing platforms. In China, live audio broadcast, group karaoke, and even audio dating products are flourishing, and there may be an opportunity to create an audio product that is more interactive and social for U.S. audiences, too.

3. Platforms that helps creators own their end users and monetize content

Most creators are disintermediated from their end listeners, since they produce content that is distributed solely through various third-party platforms. Given the brand equity and large followings that some creators have established, we believe that there’s an opportunity to give these creators a way to distribute their own content, own their customers, and to monetize through alternative sources besides advertising and off-platform donations.

Some influential podcast publishers have developed their own solutions to engage and/or monetize their own audiences, including Slate Plus, a paid membership program from Slate with podcast benefits including ad-free and bonus podcast; The Athletic, which launched over 20 exclusive shows behind a paywall in April; and BBC Sounds, an app that puts music, podcasts, and radio from BBC into one personalized destination.

But for creators who don’t have the technical or financial resources to develop their own apps or piece together various third-party solutions to accept payments or manage members, there could be a turnkey platform for creators. Down the line, there’s opportunity to create a network of these creators and listeners, along the lines of “come for the tool, stay for the network.”

The future

“If you think of audio as the way you think of, say, film, like we’re still in the black-and-white period of podcasting. What’s color going to look like? What’s 3-D going to look like?”

I love this quote from the host of Today, Explained, Sean Rameswaram — since we are still indeed in the black-and-white phase of podcasting.

Taking a step back, it’s amazing how much progress the industry has made since the Apple Podcasts app was introduced 7 years. It’s still early days, so if you’re building something that is related to any of these aspects, please drop me a line!

Am I just getting old?
When I encounter a new product idea for the first time, I find myself asking: Is this idea dumb? Or am I just getting old?

Early on, there’s often not much to judge it on besides the idea. Sometimes the idea sounds either dumb or trivial. But over the years, I’ve started to not try to judge too much, especially when it’s early

Ideas seem pretty random because in the past few years, some of the biggest wins were: An app that lets you get into strangers’ cars. An app that lets you stay at random peoples’ houses. Disappearing photos. A site that doesn’t let you play video games, but you can watch other people play. Seriously?

And if you go back a few years earlier, I remember having entire convos about why anyone in the world would want a profile or a website on the internet. Or why phones should be used for calling, and adding email was dumb. It sounds silly, but that was the perspective then

The Dumb Idea Paradox – the official definition
The dumb idea paradox is what happens when an idea sounds dumb, and yet you have a (usually very small) group of people highly engaged in doing it. And maybe that group of people seem to getting bigger and bigger. Will it continue? Will millions ultimately do this thing?

When products that have this property — it’s counterintuitive behavior PLUS it has traction — imho they are the most attractive startups in existence. After all, this is an indicator it’s likely in a new market, and often times, the TAM of these markets ends up being huge!

In other words, this handy graphic:

(Note I made that as a screenshot in GSheets. You’re welcome)

Natives versus immigrants want different kinds of products
Furthermore, these ideas often formed at the seam of the “natives” versus the “immigrants.” If you are Instagram-native, what you consider a great idea for a new retail space or ecomm brand is likely very different than someone who isn’t exposed to the same thousands of pics

The upcoming generation are using tech in a different way. They are Fortnite-native. Minecraft-native. They are streaming-native. They use “insta” differently. Food delivery will be considered a human right. The expectations will be very different.

For network effects-driven products, it matters that your friends are also into the same thing. If my peers aren’t playing Fortnite every day, then I won’t see the same value and engagement. Contrast that to a fully activated network of kids that are on it every day

Thus, I’m sure that the first time I hear about a wild idea that appeals to this group, it will be easy to dismiss out of hand. And perhaps I’d be more attracted in something that takes on the same trends, but is more familiar

Strong and weak technologies
My partner Chris Dixon has written about the idea of strong and weak technologies, which often arrive in pairs at the start of a new technological age. The weak version often sounds more practical, but the strong ones often win. Here are some examples:

The weak version of a technology is often the more plausible, “immigrant” version of an idea. The stronger version will sound better to folks who are natives.

As an investor in consumer companies, I’m always startled when I see surprisingly strong growth metrics on top of an idea that I don’t get. It’s always a signal that I need to dig deeper, at least until I feel like I’m starting to get it. But it’s hard.

So I repeat the question: The next time you hear an idea that sounds dumb, ask yourself — is it really dumb? Or are you just getting old?

Dear readers,

Many of you may have read the recent New York Times article “Uber and Airbnb Alumni Fuel Tech’s Next Wave” which is about how alumni of successful startups often split off and start new companies, and how ecosystems of investors/advisors form around these new companies to support them. In the NYT article, I mention that a16z has invested in 2 ex-Uber teams, and today, I’m excited to announce the first one — Pietra – a new startup building a marketplace for the jewelry industry.

In the announcement, I talk both about Pietra, and Ro/Pan and the team and also about the characteristics of ex-Uber alumni that make me excited to invest in them: 1) the ceo mentality of ops teams and “programs and platforms.” 2) unique expertise with marketplaces and network effects, and 3) deep scaling and technology infra.

I included the entire Pietra announcement below, which was originally published on the a16z blog here.

Thanks!
Andrew

Marketplaces, Pietra, and the Network Effects of Next Startup Talent

Dear readers,

I recently hosted an AMA on Quora where folks asked a bunch of really fantastic questions. Thanks to Adam D’Angelo and Alecia/Adrienne for getting this set up.

Wanted to share a couple of the most upvoted answers below:

• What do you look for in an investment?
• How long should a founder be without salary?
• What distribution channels should a new consumer internet startup consider in 2019?
• What investment have you made that is the most out there?
• Which commonly-discussed growth metrics in consumer tech businesses are the most meaningless and/or misleading?
• What is your advice for startup CEOs?

Enjoy!

Andrew

1. What do you look for in an investment?

This one is hard to answer generically — it’s easy to say, great team! Or big market! Or technology differentiation! Or something generic like that. However, being in venture capital is about being in the “exceptions” business.

There were hundreds of mobile photo apps prior to Instagram and Snapchat, and they would have been money-losing investments. Same for social networks before Facebook, or there were more than a dozen investor-backed search engines before Google.

My job is to find the exception to the rule, and pick an individual company that will stand out, and I don’t have to be bullish about an entire category of companies. In practice, this happens also because individually, I’m focused on doing 2–3 investments per year, and don’t have the capacity to, say, invest in every single company working on XYZ.

All of that said, beyond the obvious things (team, market, product, etc.) there are a few things that make me lean into understanding a company, in particular.

First, it’s interesting when a startup using a new platform or a new technology in a clever way. For example, Instagram Stories and Snap Stories are a huge new short-form video format, and an app that might interact with these stories in an interesting way might be compelling. Or because esports is so huge, if someone builds on the idea that perhaps games content could be streamable-first, then that’s intriguing too. Taking advantage of a new technology helps answer the “Why now?” question and explains why it’s a fresh opportunity that should be tried. If your new startup could have been built 15 years ago, perhaps the idea’s already been tried and just isn’t that good.

Second, technology changes constantly but people stay the same. And their motivations — in particular, to spend time with friends, to date, to be able to earn more, to find better work over their careers, to take care of their pets, etc., etc. — also stay constant over time. So when a new startup purports to create new consumer behavior, I’m sometimes skeptical. But if a product allows people to tap into a pre-existing motivation but in a new, fresh way, then I’m interested.

Third, I like to see a strong insight around how the product will grow. For example, it’s important if a new video streaming startup, for instance, has deep relationships with the YouTube/Instagram influencer community to get it off the ground. Or if a new workplace collaboration tool is built to tap into calendars and be inherently viral through cal invites. The reason for this is that we are in an interesting era of new technology products where in general building the technology is not all that hard. Startups typically don’t fail because of technology issues, given open source, AWS, lots of collaboration tools, a network of smart people, etc., etc. This used to be the case decades ago, but these days, startups fail because they don’t get traction in the market. As a result, I like to see something clever and insightful in how the product will get off the ground — especially if it’s driven by viral growth, or some form of organic, as opposed to paid marketing.

Usually at the stage where I am seeing companies, one of the big things I’m evaluating for is “it works!” I usually look at their growth metrics, cohort charts, acquisition mix, engagement data, etc., and try to make sure that it’s sticky now and will remain so over time. Once I validate this, then I move onto some of the bigger qualitative questions like the ones above — what’s the trick that makes it grow? Why now? What new technology does it exploit? What classic human motivation does it tap into?

And finally, I want to reiterate that it’s all about finding the exceptions. You can spend as much time as you want analyzing a space, but it’s just about picking the individual startup you like most.

[PS. Here’s also a deck I published a few months back that is the more visual, longer-form answer to this question]

2. How long should a founder be without salary?

I’m a believer in free markets, and also in thinking long-term.

When founders first get their company off the ground, they often take risk and go without salary. However, as soon as they raise a real amount of money — either from institutional seed funds, a large group of friends/family, or with a VC — I think the founders should pay themselves basically market rate (within reason)

The reason for this, especially if there are cofounders, is that starting a company is already hard enough. Your customers are leaving you, recruiting is hard, employees will occasionally quit. It’s hard to think long term, about all of this when you’re worried about your paycheck.

If on top of all of this stress, the founders are paying themselves way below market, to the point where they are burning their savings, that’s just not a good thing. It creates a lot of stress, and unwanted behavior from the perspective of an investor.

Obviously if there’s a case where the founders were highly compensated before and it would impact the runway, OK, then great, there’s an opportunity to trade off a longer runway by capping the cash compensation. If the team wants to do that, great.

But in general, I believe in market rates for everyone, including the founders and the employees, within reason.

[PS. I tweeted this out and my friend Suhail Doshi responded with a pretty cool rule of thumb:

My rule of thumb is…
– seed funding: what you’d pay your lowest paid employee
– when you’re growing a bit: your lowest paid engineer
– scaling: mid level engineer
– successful: market for ceo pay
– not growing: cut back to your previous comp until you are / helps survive

This is pretty great. Thanks Suhail!]

3. What distribution channels should a new consumer internet startup consider in 2019?

First, let me start with the negative. It’s been said (and written) that we are kind of in a funky consumer internet winter, compared to 2007 when we had the Facebook platform and the iOS/Android platforms and so on. As a result, the conclusion is that there’s a general industry malaise and everything sucks and we should all go home, etc., etc.

It’s my conclusion that this is a vastly overhyped POV about consumer.

Last year, when Fortnite went from zero to 200M+ users, how could you not be excited about consumer tech? Or where we see Kylie Jenner built a multi-hundred million dollar revenue stream selling stuff on Instagram? Or you have a content creator like Ryan, the kid that makes unboxing videos, generating $20M+ per year?

There’s a lot of exciting opportunities out there. In my first few months at a16z, I met hundreds of companies in my first 3 months. Hundreds! There’s a lot of innovation and entrepreneurs out there trying to do great things.

Yes, it’s true that you can’t just build well-designed social photo apps and still expect to succeed. You have to do something different, and evolve with the time. But IMHO there are still fantastic opportunities.

OK, now going past the preamble and answering the question directly:

The best distribution channels for your startup are the ones that only make sense for your product to use — meaning it’s proprietary, and people can’t just tap into the same channel right away. The problem with Facebook ads as a channel, for instance, is that if you’re a mattress startup buying ads, you’re not just competing against all the other mattress companies but you’re also competing with the cool new protein shake company. Contrast that to Dropbox, which has primarily grown using shared folders inside the workplace — they own that channel, and the only others who could compete on that are folks who have some kind of shared folder functionality. The performance of the channel is unlikely to degrade over time via competition because it’s proprietary.

If you agree, then the obvious question is, if I’m a startup looking for a proprietary channel, which one do I use? That’s hard to answer generically, so I won’t attempt to do so. However, the better observation is that if you are starting a brand new company, then you have the opportunity to both pick the idea — and have a hypothesis about product/market fit — as well as to pick its growth strategy at the same time. If you can think about both at its inception, then you can start thinking about a proprietary channel from day 1.

I think this is not the answer the person who wrote the question wanted to hear, so let me also try to give some more trend-driven ideas too.

I like video. There’s a lot of video being created and consumed, and I like the idea of a “video-native” product that is designed to create a lot of video as part of user engagement. Or create a lot of opportunities for streaming.

I like social data in the workplace. If you are building a workplace collaboration tool, whether it’s horizontal like Slack or more vertical like Figma, most of the files and systems you touch understand who all the users are inside the company. In particular, the calendar is a very rich data asset full of people and their relationships, and I feel that’s underleveraged by startups seeking to grow. I love the pattern of putting, say, ZOOM links, inside of calendar requests, and think more startups might end up finding opportunities to do the same.

I also like “in-real-life virality.” If you walk around and see a bunch of lime green scooters, and people are using them, then you will want to try it too. Magically, no customer acquisition cost! Or if you see people walking around playing Pokemon Go, then you might want to try it also, since they are out and about, and enjoying it so much. I think this is an underrated channel.

4. What investment have you made that is the most out there?

One day I was in the Mission district of San Francisco, and saw a huge line of people. I wondered what they were waiting for, and naturally, the curiosity got the best of me and I got in line too. As I looked around in line, I read the sign for the place. There was a huge aardvark icon, and lettering that said BOBA GUYS.

I had heard of Boba Guys before, and remember that every time I saw one of their stores, I would skip it because the line was too long. Business was that good.

While waiting, I tried to google to figure out who their founders were. No luck. Eventually I found a Kickstarter page with some info, for a store they had opened near Union Square, and found their names. Just my luck, they were already following me on Twitter. I DM’d them, ordered my boba — hong kong style with pearls — and waited.

A week later, they replied. We met for lunch near Hayes Valley, and I didn’t know what to expect. Maybe I could invest money into this thing? Did I even want to? It’s just milk and tea, right? But so was Coca Cola, or Starbucks, or Blue Bottle.

To my surprise, both Andrew and Bin were fantastic. They had great consumer packaged goods experience, had worked at Timbuk2, and came with a 20-slide deck prepared. The deck had retail comps versus other high-end stores, financial projections, and more. It blew my mind. These were very obviously the most talented bubble tea store operators on the planet.

As a quick segue, I had been going to pitches for high-end restaurants with a few friends prior to that, but had never invested. Going to a restaurant pitch was extremely fun, as you went with a group of friends, met the chef, and they made the entire food menu and all the drinks too. You hung out and could invest after. But I never liked the model because it felt like it could never scale. It’d be a fun hobby, but it’d be hard to make money. But it helped prepare my mind for investing in retail, and a beverage play like Boba Guys.

Back to bubble tea, I realized after the pitch that although it wasn’t a tech company, I should figure out a way to invest. Andrew, Bin, and I had a great conversation — the first of many, and then I rallied some of my friends to put a syndicate together to invest.

The bonus to all of this is that I now have a Boba Guys Black Card. This is a special investor card that lets me get my daily bubble tea fix for free. It’s amazing, and the investment was worth it just for the bragging rights with that.

5. Which commonly-discussed growth metrics in consumer tech businesses are the most meaningless and/or misleading?

Above: One of my favorite moments in 2018, with the a16z team and POTUS44. 

Dear readers,

Wow, so 2018 was a year with a lot of change – I started a new job, recommitted myself to writing (and tweeting), traveled a little too much, moved back down to Palo Alto (temporarily!), and much more. And in one of my favorite moments of the year, the office got swarmed by the Secret Service because Barack Obama came to visit – that was fun.

I’m also happy to redouble my efforts to writing and publish more, which I can do my new role as an investor at a16z. Previously, my pace was maybe once every other month – things were always too crazy at Uber, and it didn’t directly help my job there, so I couldn’t carve out time. These days, I consider writing as part of my work and dedicate time to it, blocked out on my calendar. As a result, I’ve been able to publish a few times a month lately – I want to continue pace into 2019!

In the spirit of trying something new, I decided to take all of my 2018 essays and turn it into an ebook PDF that you can read at your own leisure. It’s over 50 pages, includes all my essays, and alongside 200 slides in decks I published this year, you should have more than enough content to read through for a while. If you have feedback on this format, shoot me a tweet! And as always, you can get future updates by subscribing to the newsletter or follow me at @andrewchen.

Thank you for reading! And happy 2019.

Best,
Andrew

Download a PDF with 2018 essays

Links and notes

The red flags and magic numbers that investors look for in your startup’s metrics – 80 slide deck included! I put together a deck that summarizes the way that I think about evaluating the “quality” of growth for a new product. The deck unpacks a lot of different topics: How “growth accounting” metrics are great, but are lagging indicators. How to think about acquisition loops and engagement loops, and how to look for red flags like being overly dependent on a channel, or abusing notifications, to artificially boost metrics. The deck was designed for investors, but for every entrepreneur that wants to honestly evaluate where they are, it’s a good read too.

Consumer startups are awesome, and here’s what I’m looking for at a16z (70 slide deck). For the a16z annual summit, I put together a presentation introducing myself and what I’m excited about in the consumer investing world. It goes over historical precedents for give/get referrals, content marketing, and trying to bootstrap two-sided marketplaces. The deck also explains some of the big technologies and platforms coming down the pipe, and why I’m particularly excited about esports/gaming, offline experiences, and much more.

How to build a growth team – lessons from Uber, Hubspot, and others (50 slides). For a recent conference, I put together a series of lessons for companies that are looking to start growth teams. It starts simple, with the question of what growth teams are meant to solve, but also goes into organizational structure, ideal profiles/backgrounds for the team, how to ideate and prioritize projects, and more.

How startups die from their addiction to paid marketing. It’s so easy to get your product jumpstarted by buying ads to drive users, and hey, the LTVs and CAC ratios are working! But as I describe in this essay, it’s also easy to get addicted and ride the cost curves all the way up to the point where it makes no sense, and the degradation of these channels is a given.

What’s next for marketplace startups? Reinventing the $10 trillion service economy, that’s what. Co-authored with a16z partner Li Jin, we write about the next generation of marketplace startups. Whereas the previous generations have been about getting “stuff” to people, the next big opportunity will be to get services. The essay talks through why this has been so hard in the past, the benefits of having software intermediate the interactions, and the various ways that supply can get unlocked using technology platforms as the foundation.

Required reading for marketplace startups: The 20 best essays. This one’s not included in the PDF since it’s just a bunch of links, but wanted to include it here anyway. It’s a collection of links about marketplaces – from solving the cold start problem to metrics on marketplaces to specific case studies. It’s a must-read for anyone working in the space.

Why “Uber for X” startups failed: The supply side is king. One of my big lessons from Uber is that the supply side of the market is critical for any startup. I explain in this tweetstorm-turned-essay why the various “Uber for X” startups did a poor job satisfying that side of the market, even as the promise for us as consumers sounded great.

The Power User Curve: The best way to understand your most engaged users. At a16z, we often use frequency histograms – aka “Power User Curves” – to evaluate whether or not there’s a core community of users who are highly engaged. In this essay, co-authored by Li Jin from a16z, we break down what we look for, the variations on the curves you might see, and how this curve relates to the popular DAU/MAU we also ask for.

DAU/MAU is an important metric to measure engagement, but here’s where it fails. The DAU/MAU metric is an important measure of usage frequency, and was popularized by Facebook from the early days. This essay breaks down when its history, when it’s useful, and where it breaks down.

Conservation of Intent: The hidden reason why A/B tests aren’t as effective as they look. Everyone’s had the frustrating experience of running an A/B test, seeing a big lift, closing it out, and expecting the top level metrics to move a lot. But they don’t. This post explains why – “user intent” can be thought of as a fixed amount of energy as they approach the top of your funnels, and it’s hard to move it a lot.

The Startup Brand Fallacy: Why brand marketing is mostly useless for consumer startups. One of the opinions that always stirs up the hornet’s nest on Twitter is my opinion is that startups should do less brand marketing, PR, and other related activities and instead just focus on product/market fit and highly accountable performance metrics.

The Scooter Platform Play: Why scooter startups are important and strategic to the future of transportation. I’m a big fan of scooters, and here, I unpack why I’m excited about the entire category. Because scooters are used more frequently, and for shorter trips than rideshare, it creates a huge opportunity to be the “starting point” for transportation.

The IRL channel: Offline to online, Online to offline. As digital customer acquisition channels become saturated and easily copyable, one of the unique opportunities is the “IRL Channel” where people engage your product in their everyday, physical lives. Whether it’s a group of people walking around playing Pokemon Go, a microwave that has Alexa embedded, or scooters, this is one of the opportunities to combine our offline and online worlds.

I’m joining Andreessen Horowitz!. Here’s the initial announcement I made about joining a16z! Includes a few notes on how I know the folks at the firm, and what prompted my decision.

Podcasts
I didn’t include any of the podcast transcripts into the downloadable PDF, but wanted to include the essays here for completeness.

a16z Podcast: Why paid marketing sucks, Network effects, Viral Growth, and more. An interview with my a16z partner Jeff Jordan (who led our investments into Airbnb, Instacart, Pinterest, etc.) and we discuss some of the nuances of growing marketplaces, how to measure traction, and things to watch out for.

a16z Podcast: When Organic Growth Goes Enterprise. In this podcast, a16z partner Martin Casado and I talk about the intersection of enterprise sales and consumerized growth tactics. He’s on the enterprise team and I generally focus on consumer, but we look at a lot of companies together.

Product Hunt Podcast: Silicon Valley network effects, OKRs for your personal life, and more. My sister Ada (ex-Linkedin, SurveyMonkey) talk about life in the tech industry together, why we moved to the Bay Area, using OKRs to set goals, and a breadth of other topics.

I recently did a podcast with Ryan Hoover, co-founder of Product Hunt and my sister Ada Chen Rekhi, previously SVP Marketing at Survey Monkey – here’s what we talk about:

• The network effects that makes Silicon Valley what it is. The uniqueness of the Silicon Valley tech ecosystem, how network effects conspire to create a “rich get richer” situation for cities, and why new communication tools enabling distributed teams to work together across continents could mean that there will be no “next Silicon Valley.”
• Big companies versus small ones. Ada shares her insights on the contrasting skill sets needed when working at a big company versus a small startup, after having herself gone from a small startup to a huge organization like LinkedIn back to a two-person startup with her husband.
• Personal life OKRs. How to port the concept of OKRs — objectives and key results, a personnel management framework originated by legendary Intel CEO Andy Grove — to your personal life from your business (and why you would want to). We talk about you can use them to help manage your exercise, social life and relationship with your SO.

Of course, we also chat about some of our favorite products, including an app that lets you pop in to a luxury hotel for a few hours to shower or have a nap, a super cool way to greet visitors to your office, and a new app for emailing yourself.

Here it is below as an embed, but if you don’t see it inline, you can listen to the podcast via this link too. If you like the podcast, you can subscribe here. Thanks to Ryan for putting this together, including the transcript!

Some quotes from the episode

“When you’re executing at a small startup, or a small team, or just by yourself, it really comes down to ideating, picking and prioritizing, and then rolling up your sleeves and just getting things done as quickly as possible. It’s a night and day difference from a big company.” — Ada

“If you graph cities, there’s a power law: the biggest cities are really big and there’s this long tail of all these little tiny cities, and the reason for that is that there’s a network effect within cities. These ecosystems emerge because the designers are here, because the engineers are here, because the capital is here, because the marketing people are here, and on and on and on.” — Andrew

“When it comes to working at a large company, it’s much more cerebral and much more about the heart. You’re thinking about how to collaborate and communicate across a cross-functional team to get the initiative done: can you communicate what it’s about; can you motivate people to get it done; can you manage all the working pieces?” — Ada

“Either these network effects will continue to hold and the Bay Area will continue to be strong, or we make big structural shifts in how we organize teams and workforces and the network effects become less strong. But that doesn’t mean some other city becomes the next Silicon Valley, there won’t actually be a “next” Silicon Valley — it either continues or will just be distributed.” — Andrew

“The irony of it is that sometimes when you are working on projects with such large scale, because the skill set is so different, it actually feels like you’re not doing anything at all — you’re merely managing the appendages of the other groups and trying to make sure everyone is staying on track and executing.” — Ada

On joining a venture capital firm: “The idea that I would do the thing I want to do for fun as my full-time job feels like I’ve won an ice cream eating competition, and the prize is more ice cream.” — Andrew

Companies and Products Mentioned in This Episode

• Bose — Noise-cancelling earbuds.
• Breather — Peace and quiet, on-demand. Reserve a private space on the go
• Captio — Email yourself with one tap.
• Envoy — Elegantly greet visitors to your office.
• HabitShare — The only habit-tracking app that is social to its core.
• NotaBene — Shortcuts for quickly emailing notes to yourself and others.
• Notejoy — Collaborative notes for your entire team.
• Recharge — Take a nap or shower in a luxury hotel.
• Reddit — r/InternetIsBeautiful, r/bestof and
• r/firstworldanarchists
• Spacious — Flexible, drop-in workspace.
• VIPKID — Teach Chinese children from your home.
• We Are The Nerds: The Birth and Tumultuous Life of Reddit, the Internet’s Culture Laboratory by Christine Lagorio-Chafkin

Transcript

Ryan: Hey everybody, this is Ryan Hoover with Product Hunt Radio and I’m here at Andreessen Horowitz down in Menlo Park with two people I’ve known for a little while now, two brothers and sisters, Andrew Chen and Ada Chen. This is the first brother and sister duo and hopefully the first of many. Thanks for having me over here. First off, Andrew, you joined Andreessen Horowitz, is it six months ago?

Andrew: Yeah, I think I’m on month five. I’m quickly reaching my half year mark, which has gone incredibly fast.

Ryan: Are you completely swamped with meetings and pitches or how has it changed since before Andreessen Horowitz?

Andrew: Yeah, so when I was at Uber I really loved meeting with startups and hearing about new ideas and staying in touch with the tech community, but I can only do it first thing in the morning and on weekends and it quickly filled up my schedule. So I would work at Uber and then I would do that [meet with founders] basically. The idea that I would do the thing that I wanted to do for fun, like as my full time job sort of feels like I’ve won an ice cream eating competition and the prize is more ice cream. I could do as much as I want, which is super awesome.

Ryan: Yeah. And so your, your background, just maybe for those that aren’t super familiar, you were at Uber right before this and then what’s your short version of your history?

Andrew: Yeah, yeah, totally. We were just talking about. So Ada and I, who’s my little sister, by the way, I want to clarify —

Ada: [laughter, eye-rolling and protestation]

Andrew: So we grew up in Seattle, and we both made our way to the Bay Area. Actually, the funny thing is my first job ever was actually in venture capital and was something I did right after college. Then after that I ended up working at a series of startups, I moved to the Bay Area 10 years ago to start my own company. I had actually met Marc and Ben [of Andreesen Horowitz] here and they actually led the seed round for a startup I was working on during the Facebook platform days when everyone was working on crazy viral apps.

Ryan: So that’s around when we met.

Andrew: Yeah, right. Yeah, that’s exactly, that’s right around when we met and they invested out of a Horowitz Andreessen Angel Fund, which was really funny because that would have been like H16N and so different. So, I met them and I worked on that for a while and ended up basically deciding that it’d be better to go to a larger organization, ended up at Uber running various growth teams there. So I spent three years there, like a really, really fun experience —

Ryan: Probably pretty wild too, right?

Andrew: — Yeah, the first 18 months was like really, really incredible startup like hockey stick growth, then the last 18 months were very eventful and everyone’s read about it in the news. So I don’t have to summarize that.

Ryan: Yeah, and Ada, you’ve had a pretty interesting journey at Microsoft, LinkedIn, Survey Monkey, and then a two-person startup with your husband.

Ada: Yeah. Yeah. Actually multiple two person startups as well as, I spent some time in the game space as well at Mochi Media. So, after I graduated from college, I was in Seattle at Microsoft for a year and Microsoft at the time I think was around 80,000-100,000 employees? Very, very structured. Worked in the ad center space and the online advertising space when search marketing was just becoming a thing and exactly 367 days or so later, moved out to the Bay Area in 2007 and so worked at a tiny little startup that had just raised Series A called Mochi Media, which was an online games ad network, and spent multiple years there after it was ultimately acquired by Shanda Games and then actually started my first company which was a contact management app called Connected. It was all about contact management without the work. We raised some funding for that, ultimately sold it to LinkedIn and I had my experience sort of joining LinkedIn as a just as a company that was really maturing at the time. They had just had their IPO. There are about 1700 employees and experienced hyper growth for the first time, focused on things like relaunching Connected as LinkedIn Contacts, growth, learning a lot about subscriptions and consumer SaaS and was recruited out of that to work at Survey Monkey, where I was SVP of Marketing and then recently left a couple of years back to start a new company that’s actually a husband and wife team with Sachin Rekhi and we started a company called Notejoy, which is a collaborative notes app for teams and so we’re really focused on, how do we actually create a fast and focused workspace for teams that gets them out of the noise of chat and email.

Ryan: Yeah, team collaboration and productivity is so important because if you can even improve collaboration and efficiency within a team by like even just 10 percent, it can have such a huge impact on both your productivity but also just like your joy.

Ada: That was actually part of the inspiration behind the name and it’s one of those things where even when you go to a small team like small tight-fitting teams or larger organizations, you see this friction today that still exists when it comes to communication and collaboration and just think about how many decrepit out-of-date Wikis you see and Google Docs that are sort of lost in the ether and then people joining and getting forwarded random emails from way back when because that’s the only place that knowledge lives, we were really thinking about how do we create something that tackles that and productivity has always been a huge space where I’ve been passionate about.

Ryan: This is a really broad question, but what’s it like working at such a big company like LinkedIn and Microsoft and others to now just you and your husband?

Ada: Yeah, I mean it’s hugely different and I think the biggest dimension where I would say working at a large company versus a small startup is different is that effective execution looks completely different. It’s a night and day difference. So when you’re executing at a small startup or a small team or even just by yourself, it really comes down to ideating, picking and prioritizing and then rolling up your sleeves and getting things done as quickly as possible from an execution pace. When it comes to working at a large company, it’s actually much more cerebral, right? And it’s much more in the heart. You’re actually thinking about how do you communicate and collaborate across the cross functional group of teams to get the initiative done. So can you communicate what it’s about? Can you motivate people to get it done? Can you manage all of the pieces?

Ada: And the irony of it is that sometimes when you’re working on projects with large scale, because the skillset is so different, it actually feels like you’re not doing anything at all yourself. You’re actually merely managing the appendages of all the other groups and trying to make sure that everyone’s staying on track and executing. And so as organizations scale, the execution work around how much collaboration it takes gets orders of magnitude greater in terms of how hard it is to get everyone aligned and marching in the same direction versus one person. And so, I really think that that’s one of the biggest differences, like you go to a startup to learn how to do things and maybe not very well and you go to a large company to see how things are done really well, but across a broad range of disciplines and functions and really see how the whole thing comes together as an engine sort of humming smoothly and operating.

Ryan: You mentioned communication is one skill or trait of people in larger companies. And Andrew, you used to blog, I mean you still do, but you used to blog a lot. That’s largely how I think you built a pretty massive following over the past decade or so. How did you even get into writing to begin with?

Andrew: So, first I love writing. That’s kind of the very first thing, and I was always one of these, teenagers where like, I kept a journal and I would like write in it and then delete it and then start a new one and literally I was the only audience. I just like enjoyed it myself. And so before starting my current professional blog, I think I had like three other blogs that I had started over the years. Just basically, just getting going and then deleting them and not really sticking with it over time.

Ryan: Why did you delete the previous blogs?

Andrew: Because you get bored with it, and you’re just kinda like, okay, I’m done, kind of thing. And then like I think on those it was literally, it’s like who’s reading it? It’s like Ada, like my parents, like —

Ada: — Fun fact about Andrew’s early blogs: he would actually forcibly subscribe us to the emails to make sure that we wouldn’t miss anything.

Ryan: That was before some of the ICANN email laws and certainly before GDPR.

Andrew: Yeah, right. Yeah, exactly. So I think, don’t tell a 20 year old who they can subscribe to a blog or not. So I really enjoyed that. And then when I moved to the Bay Area 10 years ago, what I basically decided to do was I was like, I’m gonna write down everything that I’m learning and I’m just gonna start, like going out and so the funny thing, I was learning so much in my first year that I was just writing a lot of, like pretty random snippets, some of it would be like a paragraph or two, and I would do it like, maybe twice a week or something like that. So like pretty often and that’s actually how I met Marc Andreessen originally. It turned out that he somehow randomly had stumbled on my blog via Hacker News and then through that, had ended up seeing some of my content and then he cold emailed me and that’s how I met him in 2007. So it was like a pretty random and amazing adventure but at the time, I was an entrepreneur in residence. I was a 24 year old entrepreneur in residence actually across the street from here, which is really funny. And one of the things that my colleagues would tell me is they would say like, why are you wasting your time blogging, you’re giving away all your best ideas? Like, what are you doing? Like, these are the secrets that you’re going to use to understand the thing. And at the time I was like, well, I’m never going to be a venture capitalist so like it doesn’t matter. And so as a result, I’m just going to give away all this stuff and then, and it’s obviously so ironic now that like, so much of the job is, is obviously, sharing your ideas and giving back to the community via Twitter and Medium and writing, writing essays and all that.

Ryan: Now that’s the norm.

Andrew: Yeah, right, exactly. Yeah. And in fact it was like, it would have been considered very contrarian I think to actually share a bunch. But anyway, so I’ve kept it up and I think, I’m, I’m well into the many, many hundreds of essays, over 10 years and I think at times I’ve taken like a hiatus, I think I took a two year hiatus in the middle. But like I think my goal now is really to publish like regularly, but to do it at the kind of like a high level of quality and to go deeper into ideas and to sort of break new concepts and new kinds of data to the community versus literally the, the early days it was like, it’d be like 500 words, like what did I learn today kind of thing.

Ryan: So I’m going to take a tie into that a little bit. You mentioned a term called, correct me if I’m wrong, but something along the lines of mullet startups, is that correct? Or do you remember that there’s a tweet in a conversation with you and some others around the distributed nature of companies?

Andrew: Oh yeah, okay, mullet, yes.

Ryan: Mullet startups is a catchy term because it’s a trend that we’ve identified. Product Hunt is a mullet startup I guess, we’re headquartered in San Francisco, but we have a distributed team.

Andrew: So The Economist’s cover for this week is Peak Valley, is, is it over in Silicon Valley?

Ryan: Right.

Andrew: So then I think there’s been, there’s been a lot of like really interesting dialogue around that. I think, and obviously a lot of it has to do with like housing and the Bay Area and there’s so much to unpack there, right? But I think that one of the reactions to it has been that we see many companies, with their leadership and their executives in the Bay Area, but when it comes to hiring engineers and designers and all sorts of other folks, then they’re much more likely to distribute the team, anywhere.

Ryan: Right.

Andrew: And so, yeah, to your point, this is sort of the mullet, because it’s sort of business in the front and party in the back kind of thing. AndI think it’s fascinating because it is actually just the reverse of one of the models that we’ve seen over the years where, for example, you’ll have a really strong technical team out of Paris or out of Israel or out of Singapore and they’ll get started, they’ll get funded and then they’ll realize, okay, hey, all of our customers are in the US, let’s move the CEO and the sales and marketing function to the Bay Area. And so you end up with the, the mullet, but just like kind of, but now you do it in reverse. Right. So I think that’s like a pretty interesting, reverse mullet, which is kind of an interesting trend these days.

Ryan: Yeah. So it’s just you two right now Ada at Notejoy, but if you were to, let’s say you needed to hire 10 people tomorrow, how would you approach it? Would you hire in the Bay Area or would you go remote?

Ada: Yeah, I mean that’s actually a fascinating question because it’s something that we’ve debated and thought about because things have changed so much. Not only from the costs, but then also, what is the ability for you to access and interact with people at scale, if they’re located in other places. We actually talked to this close friend of mine who’s a founder who, built his company and scaled it to revenue, pretty substantial revenue in the Bay Area. And he basically said to us, if I were to do it again, I believe that Silicon Valley is the worst place to self-fund a company or to start a company or even to have funding and try to build a team. And the biggest challenge that he was having was actually access to talent. I think it would really depend. I think on one hand I think we have really strong networks within the Bay Area and so it would be possible to kind of peel people off and that’s really how many startups start with their founding team. They pull people that they respect, that they work with, that have shared belief in to kind of create that initial nucleus of a team and that gets you to your first couple of headcount. So maybe we can get to 10 that way, but I do think that now when it’s coming to scale, like yeah, we would definitely be looking very closely at could we build a remote team and create a really distributed workforce for Notejoy.

Andrew: I think one of the distinctions is do you hire a lot of folks who are doing the kind of individual contributor work versus the managers because I do think that it ends up being really hard once you want to find the engineering director that’s managed 200 engineers to find that elsewhere, versus it being, kind of a main thing. So, so there’s a really interesting thing about cities, right? Which is like if you graph the population of cities and sort of like, stack rank them, you’ll see that there’s a power law in it. And like the biggest cities are really, really, really big and then there’s this like there’s this long tail of all these like little tiny cities. And the reason for that is that there’s really like a network effect within cities, right? Like, whether it’s show business in LA or it’s, finance in New York, like these ecosystems that emerge happen because, you end up with the designers who are here because the engineers are here because the marketing people are here because the capital is here because and on and on and on and all in one place. And so one of my colleagues here at Andreessen Horowitz, Darcy, had mentioned, he tweeted the idea that, one of two things will happen, right? Either these network effects continue to hold, meaning that then, actually the Bay Area will just continue to be what it is, right? Or, we actually make really interesting structural shifts in how we organize teams and workforces and all that stuff. In which case the network effects become less strong. But what that means is not that then all of a sudden, some other city like becomes a quote unquote the next Silicon Valley. It actually just means that everyone just lives where they want to live and eat and that’s that. And so, so if you believe that thesis, then you’d actually say there is no quote unquote next Silicon Valley. It either just continues or it’ll just be distributed. Right. I think that’s like a pretty interesting —

Ada: — I think you see that already emerging even within online communities. So when you think about where the discourse actually taking place, right, it’s taking place on Medium, it’s taking place on Twitter, it’s taking place on Product Hunt. We went through the experience of launching on Product Hunt and we were really amazed by how international the community was in contrast to the earlier startup Connected that we’d done several years before that. It may not be as important in the future for everyone to be physically co-located in the same space.

Ryan: Yeah. I’m super fascinated by this space and I’m actually committed to investing in a company that’s rethinking how people communicate with a distributed and remote team by video because we have a lot of different tools out there like Zoom and Google Hangouts and others and they are all kind of utilities in that they’re not much different from each other. It’s just like a big screen with your face on it and they’re rethinking, in a world where everyone is distributed or a group of people are distributed and another group of people are working from their home, how do you communicate more effectively? Yeah, I find it an interesting trend. I think one observation too is that the mullet strategy can work really well if your home base is where your customers are. So like you said Andrew earlier, like if you’re building an entertainment company, it’s probably good to have connections and live in LA so that you can be around those people and that can create a lot of serendipity in business partnerships and so on, but you don’t necessarily need your entire team there. You can also have them around the world. If you can build a culture that can facilitate working effectively remote. I’m pro-remote, if it makes sense for your company. Just saying, I’m slightly biased. It’s been five years now with Product Hunt running distributed.

Andrew: I think what’s hard is that basically there’s a whole class of interactions were being in person is actually better. And so if you’re meeting people for the first time in a partnership type scenario or a sales kind of scenario or in investing kind of scenario, like you do want to go old school, you do want to see the other person. and so I think in those cases, that’s where, that’s actually, I think where the network effects actually kick it right where then it’s like, okay, yeah, let’s get everyone clustered together, in those cases.

Ryan: SF in particular, is so dense. I mean, granted I’m driving down to Menlo Park, but it’s a small, short trip. Whereas LA and New York as well, it’s actually hard to have a lot of meetings within a five hour period because everyone is distributed across different locations. I’m curious to hear from an investing side, are you actively looking at sort of the future of work or distributed teams and looking to invest in companies building for that?

Andrew: Totally. I mean I think, I think there’s a couple different angles on the future of work that are, that are worth mentioning. So I think one is, I learned a ton of really, really interesting lessons at Uber, but I think one of the most important ones is that there are 80 million hourly workers in America. Right? And so these are folks that are often working multiple part time jobs, they don’t have steady sources of income, and what they’re often doing is they are driving Uber kind of between their other things, right. And so I think when you look at that, you’re like, wow, like the future of work has to encompass that industry, which is what are all the other kinds of interesting work that can actually happen? So like just to call out a couple really interesting ones: there’s a company called VIPKID which caters to — the consumer side is basically kids in China and then the supply side of the market is basically often like Midwestern like ex-teachers, stay at home moms, that kind of thing and they’re spending time on video together and they’re getting this whole experience around teaching and tutoring. And this is something that you can do from your home. Like super interesting. Right? There’s obviously lots of really interesting things happening in real estate. Our portfolio company, Airbnb obviously provides a lot of really important, supplemental income —

Ryan: If your HOA will actually allow it. I’m speaking from experience. They will not allow me, unfortunately to rent my place out. But it’s pretty typical, right?

Andrew: I mean, I think within all these different kinds of work, there’s obviously different rules that need to be be in place and that’s true for rideshare and that’s true for many other things as well. But I think that’s kind of one notion of a future of work that I think is important for us to consider even though it’s sort of outside the tech bubble a little bit, but it’s a really huge market. I think the flip side is, I’ve been an on again off again advisor to Dropbox for many years and I’ve known that team for a while and when you look at what these horizontal products are trying to do, it’s sort of like, we’re in a world where, if we can get all of these professional white collar workers — just make their jobs better, right? And just like make all these workflows, these really complicated workflows that you know for the most part are still being managed in spreadsheets and docs and chat and sort of like streamline all that. There’s tons and tons of opportunity across many different dimensions.

Ryan: So let’s talk about some apps or products you guys love. Ada, what’s on your home screen that people need to know about or is there a product you use maybe every day, every week that is bettering your life, changing your life?

Ada: Yeah, that’s a great question. So I am a huge fan of personal productivity and so, every year I make my New Year’s resolutions and one of my resolutions for example, was get to a point where I was working out three times a week and the challenge that I always had was the accountability, right? And tracking. And so this is probably not a particularly popular app, but one of my favorite apps for that is actually this iOS app called HabitShare. And it basically lets you share accountability, like share your to do list, like check I did it today and set a goal and make sure that you’re keeping track of how accountable you are against it. I’m a huge fan of that. And then I think Andrew actually introduced me to this, but I love this app called Captio as well and it’s a very quick way to email yourself and you wouldn’t think that it’s that many taps to email yourself to remember a quick idea. But after you experienced it, it’s pretty mind-blowing.

Andrew: Can we just go on a quick tangent about Ada’s goal setting strategy?

Ryan: Yes. This is one of the reasons why we had brothers and sisters on the show.

Andrew: Yeah. So one of the things that’s impressive, but also a little bit scary is the the level of — she actually uses OKRs, objectives and key results. There’s a whole book about it. In order to handle her goals, but this is the best part. Her husband also does the same, Sachin also does the same and they actually will score each other on the OKRs. Do you want to talk about this a little?

Ada: True story. So, both my husband and I love productivity. I mean, this is why we’ve been spending all of our time working on Notejoy, but I spent probably a decade of my life at this point thinking about productivity apps and so OKRs is actually something that we’ve adopted as a process from LinkedIn, which originally came from Google, which originally came from another company before that. It’s widely adopted.

Ryan: John Doerr actually wrote a book about it recently.

Ada: Yeah, that’s right. And so with objectives and key results, we actually found that it was a really good way of establishing goals that are both measurable as well as very distinct specific. And so we actually do annual OKRs is on a personal level, whether it’s around like personal infrastructure, like fitness or how to relate to your life —

Andrew: — You have like a KR that’s like hanging out with friends three times per month.

Ada: Yeah. So I actually had a reconnection OKR at one point where I basically made a list of people, 50 people that brought me joy that I was really engaged with, always wanted to get to know better, the bar was basically just interesting and that I hadn’t spoken to in four to six months and then the goal was basically to take a one month period and meet with half of them and it was actually one of the most energizing and transforming goals that I’d had because it was a great way to kind of have a focused effort at reconnecting with people and building relationships. And yeah, we score each other on it so we actually have business OKRs in terms of managing the business that we do on a quarterly basis and then I have annual OKRs around some of my goals such as like learning a new skill or whatever else. Thanks for bringing that up.

Andrew: I do not use OKRs to score anything in my life. Do you?

Ryan: Not really. I mean, that is extremely nerdy but also I’m kind of inspired because the beauty of OKRs is when you craft the right OKRs it’s binary, like you pass it or you didn’t and a lot of people they set goals like New Year’s resolutions and they’re like, I want to work out more and that’s their goal. And they end up not actually pursuing it oftentimes in part because it’s not specific. It’s like, well does that mean you need to work out three times a week, minimum, for the rest of the year, and what are your goals and what are your outcomes and expectations out of that?

Ada: Right. Yes. I actually tapped into this HabitShare app in addition to that and specifically with the fitness goal, it was actually, Q1 was like, okay, get to once a week, twice a week, Q3, is at three times a week. And so that’s actually how I’ve been tracking and achieving it.

Ryan: Love it.

Andrew: Okay. So one of my favorite things on Twitter is I, I tried to do this at least once a year where I will just screenshot my, my homescreen and then I’ll just ask everybody else to just do the same and then like reply and it’s really cool. First of all it’s a very personal thing what your home screen is and so I always have to look at it and be like, is there anything like weird on here I don’t want, a stealth beta company —

Ryan: — Right, right, exactly. Yeah, stuff like that.

Andrew: Exactly. And then, and then similarly like looking at other people’s homescreens are really interesting. Like occasionally you’ll see people where they’ll, they’ll like sort their homescreen by like color and that’s how they organize everything and I’m just like, that’s insane. Anyway, so I was going to mention some of the apps that are like on my home screen these days. So I think one, as Ada mentioned, there’s an app called Captio which is great but this morning actually you had tweeted something that the Fin team had come out with a new app called Nota Bene, which is sort of like Captio on steroids. So I actually just installed, I put it on my home screen, I’m actually really excited to try it.

Ryan: Yeah. What, what does it allow you to do for those that aren’t familiar with Kaptio?

Andrew: Right. So, basically both of these apps they allow you to, you basically open up the app, there’s a blank text screen, you type in whatever you want and then you just hit send and then it emails you. So that’s Captio. And then what Nota Bene does is it has a couple more aliases. It has things like, this is something that like I actually really want and need which is sending to my work email versus my home email. And then I might do like work email plus like admin, is a thing. And so I think that’s Fin’s hook to try to get you into the workflow, that way. But yeah. So, I think that that one I use all the time. I was mentioning that, for my first year on the job at Andreessen Horowitz, I moved down to downtown Palo Alto but I’m spending two days a week in the city and so one of the things that I’m finding is that, I’m trying different kinds of like, solutions for like, oh, if I want a place to hang out and do email, like what should I use? And so one of the things that I’ve been trying out over over the last couple months now has been Breather, which lets you rent , basically like a conference room that’s been built out and kind of doing meetings there. Another one is Spacious, which just got launched actually I think in the last couple of weeks. It’s a really cool concept. So what they do is they basically, you have these really high end restaurants, right? And like they have a very nice interior and all that stuff, but they’re basically closed the entire day all the way until like 5:00 PM. And so the idea is between nine to five or eight to five or whatever the hours are, can they actually just literally put like one person there and just have like coffee and water and then you use the interior of this beautiful restaurant. So one of the places in SF is the Press Club, right? Which is this great —

Ryan: — Yeah, great spot.

Andrew: It’s tons of space and so, you can basically just hang out in the press club during the day and it’s basically completely empty and it’s like —

Ryan: — How much does it cost typically?

Andrew: It’s like a membership basis. I think it’s like 90 bucks a month or something like that. And they have spots in Cole Valley and Hayes Valley and the Castro and a bunch of other places. Another one that’s kind of like this, it’s pretty interesting I think when I first heard this idea I like laughed because I thought it was so funny, but, but now I actually have like used it like in a real way which is a company called Recharge and that it lets you rent hotels by the minute. And so you’re kind of like, what is the use case for that? The actual use case is, you need a place to make a phone call. Right. And so in the same way that like Breather or Spacious, it’s like, it’s sort of like, oh well you have all this built-in inventory and like maybe you hold out a room —

Ryan: — Or maybe just a shower. Some people are traveling, flying. I just need in between meetings to have a shower.

Andrew: Right, right. And then you can pay like one fourth the price of a hotel, and like that actually is like kind of useful. So anyway, those are fun. I think I’m now up to, I’m trying one of these like kind of almost on a weekly basis which has been pretty cool.

Andrew: We’re going to talk about Reddit a little bit? Yeah, because I’m like a daily active user. I don’t check it all the time, like I’d probably check it not as often as Twitter, but it’s like, it’s like my default late night read, when I want to just like chill out,

Ryan: You can just turn your brain off. It’s different than Twitter, it’s different from any other community. I actually got a book, a pre-release book by Christine. She’s been writing about this for years now, about the history of Reddit. It’s about 400-500 pages long. You would enjoy it.

Andrew: Well, I just bought a Alexis’s book that he had written a couple years ago, so yeah. I’ve been into Reddit for like several years now, but it’s funny, one of my good friends, Noah Kagan was literally like, you need to go to your favorite subreddits and sign up and follow and actually set up your Reddit and then it’s amazing.

Ryan: Kind of like Twitter.

Andrew: Exactly. Right. Right. And I think I didn’t get it because I would go to the homepage and I would kind of be like, well this is kinda fun, is this like another cat memes website or whatever? I think the one that I want to recommend that folks start out with is actually if you just go to the /bestof subreddit. So it’s like reddit.com/r/bestof. Then it basically just links to some of the best comments on Reddit over the last 24 hours and then you can actually sort it by the last month or something like that. So I think anyway, that that’s a good one. But yeah, I follow a ton of different subreddits at this point. The other one I really like is r/firstworldanarchists and that’s basically, when you have like a sign that’s like don’t step here and then someone takes a photo, like they’re stepping in the grass or whatever, that’s like my kind of like rule breaking. Anyway. So yeah, so there’s that. And then the one, one, one last thing I’ll mention is Bose Quiet Comfort — the wireless noise canceling things as I’ve been commuting from SF and Palo Alto are like amazing. And so they actually have an app that lets you like adjust, how much noise cancellation you want so I use it all the time.

Ryan: These are the ones that just go in your ear, right? Kinda like Airpods?

Andrew: Right. The battery actually hangs on your neck and then they go into your ear. So they’re not over-ear, they’re the ones that go in. But I actually, I have both and yeah, I prefer this one the most and I like bring it with me everywhere at this point.

Ryan: Do you live on, on Reddit at all Ada?

Ada: I’m probably a weekly active user. I check it in and I just like to see r/bestof and see what people are talking about. But you’re right. I mean it’s such a, it’s such a passive way to kind of see a lot of interesting content stream by.

Ryan: What’s also nice about Reddit in a world where content is delivered by algorithms and people you follow and things like that, like on Twitter and Facebook and so on. It’s kind of refreshing to go to a place like Reddit where you can get out of your bubble and you can explore the weirdest stuff if you really want to. It’s not socially curated, it’s not personally curated necessarily to like everything that Reddit knows about you, but it’s really a community of people geeking out about this stuff. One of my favorite subreddits, I don’t visit Reddit all that often, I try to actually avoid it because it’s kind of a rabbit hole, but one subreddit I love is called r/internetisbeautiful and we’ll find there’s actually a lot of really weird projects and websites and little hacks that people are building and it’s almost always delightful. You go there and find something weird, just some crazy website that someone created and the name, r/internetisbeautiful is just such a wonderful feeling. It’s really, really well crafted subreddit. Cool. Thanks for having me over here. By the way, is there anything else you’d like to to plug — anything in the portfolio maybe Andrew? Or anything at Notejoy, Ada?

Andrew: We were talking about how when when you were just signing in at Andreessen Horowitz, I was like, oh, you should install the Envoy app. Yeah. Because it makes it so much easier. You literally get a photo of your face on it and you just tap on it and then like, and you go in and it works over Bluetooth. So anyway, so I always like to plug Envoy, it’s one of our portfolio companies.

Ryan: I mentioned this earlier, but we didn’t get into it. Envoy is such an interesting company. I’m fascinated by social graphs and when you look at the uniqueness of a social graph like Twitter and Facebook and LinkedIn and, and I think some of the most interesting ones that are less talked about is one, like Slack is very interesting, like the people that are in your Slack team or the people that you actually work with, no one else has access to that sort of graph right now. And Envoy is also really fascinating. It’s like a graph of the people and business partners and people that you’re meeting at your company. No one else has that.

Andrew: Well, I mean I think it wouldn’t surprise you to know that the CEO, Larry, was actually really early employee at Twitter. Right. And so a lot of the sort of thinking around both the product experience and just like how nice it is. I think we’ve gotten to, to this whole trend now where your office and your office experience is this extension of your brand. And so now people like really care about it. So they don’t want like this kind of kludgy pen and paper thing. And that product experience is so important. But to your point on the graph is, totally agree — I think that’s also one of the reasons why it’s like, Envoy’s pretty special in sort of the pantheon of these like B2B companies in that it actually grows virally. Like the way that the company grows is that people experience it and they’re like wow, this is really nice. And then as soon as they go back to their office they’re like we should have one. And there’s really not that many products that grow that way. Dropbox grows that way, Slack grows that way. It’s like a viral B2B thing. And so I think, in, in the same way it’s like that, that graph means that not only is it spreading virally and enables that spread, but then the other part I think is wow, okay, cool, you get a list of all the people that are visiting and who they’re visiting and then that can then feed into like all your other like, offline data.

Ryan: Or CRM.

Andrew: Yeah, so you take your offline data and turn it into online stuff and it’s another touchpoint which is super important.

Ryan: You don’t have Envoy at the headquarters, Ada?

Ada: No.

Andrew: I think she has a doorbell.

Ryan: Old school doorbell. So yeah, what’s down the pipe?

Ada: What’s new on the pipe is that we’ve actually been doing a ton of mobile enhancements and so we’re actually bringing Android very soon in terms of bringing it out as an app. It’s interesting now because the bar for consumer and business apps is so much higher than it used to be, right? Like it’s actually really important to be fully multi-platform, so we’ve always had Mac and PC and then the browser and we’ve had iOS but it’s really exciting to actually bring that to Android because that’s been a big factor for a lot of teams that are trying to adopt Notejoy as an overall group and so yeah, just cranking and hard at work.

Ryan: Cool. Awesome. Well thanks for coming on. This will be the first of hopefully many brother-sister Product Hunt Radio shows.

Andrew: Awesome. Thanks for having us.

Ada: Thanks.

Above: New technology has always captivated consumers!

Dear readers,

I’m often asked- so what kind of startups are you investing in at Andreessen Horowitz? And since I’m focused mostly on consumer companies – is there anything exciting happening? After all, if we’re “between” platforms, and there isn’t something as big as the iPhone coming up, is there anything interesting left?

I’m really bullish about what’s around the corner – and I want to unpack what I’m looking for, how I’ve drawn insights from history, and what’s around the corner. In the 70 slide deck below, I cover a couple key concepts:

• Accelerating technology adoption. Why the telephone took 50+ years to adopt, but the mobile phone was <10 years
• Three historical examples and their modern antecedents
  • Content marketing. The origin of the Michelin Guide and why content marketing still works
  • Viral growth. How chain letters were invented and rethinking its effectiveness in the framework of viral growth
  • Marketplaces. How to bootstrap marketplace businesses and the cold-start problem, and what the story of toothpaste can tell us about that
• The most exciting new technologies coming around the corner, and how to evaluate them for producing new startups
  • Video. Why video is big, and will get even bigger
  • Offline. How the offline-to-online channel has been used by scooters and rideshare, to great effect
• My investing thesis. The intersection of growth hacking, new tech, and pre-existing consumer motivations
• Closing. Technology changes, but people stay the same

I presented all of this at the Andreessen Horowitz Summit in 2018, which gathers our portfolio companies, partners, LPs, and close friends. It’s great to be able to publish it here as well. Hope you enjoy it.

Another note is that this is closely related to, and complimentary, to this deck: The red flags and magic numbers that investors look for in your startup’s metrics. If the below deck is the macro view of how I’m looking at markets, industries, and technologies, then the metrics deck gives my POV on how to diligence each company.

Finally, before I jump in, it’s true that I talk about what sectors I’m into as well – and here are few areas I’m digging into:

• Unbundling my Uber expertise
  • Marketplaces (particularly the $10T service economy – more on that here)
  • Transportation and travel
  • The future of work (Bottoms up SaaS, full-stack autonomy, etc)
• Next generation entertainment and networks of people+content
  • esports, gaming, virtual worlds
  • Reinventing traditional media (Podcasting, eBooks, etc)
  • Content creator / influencer economy
• … plus, anything else that looks like a network with network effects

Obviously if you are working on anything in this area, and have some traction in the US, would love to talk more. Get an intro through your investors and come find me! Happy to chat.

Thanks again!

Andrew
San Francisco, CA

Download the deck

The Deck

Today, we’re going to talk about what’s exciting and new in consumer startups, and what I’m investing in at Andreessen Horowitz. But to hit this topic, I want to start by zooming out. Here’s a graph of many of the new consumer technologies that have been introduced in the US over the past 100 years. The X-axis is years, and the Y-axis is the % of US households that tech reached.

Each line represents a different technology – you can see the car, the radio, air conditioning, the microwave, and so on. Lots of important consumer tech that was new at some point. But you also see something pretty interesting – some of the most important tech took decades to adopt.

Let’s take a look at the telephone, in particular.

Above: Now, remember that the motivation of communicating with your friends and family has been around since the dawn of time. But when you look at the phone, it took 5 decades to break into the majority of US households. Wow! And of course at the time, there were other technologies competing for engagement – there was the telegraph, postal mail, etc. In fact, early on the phone was marketed as the “speaking telegraph.” Nevertheless, for something we now take for granted, several decades is a long time.

Why is that?

Above: Here’s why. These were the kinds of instructions that had to be packaged alongside the Bell Telephone System – how to hold the phone, which side went to your ear and which side was next to your mouth. And if someone called, you were supposed to say hello!

While the human motivation was there to speak to friends and family, we had to build the behavior from scratch.

Above: Let’s contrast this to the cell phone, which took a much shorter time to conquer the market. And in fact, if you were to think about the next few years after it hit mass penetration in the US households, we also know it hit several billion active handsets worldwide. Some developing countries are truly mobile-first – they have mobile phones before they have computers, land lines, or reliable access to water.

Above: Each new technology is able to build on each other. You can use radio ads to market the TV. You can use the TV to market mobile phone services. And so we see an accelerating adoption rate of new technology introduction.

What a time to be alive! It’s only going to go faster.

Above: And yet, even with the backdrop of all of these new technologies, we are still fundamentally the same people from many eras ago. We haven’t physically changed much.

Above: We are the same humans who painted the walls of caves, because we love art, and love creativity.

Above: We are the same people who built massive theaters, because we love to be entertained.

We took selfies as soon as the technology allowed.

… and it turns out, we have always loved scooters. In fact, the US Postal Service tried these gas-powered units out to deliver mail a century ago.

Above: In other words, while technology changes rapidly, people stay the same. And that’s the opportunity.

When we spot new startups who can take advantage of a moment in time, at the intersection of new technology, pre-existing human motivation, and can find a clever growth trick to get going – that’s exciting. That gets my attention.

Let’s look at a couple historical examples where these kinds of intersections have happened, and also some modern echoes of their impact.

First example, we’ll go back in time.

Above: It’s 1900, and there’s a new technology – cars. But there’s only 3,000 automobiles in France, because they’re hand-made, they break down all the time, and it’s not actually clear why they are superior to horses.

Above: They look like this.

In the introduction of these new technology, there forms an ecosystem of new companies that stand to benefit from more cars on the road. There’s gas stations, there’s maintenance businesses, and there’s also tires.

Above: One of these companies you’re familiar with – they’re called the Michelin Tire Company. And certainly you recognize the Michelin Man on the left over here.

Now they have a tough problem to get their business to grow. Remember, there’s only 3,000 cars. Selling tires is hard because what you actually need is to get car owners to drive more, and to create more car owners as well. That’s tricky! It’s a very indirect problem that requires a clever solution.

What was Michelin’s solution? We’re all familiar with the answer: They created the Michelin Guide.

Above: This small red book is one of the first Michelin Guides, given out with the subtitle, “Free for Drivers.”

This is a really clever effort for Michelin, because by packaging all the destination restaurants across France, and eventually Europe and the world, they gave people a reason to drive. And for existing automobile-owners, a reason to visit more towns and drive longer. And in fact, the Michelin Guide is so successful that many of us today don’t have much need for their tires, but certainly rely on their recommended restaurants.

This is a great example of a “hack” that gets their core business growing. And today, we’d call it Content Marketing, and it still works.

Let’s us a contemporary example that builds on their content marketing push.

Google wants us all to be engaged in their mobile apps, search functions, and other properties – but they want to be relevant in our lives in other ways too, for example in our culture and media. One way Google does this is that they have a great app, called the “Google Arts and Culture” app, which demonstrates the world’s great works of art. They have virtual tours of museums, 360 degree photos, videos, and more.

But the best feature they built is the “take a selfie and see what kind of famous artwork you resemble” feature. As we saw earlier, we’ve always been obsessed with selfies. So this was successful. Very, very successful.

Above: We saw famous people like Kumail from HBO’s Silicon Valley take selfies and publish them – this is a pretty good one! And not only did celebrities  share their photos, many everyday consumers did too. A lot of them.

This was so viral, in fact, that eventually this app was downloaded millions of times.

In early 2018, it became the most downloaded app at that time. More than YouTube. More than Facebook. Wow! That’s fantastic.

But what does this have to do with Google? This is such an indirect way for Google to tell their message, and to engage us in their products. But it’s a much fancier form of content marketing that lives in a mobile app. It worked for Michelin a hundred years ago, and it works for Google today too.

The second example I’ll talk about is more of a consumer user-generated content play. It starts in 1775.

Above: In 1775, the US Postal Service was founded!

You may know that this guy – Benjamin Franklin – started it.

One way to think of the service, in contemporary jargon: The postal service was a new user-to-user communications platform that allows millions of consumers to communicate with each other for the first time. Before social media, and before email, the postal service let people do what we now take for granted.

There are, of course, a lot of reasons to use the postal service – there’s personal correspondence, bills, advertising, and many other uses. But one of the major uses of mail came unexpectedly, and introduced millions of people to new ways to use mail – the chain letter! It turns out sometimes, as a platform, you’re super lucky, and your customers find new ways to engage and grow your service for you.

In the photo above, you can see one of the world’s first chain letters. When enterprising individuals started to experiment with postal mail, they figured out they could get a ton of engagement when they worded the letters a certain way, and promised certain things.

The variant above behaved like the following: When you received one, it asked you to remove the top name, add your name to the bottom of the list, and mail a new dime to everyone on the list. And then to share the chain letter with 5 of your friends within 3 days. Specific, clear call-to-actions. If you followed the instructions in the letter, you would receive 15,625 letters with $1,500+ in dimes. In today’s dollars, this is about $33,000. What a great outcome! For folks who’d never seen this kind of letter, and who saw their friends slowly getting rich – one dime at a time – this was enticing.

These chain letters worked. In fact, they worked really well – too well. Within the first few months, this chain letter reached tens of millions of copies. It eventually became so successful that the US Postal Service had to shut it all down.

And thus, to this day, chain letters are illegal to send on the US Postal Service!

The chain letter was a clever creation, of course, but today we’d just call this viral user acquisition. Getting people to tell their friends and family to spread the word is something that’s always worked – and it works today as well! The modern version is far more sophisticated.

Above: Companies like Airbnb and Uber have referral programs, where you can send credits to your friends that can be redeemed on their trips. And you get credits in your account when they accept it too – it’s a reciprocal give/get program. Of course, we’ve improved the whole thing based on the latest tech. It integrates into Facebook Messenger and your email addressbook. It has tracking codes so you can see how well it circulates, and you A/B test the whole thing to make sure it’s highly optimized to be viral and spread.

Yet in the end, the mechanics are the same – you can get people to tell their friends and family, if you make it enticing for them, and also for yourself.

The last historical example I’ll use is a story about the “cold start” problem – but we’ll use grocery stories and toothpaste as our example.

In the early 1900s, it was the dawn of consumer packaged goods companies, who were still figuring out their distribution models. Amazingly, many of the household goods that we’re now familiar with hadn’t been invented yet. People still weren’t really bathing on a regular basis. It was an earlier, simpler time for CPG companies.

The amazing thing about the story of toothpaste – the above is a box by Pepsodent – is that toothpaste had to be invented. Even more amazing, people needed to be taught how to use toothpaste, and why.

You could advertise to spread the word with consumers, of course, but there was a second problem: How do you get the toothpaste in the hands of consumers?

Above: Across the US, there were tons of “mom and pop” grocery stores like these. They needed to carry the toothpaste so that consumers could come in and buy them. The problem is, they don’t want to stock the toothpaste (which they would need to buy) if consumers weren’t asking for it. And of course, consumers wouldn’t ask for the product – at least you couldn’t count on it – unless it was in stock.

This is a classic chicken and egg problem. So how do you solve this?

Above: The answer was simple: Advertising, and lots of it, and coupons too. First, it was important for the CPG companies to convince consumers that they had a yucky film on their teeth that could only be solved with toothpaste. And then they offered them coupons to come and try it.

Before running a big campaign like this, they could go to the grocery stores and say, “We’re about to create a ton of consumer demand! Folks are going to come in and ask for toothpaste, so now’s the time to stock it.” This solves the chicken and egg.

Solving the chicken and egg problem was hard then, and it’s hard now. And yet it’s something that every marketplace company has to do.

Above: If this example sounds familiar, it’s because it was used recently by our portfolio company Instacart too. Today, Instacart has deep relationships with the nation’s top grocers. But when they first got started, they just built a great app, got consumers buying things, and started dispatching shoppers to pick up their orders. As more demand was built, eventually Instacart could approach the grocery chains and set up a formal partnership to make the experience even better.

A hundred years ago, CPGs used advertising and coupons to drive demand to solve their chicken and egg. Today, startups use awesome mobile apps to create demand and to solve the same problem. It still works.

Above: As you might imagine, you could go for hours on these kinds of historical examples. There are a ton of them.

The important, core concept here is simple:

• When there are new technologies and platforms hitting scale…
• … and products tap in pre-existing consumer motivations
• … and there are “growth hacks” that create slingshot opportunities to quickly and scalable grow

At the intersection of these three factors, amazing things can happen.

So it’s my goal to spot new products that look like this, and to evaluate them. (In a separate deck, I talk more about the extensive techniques from the metrics and growth function that can be used to evaluate startups).

Of course, the first of the triumvirate is critical – and that is new technologies and platforms. And there are a ton of exciting ones right around the corner. But let’s first cover many of the new platforms that have hit major scale.

We have IoT devices, particularly voice assistants that live inside Google Homes and Amazon Echoes.

We have over a hundred million units of smart TV devices that combine media and computing. That’s exciting.

We have platforms like YouTube with over a billion active users. Wow!

And wearables with hundreds of millions of units that sometimes run apps themselves, or help augment experiences on your phone.

Not only there many platforms at scale, but it’s exciting to see a couple emerging categories as well.

There are Nintendo Switches, which have sold tens of millions of units. They focus on games, of course, but you can run cloud-connected games like Fortnite. And perhaps people will creative about what kinds of other apps work too.

All modern appliances are adding internet-connectivity. Fridges are an obvious one, but we all have seen Amazon add Alexa to microwaves too. What’s next after that??

There continue to be companies working on smart glasses. Above is from North, who are making Augmented Reality inside a pair of glasses that almost look identical to the ones you already have on your face. I think this will be a really compelling category in the next decade.

And finally, as autonomous cars come out, we’ll have to rethink the entire driving experience to mostly be a riding experience. I expect a lot more video, gaming, and interactive media in the car. This is an emerging area too over the next decade.

So there are a ton of new technologies right around the corner. We just need one or two to break out, in addition to the surefire opportunities around marketplaces, B2B, mobile, and other existing categories.

The question is: Which platforms am I most excited about? What are examples of growth tactics that are working now that are super clever? In the intersection of the three things I mentioned earlier, what would I zoom in on?

Let’s talk through a couple.

The first category of products I’d call “Video Native” products.

Above: The new technology at scale is video. We already talked about how big it is – but let’s give a really concrete example.

You all remember Gangnam Style, our favorite Korean pop song from 2013. And we’ve all heard Despacito (even if you don’t know you have). Here’s the link if you need a refresher.

Both videos are very popular, and have been viewed billions of times.

It took Gangnam Style nearly 5 years to be viewed three billion times. It’s an amazing feat, but even more amazing is that it took Despacito just a year!

Today, as of this writing, Depacito has been viewed 5.7 billion times. Wow.

Video is huge, but not just for music videos. It can be used by many other forms of entertainment and media to boost their growth as well.

My hypothesis: One of the big opportunities right now is that any product that automatically generates video when users engage will create more video sharing activity, thus more viral acquisition and engagement.

No wonder eSports are such a big deal right now. And it’s one of the reasons I’ve been spending time in this space.

When you look at a game like League of Legends, created by Riot Games, you see some amazing stats.

The 2017 League of Legends championship was viewed by over 100 million live viewers. Compare that to Wimbeldon, which had a mere 9.4 million viewers. That’s over 10X. And yet we think of video games as a vertical niche – it’s certainly not. It’s mainstream, and it’s big.

One startup I’m excited about is Sandbox VR and the category of location-based virtual reality (LBVR). I think this is the format that is most likely to break virtual reality into the mainstream – not in-home. Sandbox asks for people to bring their friends, as a group, to a retail location to use what I think is the best VR experience on the planet. You wear haptic suits, there’s a motion capture system, props, and special effects. It’s next level.

It’s an incredible experience – you can see the trailer here and try it in San Mateo here.

With your friends, you fight pirates and zombies. And pirate zombies. They currently have two games, with more coming.

The whole experience is cool, but part of the reason I’m excited about the company is that they have an awesome growth tactic that connects directly to video.

Above: Every time you go with friends, it’s an event – you take a ton of pictures and video. In fact, Sandbox helps you generate a mixed reality video with that’s shareable. You publish it on Facebook and other social media, and it looks like so much fun that friends want to try it too. All of this generates viral growth! It’s a fantastic growth tactic.

It’s no wonder that one of the company’s slogans is – “Fun to play, but fun to watch too.”

The second example I want to use is “Offline to Online.” We all know about going online to offline, which has been enabled by companies like Amazon, eBay, and more. You can think of the first generation of marketplaces and internet products as filling this niche. However, this is the other way around.

Above: The fundamental technology shift that’s allowing this is everything to do with maps, GPS, and AR – all in your pocket, on your mobile phone. This enables both new product experiences but also new growth tactics too.

The growth hack I have in mind is that you can now have highly visible offline experiences that then drive people towards using their app. As online channels become saturated – Facebook and Google ads are expensive, there are literally millions of apps in the app store – it turns out the real world gets pretty attractive.

Let’s look at some examples:

First, there’s Pokemon Go, by Niantic. You see yourself on a map, with Pokemon all around you. Collect them all! It’s fun, but it also means that people are watching others play. Sometimes this is a small reminder, if you see a small group gathered trying to collect a rare Pokemon.

But sometimes it gets big – really big.

Here’s a photo of tends of thousands of people who showed up for a Pokemon event. This is just one example, but Niantic does events all over the world, all the time.

Of course, rideshare looks like this too. Who can forget the pink mustaches from across the city that remind us to try and use Lyft?

Transportation is an intrinsically viral product – they are social activities. You bring your friends and loved ones in the car with you, to share the costs. Even the fully utilitarian version – going from point A to point B – can be social, since there’s often a person on the other side. These mean that the rideshare companies all benefit from significant viral and organic traffic to their products.

Scooters are another great example. Our portfolio company Lime has their scooters deployed across a city, and each scooter is literally a mini-billboard to try it out. And the first time you’ve seen someone ride a scooter, they probably had a big smile on their face! It looks fun. And because of that, they benefit from the offline to online effect.

Above: So these are two quick examples – I’m keeping my eye on more, but again, it’s all about the intersection of new tech, existing consumer behavior, and an insight about growth. If you can get these three together, it’s super interesting.

There are a ton of new plaforms hitting scale. I’m also interested in GSuite, which is hitting critical mass across SMBs and enterprises. I mentioned Alexa. You can see products like Twitch and Tik Tok growing quickly – with the former adding extensions and the ability for apps to integrate. And Minecraft and Roblox are fascinating virtual worlds that bundle social networks and content together in one place – also fascinating to track.

As these platforms emerge, there will be new startups can be built adjacent or on top of them.

I’m very excited about what’s going on in consumer – and am excited to see what people build.

Again, here’s my investing framework – 1, 2, and 3. It’s important to see the intersection.

The important idea here is simple:

Technology changes, but people stay the same. If we can spot the new, breakthrough products that can grow at the intersection of this technological change, and peoples’ behaviors, then we’ll build the next generation of startups. (And yes, we have really always loved selfies – it’s not a new thing).

[Dear readers, this essay is on the future of marketplaces. Is there still room for marketplace startups to innovate? We answer, emphatically, yes! Am excited to share a vision on the past and future of the service economy, in a collaboration by my a16z colleague Li Jin. From “Unbundling Craiglist” to “Uber for X” – we lay it all out in a single framework. Hope you enjoy our thinking! -A]

Above: 4 eras of marketplaces focused on the service economy – and what’s next

Goods versus Services – why a breakthrough is coming
Marketplace startups have done incredibly well over the first few decades of the internet, reinventing the way we shop for goods, but have been less successful services. In this essay, we argue that a breakthrough is on its way: While the first phase of the internet has been about creating marketplaces for goods, the next phase will be about reinventing the service economy. Startups will build on the lessons and tactics to crack the toughest service industries – including regulated markets that have withstood digital transformation for decades. In doing this, the lives of 125 million Americans who work in the services-providing industries will join the digital transformation of the economy.

In the past twenty years, we’ve transformed the way people buy goods online, and in the process created Amazon, eBay, JD.com, Alibaba, and other e-commerce giants, accounting for trillions of dollars in market capitalization. The next era will do the same to the $9.7 trillion US consumer service economy, through discontinuous innovations in AI and automation, new marketplace paradigms, and overcoming regulatory capture.

The service economy lags behind: while services make up 69% of national consumer spending, the Bureau of Economic Analysis estimated that just 7% of services were primarily digital, meaning they utilized internet to conduct transactions.

We propose that a new age of service marketplaces will emerge, driven by unlocking more complex services, including services that are regulated. In this essay, we’ll talk about:

• Why services are still primarily offline
• The history of service marketplace paradigms
  • The Listings Era
  • The Unbundled Craigslist Era
  • The “Uber for X” Era
  • The Managed Marketplace Era
• The future of service marketplaces
  • Regulated services
  • Five strategies for unlocking supply in regulated markets
• Future opportunities

Let’s start by looking at where the service economy is right now and why it’s resisted a full scale transformation by software.

Software eating the service economy, but it’s been slow
We’ve all had the experience of asking friends for recommendations for a great service provider, whether it be a great childcare provider, doctor, or hair stylist. Why is that? Why aren’t we discovering and consuming these services in the same digital way we’ve come to expect for goods?

Despite the rise of services in the overall economy, there are a few reasons why services have lagged behind goods in terms of coming online:

• Services are complex and diverse, making it challenging to capture relevant information in an online marketplace
• Success and quality in services is subjective
• Fragmentation – small service providers lack the tools or time to come online
• Real-world interaction is at the heart of services delivery, which makes it hard to disaggregate parts of a purchase that might be done online

Let’s unpack each reason below:

First, on the complexity and diversity of services, services are performed by providers who vary widely, unlike goods which are manufactured to a certain spec. Even the names of services can vary: what one home cleaning service calls a “deep clean” can be different from another provider’s definition. This lack of standardization makes it difficult for a service marketplace to capture and organize the relevant information.

Second, services are often complex interactions without a clear yardstick of success or quality. The customer experience of a service is often subjective, making traditional marketplace features like reviews, recommendations, and personalization more difficult to implement. Sometimes just getting the job completed (as in rideshare) is sufficient to earn a 5-star review, whereas other higher-stakes services, like childcare, have complex customer value functions, including safety, friendliness, communicativeness, rapport with child, and other subjective measures of success.

Third, small service providers often lack the tools or time to come online. In many service industries, providers are small business owners with low margins; contrast this with goods manufacturing where there are economies of scale in production, and thus consolidation into large consumer products companies. As a result of industry fragmentation, service providers often don’t have time or budget to devote to key business functions, such as responding to customer requests, promoting and marketing themselves, maintaining a website, and other core functions. While major e-commerce platforms have taken on the role of distribution, merchandising, and fulfilling orders for goods, there are few platforms that service providers can plug into to manage their businesses and reach customers.

Fourth, real-world interaction is central to services, which can pull other steps of the services funnel into the offline world as well. Many services are produced and consumed simultaneously in real-world interactions, whereas goods entail independent stages of production, distribution, and consumption. The various stages of the goods value chain can be easily unbundled, with e-commerce marketplaces comprising the discovery, transaction, and fulfillment steps. Conversely, since the production and consumption of services usually occur simultaneously offline, the discovery, distribution, and transaction pieces are also often integrated into the offline experience. For instance, since getting a haircut entails going to a salon and having interactions with the providers there, the stages of the value chain that precede and follow that interaction (discovery, booking, and payment) also often get incorporated into the in-person experience.

All of these factors make it very hard for services to come online as comprehensively and widely as commerce – but there’s hope. We’ve seen multiple eras of bringing the service economy online, and we’re on the verge of a breakthrough!

The 4 eras of Service Marketplaces, and what’s next 
There have been 4 major generations of service marketplaces, but coverage of services and providers remains spotty, and many don’t provide end-to-end, seamless consumer experiences. Let’s zoom out and talk through each historical marketplace paradigm, and what we’ve learned so far.

Above, you can see that there have roughly been four major eras of marketplace innovation when it comes to the service economy.

1. The Listings Era (1990s)
The first iteration of bringing services online involved unmanaged horizontal marketplaces, essentially listing platforms that helped demand search for supply and vice versa. These marketplaces were the digital version of the Yellow Pages, enabling visibility into which service providers existed, but placing the onus on the user to assess providers, contact them, arrange times to meet, and transact. The dynamic here is “caveat emptor”–users assume the responsibility of vetting their counterparties and establishing trust, and there’s little in the way of platform standards, protections, or guarantees.

Craigslist’s Services category is the archetypal unmanaged service marketplace. It includes a jumble of house remodeling, painting, carpet cleaners, wedding photographers, and other services. But limited tech functionality means that it feels disorganized and hard to navigate, and there’s no way to transact or contact the provider without moving off the platform.

We’ve all had the experience of a listings-oriented product, like Craigslist. You find something you want, but everything else – trust/reviews/payments/etc – that’s all up to you!

2. The Unbundled Craigslist Era (2000s)
Companies iterated on the horizontal marketplace model by focusing on a specific sub-vertical, enabling them to offer features tailored to a specific industry. We’ve all seen the diagram of various companies picking off Craigslist verticals – it looks something like this:

As a reaction to the “Wild West” nature of Craigslist, to improve the customer experience, each startup would create value-add via software. For instance, Care.com carves off the Childcare section of Craigslist, and provides tech value-add in the form of filters, structured information, and other features to improve the customer experience of finding a local caregiver. It’s a huge leap in terms of user experience over Craigslist’s Childcare section.

Angie’s List, a home services site founded in 2005, carves off Craigslist’s household services category. The platform has features including reviews, profiles, certified providers, and an online quote submission process. But the marketplace doesn’t encompass the entire end-to-end experience: users turn to Angie’s List for discovery, but still need to message or call providers and coordinate offline.

Unmanaged vertical marketplaces like Angie’s List go a step beyond Craigslist and take on some value-add services like certifying providers when they meet certain standards, but customers still need to select and contact the service provider, place their trust in the provider rather than the platform, and transact offline.

Like previous listing sites, these platforms in this era try to use the ‘wisdom of the crowds’ to promote trust. These platforms have a network effect in that more reviews means more users and more reviews. But user reviews have their limitations, as every user has a unique value function that they’re judging a service against. Without standardized moderation or curation, and without machine learning to automate this process, customers have the onus of sifting through countless reviews and selecting among thousands of providers.

3. The “Uber for X” Era (2009-)
In the early 2010s, a wave of on-demand marketplaces for simple services arose, including transportation, food delivery, and valet parking. These marketplaces were enabled by widespread mobile adoption, making it possible to book a service or accept a job with the tap of a button.

Companies like Handy, Lugg, Lyft, Rinse, Uber and many others made it efficient to connect to service providers in real-time. They created a full-stack experience around a particular service, optimizing for liquidity in one category. For these transactions, quality and success were more or less binary–either the service was fulfilled or it wasn’t–making them conducive to an on-demand model.

These platforms took on various functions to establish an end-to-end, seamless user experience: automatically matching supply and demand, setting prices, handling transactions, and establishing trust through guarantees and protections. They also often commoditized the underlying service provider (for instance, widespread variance on the driver side of rideshare marketplaces is distilled into Uber X, Uber Pool, Uber Black, Uber XL, etc.).

Unlike the previous generations of marketplaces, in which the provider ultimately owns the end customer relationship, these on-demand marketplaces became thought of as the service provider, e.g. “I ordered food from DoorDash” or “Let’s Uber there,” rather than the underlying person or business that actually rendered the service.

Over time, many startups in this category failed, and the ones that survived did so by focusing on and nailing a frequent use case, offering compelling value propositions to demand and supply (potentially removing the on-demand component, which wasn’t valuable for some services), and putting in place incentives and structures to promote liquidity, trust, safety, and reliability.

4. The Managed Marketplace Era (Mid-2010s)
In the last few years, we’ve seen a rise in the number of full-stack or managed marketplaces, or marketplaces that take on additional operational value-add in terms of intermediating the service delivery. While “Uber for X” models were well-suited to simple services, managed marketplaces evolved to better tackle services that were more complex, higher priced, and that required greater trust.

Managed marketplaces take on additional work of actually influencing or managing the service experience, and in doing so, create a step-function improvement in the customer experience. Rather than just enabling customers to discover and build trust with the end provider, these marketplaces take on the work of actually creating trust.

In the a16z portfolio, Honor is building a managed marketplace for in-home care, and interviews and screens every care professional before they are onboarded and provides new customers with a Care Advisor to design a personalized care plan. Opendoor is a managed marketplace that creates a radically different experience for buying and selling a home. When a customer wants to sell their home, Opendoor actually buys the home, performs maintenance, markets the home, and finds the next buyer. Contrast this with the traditional experience of selling a home, where there is the hassle of repairs, listing, showings, and potentially months of uncertainty.

Managed marketplaces like Honor and Opendoor take on steps of the value chain that platforms traditionally left to customers or providers, such as vetting supply. Customers place their trust in the platform, rather than the counterparty of the transaction. To compensate for heavier operational costs, it’s common for managed marketplaces to actually dictate pricing for services and charge a higher take rate than less-managed marketplace models.

Managed marketplaces are a tactic to solve a broader problem around accessing high-quality supply, especially for services that require greater trust and/or entail high transaction value. If we zoom out further, there’s many more categories of services that can benefit from managed models and other tactics to unlock supply.

What’s next: The future of Service Marketplaces (2018-?)
We think the next era of service marketplaces have potential to unlock a huge swath of the 125 million service jobs in the US. These marketplaces will tackle the opportunities that have eluded previous eras of service marketplaces, and will bring the most difficult services categories online–in particular, services that are regulated. Regulated services–in which suppliers are licensed by a government agency or certified by a professional or industry organization–include engineering, accounting, teaching, law, and other professions that impact many people’s lives directly to a large degree. In 2015, 26% of employed people had a certification or license.

Regulation of services was critical pre-internet, since it served to signify a certain level of skill or knowledge required to perform a job. But digital platforms mitigate the need for licensing by exposing relevant information about providers and by establishing trust through reviews, managed models, guarantees, platform requirements, and other mechanisms. For instance, most of us were taught since childhood never to get into cars with strangers; with Lyft and Uber, consumers are comfortable doing exactly that, millions of times per day, as a direct result of the trust those platforms have built.

Licensing of service professions create an important standard, but also severely constrains supply. The time and money associated with getting licensed or certified can lock out otherwise qualified suppliers (for instance, some states require a license to braid hair or to be a florist), and often translates into higher fees, long waitlists, and difficulty accessing the service. The criteria involved in getting licensed also do not always map to what consumers actually value, and can hinder the discovery and access of otherwise suitable supply.

Above: Bureau of Labor Statistics. (11/9/18)

Five strategies to unlock regulated industries
We’re starting to see a number of startups tackling regulated services industries. As with each wave of previous service marketplaces, these new approaches bring more value-add to unlock the market, with variations in models that are well-suited to different categories.

The major approaches in unlocking supply in these regulated industries include:

1. Making discovery of licensed providers easier
2. Hiring and managing existing providers to maintain quality
3. Expanding or augmenting the licensed supply pool
4. Utilizing unlicensed supply
5. Automation and AI

1) Making discovery of licensed providers easier
Some startups are tackling verticals that lack good discovery of licensed providers. Examples include Houzz, which enables users to search for and contact licensed home improvement professionals, and StyleSeat, which helps users find and book beauty appointments with licensed cosmetologists.

2) Hiring and managing existing providers to maintain quality
Companies can raise the quality of service by hiring and managing providers themselves, and by managing the end-to-end customer experience. Examples are Honor and Trusted, managed marketplaces for elder care and childcare, respectively, which employ caregivers as W-2 employees and provide them with training and tools. In the real estate world, Redfin agents are employees whose compensation is tied to customer satisfaction, unlike most real estate agents who are independent contractors working on commission.

3) Expanding or augmenting the licensed supply pool
Expanding the licensed supply pool can take the form of leveraging geographic arbitrage to access supply that’s not located near demand. Decorist, Havenly, Laurel & Wolf, and other online interior design companies enable interior designers around the world to provide design services to consumers without physically visiting their homes (yes, in many parts of the US interior design requires a license!). With improvements in real-time video, richer telepresence technologies, and better visualization technologies, more synchronous services are also shifting from being delivered in-person to online. Outschool and Lambda School are examples of de-localizing instruction, enabling teachers and students to participate remotely while preserving real-time interaction.

Another approach is to help suppliers navigate the certification process. A16z portfolio company Wonderschool makes it easier for individuals to get licensed and operate in-home daycares.

Lastly, there’s the approach of augmenting certified providers so they can serve more customers. Fuzzy, an in-home veterinary service, uses AI and vet technicians to augment the productivity of licensed veterinarians; and a16z portfolio company Atrium builds automation and workflow management to provide efficiency gains in the legal industry.

4) Utilizing unlicensed supply
Some companies utilize unlicensed supply–notably Lyft, Uber, and other peer-to-peer rideshare networks. Another example is Basis, a managed marketplace for guided conversations with trained but unlicensed specialists to help people with anxiety, depression and other mild to moderate mental health issues.

In the pet space, Good Dog is a marketplace that brings together responsible pet breeders and consumers looking for a dog. Going beyond existing breeder licensing, which the company felt didn’t map to what consumers valued, Good Dog established its own higher set of standards and screening process in conjunction with veterinary and academic experts.

5) Automation and AI
Other startups automate away the need for a licensed service provider altogether. These include MDAcne, which uses computer vision to diagnose and treat acne; and Ike Robotics and other autonomous trucking startups which remove the need for a licensed truck driver.

Opportunities for companies addressing regulated services
The last twenty years saw the explosion of a number of services coming online, from transportation to food delivery to home services, as well as an evolution of marketplace models from listings to full-stack, managed marketplaces. The next twenty years will be about the harder opportunities that software hasn’t yet infiltrated–those filled with technological, operational, and regulatory hurdles–where there is room to have massive impact on the quality and convenience of consumers’ everyday lives.

The services sector represents two-thirds of US consumer spending and employs 80% of the workforce. The companies that reinvent various service categories can improve both consumers’ and professionals’ lives–by creating more jobs and income, providing more flexible work arrangements, and improving consumer access and lowering cost.

The companies mentioned in this essay just scratch the surface of regulated industries. You can imagine a marketplace for every service that is regulated, with unique features and attributes designed to optimize for the customer and provider needs for that industry. (A full list of regulated professions in the US can be found here.) We fully expect more Airbnb- and rideshare-sized outcomes in the service economy.

If you’re a founder who is looking to take on the challenge of tackling more complex services and bringing them online, we’d love to hear from you.

Thank you for reading!

Dear readers,

Building a new growth team is hard. You have to figure out the macro organizational issues – how it fits in with marketing, product, and other functions – as well as the micro, like how to measure the success of these teams. It’s a tricky topic and something that a lot of teams are thinking about right now.

A few months ago, I spoke on lessons learned from various organizational structures for the growth teams at Uber, organized as 5 broad topics:

1. Why create a growth team?
2. What’s the difference between a “growth hacker” and a growth team?
3. What’s the difference between growth and marketing/product/whatever?
4. Where should growth teams focus?
5. I’m starting or joining a growth team! What should I expect?

To answer these questions, Brian Balfour and I worked on a deck, based on materials from Reforge. (Check them out for more practical reference materials on this topic)

The deck is presented below! Hope you enjoy the materials, and feel free to reach out or follow me for realtime updates at @andrewchen on Twitter.

Thanks,
Andrew

The Deck

Above: Today, I’m going to present a few key topics that you need to figure out as you build a growth team for your company. First, why you might want to create one in the first place. Then, the differences in skillsets for both individual practitioners versus the org – and versus existing functions like Product and Marketing. And finally, where teams should focus and how to make an impact in the early days.

The ideas within these topics are drawn from several places – interviews and discussion with the folks who lead growth teams at places like Slack, Dropbox, Hubspot, Pinterest, and others, but also my own personal experiences at Uber.

Above: Many of you may remember when Uber looked like this. It was all up and to the right.

The growth team was originally created in 2013, founded by Ed Baker. It experimented with a ton of different organizational configurations – I joined a few years after it was created and spent about 3 years there, and spent most of my time on driving growth on the rider side of the platform.

Above: While I was at Uber, a lot of amazing projects were run out of the growth team. My colleagues in China Growth made incredible progress – shoutout to Ben Chiang, Han Qin, Michelle Chen, Jia Zou, Vinay Ramani, and many others – in addition to much of the progress being made across the US and the rest of the world.

At its peak , the growth team included China Growth and had over 500+ people. It was an amazing, dynamic time for the company. I learned a ton and am excited to share some of the ideas today.

Uber has changed a lot over the years. We certainly changed logos many times. But I think there are some really critical things that we can pass along to others in the ecosystem.

Let’s start with the basics…

Above: First, why create a growth team in the first place? We know that a lot of companies have folks with formal growth teams, and informal ones with growth PMs/marketers/etc running around.

Above: When you just look at the cross-section of companies in the industry, many of the newest and best B2B and consumer companies have all built growth teams.

We’ve also heard many Boards ask their CEOs to invest in growth teams. Why did this even emerge in the first place?

Above: The easiest way to talk about The Product Death Cycle.

Unfortunately, this is how products are often shipped and released. You have someone with a vision, who builds some features and does a launch. They might get an initial spike of traction, but when growth flattens, it’s not clear where to take things. They talk to some customers, ask what they want, and try again. They add a few more features, re-launch, and so the cycle goes on.

Do that too many times, and all of a sudden, you’re dead.

Why?

Above: If you build it, they may not come, it turns out. Better products, and more features, do not necessarily equal growth.

Many of the key levers for driving more user acquisition, retention, engagement, can sometimes sit outside the toolkit for most great product leaders. There’s a long laundry list of skills that are critical, but not often considered core to the product: adtech integrations, signup funnel A/B testing, optimizing notification delivery, testing price points, testing cohort curves, etc. Yes, occasionally there are people who know all of it – but they are rare!

Furthermore, no one individual can drive this. Instead, you need to bake this into your organizational goals and DNA. You need to collect these efforts within the larger framework of the company.

Above: Thus, we seek to build a framework for growth that’s a discipline and organizational structure within its own right.

We’ve come to see that “design thinking” and “agile engineering” are their own systems of organizational structures, workflows, philosophies, and skillsets. They are key to how we work within a company.

In the same way, we can build growth teams as a system too.

Above: Product Growth is the discipline of applying the scientific method to business KPIs.

It provides an underlying system for increasing metrics whether it’s revenue, acquisition, retention, engagement, or another key business metric.

Above: And just as you’d expect with the scientific method, the steps are build on understanding the data, creating hypotheses that identify why certain processes are happening, prioritizing those ideas, running the experiments, and then repeating the cycle.

That way, if you think your active user count is low, you can analyze the data to understand that you need more top of funnel user acquisition, then hypothesize that a combo of paid advertising and referrals can help, and then execute against that.

That is much, much better and more targeted than just building more features that your users ask for, and expecting growth to magically increase as a result. (Maybe you should build those features anyway, but don’t do it for growth!)

Above: Second topic. Let’s talk about the difference between the “Growth Hacker” – a term that Sean Ellis invented and I helped popularize – and a “Growth Team.” This is an important one.

Above: In the early stages of the growth skillset, there were no teams. There were a number of individuals and startup founders who were putting the necessary ideas, workflows, and tactics together. Some of these folks would refer to themselves as “growth hackers” in a tongue-in-cheek way.

As the skillset grew, it was clear that to do anything impactful, especially within the context of a larger/complex product, you needed to organize entire groups of people.

Above: Thus the growth team emerged, with the philosophy that you don’t want a lone genius with all the levers, and a team of helpers. Instead, you need to create an organization with a broad set of skills.

Growth is a team sport, and to run the scientific method on your KPIs, you need a lot of people who can help you.

Above: For most of the missions for a growth team, you need many different functional roles to help – from Product, Marketing, Engineering, Data, Ops, Finance, etc., etc. You combine all of these folks into individual teams and organize them together into a growth org.

Above: What are people doing within all of these roles?

• Growth PM: A product manager that’s responsible for the experiment roadmap
• Growth engineer: An engineer who’s focused on technical decisions and implementing experiments
• Growth marketer: A versatile marketer with an expertise in a given channel – from paid marketing to SEO to email to others
• Growth data: An analyst focused on creating insights – both macro on the user lifecycle, and micro, on specific experiments
• Growth design: A designer leading the UX, but with an emphasis on speed

You might also loop in other function – for example at Uber, a lot of decisions around incentive spend had to include folks from Finance or Pricing. And you’d always have to include Ops to think through how it affected things on the ground.

Above: Depending on the problem you’re trying to solve, you might have a different makeup on the team. For the new user experience – which might include increasing signup conversion, and maybe even integration into ads – you’d probably emphasize engineers. You’d want an Android and iOS engineers. Plus even performance marketing folks, some data analysts to look at the metrics, etc.

Above: If you were working on SEO, on the other hand, then maybe you wouldn’t need designers. This might be more about optimizing page structure, where the content goes, etc. In this case, you might emphasize SEO marketing, data, and a full-stack engineer for web.

Ultimately, the goal is to define the problem based on your insights and hypotheses, and staff the team to solve that particular problem. The individual teams might emphasize different skills, and the macro organizational structure of where the growth team fits has the some complexities depending on the missions of other teams.

Above: One common structure is to treat the Growth Team as a set of pods, each one matrixed to their respective functions. So you might have a Growth PM that reports into Product, plus the others, and all together they are the growth team. Many product teams look like this, and this is set up to match.

Alternatively, at Facebook and in an early incarnation of the Uber growth team, you have things set up more like a business unit. You have functions reporting into a GM, and the pods underneath. This has the advantages of creating a lot of independence within the team, with the complication that you split the various orgs – this can cause complexity and sometimes conflict as well.

Above: You can obviously pick and choose and have hybrid models as well.

Above: Too many startups are beginning with “I need a growth team!” and accepting a random org configuration, without thinking it through from the fundamentals. Ultimately, You have to start with the problems you are trying to solve. Begin with the KPIs, the insights you’ve generated, and then move onto execution. You staff the problem area and the type of execution you want. The organizational structure follows from there.

Above: This is a question I often get. Isn’t growth and marketing just the same thing? Isn’t growth and product just the same thing? Can’t everyone just be responsible for growth?

In this section, I’ll walk through some of the practical differences.

First, when it comes to Marketing and Growth, there are a lot of specialties that you want to solve:

• Brand marketing
• PR
• Events
• Content marketing
• Email
• SEO
• Paid marketing
• Viral/referral features
• New user experience
• User-to-user notifications
• etc

You could house all of these in a bunch of different configurations, but roughly speaking, you often have three categories of functions:

• Brand
• Growth marketing
• Growth product

It’s usually obvious that Brand ends up in Marketing. And similarly, things like NUX and product-generated notifications end up in a Growth team. But some of the middle levers, like SEO/Paid marketing/Email/etc, could potentially sit in either. I’ve seen both. Facebook has much of performance marketing sitting inside the Growth team. Uber started that way, but ended up having it all go into Marketing. There’s a lot of different possible configurations.

Above: If core product teams have engineers, designers, and PMs, and so do growth teams, what’s the difference? It’s all dependent on what they do. Product teams focus on creating core value. Enhancing product/market fit over time. This means obsessing over every little interaction in the core engagement loop – it’s a game of inches, and those inches count.

On the other hand, growth teams should focus on getting the core value out there to the world – getting as many folks as possible to experience that value.

There’s a middle ground on making users experience core value as frequently as possible – you could imagine putting that in either team, but if the solutions tend to be very iteratively/quantitatively-driven, then maybe put it in the Growth team.

Above: You also have to decide the ownership model. There are two extremes: Growth-as-a-Service and Autonomous. And everything in between.

In “Growth as a Service” – the team doesn’t technically own any feature or codebase. They jump into the highest value part of the product, do their analysis and optimizations, ship a bunch of improvements, and move on. It’s important for the team to be gentle, as they are the guests, but it’s also important that they stay lightweight. If the growth team ends up owning every piece of code they touch, then they would eventually get stuck in maintenance mode for everything.

On the other hand, a full ownership model means that the growth team could own the new user funnel, notifications, ad tech, the A/B testing platform, payment flows, and many other critical areas where numbers trump intuition. This can work, but then the team needs to be staffed properly.

Above: There are ultimately lots of pros and cons to each model. Uber went through the entire spectrum, but over time, came to own more and more pieces of the product. But you’ll have to decide based on your own constraints, org, and product requirements.

Once the growth team’s been set up, where should they focus? As discussed previously, their mission and toolkit ought to be distinct from those used by the marketing or product team. Especially in the early days of the team, there should be low-hanging fruit that can be picked off easily.

Although it’s easy to jump right into user acquisition, or looking at churn, let’s zoom out and look at the system. Let’s start with a prioritization framework.

Above: Ultimately there are three key things you’re trying to trade off – and one is particularly tricky:

• Effort. How much design/eng/marketing resources does it take to execute?
• Success. How likely will it be to be successful?
• Upside. This is the tricky one – but if it works, how much will it affect overall growth?

Every growth experiment is ultimately a prioritization based on the ranking of these three axes, and over time, your growth team will be smarter about how to pick. But I wanted to provide some notes on where a growth team is likely to go wrong in their prioritization.

The most common anti-pattern on picking growth projects is where a +50% increase on a feature touching 0.01% of users is celebrated, but a +5% increase that touches 50% of your active users feels smaller. Of course when you do the math, the latter is much more important as you ultimately want these bottoms up experiments to hit your top-down KPIs.

Another common anti-pattern is to focus on large effort, large upside projects over low effort, low/medium upside projects. Almost everyone overestimates their chances of success, so it’s better to go for more execution throughout over big bets… until you run out of easy ideas or you have enough resourcing to build a portfolio of small and big projects.

Some notes on each factor:

• In general, Effort is the easiest to understand. If you define a project, your team will be able to execute against it like anything else. The usual advice I give here is to bias towards low effort projects early on in a growth team
• Success rate can be controversial, because the things that work in growth are not necessarily things that users will self-report – and thus, people on teams will usually say, “I would never want this. I would never do this.” And yes, you implement the best practices and things work. The classic example here is the desire to add comprehensive content on landing pages, with links to a million other places. It’s a well understood design pattern to provide just as much information as is needed to get the signup – nothing more.
• Upside, of the three, is the trickiest thing to understand though. It’s also the lever with the most power, as it provides strong guidance on where in the product the growth team should be focusing.

Let’s do a deep dive on Upside.

Above: Upside is ultimately measured in absolute terms – how many additional subscribers did you gain, the number of signups generated, etc. You calculate it using two components – Reach and Impact. Reach is the measurement of how many end users are touched by the change of a feature. Impact is how much the metric moves as a result of the change.

Between these two factors, Impact tends to be the most random. Sometimes a change you’ll make moves things by +5% and sometimes it can move things +50%. In the main, you’ll get something in between for the vast majority of your projects. For some projects, impact can be huge if it’s a product experience that can happen multiple times – for example, a new highly-relevant notification that’s sent in the core engagement loop of a product. Or something that significantly amplifies a viral loop, causing the flywheel to spin faster and faster. (But that’s out of the norm- but also tells you that you might want to focus on these outsized impact cases)

Reach, on the other hand, is an amazing lever that is often misunderstood. This is often the sweet spot for understanding the best kinds of projects.

Above: In the main, most product teams focus on making their core product experience better, which benefits their core users. This has a lot of benefits – after all, they are the most engaged, the most valuable from a monetization perspective, and in a multi-sided platform, they produce the photos/content/sales/etc that sustain the rest of the ecosystem.

On the other hand, core users are often only a small % of your total active users.

Above: Depending on how you define core users, they are usually only 5-25% of your active user base. If you are looking at the segment of your userbase that actually produces content, rather than just consuming it, you’ll see it’s usually a small %. Or the ratio of your hardcore users who are generating a ton of content, versus purely passive consumers. It’s always a small amount.

As a result, if you have projects that can target your active users, but not your core ones, then you might have 4-20X more reach! Wow.

But that’s not all, there are more concentric circles.

Above: On average, only 10-50% of your registered users might be active in any given month. 50% is world-class good – like Facebook and their ilk. Usually most products are closer to 10-20% because the vast majority of products have a ton of one-hit wonders: People who try the product once, but then forget to ever come back.

Projects at this level ought to focus on activation. If you can understand what gets a user to become active, then you can introduce that during the onboarding flow, thus converting them to active or core users.

The other set of activities here – for products with large, established audiences – is the flow from being inactive/churned to coming back into the product. Are they getting relevant emails to get reactivated? If they’ve forgotten their password, are you optimizing that flow as critically as if it were a signup flow?

Above: Of course, for many products – and this is more of a web thing – there are people who look at a product but never sign up. Most landing pages might only have 10-50% conversion rate to signup! Furthermore, a lot of products have “side doors” – like Dropbox shared file links, or YouTube video pages – that get the majority of the traffic. Those become critical places to optimize.

Above: Of course, even bigger than all the people who have interacted with your product at all – even in a logged out state – there’s everyone in your primary acquisition channel (whether that’s on Facebook or Google or something else) that have never heard of you before. This is true top of funnel acquisition.

And of course, there are all the channels you haven’t even experimented with. That’s why adding a new channel – like trying a referral system when it doesn’t yet exist – can be such a big needle mover.

Above: All of this is to say that if you are looking for the biggest lever on growth upside, it’s probably in addressing  Reach. And think of the concentric circles when you are finding that your growth team’s projects collide with the core product team – move to further and further concentric circles, whether that’s targeting new users, churned users, and everyone out on the edge who hasn’t yet bought into your product.

The other fascinating exercise is to look through your existing features and roadmap and circle everything that touches non-users (or inactives) as opposed to active/core users. You’ll be surprised that there are generally very few.

The above was shared from Airbnb’s growth team who did exactly this exercise – only 6 items out of 33 were for non-users. (Shoutout to Gustaf, who ran their guest growth!) A growth team can rapidly expand this list and give some love to everyone out on the edges of the audience.

Above: Final topic. Let’s say that you as an individual are thinking about joining (or starting) a growth team at a company. What should you expect, and how might you evaluate the opportunity?

Above: There are a lot of organizational and cultural aspects that can get in the way of setting up a successful growth team. First, there’s leadership DNA – is there an understanding of what the growth team does? In particular within the Product and Marketing peers? Or is this something that’s being forced top-down by the CEO or board without leadership buy-in? It can get painful if people don’t fundamentally get the mission of the growth team.

Company culture is an important aspect too. If the culture is like Uber 1.0’s, where experimentation is encouraged – as long as it’s scoped to a city or two at a time, or as a 1% test – then that’s great. “Move fast and break things.” On the other hand, if the company is extremely design and brand conscious, it can be harder. Famously, Apple and Snap are two companies that rarely ran A/B tests until the recent era. In evaluating a company for experimentation, it’s good to understand how open folks are to a big change to the homepage, for instance, even if it’s 1%. Or in the new user flow.

The ownership model, as previously discussed, can either on a spectrum: SWAT team model, with little/no code ownership, or strong ownership of areas like NUX/notifications/adtech/etc. Both can work, just make sure you know what you’re getting into and that the staffing reflects it.

IMHO, the best case scenario, in my opinion, is to have a team that is:

• Bought into having a growth team, and knows how it compliments the existing functions
• Supports experimentation, even extreme as long as its tested with a small group
• Dedicated staffing that’s already in place, with a bias towards strong ownership on for everything outside of the active user base

The worst version, of course, is where people don’t really get why you have the growth team, there’s a ton of risk aversion on rapid experimentation, and no staff… just an expectation to run around and convince other teams to build your amazing ideas. That’s a recipe for failure.

Above: There are common lines of disagreement to implementing a growth team. Sometimes the incentives of a company are set up to reward large, complex projects (with codenames and executive oversight) rather than many lightweight changes that move the business along. This can get baked into everything from how projects are reviewed to perf review, to everything else.

Similarly, before starting a growth team, almost certainly there were also folks looking after growthy parts of the product. By moving those responsibilities away, or starting to encroach on “engagement” which overlaps with the core product team, there can be anti-bodies that make growth projects much, much slower.

Above: The foundation of the organization has to be ready to accept a growth team, and that starts with a fundamental understanding that the environment has changed:

• Growing tech products has changed, and the playbook has changed in the last decade
• Explicit headcount/roadmap has to be dedicated towards making growth happen – “build it and they will come” doesn’t work
• Creating a pipeline of growth experiments will need a different process. The scientific method as applied to KPIs. Not just a subset of marketing and product projects
• And finally, the team structure and skillsets to make this successful are different

As you might imagine, creating this foundation of mutual understanding is a big effort by itself. And y0u’ll need the help of your startup’s CEO, or your business unit’s GM, and the layer above them too. And all your peers.

Above: There are tactics to overcome the inevitable organizational friction you’ll hit. Here are a few of them.

OK, that’s all folks! Thanks for reading this far, and hope you enjoyed this deck

Growing startups and evaluating startups share common skills
Earlier this year, I joined Andreessen Horowitz as a General Partner, where I focus on a broad spectrum of consumer startups: marketplaces, entertainment/media, and social platforms. This was a big moment for me, and the result of a long relationship that began a decade ago, when Horowitz Andreessen Angel Fund funded a (now defunct) startup I had co-founded. One of the reasons I’ve been excited about being a professional investor is the ability to apply my skills as an operator. The same skills needed to grow new products can be used both to evaluate new startups to invest in, and once we’ve invested; to help them grow.

The reason for this is that the steps for starting and scaling a new startup share many of the same skills as investing in a new startup: 1) First, we seek to understand the existing state of customer growth – including growth loops, the quality of acquisition, engagement, churn, and monetization. 2) Then, to identify potential upside based learnings from within the company as well as across benchmarks from across industry. 3) And finally, to prioritize and make decisions that impact the future. Of course, as an investor you can’t run A/B tests or analyze results directly, but you can form hypotheses, ideate, and apply the same type of thinking.

As part of my interview process at a16z, I eventually put together an 80 slide deck on how to use growth ideas to evaluate startups. In the spirit that this perspective can help others in the ecosystem, and to share my thinking, I’m excited to publish the deck below.

Disclaimer: This was just one presentation in a 10 year relationship
But before I fully share, I have a disclaimer. This is one presentation I made within a series of dozens of meetings and interactions I had with the Andreessen Horowitz team. It was just one ingredient. I’ve been asked by friends and folks on the best path into venture capital. From my experience, it’s a long, windy experience – others have written about their processes as well.

My journey took a while too:

• 10 years in the Bay Area (and blogging, building my network, etc)
• Dozens of angel investments and advisory roles in SaaS, marketplaces, etc
• Once kicked off, 6 months of interviews (dinners, sitting in pitches, analyzing startups)
• 100+ hours of interviewing and prep

This deck was just one step, but one that I’m proud of, and want to show y’all.

The Deck

Above: I presented this deck as part of my interview to join Andreessen Horowitz to help demonstrate my expertise and “superpower” and how it might be used in an investing context.

As a result, it’s split into three sections:

• About me and my superpower
• How to apply user growth ideas in an investing context
• My continuing leadership in the field

Let’s get started!

Above: When I first arrived in the Bay area, if you had searched for “growth hacking” – you would have gotten zero results. It wasn’t a thing. Some early companies like Linkedin and Facebook had started the notion of “growth teams” but this wasn’t a widely understood set of ideas in the industry.

While there were people thinking about user acquisition and ad tech, and some early consumer teams (like Eric Ries’s IMVU) thinking about cohort curves to mention retention, it hadn’t been centralized into a team that could execute against it.

I started my blog originally to write down everything I was learning. My previous background up to that point was in user acquisition and ad tech, and I was making the pivot to consumer products. There was a lot to learn.

As I learned from the best in the industry – in particular from the PayPal mafia who had employed a metrics-driven viral approach to build some of their most iconic companies – I started to write about what we’d now call growth.

If you look at Google Trends, you’ll see that “growth hacking” all of a sudden became a term people in the industry were interested, and were searching for, in 2012.

There’s a reason for that. I’d like to take some credit :)

I was lucky with the right timing, the right content, and with inspiration from my friend Sean Ellis to be able to popularize the terminology and ideas around “growth hacking” in an essay I wrote in 2012.

And these days, it’s spread and become its own ecosystem.

Teams focusing on user growth have spun up across some of the best companies in the ecosystem!

(As of early 2018, when I had presented this, these were some of the companies that had growth titles or formal growth teams)

Of course “growth hacking” has changed a lot – it’s no longer about hacks as much as a much bigger umbrella as it’s become a more professionalized, formal function within a team.

One evolution is the number of books and conferences now dedicated to growth.

The other evolution in the ecosystem is that people are thinking about different things – about how to build growth teams, not just hacks. Thinking about new user experience, engagement metrics, and other important concepts.

I continue to contribute to this ecosystem by writing, being involved in social media, and press.

As part of that, as folks search for important concepts like “product market fit” and “user growth” – my essays are often on the front page. These are evergreen concepts and were relevant 5 years ago, relevant today, and will be important in the next phase of tech as well.

Beyond writing, I’ve also extended my efforts to bring together the high-end professional network of people working on startup growth. This hits a different part of my network as it’s a deeper relationship, and Bay Area focused, as opposed to my essays and social media which are global.

To accomplish this, I’ve been working with Brian Balfour (ex-VP growth from Hubspot) to start up Reforge which has educated 1000s of employees from top tech companies.

The flagship program on growth is 8 weeks and pulls together some of the foundational concepts.

The speakers include executives who run growth or related functions from across the industry. (Thank you to all the wonderful people who are involved with Reforge! Y’all are awesome and I’m happy to count you as my friends)

In the past few years, over 1500+ folks have attended the program from almost every company in the Bay Area and many F500 enterprises as well. This includes CEOs/founders, VPs, PMs, marketing folks, data science, engineers, and so on.

In the coming years, I want to stay as active as possible – to stay ahead of the curve by spending time with the smartest people from across industry, to bring communities together, and to continue to publish ideas. Establishing myself in the industry has taken a decade in the Bay Area and I intend to spend the next few decades at the same pace!

Next, let’s change gears. After all this talk about startup growth, how might you use this to evaluate new products in an investment context?

In this next section, I’ll present some of the central ideas in user growth and how you might use that to evaluate the quality of a startup’s growth as opposed to getting stuck on vanity metrics.

Above: To start, oftentimes you’ll find a new startup that presents their growth curve, which might look something like this – up and to the right! This is great. Time to invest, right?

The problem is, you don’t know where it’s going to go.

In the long run, over the course of an investment, you’ll find that this curve might go in a direction you may not want it to go – perhaps it’ll plateau. Perhaps it’ll even collapse. Or you may find that it’s going to continue going up, and even hockey-sticking.

How do you predict the future? Is it working and will it sustain? Will it even accelerate?

There’s a couple common frameworks to try to understand this, and one is the Growth Accounting Framework.

The Growth Accounting Framework looks something like this – within each time period (say a week, or a month) – you’ll add some users, reactivate some folks who had previously churned, and some go inactive. You add this up and it’s the “Net MAU” for a product – the difference between each time period.

If your positive terms (New+Reactivated) are smaller than your negative terms (the number who become Inactive) then you stop growing, and the whole thing goes negative.

Let’s look at each term in isolation.

The New+Reactivated term tends to look linear or be an S-curve. The reason is that it’s really really hard to scale acquisition – only a few, like viral loops, paid marketing, and SEO can bring you to millions or tens of millions of users. And as the acquisition channel gets bigger, it tends to get less effective. Ads become more expensive to buy, viral loops end up saturating your target market, etc. This term dominates.

Reactivation tends to be hard to control. If someone quits your product, emailing them a bunch of times probably won’t help. (But if you have a network, something like photo-tagging or @mentions might!). But most products don’t have a network, and as a result, the acquisition term tends to be much bigger than the reactivation one.

Above: The Inactive curve is also an S-curve, but it lags acquisition. It’s simple to understand why, which is that until you have a base of active users, you can’t really churn. You can’t churn anyone when you have zero users. So it goes up over time. So usually your acquisition curve pushes you up, and then churn starts.

At the moment that your New+Reactivated is equal to your Inactive users, each time period, then you hit peak MAUs. This is the thing to watch for, because then it’s all flat or down from there.

I use MAUs in this example but you could also use active subscribers, or users who have bought something in the past 30 days, or some other definition. The underlying physics are the same.

If you’re following all of this, it’s already a pretty profound insight. We’ve moved from looking at a single curve that might have been growing and decomposed it into its underlying terms, and shown how a curve that’s been going up and to the right for a while might go flat the next month. And why. That’s important.

But there’s a problem.

The problem is that the Growth Accounting Framework provides for lagging metrics. It’s hard to predict the future. It’s the equivalent at looking at company’s current year P&L and its constituent parts – it’s useful, but not enough. It’s hard to be predictive. It’s also hard to be actionable for product teams.

That’s why for the growth and product teams I’ve advised over the years, this isn’t something you can look at every day or every week. It’s not helpful.

Instead, you need leading indicators and a more predictive conceptual model.

Above: To do this, I advocate that we look at two key loops:

• Acquisition loops, which power the positive term for New
• Engagement loops, which power the negative terms on Reactivation and Inactive

Understanding these underlying loops is the key to the whole problem of predicting where a graph is going to go.

In understanding these loops, I don’t mean to simply chart them out in a spreadsheet. I mean to think about the quality of the loops – how defensible and proprietary are they? How scalable and repeatable? Is there upside in optimizing them or adding to them further?

In other words, we want to understand the quality of the user growth. If we understand that, we can forecast into the future as opposed to looking backwards.

To start, let’s look at the Acquisition Loop.

Above: There’s 4 sections of content we’ll go through- first, to understand the examples, then what metrics to examine. Then to look at how to best improve the loops. And finally, we’ll try to apply the framework.

Let’s start with examples.

Above: The key thing to ask for the Acquisition Loop is to understand how a cohort of new users leads to another set of new users. If you can get that going, then by a conceptual proof by induction, you’ll be able to show how it scales.

Importantly, these loops are flows within the product that are created on top of pre-existing, large platforms. Sometimes the loops are built because they are bought – via Ads. Sometimes they are built via API integrations, to allow for easier/faster sharing. And sometimes it’s via a partnership.

Let me talk through some examples.

A product like Yelp or Houzz fundamentally is a UGC SEO driven loop. New users find content through Google, a small % of them generate more content, which then gets indexed by Google, and then the loop repeats. Reddit is also like this. So is Glassdoor. And so on.

Paid marketing is also an obvious loop. Spend money, sell products, take the money and buy more ads. Keep going.

Above: Viral loops are important because they are extremely scalable, free, and don’t require a formal partnership. This is based on users directly or indirectly sharing a product with their friends/colleagues, and having that loop repeat itself.

The important point here is that loops aren’t just conceptual, but you can actually measure their efficiency as well. If you can get 1000 users to invite and sign up +600 of their friends, then you have a ratio of 0.6. But that’s just in the first cycle of the loop. But then those 600 new users generate 0.6*600=360 new users, who then generate 216, and so on, until the entire cohort is +1500 signups total from a base of 1000. Wow! That’s meaningful because then for every user you get through other means, you’re amplifying their effect.

This can be particularly important when you have a large paid marketing budget, because it can drive down your cost of acquisition by blending in a scalable form of organic. It can be a huge advantage.

Above: What about PR, conferences, in-house content marketing, etc.? Aren’t they important? Yes, they can be- but they don’t scale. For example, conferences happen irregularly, have poor ROI/attribution tracking, and every dollar made from a conference can’t quickly be reinvested. Contrast that to paid marketing, which can be highly accountable, become very optimized, and can scale to $1B+ spend/year.

So when it comes to PR, conferences, partnerships, etc. – they’re useful, but they are more like one-off opportunities, and certainly not where the bulk of your customer acquisition takes place. Instead, you use them to drive traffic into your loop, which then gets amplified.

As a result of this model of linear channels versus loops, when you are meeting a company for the first time, you have a framework to understand if their growth will scale over time or not. If it’s a one-time launch, like they just got announced as part of the latest YC batch, well that’s not a loop.

If they have been quiet on PR, conferences, etc., but users are telling each other as part of the native functionality of the product – okay then you have my attention!

Once you understand the loop, you have you understand if there’s upside. Is it possible to improve the loop? Maybe it sucks now, but maybe it can be fixed? Or even better, maybe there’s a product growing like gangbusters but you could accelerate even further.

To understand this, you have to move out of spreadsheet world and get into product experiences.

The first move is to decompose the simplified loops we were looking and actually get into the details.

Above: Instead of just 4 steps, as shown before, now we go even more tactical. Of course new users will have to land on the app store page, then sign up. They have to mobile verify. They have to go to a certain screen on the product, then add something to their cart – hypothetically. And so on. Each step is friction. Each step drives down performance.

We ought to be able to look at every single one of these steps and improve them further.

Let’s dive into one example, which is the app store screen.

On the app store screen – and this is a real example – there’s reviews. There’s a star rating. The bounce rate on the app store screen can often be very high, sometimes 50-80%.

In 2016, the star rating on Uber’s rider app was low. 1.7 stars, in fact. Ouch.

There were a lot of reasons for this, but on fundamental issue was that only unhappy riders were rating the app. It’s a common best practice to ask a broad spectrum of users to rate your app, and the Uber app wasn’t doing that. This was controversial because there was some desire to “cherry pick” only happy riders, for fear that the rating might stay low.

Nevertheless, the best practice was implemented and shipped.

Here’s what it looked like- after a trip, regardless of what the rider rated their trip experience, it would ask the rider to rate the app. And very quickly, the 10s of millions of users who had happy, successful trips weighed in. Quickly things moved from 1.7 stars to over 4.7 stars, where it still sits today.

A change like this is worth on the order of millions of incremental downloads for Uber. It’s a small change, but had a lot of upside. (Congrats to the Rider Growth team for shipping this! Miss you guys!)

Let’s look at another example- having all of your users verify their phone numbers. You’ve done this a million times.

It turns out, having people verify their numbers is a high friction step and oftentimes, there’s a 10-40% dropoff rate just on this screen. It might be because your phone number was entered incorrectly. Maybe you’re international – an important use case for travel-oriented apps like Uber. There’s a whole series of updates you can make to improve this step – from partnering with carriers, allowing a voice call to verify, and so on.

One more example on creating upside – which is on the back part of the paid marketing loop, when a new user clicks on an ad and lands into the product. The landing page they see is important.

And it’s so important, years later, they all look the same.

There’s a reason why so many landing pages are just signup forms. Not a ton of information about the product, not a lot of frills- just an ask to sign up. The reason for this is that after years of testing, this is what performs best when you are invited by a friend.

So if I see a startup that doesn’t directly ask for the signup, I assume there’s upside that can be gained.

These landing pages – often the first experience of a new user – are super important because the bounce rates are often over 80%. Wow. That’s almost everyone! So there’s a playbook of common changes you can make – from removing friction, pre-filling fields, adding video, optimizing for everything being above the fold, etc.

OK, we’re done with the examples. Now once you understand the upside, let’s say you want to dig into the data. What KPIs do you look at, and what are you looking for?

Above: The first thing to ask for is the product’s Acquisition Mix. This is a look at signups broken down by channels/loops and by time period (ideally weeks). I’m looking for signals that the dominant channel(s) are proprietary and repeatable. Ideally they are loops. I want low platform risk, where there isn’t a dependency on a larger company that might change their mind. (I.e., Instagram, Google SEO, etc.). A good mix might be 33/33/33 where you have a third organic, plus two loops, like viral and SEO.

The red flags I look for are around new channels appearing, but which aren’t sustainable. Especially ad spend that comes and goes, indicating maybe everything’s been juiced for before the fundraise. I don’t love to see spikes for that reason.

But a signup isn’t always a signup – thus it’s important to understand the quality of a signup.

A startup shouldn’t care much about signups, they should care about how well they translate into paying customers, or active users, or whatever an “activated user” looks like. It turns out that one of the biggest determinants of “quality” of new users is the source of the user. As a result, you want to understand both how signups are being generated by various channels, via the Acquisition Mix report above, but also a sense of the quality by understanding the activation rate by channel.

The red flags here are a bunch of new users from a new channel that’s actually low quality. Or a doubling down on a new low-quality channel just to pump up the signup numbers. After all, a spike of new users count into whatever month’s MAU metric that they joined under, and it’s an easy way to juice their short-term MAU. Watch for that.

The other aspect to analyze is the concentration of new users from different sources. Perhaps a particular channel/loop dominates but seems brittle or is expensive. If all the users have come from beta users list or Product Hunt, that won’t scale over time.

On the other hand, if marketing spend and product efforts are going towards high-quality channels, that’s fantastic.

Above: As noted before, loops are usually build on top of another platform. Sometimes that’s Google SEO, email systems, Instagram, or more.

If the startup’s new product adds value to the underlying platform, and isn’t too horizontal, it might be stable. There might be a strategy to become a destination product in itself. That’d be great. But that’s often not the case.

The red flags here are focused on the integrations between the growing product and its platform- if it’s built on iOS and one of the core integrations is push notifications (like the recent live quiz apps), then look at the clickthrough rate trend for the notifications. If it’s decreasing over time, then you know it’s not working. Or on a per user basis, perhaps the average user is tapping through on the first push but isn’t engaging much with the fifth. Or perhaps the underlying platform is shrinking. If you built a product that depended on AOL Instant Messenger to thrive, that’s not a smart bet.

It’s important to understand the underlying platform of any acquisition loop because things can collapse quickly.

One cautionary tale is what happened with Branchout, which was trying to build a Linkedin on top of Facebook Platform. You can see how fast it grew – to 14 million Daily Active Users, and how it was 1/10 the size just 4 months later. You don’t want to invest at its peak.

Once you understand the acquisition loop concept, can forecast the upside, and have metrics to look at to evaluate quality- then it’s time to go back to our original challenge: The up-and-to-the-right graph.

OK so does this go up, or not?

The key here is to ignore the graph, and instead use all the tools we discussed to create a baseline forecast on the engagement and user growth. Do the signups stay linear? Grow as a percentage over time? Or go flat?

Above: Using our understanding of the potential product improvements, we ought to be able to create a bottoms up roadmap of all the improvements. We can use our expertise to understand when changes might be a +5% and when they might be a +20%. Combine all of it together, and you get a picture of the upside.

Once you have all of this together, then you ought to be able to create a series of scenarios on where your growth curves are going to go. Perhaps you can assume the product and marketing teams execute aggressively, and capture all the upside you saw. Or perhaps you can assume there’s no engineering help, and it’s just a matter of adding a few new advertising channels. All of these scenarios can be combined to create a new curve. This is your forecast. It’s a prediction of the future.

If you did all of this, you’d still have a major problem. Your prediction would suck, because you only looked at one half of the problem. The other side is Engagement, and all the loops there.

There’s an Engagement Loop, similar to what we looked at with the Acquisition Loop. Let’s take a look there.

Above: We’ll go through the same format. First examples, then how to improve, then how to measure, and then let’s bring it together and apply it.

Above: The key question with engagement is similar to the one we asked on acquisition. If you have a network-based product, like Dropbox or Slack, then you need active users to engage each other. If it’s purely a utility, then you want engagement in one time period to help set up engagement in a future time period.

Let’s run through some examples.

In an engagement loop that’s based on social feedback, you get a game of ping pong. One user messages/follows/mentions another, and they draw them back. And then that user might do the same, and draw in a different user. And this repeats. This is why achieving network density and easy content creation is so important- you need ways to bring people back into the network.

On the other hand, there are engagement loops that are more like planting seeds. If you sign up for Zillow and put in your home address, and favorite a couple new real estate listings, then Zillow will start re-engaging you with personalized emails. Sometimes it’ll be when your house goes up in value, other times it’ll be when new listings show up in your neighborhoods. Credit Karma is the same, where a single setup session leads to important notifications about credit score changes over time.

These are just two engagement loops, and there are many more.

Another fun one is rideshare, where seeing physical on-the-street reminders of the product might prompt you to use it too. Mapping works in a similar way, often starting with a real-life trigger of “I’m lost!”

Just like the acquisition loop, there are linear channels to re-engage users. These are useful, of course, but again, they don’t scale. It’s better when users re-engage each other or when users re-engage themselves.

This is part of why marketing-driven one-off email campaigns are often ineffective. They don’t scale, aren’t interesting to users, and with enough volume, can cause folks to churn. Not good.

It’s much better to see a natural engagement loop that leverages push notifications and email in a way that’s user-initiated.

In the same way we analyzed acquisition loops to understand upside, we can do so for engagement loops.

The first step is to break down the loop into much smaller, more granular steps.

Above: Here, we’ve taken a Social Feedback loop that starts with a user creating content and publishing, to their friends viewing, adding comments, and then the notification back to the original user.

Now let’s zoom in on a particular step.

Above: The social feedback loop fundamentally is built on the content creation step. If it’s not easy, then it won’t work. So it has to be an activity that a lot of users want to do. That’s why taking a photo, typing in a text, or hitting a heart are all so effective. They’re dead simple actions.

Above: Pinterest has many examples where they’ve optimized content creation – or more specifically, more pinning/repinning per new signed up user. One method is to use the term “Save” as opposed to the more wonky term “Pin it.” Another is to up-sell the mobile app where it’s easy to interact. Education during onboarding helps too. All of these changes doubled the activation rate for new users, causing them to repin more, kicking off engagement loops for themselves and other users.

Once you create content, then you need to circulate it within your network.

One key aspect of every network is the density of connections. It’s important to build the number of connections up, but they have to be relevant. And there’s diminishing returns too.

A decade+ into the social platform paradigm, there’s now a playbook for how to do this. Let’s cover some of these ideas.

Above: An important way to build a social graph is to bootstrap on an existing network. For consumer products, that might be your phone’s addressbook or Facebook. Within the enterprise, it might be your colleagues’ emails in ActiveDirectory or GSuite or your work email. There’s tactics like asking people to “Find Friends” and to build “People You May Know” features to increase density.

The red flags here are folks who claim to have explosive viral growth just based on inviting. It won’t last, and they’ll be low quality signups. Similarly, if the core activity is all inviting and friending and there’s no main activity, that’s not good either. Better to let those ones go.

As a final examination of looking for upside in user engagement, it’s important think about an otherwise innocuous step- your users clicking on a notification, trying to get back into your product, but perhaps they’ve logged out.

How bad can it be to get logged out?

Turns out, being logged out and failing your password attempts can become a huge drag for established products with large audiences. It’s common for 50-75% of signed up users to actually be inactive – that is, the majority of your users will have tried the product but never get hooked.

The problem is when those inactive users come back, perhaps because of a notification or some other reason, and try to log back in. They often are locked, can’t remember their password, and become permanently inactive. Not great. The solution is manifold – first to treat this flow seriously, with KPIs and optimizations. There’s tactical things, like integrating into iCloud keychain, logging in with other apps if you have a multi-app strategy, and so on.

A company like Uber might literally see tens of millions of failed sign in attempts. Amazing. And perhaps a good percentage of those riders are trying to log back in, standing at an airport wanting to take a trip, and eventually, in frustration, they walk across the street and grab a cab. It’s worth fixing.

Now that we have the conceptual idea of an engagement loop set, and understand potential upsides, let’s dig into the metrics. What should we look for?

Above: The first, as everyone knows, is to look at everything in cohorts. We want to understand conceptually why the user cohorts are being brought back – is there value being created at each visit that makes the product more sticky over time? Are they building a network? We want to understand the classic D1/D7/D30 metrics – for which there are many comps – and also look at the month to month numbers.

There are a couple key things to watch for: The cohort curves need to flatten. Ideally >20%, so that each signup activates into a sticky, active user over time. If only 5% of users stick, then you’d have to sign up 2B users to get 100M MAUs. Not tenable.

You can project out the total size of the company with this, by combining TAM with the cohort % you have left after a year (D365 or D730) and then the ARPU. This needs to be big enough to have venture scale.

Above: One of the key tools for the engagement loop is the use of notifications – whether that’s email, push notifications, or some other on-platform channel. They are easy to be abused.

To detect artificial engagement that’s being manufactured, not organically created by users, you can look at a breakdown of every notification that a product sends out. And the volume and CTRs over time. You should do a quick spam check on Reddit, Twitter, Google, and other places.

Ultimately, the right attitude towards notifications is that they accelerate engagement that’s already there – you can’t make it out of thin air. Some products naturally generate a lot of notifications, and others don’t. Some are higher CTR than others.

Above: This is one push notification chart I’ve used in the past. Ecommerce companies often use push to advertise sales- no wonder the CTRs are low. But if you are looking at ridesharing, you’ll probably interact with the push because you want to make sure your car is here!

Another set of metrics we want to understand on user engagement is frequency of use. Almost every product I’ve seen has a “ladder of engagement” where you come for one use case, but ultimately become stickier and higher frequency by adding use cases.

For Uber, riders would often do their first trip because of travel use cases, like getting to the airport – this is a 2 trips/year activity. Then they’d layer on “going out” – like dinners on the weekend, which might be 1 trip/week. And eventually a number of other use cases until they got to commuting, which could be 2 trips/day.

What I want to understand with a Frequency diagram is to segment high- and low- frequency segments, and start digging into their usage of the product. If you can upsell new use cases, then there’s a ton of upside.

Now that we have all the tools, we can build the forecast.

The prior forecast on the acquisition loops can plug into this, because each cohort starts with the number of new users who have been acquired. We can then use the cohort retention curves to build curves that translate to monthly actives or customers.

We can forecast MAUs once we have both the acquisition and engagement curves. Project that out a few quarters, and you can get a fine-grained understanding of where MAUs will be in 2 years.

Engagement metrics are very hard to move compared to Acquisition. As a result, it’s better to assume the curves are what they are. But if you must add a bullish forecast, the right way to go is to focus on new user activation. And up-selling users from one frequency segment into the other. That’s the quantitative way to do it.

And so there we have it!

We have the engagement loop, and the acquisition. We have forecasts for each. We have upside scenarios.

So what can we do with this?

This whole discussion started with the Growth Accounting Framework. If we have a deep understanding of both acquisition and engagement, then we have the inputs.

With the inputs, we can build scenarios that model the outputs.

We can get a granular sense of the risks involved. Ultimately this is about a forecast that’s about the quality of acquisition, and the quality of engagement, not a single number in 2 years.

Startups aren’t spreadsheets.

With all of this, we can answer the questions that matter. If a startup walks in the door, and shows a graph, we can have a real discussion of what might happen next.

OK, and that was it. (I chopped off a couple slides off the end since it’s more self-promotion – you got the meat of it!)

The epilogue
One month after I presented this deck, I got the offer to join a16z! So it worked. 10 years in the bay area, dozens of angel investments, 6 months of interviewing, culminating in my new role.

For all of you read this far – thank you! Hope you enjoyed this deck and essay. If you have feedback, shoot me a tweet: @andrewchen.

Thank you
Also, special shoutout to Brian Balfour, Shaun Clowes, Casey Winters, Bubba Murarka, and Aatif Awan who helped me at various points in iterating the content here. Couldn’t have done it without you guys! Appreciate your help on this.

The consumerization and developerization of B2B
Dropbox is the fastest SaaS company to $1B in revenue run rate with 600+ million users. This is just an example showing that companies are adopting software in a completely different way in recent years – we have individual users/developers picking out products that they want to use, and then it eventually spreads inside the organization.

This is the engine that powers Dropbox, Slack, Asana, and many other new companies. It brings together all the growth levers: Viral growth, performance advertising, consumer growth techniques – but also inbound marketing, enterprise sales, etc., etc.. It’s a great trend that brings together folks with consumery backgrounds (like myself!) and my colleague Martin Casado (prev Nicira, acquired by VMWare).

There’s a spectrum that goes from Atlassian (all self-serve, no enterprise sales team) all the way to a traditional enterprise company like Oracle. Startups have to choose where they want to play, and what organization they want to build. A lot of interesting nuances here.

a16z Podcast
Today, I want to share a new podcast on When Organic Growth Goes Enterprise – this is a podcast that includes Martin and myself, with DocSend CEO and co-founder Russ Heddleston, in conversation with Hanne Tidnam.

(I’ve previously been interviewed on the Andreessen Horowitz podcast – you can subscribe here. My previous one was a two-part series on the basics of thinking about growth, from acquisition to engagement.)

Topics
Questions we talk about:

• What exactly does more bottoms up growth for enterprise look like?
• How does organic growth map into the direct sales model we traditionally see in enterprise?
• How does it affect company building overall?
• What changes in how we evaluate growth
• How can those two different models work best together?

Transcript

Dear readers,
It was my pleasure to be on my first ever Andreessen Horowitz podcast! if you haven’t checked it out, you can subscribe here. I’ve linked to the Soundcloud and included a transcript below.

In the podcast, we cover a broad overview of growth/marketing topics, including:

• The natural “gravity” that slows down high-growth businesses
• What’s really happening beneath the surface of exponential growth curves
• Organic, paid marketing, and LTV/CAC
• Why blended CAC numbers are misleading
• Why offline products are so compelling for acquiring customers
• Cohort analysis and looking for “smile curves”
• The Power User Curve aka L28
• Why onboarding is so important for retention/churn
• Phases of growth- why early companies focus on acquisition, but big companies focus on churn
• High frequency versus episodic usage products
• Why adding lots of spammy email notifications decreases your DAU/MAU
• Network effects and why different products’ network effects are different from each other
• Why Google measures many short sessions, versus other products focus on long sessions

Hope you enjoy it!

And thank you to my colleagues Sonal and Jeff for making this happen :)

Andrew
Palo Alto, CA

Part 1: User Acquisition

Hi everyone welcome to the a16z Podcast, I’m Sonal. Today’s episode is all about growth, one of the most top of mind questions for entrepreneurs — of all kinds of startups, and especially for consumer ones.

So joining to have this conversation, we have a16z general partners Andrew Chen and Jeff Jordan. And we cover everything from the basics of growth and defining key metrics to know, to the nuances of paid vs organic marketing and the role of network effects and more.

Part one of this conversation focuses specifically on the aspect of user acquisition for growth, and then we cut off and go into the aspects of growth for user engagement and retention, in the next episode. But first, we begin by going beyond the concept of growth hacks — and beginning with the fundamental premise that businesses do not grow themselves…

Sonal: So the topic we wanted to talk about today is growth, which is a big topic. What would you say are the biggest myths and misconceptions that entrepreneurs have about growth?

Andrew: You know, not only is there the misconception that it happens magically, then the next layer I think is that it’s really just like, oh, a series of, you know, tips and tricks and growth hacks that kind of keep things going as opposed to like a really rigorous understanding of, you know, how to think about growth not just, as kind of the top line thing but actually that there’s acquisition, that there’s engagement, that there’s retention, and each one of those pieces is very different than the other and you have to like tackle them systematically.

Jeff: It is a scientific discipline, done right, because it requires you to understand your business and business dynamics at this incredibly micro level.  

Sonal: I love that you said that because one of the complaints I’ve heard about “growth hacking” is that it’s just marketing by a different name, and what I’m really hearing you guys say is that there’s a systemic point of view, there’s rigor to it, there’s stages, there’s a program you build out.

Jeff: If you’re fortunate enough to achieve product-market fit and your business starts to take off, typically, you know, when in the wonderful situation do you get this hyper growth where you’ll grow year over year, you know, it’s triple digits. It’s just exploding. And then gradually the law of the large numbers starts to kick in and maybe the 100% growth becomes 50% growth the next year, and then the law of large numbers continue to kick in and there’s 25% and then it’s 12.5% and so growth tends to decay over time even in the best businesses. And so the–  

Sonal: — Didn’t you use to call it like “gravity”?  

Jeff: I called it gravity, you just would…it comes down to earth. And then the job of the entrepreneur is to be looking years down the road and say, “Okay, at some point growth in business A is going to stop and so I want to keep it going as long as I can and there’s a whole bunch of tactics to do that,” but then the other tactic, the other strategies, okay, I need new layers on the cake of growth.

At eBay the original business was an auction business in the U.S. and so, you know, some of the things we layered on early days we layered on fixed price in the U.S. — it’s not revolutionary but it really did grow then we went international. And then we layered in payment integration and each time we did that the total growth of the company would actually accelerate which is very hard to do at scale.  

Sonal: That’s the whole point… like there’s intentionality to it. It’s not an accident that you guys introduce new businesses, new layers on the cake.

Jeff: Businesses don’t grow themselves, the entrepreneur has to grow them. And, you know, occasionally, you stumble into a business that seems to almost grow itself but they’re just aren’t many of those in the world and that growth almost never persists for long periods of time unless the entrepreneur can figure out how to continue its growth.  

Sonal: Right. I remember a post you wrote actually a few years ago on “The ‘Oh, Shit Moment!’ When Growth Stops” because people are a little blindsided by it.  

Jeff: And that’s the flip side of it. You know, early on you get this great growth, you had to keep it going. When it stops your strategic options had been constrained dramatically.

Andrew: A lot of times when you’re looking at what seemingly is an exponential growth curve. In fact, it’s really something like, oh, you’re opening in a bunch of new markets, right, so there’s sort of a linear line there, but then you’re also introducing products at the same time and you’re also reducing friction and, you know, sign-ups or retention or whatever, and so, the whole combination of those things is really kind of like a whole series of accelerating pieces that looks like it’s, you know, this amazing viral growth curve. But it’s actually like so much work underneath.  <Sonal: Right.> You know, that makes that happen.

Sonal: I’ve also heard you [Andrew] talk about, being able to distinguish what is specifically driving that growth, so you don’t have this like sort of exponential-looking curve without knowing what that lever that you’re pulling to make that happen or knowing what’s happening even if it’s kind of happening naturally or organically. Can we break down some of the key metrics that are often used in these discussions including just what the definitions are and maybe just talk through how to think about them?  

Andrew: Right. Yeah, so when you look at a large aggregate number like, you know, total monthly active users, right, or you’re looking at like —   

Sonal: — “MAUs”  

Andrew: –Yeah, MAUs, right. Or you’re looking at, you know, the GMV like all the…adding up all the transactions in your marketplace–

Sonal: — So, “gross merchandise value”.

Andrew: Yup. And so, you know, when you look at something like that and if it’s going up or down, you don’t have the levers at that level to really understand like what’s really going on. You want to go a couple levels even deeper: How many new customers are you adding? As you’re growing more and more new customers, a bunch of things happen. If you’re using paid advertisement channels, things tend to get more expensive over time because — you know, your initially super, super excited core demographic of customers — like they’re gonna convert the best and as you start reaching into different geographies, different kinds of demos, all of a sudden they’re not gonna convert as well, right?

Sonal: Just to pause in that for a quick moment, you’re basically arguing that growth itself halts growth in that context.

Andrew: Right. Yeah. So the law of large numbers means that you know there’s only a fixed number of humans on the planet, there’s only a fixed number of people that are in your core demographic, right? Once you surpass a certain point, it’s not like it’s it falls off a cliff, it’s just more gradual that you know that the customer behavior really changes.  

Sonal: How do you determine what’s what when you don’t have product-market fit? Sometimes aren’t these metrics ways to figure that out or is this all when you have product-market fit… like is there a pre- and a post- difference between these?

Andrew: Very concretely, you want to understand how much of the acquisition is coming from purely organic (people discovering it, people talking to each other), as opposed to, oftentimes you’ll run into the companies that have over 50% of their acquisition coming from paid marketing and that tells you something that you’re, you know, needing to spend that much money to get people in the door.  

Sonal: Yeah. So CAC, “customer acquisition cost”, that’s what you’re talking about when you talk about acquisition.

Jeff: CAC is what it cost to acquire a user, “blended CAC” is what it costs to acquire a user on a paid basis plus then also what free users you acquire. So if you’re acquiring half your users through paid marketing you’re paying a $100 to acquire a user but half of your users are coming at zero, paid CAC is 100, blended CAC is 50.

I think blended time is a really dangerous number. Most of the best businesses in the internet age of technology haven’t spent a ton on paid acquisition. And so the truly magical businesses, you know, a lot of them aren’t buying tons of users… Amazon’s key marketing right now is free shipping. And then, yeah, the economics of paid acquisition tend to degrade overtime.  

Sonal: As it grows.

Jeff: As it grows and you just try to scale it and, you know, largely you’re cherrypicking the best users and then you’re trying to also scale the number you get to grow. I need twice as many new users this year as last year and you typically pay more so that magical LTV to CAC ratio which early on says, “Oh, we are three to one, you know, in two years it’ll probably be one and a half to one if you’re lucky,” or something like that. So we typically do try to look for these other sources of acquisition be it viral, be it, you know, some other form of non paid <crosstalk>

Sonal: I want to quickly define LTV — it’s “lifetime value” of the customer, but what does that mean?  

Jeff: When you’re showing an LTV to CAC ratio you have no idea of what you’re seeing essentially given all the potential variations of the numbers. So we will almost always go for clarity. LTV, lifetime value, should be the profits, the contribution from that user after all direct costs.

Sonal: How do we define the LTV to CAC ratio? What do the two of them in conjunction mean?  

Jeff: Well, let’s break them down. LTV is lifetime value. What you’re describing there is the incremental profit contribution for a user over the projected life of that user. So not revenue per CAC is that you know typically there’s cost associated to user. What’s the incremental contribution that the user brought from that <crosstalk> <Sonal: And that you mean the user brought to your company’s value.> To the company, yeah.

Sonal: So it’s a value of your customer to the bottom line?  

Jeff: It’s the value of each customer to the bottom line, and then you compare that to the CAC or “cost of acquired customer” to understand the leverage you have between what I need to spend to acquire a customer and how much they’re worth. If your CAC is higher than your LTV you’re sunk. Because it’s costing you more to acquire a user…

Sonal: Than the value you get out of it. Now I get it.

Jeff: …then you’re going to get out of that user.

Sonal: Yeah.  

Jeff: If it’s the opposite, at least you’re in the game. You know, I get more profit out of the user than I get the cost to acquire that user. And then there’s this dynamics on how does it scale over time, CAC tends to go up, LTV tends to go down. Because you’re, on the CAC side, you’re acquiring the less interested users over time. So they cost more to acquire and they’re worth less, and so that the LTV to CAC ratio, in our experience, almost always degrades as over time with scale.

And so, you know, when you’re in that conversation, you’re in a very specific conversation of, “Okay, how much room do you have?” “How is it gonna scale?” “You know, what’s gonna impact your CAC like a competitive thing?” So there has to be a lot, it had to be like 10 to kind of get you over that concern that oh, my goodness, those two were so close, that you have no margin for error.

Sonal: Right. This also goes back to the big picture, the layers on the cake, because if you have other layers you don’t have to only worry about one layer CAC to LTV ratio.  

Jeff: It really does affect the calculation. If it’s, I’m in a new business, and I have a whole different CAC versus, you know, LTV ratio then that’s a different conversation as well.

Sonal: And the big picture there, is that if you don’t know the difference of what’s doing what when you may get very mistaken signals, mixed signals about your business, and so you guys don’t want blended CAC because you want to know what’s driving the growth.

Andrew: I think what blended CAC gives you is it gives you a sense for at this particular moment in time, you know, what’s happening. The challenge is that when it comes to paid marketing, in particular, it’s easy to just add way more budget and a scale that than it is to scale organic or to scale SEO. So your CAC is giving you a snapshot, but then as you’re trying to scale the business you’re trying to increase everything by 100% over the next, you’re trying to double everything then all of a sudden, you know, your blended CAC starts to approach whatever your dominant channel actually looks like.  

And so if you’re spending a bunch of money then it’ll just approach whatever is your paid marketing, you know, CAC. What entrepreneurs should think about is what is the unique organic new thing that’s gonna get it in front of people, without spending a bunch of money, right?

Jeff: A lot of the best businesses have this very interesting, I’ll call it a growth hack. I mean OpenTable, when I was managing it, did not pay any money at all to acquire consumers. Like how can you do that? You know, it had millions of consumers. The restaurants would mark it OpenTable on our behalf.  

Sonal: Right.

Jeff: They go to The Slanted Door website like when they were an OpenTable customer and you’d see, you’re looking…you go there to try to get the phone number to make a reservation and they’d say, “Oh, make an online reservation.” And we then got paid to acquire that user in its core form. But that hack was a wonderful thing. It scaled with the business and got us tons of free users.

Sonal: To be fair, and this is another definition we should tease apart really quickly before we move on to more metrics, that also had a quality of network effects which we’ve talked a lot about in terms of these things growing more valuable to more people that use it… is that growth? What’s the difference there?  

Jeff: Well, the business grew into the network effect. The key tactic to build the network effect was that free acquisition of consumers that the more restaurants we had, the more attractive it was to consumers the more consumers who came, the more attractive it was to restaurants. So there is a wicked network effect.

Sonal: Like a flywheel effect, right.

Jeff: If you’re not spending anything on paid acquisition of consumers, how do you start it? And the placements that OpenTable got in the restaurant book both physically in the restaurant but particularly in the restaurant’s website was the key engine that got the network effect started. You had to manually sell some restaurants come for the tools, stay for the network, but then once the consumers got enough of a selection and started to use it, it was game over.  

Sonal: Right, that was one way of going around the bootstrapping or the chicken-egg problem and seeding a network.

Andrew: Network effects have…there’s a lot of really positive things about them and one of the big pieces is that virality is a form of like something that you get with the network. You know, the larger your network is, the more surface area, the more opportunities you have in order to encounter it, right. And so, you know, in the case of Uber (where I was recently), by seeing all the cars with the Uber logo like those are all opportunities to be like, “Oh, what is this app? I should try it out.” And so it’s mutually reinforcing: then you get more riders and then you get more drivers that are into it and so, I think all of that kind of plays together.

Jeff: I’ll bring two examples up, the pink Lyft mustache when I first got to San Francisco.  

Sonal: I remember that.

Jeff: You can see it once in the car and you’d go, “Oh, that’s pretty weird.” You see it twice in the car and you say, “Something is going on here that I don’t know about, and I have to understand what it is.” Lime is the same kind of thing.

Sonal: Right.

Jeff: They’re bright green and they glow essentially. So when someone sees one in the wild, someone bolts by them in a glowing green electric scooter and you’re just like, “Okay…what is that?” And Lime hasn’t spent a penny on consumer acquisition. <Sonal: Right.> Because their model is such that physical cue in the real world leads to it.  

Andrew: The other one I’ll throw in as well is within workplace enterprise products there’s a lot of kind of bottoms-up virality that comes out of people, you know, kind of sharing and collaborating.  

Sonal: Like with Slack.

Andrew: Yeah, like for example Slack is a great, it’s an example of this. And so, these are all kind of really unique ways that you can, you know, get acquisition for free. And so then your CAC is, you know, “zero” as a result.

Sonal: Yeah.

You guys have talked a lot, about organic. It makes it sound to me as a layperson that you don’t want paid marketing! Like what’s your views on this — is it a bad thing, is it a good thing; I don’t mean to moralize it but — help me unpack more where it’s helpful and where it’s not. Are they any rules-of-thumb to use there?

Jeff: I mean a lot of great businesses that have leveraged paid marketing. The OTA sites (online travel agencies – Priceline and Expedia) just spends, you know, they spend a GDP of many large countries in their acquisition; and then it’s often a tactic in some good business. But if it’s your primary engine, a couple of things happen: One is it tends…the acquisition economics tend to degrade over time for the reason we’re saying…  <Sonal: Right this is…> And it leaves you wide open to competition.

Sonal: It gets commoditized basically.

Jeff: If you need to buy users, I mean if you’re selling, you know, the new breed of mattress and you need to buy users and early on, you’re the only person competing for that word, flas-hforward a year or two, they’re like six new age mattress manufacturers with virtually identical products competing for the same consumer. The economics are not going to persist over time. And so, you know, one of the key questions in businesses driven by heavy user acquisition is how does the play end? You know, it usually looks pretty good at the beginning of the play but in the middle it’s starts getting a little complex and there’s tragedies at the ends.  

Sonal: There’s literally an arc.

Andrew: And I think, you know, if it is something that you’re using in conjunction with a bunch of other channels and you’re kind of accelerating things, that can be great. For example when Facebook in the past broke into new markets they started with paid marketing to get it going. And so in a case like that really paid marketing is a tactic to kind of get a network affect jumpstarted right? <Sonal: Gotcha.> And then you can kind of like pull off from that if you’d like. <Sonal: Right.>

Andrew: But if you’re super, super dependent on it and you don’t have a plan for a world that you know all the channels atregonna degrade [in] then you’re gonna be in a tough spot in a couple of years.  

Sonal: Totally. Do you have sort of a heuristic for when to stop the paid? Is there like a tipping point, you know, THIS is when you move?

Andrew: I think in terms of how much paid should you do as part of your portfolio, I think that’s the right way to think of it is it’s one out of a bunch of different channels, right? And so I would argue the following: First is you really have to measure the CAC and the LTV and be super disciplined about not spending ahead of where you want it to be and not to do it on some, you know, blended number that doesn’t make any sense. <Sonal: Right.> And then I think the other part is you really want it to be kind of a small enough minority of your channels. Such that if you were to get to a point where it turns out to be capped that you’re okay, that you can live with that.  

Sonal: Your business will survive and you continue to grow and be healthy.  

Andrew: Right, exactly, and you can still get the growth rates you want and you can still, you have such strong product-market fit that you’re able to maintain that.

Jeff: Take a couple of sector examples. You know, ecommerce, a lot of companies struggle with, “Okay, how do I get organic ecommerce traffic?” So most ecommerce companies rely heavily on paid user acquisition, you know, typically one of the interesting things is they degrade over time and they’re all competing for the same user. It’s hard for ecommerce companies in most segments to be profitable and you’d look at the same kinds of dynamic and restaurants delivery. You know, if you can’t differentiate yourself and you’re highly reliant on paid marketing, the movie typically doesn’t end really great, and so, we look for segments where there’s a balance or they come up with that really unique growth hack and they’re not then reliant on paid channels.

And then by the way, paid channels can degrade too. I mean, I’d made a couple of investment mistakes where the paid acquisitions looked really good and then actually what they were doing is they’re arbitraging something like Facebook’s early mobile attempts where the people who participated with Facebook mobile ads early got real deals. They were nowhere near kind of the price they should have been trending at, so you’re like, “Look at these user analytics. They’re awesome!” And then Facebook, you know, kind of got the equilibrium when supply and demand met and the cost went up multiples, and those businesses that looked so good early just got incredibly stressed because they had no alternative to that inflation.  

Sonal: That’s the case of platform risk where you’re dependent on the channel of on Facebook mobile or whatever the specific channel was there. But Andrew, you were also earlier talking about just a cap on how much is possible, and you both referenced the fact that things can become very competitive, that your competitors can also buy the same channels and then it gets very crowded or very expensive. So there’s multiple layers of the risk of the paid is what I’m hearing, but you have to be aware of that.

Andrew: Yeah. So I think on the acquisition side today, there’s a couple of really interesting opportunities that might be, you know, temporal, right, and like it may go away, right? <Sonal: Like, anything that crosstalk> For example, I think that if you have a product that is very highly visual, and I think this is, you know, one of the reasons why eSports has gotten so huge is because you have a product that naturally generates a ton of video in an age which all the platforms are trying to rush to video.  

Sonal: That’s fascinating.  

Andrew: Right? And so, you know, maybe this will be less of an opportunity coming up but like, you know, that’s a thing.  

Sonal: Why would you say that’s temporal because it seems like…  

Andrew: Because the competition will…

Sonal: …Do the same thing?

Andrew: …Yeah, will do the same thing, right. I think we’re now gonna move to a thing where all of these different kind of software experiences all are incredibly sharable. Like there’s no point these days in building a new game that doesn’t have like built-in recording and publishing the Twitch stream. And built-in tournament systems and all the community features and all that stuff that you need and, you know, I think it used to be that you would think of a game is just the actual IP but in fact, it’s sort of these layers and layers and like social interaction and content around it. And I think that’s about true as well as, all of these different brick-and-mortar experiences that are making themselves highly Instagrammable, they are adding the areas where you actually stand there and pose…  

Sonal: Oh, my god, my favorite story about this is the restaurant trend of making square plates and layouts so it really fit beautifully with Instagram. That’s like one of my favorite cases; that’s one of my favorite things in the world is when the physical world adapts to the digital!

Andrew: And then you can go the other way too which is, physical products like scooters that remind you to engage digitally. The other, fun example I always like is everyone’s had the experience now where they’re just like in the room talking and then their Amazon Echo just turns on and it’s trying to go and I’m like, you know, they have no incentive to fix that. <Sonal: Yeah.> Because it reminds you that it’s there and reminds you to talk to it.

I think the big takeaway here is that you have to really be creative and really be on the edge of what everyone’s doing, right? And so if it turns out that everyone’s really into video and they’re really into Instagram right now, you have to think about like how does my product actually fit into that trend? <Sonal: Yeah.> And if you can find it, then you can get an amazing killer way to get jumpstarted and if the trend lasts then great, accelerate it with paid marketing, accelerate it with PR, do all that stuff to kind of keep it going.

I also want to make the distinction that we’re mostly talking about growth and acquisition.

Sonal: Yesss!

Andrew: And that is what startups mostly care about in the early days, because you don’t really have any active users, right? But the other part of this is that you see all the users would show up and how active they are starts to change over time… <overlap/crosstalk>

Sonal: <overlap/crosstalk>…The engagement. Well, thank you guys for joining the a16z Podcast.

Part 2: Engagement and Retention

Hi everyone welcome to the a16z Podcast, I’m Sonal. Today’s episode continues our series on growth — the first part covered the basics of user acquisition — and so this part covers, more specifically, engagement and retention. Including, as always, key metrics and how to think about them.

Joining us to have this conversation, we again have general partners Andrew Chen and Jeff Jordan. And we cover everything from how do network effects come in to is there really a magic number or aha moment for a product? To who are the power users and what is the power user curve for measuring them. But first, we begin with what happens after the initial acquisition phase, as different kinds of users join a product or platform over time — what does that mean for engagement; and how do you analyze them, using cohort analyses?

Andrew: One of the things that you see is that people end up using these products very differently. Because the kinds of users that you’re getting are changing over time. When you look at something like rideshare, you know, all the early cohorts are basically people in urban areas. And in these days all of rideshare is more like suburban or rural folks because you’ve saturated all of the center. And so what you tend to see is as you acquire your folks, your core demographic out that actually ends up showing up in the engagement.

And so, you know, going back to a natural “gravity” to the whole thing [discussed in episode one], this gravity also hits the engagement side of things as well — and then ultimately the LTV because your users were typically getting less valuable. I may take years to see this kind of play out but that’s kind of the natural law of things.

Jeff: There is a progression in these and particularly the ones that are really successful. Early on it’s all about getting users. <Sonal: Right.>  
And it’s just like users, users, users. If you’re widely successful at doing that you run out of users (or you start running low on users) and you have to go to engagement. So Pinterest has a very high-quality problem right now. Most women in America, have downloaded the Pinterest app.

Sonal: Oh yah, I’ve had it for years.

Jeff: Some growth can come through, okay, there’s some number of women who never heard of Pinterest somewhere in the country. But much more so they need to engage and re-engage the existing audience. I mean, we love engagement from an investor standpoint because it’s just, you know, that [crosstalk]  

Sonal: [crosstalk] It shows stickiness.  

Jeff: You can often hack your way into new users. It’s really hard to hack your way into true engagement. <Sonal: Keeping them.> Someone is spending 20 minutes a day on your site. Offerup, Pinterest the major investment thesis was, “Oh, my God!” look at that engagement … And, you know, if they can scale the userbase it’s a beautiful thing.

Sonal: Right. What we mean by engagement is actually interacting with them and seeing their activity. Because to Andrew’s three points of acquisition, engagement, retention, the third piece is keeping them.

Andrew: The way that we’ll often analyze this is looking at cohort analysis.

Sonal: Yesss.

Andrew: Where we’ll look at kind of each batch of users that’s joining in each week and really start to dissect like well, how active are they really and to compare all these cohorts over time. You’re basically putting the users that come in from a particular timeframe, let’s say it’s a week, and you’re putting them into a bucket, right? And what you’re doing is you want to compare all of these different buckets against each other.

And so what you typically do is you look at a bucket of a cohort of users and you say, “Okay, well, you know, once they’ve signed up the week after, how active are they?” And what about the week after that and the week after that and you kind of like can build out a curve. And it just turns out that these curves once you’ve looked at enough of them surprisingly, human nature, they all look kind of the same. They kind of all kind of curve down and for the good ones they start to flatten out and plateau and then, for the really good ones they’ll actually swing back up and people will come back to the surface. What you want to do is you want to compare the various cohorts against each other in time to see if you can spot any trends on how the usage patterns are, increasing or decreasing. When you add a new layer to a layer cake, you might unlock a bunch of new behavior. You might unlock a bunch of new frequency that didn’t exist before. Or alternatively, over long thresholds of time, people tend to become less active as you move out of your cohort segment.  

Sonal: The cohort graduates.

Andrew: Whether or not a specific cohort of users flattens out is really important, right? Because, you know, if you’re in a world where they kind of slowly degrads and then all of a sudden it’ll actually go to zero, that means that you’re always kind of filling up the bucket — You have a leaky bucket, you’re constantly filling it up.  

Sonal: You’re always filling it up. Right.

Andrew: Right, and what happens is that gets progressively harder because, if you want to keep your overall growth rate, because that means you have to double, triple, quadruple your acquisition in order to counteract for that.

One growth accounting equation that’s often thrown around is that you know your incremental — your net — MAUs, right? So your net monthly active users equals all the new people that you’re acquiring, minus all the people that are churned, and then plus all the people that you’re resurrecting…  

Sonal: …Re-engaging.

Andrew: Re-engaging, exactly, that are coming back after they’ve churned. And so what happens is for a new startup you are completely focused on new users because you don’t really have that many users to churn, and over time as you get bigger and bigger and bigger what you find is that your churn rate starts to — it’s a percentage of your actives.

And so the evolution of most of these companies as they’re getting bigger tends to start with acquisition, then focus much more on churn and retention, and then ultimately also to layer in resurrection as well.  

Jeff: And the cohort curves have a couple of other features that I love. Usually in marketplace businesses, the best models are built off of the cohort curves.  

Sonal: Interesting!  

Jeff: Because you have to understand that degradation and where it goes. Using cohorts really give you a sense of their network effects, and network effect is the business gets more valuable to more users that use it; if it gets more valuable, your newer cohorts should behave better than your early cohorts.

Sonal: Why is that?

Jeff: Because the service is more valuable given how they are.

Sonal: Interesting. So that’s kind of a tip–

Jeff: So in OpenTable if there’s ten times more restaurants you’re going to get a whole lot more reservations per diner because you were serving more of their needs. The OpenTable cores would climb up and get more attractive over time versus, you know, we talk about typically they tend to degrade over time. If you’ve reversed the polarity and they’re growing over time it means you’ve made the business more valuable. And then you start projecting forward. Okay [crosstalk]

Sonal: What a better way to know the business is actually more valuable than thinking it’s valuable and believing your own myth.  

Jeff: In a network effects businesses we always ask, show us the cohorts. Everyone is [inaudible] on network effect, I’m a network effect But, you know, when you say, “Show me the data, cohort curves, or [crosstalk].” They don’t like it.  

Sonal: It’s like show me the money, it’s now show us the cohorts, I get it.

Jeff: They don’t lie.

Andrew: The other really interesting part is segmenting it.

Sonal: I was about to actually ask you what are “the buckets” of cohorts? Are they all demographic data?

Andrew: For a bunch of hyper-local type businesses, the reason why segmenting it based on market geography, why that’s so valuable is because then you can compare markets against each other. You can say, “Well, you know, this market which is like, has much more density in terms of the numbers of scooters behaves like this.” And you can start to draw conclusions, sort of a natural A/B test in order to do that.

And I think the similar kind of analysis you can do for B2B companies is for products that have different sized teams using it. If you have a really large team that they are all using a product, well, are they all using the product more as a result? And let’s compare that to something that maybe only has a couple. … And so this way you can start to kind of disassemble the structure of these networks and do they actually lead to higher engagement.

Jeff: Slack would be a perfect example of that, you know, just if you have five people in the organization using Slack you get one use curve. If you have the organization it’s the operating system for the organization; you have a very different curve.

Sonal: Though it’s not just an accident, you have to sort of architect it, not just expect, like, serendipity to fall into place.

Andrew: So after you get the new users, the way that you have to think about it is around quality, right? You have to make sure that the new users turn into engaged users. One of the things people often talk about is just sort of this idea of like an “a-ha” moment or a magic moment where the user really understands the true value of the product. But often that involves a bunch of setup. So, for example, you know, for all the different social products (whether that’s Twitter or Facebook or Pinterest, etc.), you have to make sure that when you first bring a new user in, they have to follow all the right people. They have to get, you know…

Sonal: It’s like the onboarding experience.

Andrew: …which, by the way, isn’t just signing up but it’s actually doing all the things to get to this a-ha where you’re like, “Oh.”

Sonal: “I get this product.”

Andrew: I get this product. It’s for me, And once you get that, then they’re kind of, you know, then you have the opportunity to keep them in this engaged state over time.

Sonal: Is that really such a thing that there is, like, an a-ha moment? Or is it sort of like a cumulative… a lot of the later users on Facebook came because everyone else was already there. Is this only tied to new users?

Andrew: In the case of Facebook actually, the fact that everyone was already there makes the a-ha moment that much more powerful, right? Because all your friends and family, they’re already there; your feed’s already full of content. And the first time that you see photos that maybe, you know, someone that you went to high school with, right? That is like whoa.

Sonal: That’s actually what happened to me. I was so excited when I saw an old friend, right?

Andrew: Right. Yeah, exactly. And so what that means is, you get the product and then afterwards, when you actually are getting these push notifications or emails that are like, “Hey, it’s someone’s birthday,” or whatever, you’ve internalized what that product is. And, you know, this moment is different for all sorts of different companies.

Jeff: I’ve always heard this referred to as the magic number. When you show up and it’s a blank slate, it’s like, “What is this about?” But they would drive you maniacally to follow people because when you got to their magic number where they had statistically correlated the number of followers with long-term engagement and retention — they would kill you to get you there, doing what felt like unnatural acts of, like, you log on, follow, and you say no, and they say yes — but when they got you there, it kicked in, and the service then quote/unquote worked for you.

A lot of the entrepreneurs I work with are trying to figure out what is my magic moment that then creates the awareness of the value prop. So take the example of Pinterest. Pinterest when it goes into a new market, first of all, they figured out they need a lot of local content to make it compelling to local users. The U.S. corpus of images doesn’t necessarily…is helpful in international markets but isn’t sufficient. And so they needed to supplement…

Sonal: …You’re right. If I’m Indian, I want, like, saris. I don’t only want, like, skirts, which I may not be able to wear in certain regions.

Jeff: Yeah. Exactly. I haven’t worn a sari in North America in a long time ;) <team laughs> But then once you have the content set, then you have to get compelling information to that individual in front of them, which, you don’t know the individual when they walk in the door, the faster they do that, the more quickly, the better the business performs; engagement goes up; retention goes up; and it works. So different entrepreneurs had to figure out what is that…what experience do they want to deliver where people get it? And then how do you engineer your way into delivering it?

Sonal: Okay. So we’ve come up through acquisition and you’ve gotten new users. They get the product. You even hopefully have a way to measure that and see and track it over time. Do you want then go into trying to get different users? Do you take your existing users? One of the things that we covered very early on is that with SaaS, you always wanna try to take existing users and upsell them because it’s way more expensive to acquire a new customer in that context. (I mean, of course, you wanna grow your customers.) How does this play out in this context? What happens next?

Jeff: In a lot of companies, it’s a progression. So almost all the early activity in a company is, “Okay, how do I get the users?” As you get users, you get more and more leverage from efforts at activation and retention and engagement. So, I mean, use Pinterest as an example: again, a very high percentage of women in America have downloaded Pinterest. Then the leverage quickly goes into, “Okay, how do I keep them engaged? Reactivate the ones who disappear?” And their acquisition efforts in the U.S. get de-emphasized and all the leverage is there except as they’re going international, they’re still in that acquisition part of the curve. And so I think the leverage changes over time based on the situation of the company. Facebook hasn’t had any users in the U.S. in forever because they have them all.  

Sonal: This kind of goes back to this portfolio approach to thinking about your users.

Andrew: Once you have an active base of users and customers, what starts to get really interesting is to really analyze what are the things that actually set that group up to be successful really long-term sticky users versus what are the behaviors and profiles where users aren’t successful, right? You actually throw your data science team on it to figure out all the different weights for both behavioral as well as the demographic and sort of profile-based stuff on there. And so one of the first things that you figure out is that each one of these products actually has this ladder of engagement where oftentimes new users show up to do something that’s, valuable but potentially infrequent. And you need to actually level them up to something that happens all the time.

For example, when you first install Dropbox, the easiest thing that you can do is you can use it to just sync your home and your work computers, right? And that’s great but really the way to get those users to become really valuable is for them to share folders at work with their colleagues. Because once they have that and people are dragging files in, and they’re really starting to collaborate on things, that’s like the next level of value that you can actually have on a daily basis versus this thing that kind of is in the background that’s just syncing your storage.

Sonal: So what are some of the things that people can then do to move those users up that “ladder of engagement”?

Andrew: Step one is really segmenting your users into this kind of engagement map, oftentimes you’ll see this visualized as a kind of state machine where you have folks that are new, you have folks that are casual, and you want to track how much they’re moving up or down in each one of these steps.

And then once you have that, then the question is, okay, well, great, how do you actually get them to move from one place to the other? First there’s like content and education; they need to know in context that they can actually do something. So for example, if you can get your users to set their home and work for a transportation product then you can maybe figure out, okay, should I prompt them in the morning to try a ride based on what the ETAs are?

Sonal: Like in the app, there would be some kind of notification.

Andrew: Like lifecycle messaging kind of factors in there. The second is of course if your product has some kind of monetary component, then you can use incentives like $10 bucks off your next subscription if you do this behavior that we know for sure gets you to the next step. And then the third thing is really just like refining the product for that particular use case, maybe there are certain kinds of products that are transacted all the time and so you maybe want to waive fees or you give some credits or you do something in order to get people to get addicted to that as a thing.

Jeff: The really interesting thing is the frequency with which something is consumed. I mean, eBay had enormous levels of engagement early on for an ecommerce app in particular. People would spend hours just browsing because early on it was about collectibles and it was about people’s passion. So if someone’s passionate about Depression-era glass, they will spend hours if you give them that depth of content to say, “Oh, my God. I just found the perfect item.”

OpenTable and Airbnb are both typically much more episodic. Most people don’t dine at fine dining restaurants with high frequency; our median user dined twice a year on OpenTable. And so that has completely different marketing implications and user implications. Measurement is probably even more important in the engagement/ retention thing because we got our data scientist to understand the different consumer journeys through our product, and then we tried to develop tactics to accelerate the journeys we wanted and limit the journeys we didn’t want. But in order to develop your strategy, you really need to understand how your users are behaving at a really refined level.  

Sonal: So what are some of the engagement metrics?

Andrew: One really important area is frequency, like, just how often are you using the product regardless of the intensity and the length of the sessions and all that other stuff. Literally just frequency of sessions. We might often ask for a daily active user divided by monthly active user ratio, and that gives you a sense for how many days is a user active?

Jeff: DAU to MAU.

Sonal: You recently put a post out on the DAU/MAU metric.  

Andrew: Right.

Sonal: And when it works and when it doesn’t. There’s a lot of nuances around when to apply it and when not to.

Andrew: DAU/MAU was very much popularized by the fact that Facebook used it, including in their public financial statements, and it really makes sense for them because they’re an advertising business and it matters a lot that people use them a lot all the time.

Sonal: It’s like counting impressions and being able to sell that to advertisers.

Andrew: Exactly, their products have historically been 60% plus daily actives over monthly actives. And that’s very high. You’re using it more than half the days in a month. On the flip side, what I was talking about in my essay about this is that DAU/MAU can tell you if something’s really high frequency and if it’s working, but a lot of times products are actually lower DAU/MAU for a very good reason because there’s sort of just a natural cadence, you know, to the product. Like, you’re not gonna get somebody who is using a travel product to use it more than a couple times per year. And yet there are many valuable travel companies, obviously.  

Sonal: So you’re saying don’t live and die by that alone.

Andrew: Exactly.

Sonal: Because it really depends on product you have, the type of nature of use it has, etc.

Andrew: You just want to make sure that the metric reflects whatever strategy that you’re putting in place. If you think that your product is a daily use product and you’re gonna monetize using a little bit of money that you’re making over a long period of time but your DAU/MAU is low, is like sub 15%, then it’s probably not gonna work.

And then a metric called L28, which is something else that was pioneered certainly early at Facebook: It’s a histogram and what you want to do is —

Sonal: — A histogram is a frequency diagram.

Andrew: Right. A frequency diagram that basically says, okay, show a bar showing how many users have visited once in that month, then twice in the month, and then three times in the month, and then four times in the month. And you kind of build that all the way out to 28 days.  

Sonal: Because there’s 28 days in the month on average.  

Andrew: And the 28 days is to remove seasonality and then a related one obviously is like L7, right? So just like last seven days. And so what you want to see…

Sonal: So would this be WAUs (“wows”)? Weekly active users? Is that really a thing, by the way? Or am I just making that up?

Andrew: Right. WAUs, DAUs over WAUs.

Jeff: You just coined it.  

Sonal: I know. Great. I coined retainment. Why not?

Andrew: Right. And so the idea with L28 or an L7 is the idea that you should actually start to see when you graph this out that there’s a group of people who just use it 28 days out of 28 days, right? And that there’s a big group of people who use it 27 days out of 28 days, and that there’s a big cluster. And so that’s how you know that you actually have a hardcore segment. And that’s really important because in all these products you typically have a core part of the network that’s driving the rest of it, whether that’s power sellers or power buyers or, in a social network the creators vs. the consumers.

Jeff: I actually have heard this referred to as a smile because the one use is always pretty big. A lot of people show up once, “I don’t understand what this is,” and disappear… And then they typically slide down, more people use it…fewer people use it two days than one, three days than two. Done right, it starts to increase at the end. So you basically get a smile. [inaudible] And I mean, that’s really powerful. Facebook had a smile. WhatsApp had a smile. Instagram had a smile. If you take a step back, it’s a precondition for investing in a venture business essentially that there’s growth. If it’s end market [inaudible] you want to see growth, but growth by itself is not sufficient. Investors love engagement. So Pinterest, the key driver of Pinterest, it was growing but the users were using it maniacally.  

Sonal: Oh, my God. I think I spent an entire Thanksgiving using Pinterest.

Jeff: It was the engagement that blew my mind much more than the growth. OfferUp has engagement that’s similar to social sites like Instagram and Snap. I mean, a ecommerce site, you know, mobile classifieds, people just sit there and troll looking for bargains, looking for interesting things.

Sonal: It’s a little addictive to see what’s nearby that you can buy. Why not? Yeah.

Jeff: So DAU to MAU, smile, all these metrics are so core to us because a big engaged audience is so rare and, as a result, it’s almost always incredibly valuable.

Andrew: And the engagement ends up being very related to acquisition because when you look at all the different acquisition loops — whether it’s paid marketing or a viral loop or whatever — all of those things are actually powered by engagement ultimately. Like, you need people to get excited about a product in order to share content off of that platform to other platforms in order to get a viral loop going. And so one of the things I was gonna also add on DAU/MAU and L28 is that they’re actually really hard to game, right? Which is fascinating.  

Sonal: Yeah, why is that?

Jeff: [inaudible] growth can be very easy to game.

Andrew: Right, exactly.  

Sonal: Why is that? What’s the difference?

Andrew: The typical approach is to say, “Well, you know, I’m gonna add in email notifications. I’m gonna do more push notifications. I’m gonna do more of this and that.” And then automatically, you know, these metrics ought to go up, right? The challenging thing is actually usually sending out more notifications will actually cause more of your casual users to show up because your hardcore users were already kind of showing up already. And what that does is that’ll increase your monthly actives number but actually not increase your daily actives as much. So I’ve actually seen cases where sending out more email decreases your DAU/MAU as opposed to increasing it.

Sonal: That’s really interesting. When you think about this portfolio of metrics, it really tells you a story about people are kind of coming but not really staying–

Andrew: If you get an email or a push notification every day, eventually you turn them off, and then you just stop. So then you get counted as a MAU for that period of time and then you lose them as a DAU. Acquisition is super easy to game because you can just spend money.

Jeff: Or you’ve got a distribution hack that works. Early on in the Facebook platform, companies literally got to a million users and it felt like minutes. Just because there were so many people on Facebook and the ones who were early just got exploding user bases. There were a number of [inaudible] whose mean number of visits was one. They never came back. So you get to see these incredibly seductive growth curves but our job is essentially to be cynical and just say, okay, we need to go be it below that because there are a lot of talented growth hackers who can drive growth. I looked at a number of businesses that had tens of millions of users and no one ever came back. [inaudible]  

Sonal: This is why engagement is so, so key.

So we’ve talked especially about the fact that growth and network effects are not the exact same thing. Because network effects by definition are that a network becomes more valuable the more users that use it. What happens on the engagement side with network effects? What are the things we should be thinking about in that context?

Jeff: Typically network effects, if they’re real, manifest in data. Things like the cohort curves improve over time. Usually there’s a decay. With network effects, there often is a reversal where they’re growing because it’s more valuable. Another smile, essentially. My diligence at OpenTable was I looked at San Francisco, which was their first market, and sales rep productivity grew over time, restaurant churn decreased over time, the number of diners per restaurant increased over time, the percentage that went that booked through OpenTable versus the restaurant’s own website moved towards OpenTable dramatically. Every metric improved. And so, you know, that’s where it both drives good engagement, but also it just improves the investment thesis.

Sonal: The value overall, right?

Andrew: One of the interesting points about network effects is that we often talk about it as if it’s a binary thing.

Sonal: Right. Or homogenous, like all network effects are equal when they’re not.

Andrew: Exactly right. When you look at the data, what you really figure out is that network effect is actually like a curve, and it’s not like a binary yes/no kind of thing. So for example, [turns to Jeff] I would guess that if you plotted the number, if you took a bunch of cities, every city was a data point, and you graphed on one side the number of restaurants in the city versus the conversion rate for that city, you would quickly find that when cities have more restaurants, the conversion rate is higher, right? I’m just guessing.

Jeff: It’s actually almost perfect but with one refinement. The number of restaurants you have as a percent of that market’s restaurant universe; because having 100 restaurants in Des Moines is different than having 100 restaurants in Manhattan.

Andrew: Makes total sense. So not only that, what you then quickly figure out is that there’s some kind of a diminishing effect to these things often in many cases. So for example, in rideshare, if you are gonna get a car called 15 minutes versus 10 minutes, that’s very meaningful. But if it’s five minutes versus two minutes, your conversion rate doesn’t actually go up.

If you can express your network effect in this kind of a manner, then what you can start to show is, okay, yeah, we have a couple new investment markets that maybe don’t have as many restaurants or don’t have as many cars but if we put money into them and invest in them and build the right products, etc. then you can grow.

You can do this kind of same analysis whether you’re talking about YouTube channels and the number of subscribers you might have, having more videos is better; I’m sure you can show that. If you go into the workplace, and you start thinking about collaboration tools, then what you ought to see is that as more people use your chat platform or your collaborative document editing platform, then the engagement on that is gonna be higher. You should be able to show that in the data by comparing a whole bunch of different teams.

Sonal: Okay… So we’ve talked about engagement and also how it applies to network effects. Are engagement and retention the same thing? I mean, in all honesty, they sound like they would be the same thing.

Jeff: There’s overlap, but they’re different.  

Andrew: Yeah, there’s overlap, right. Just to give a couple exampleS: So weather is low frequency but high retention because you’re actually gonna need to know what the weather is… <Oh right!>

Sonal: Only once a day, unless you live in San Francisco and you gotta check it, like, 20 times a day with all the microclimates.

Andrew: Right, exactly.  

Jeff: Or if you live down here, you have to check it twice a year.

Sonal: That’s true, it’s actually the same year-round.

Andrew: That’s actually what it showed, was actually more that generally people didn’t really check it that often. However, you are highly likely to have it installed and running after 90 days because it’s a reference thing. You might need it.

Sonal: It’s so important, yeah.

Andrew: Like a calculator. Versus if you look at something like games or ebooks or those kinds of products, like Really high engagement because you’re like, “All right. I’m gonna get to…I’m gonna finish this like trashy science-fiction novel that I’ve been reading. I’m just gonna get to it.” But then as soon as you’re done, you’re like, “Okay, there’s no reason why I would go back and read it again.”

Sonal: So the real difference is that engagement obviously varies depending on the product, the type of thing it is, whether it’s weather or ebook, and retention is are you still using it after X amount of time.

Jeff: And different companies have different cadences. If the average user is twice a year, retention is did they book annually. Other businesses are, did they come daily? The model behind retention is completely different and the model behind engagement is completely different.

Andrew: The chart that I’d love to really see is one that was like a bunch of different categories that’s, you know, retention versus frequency versus monetization. I think you got to be, like, really good at least on one of those axes.

Sonal: So we’ve done sort of this taxonomy of metrics. We’ve talked about the acquisition metrics. We’ve talked about some engagement metrics, primarily frequency.

Jeff: On engagement, it’s also time. Not just how frequent someone is, but just how much time did they spend.

Sonal: Right. Time spent on site, on the… piece, writing comments, not just pageviews.

Jeff: Because, I mean, the number of businesses that have great engagement is not high. Because there are only so many minutes in the day. And so, you’re just looking for where, okay, they’re just passing time and enjoying, and they both have obvious monetization associated with that behavior.

Sonal: This is why Netflix is so freaking genius because when they literally invented the format of binge-watching, which you could not do — I love it because it’s a very internet native concept — I mean, they’ve literally fucked up everyone else’s engagement numbers.

Andrew: I think that’s one of the narratives on why building consumer products is much harder these days. Cuz–

Sonal: –And, do you think it’s true or not?

Andrew: Well, because it used to be. It used to be that you were…what kind of time were you competing for in the first couple years of the smartphone. [inaudible] you were competing against literally I’m gonna stare at the back of this person’s head, or I can like use some cool app that I downloaded, right? Versus these days you actually have to take minutes away from other products.

Sonal: Yes.

Jeff: And it’s typically other [?] because the top apps are almost all done by Facebook, Amazon, Google. And you know, breaking through jusT — Marc calls it the first page, the people who are on the first screen — are just such the incumbents. And sure, most people have Facebook on the screen and YouTube on the screen and Amazon on the screen.  

Sonal: It’s hard to take that down, right?  

Jeff: You have that competition. It is a big overhang right now in consumer investing because you have to displace someone’s minutes.

Sonal: Yeah. I would add one more layer to that, at least on the content side, which is I think a lot of people make a lot of category errors because they think they’re competing with like-minded players and, in fact, when it comes to things like content and attention, you’re competing with just about anything that grabs your attention. It’s not just other media outlets. It’s…

Andrew: …Tinder.

Sonal: It’s a dating app. It’s something else.

Jeff: I’m riding in the train for an hour, I could, you know, see what my friends are doing on Facebook, watch videos on YouTube.

Sonal: It actually changes with time blocks. Xerox PARC did a really interesting study on “micro-waiting moments” and they’re literally the little snatches of time, like two seconds here and there, that you might be waiting in line or doing something, so you can do a lot of snack-sized things in that period, which is also another interesting thing to think about for how people engage with various things.

Jeff: So it’s actually funny because there’s some businesses that have good engagement where it’s one session that goes on for a while, YouTube or Netflix or something like that. There are others that are multiple small sections that in aggregate…

Sonal: …Like a podcast which might not finish in one sitting.

Jeff: …Because it’s the micro-opportunities…

Andrew: …And Google is the best example of this, right? In fact if you spend a lot of time on Google.com, you know, refining your searches and clicking around, that means actually the service is doing poorly.

Jeff: They’ve failed. Their goal is to get you to their advertisers as fast as they can.

Andrew: That’s a frequency play and a monetization play ultimately as opposed to an engagement one.  

Sonal: Yes, that’s fascinating.

Andrew: And some products are more on the engagement side.

Sonal: So sometimes you have to optimize it based on how you’re monetizing. What are some of the metrics for retention? I mean, is it just should-I-stay-or-should-I-go? Is that the retention metric?

Andrew: I think the big thing is the concept of churn. Is a tricky one in some cases like subscription Hulu, Netflix, and then also in the SaaS world. Whether or not you’re still continuing to pay or not. And that’s really obvious.

The thing that’s tricky for a lot of these consumer products especially episodic ones — and, it’s actually less whether they’ve quote-unquote churned or not — it’s actually just whether or not they’re active or inactive, and whether or not that’s happening at a rate that you in your business strategy have decided is acceptable or not. If every Halloween, you know how there’s those costume stores that open all over the place. If every Halloween, you go back and you buy a costume, but you’re inactive the rest of the time, have you churned or not? It’s not clear and I would argue you’ve not churned because you’re doing exactly what they want, which is to buy a costume every Halloween.

Sonal: It seems like it smakes assessing the retention of a consumer business very difficult.

Jeff: You adjust the time period that you’re relevant on. If the average diner dines twice a year…

Sonal: …Then that’s your time frame.

Jeff: You can [inaudible] apply that metric. Travel’s a similar thing. Airbnb is for the average user relatively infrequent. You have to tailor your look to what are they trying to do, so if you’re trying to stake up with your friends and you’re doing it twice a year, yeah, that doesn’t work. So Facebook has got a whole different setup.

Andrew: One of the things that companies can often do is to measure upstream signal. So for example, Zillow, you’re probably not gonna buy a house very often. Maybe a couple times in your life. However, what’s really interesting is they can say, “Well, you know, maybe folks aren’t buying houses but at least are we top of mind? Are they checking the houses that are going on sale in their neighborhood? Are they opening up the emails? Are they doing searches?” Right?  

Sonal: Interesting. Why do you call that “upstream”?

Andrew: In the funnel. You’re kind of going up in the funnel and you’re tracking those metrics.

Sonal: I get it now!

Andrew: As opposed to, you know, purchases. So even for OpenTable, it’s like, okay, great. Well, maybe if you’re not actually completing the reservations, are you at least checking the app for availability?

Jeff: Or what’s new restaurants where I want to dine? There’s some level of content consumption.

Sonal: So throughout this entire episode, there seems to be this interesting “dance” between architecting and discovering. Like, you might know some things upfront because you’re trying to be intentional and build these things, and then there are things that you discover along the way as your product and your views and your data evolves. How do you advise people to sort of navigate that dance?

Jeff: You iterate. You develop hypotheses. You put it out there and you test the hypothesis. I think my product’s gonna behave this way. And then, did it?

Probably the most important thing is for me, marketing can be art, marketing could be science; in the consumer internet, it’s more science. Some companies can effectively do TV campaigns, large media budgets, things like that. For me, the better companies typically just rip apart their metrics, understand the dynamics of their business, and then figure out ways to improve the business through that knowledge. And that knowledge can feed back into new product executions or new marketing strategies or new something. It’s constant iteration but it’s informed by the data at a level that on the best companies is really, really deep.

Andrew: Ultimately, you have a set of strategies that you’re trying to pursue and you pick the metrics to validate that you’re on the right track, right? And a lot of what we’ve talked about today has really been the idea that actually there’s a lot of “nature versus nurture” kind of parts to this. Your product could just be low cadence but high monetization, and so you shouldn’t look at, you know, DAU/MAU. And so you have to find really the right set of metrics that show that you’re providing value to your customers first and foremost and then really build your team and your product roadmap and everything in order to reinforce that.

Find the loops and the networks that exist within your product because those are the things that are gonna keeps your engagement improving over time even in the face of competition.

Jeff: Growth is good. Growth and engagement is really really, really good. Sonal: That’s fabulous. Well, thank you, guys, for joining the a16z Podcast.  

Remember all the “Uber for x” startups?
A few years ago a ton of “Uber for x” startups got funded, but very few of them – maybe none? – worked out. It sounds good but ultimately most failed on the supply side. Let’s explore why.

Rideshare has better economics, at the same acquisition cost
Rideshare is special. Acquiring a broad base of labor for driving is expensive, often $300+. But then they can get requests all day. You can work 20 hours and even 50 hours a week if you want. You continually need the driver app to find new customers

Where a lot of “Uber for x” companies fall down – valet parking, car washing, massages, etc – is that demand is often infrequent and there’s spikes at a few points in the day. What’s your supply side supposed to do the rest of the time?

In other words, “Uber for x” cos often have the same cost of acquisition and cost of labor as rideshare, but can’t fill their time with work as smoothly / profitably

Marketplace outcomes are sensitive to unit economics
Rideshare networks are fickle and require a long period of being unit economic negative before they can break even, with enough scale/density. But a lot of “Uber for x” cos can never dig out of that hole, and stay unprofitable forever

This is one of the reasons why I’m bearish on food delivery as a stand-alone business in the long run. Uber can tap into their supply side and augment with food delivery earnings. Pure food companies have to get the same drivers but can’t pay as well

The key is to go for a different pool of workers
So what kind of “Uber for x” ideas can work? Ultimately the ones that go for a completely different pool of labor. Folks who prefer to work from home. People who don’t live near a city with rideshare. People who don’t own cars. Etc.

If you can find a different pool of labor, they still have the same motivations around flexible schedules and easy earning potential. You can use the same techniques as Uber – simple UX, transparent pricing, etc – and apply them to these marketplace opportunities

In that way, the lessons from “Uber for x” are a subset of best practices you can learn from marketplaces. You need a strong strategy to get the supply/demand flywheel going. A big market with a defensible moat. Fragmentation that can be solved w transparency and aggregation

Don’t emulate – approach from first principles, starting from the workers’ POV
IMHO “Uber for x” cos failed to become a thing because they sought to emulate ridesharing when they should have just approached their particular market from first principles. There’s still a ton of marketplace opportunities out there and am excited to see what people do!

Because all these marketplaces tend towards supply constrained, you should evaluate each opportunity/company from the POV of the supply side. Does it work for them? Can they do it 40 hours/week and stay sticky? When can you pull away subsidies? These are the key questions

The key lesson!
Supply side is 👑.

If you’re interested in more reading about Uber and marketplaces, I collected my favorite 20 links here

First published on Twitter here!

[Today we have an essay on one of the common frameworks we use to analyze investments at Andreessen Horowitz: The Power User Curve. I worked closely with Li Jin, a partner on the investing team, to collect our ideas into this essay which she wrote. You can follow @ljin18 on Twitter for more thoughts. -Andrew]

The importance of power users
Power users drive some of the most successful companies — people who love their product, are highly engaged, and contribute a ton of value to the network. In ecommerce marketplaces it’s power sellers, in ridesharing platforms it’s power riders, and in social networks it’s influencers.

All companies want more power users, but you need to measure them before you can find (and retain) them. While DAU/MAU — dividing daily active users (DAUs) by monthly active users (MAUs or monthly actives) — is a common metric for measuring engagement, it has its shortcomings.

Since companies need a richer and more nuanced way to understand user engagement, we’re going to introduce what we’ll call the “Power User Curve” — also commonly called the activity histogram or the “L30” (coined by the Facebook growth team). It’s a histogram of users’ engagement by the total number of days they were active in a month, from 1 day out of the month to all 30 (or 28, or 31) days. While typically reflecting top-level activity like app opens or logins, it can be customized for whatever action you decide is important to measure for your product.

The Power User Curve has a number of advantages over DAU/MAU:

• It shows if you have a hardcore, engaged segment that’s coming back every day.
• It shows the variability among your users: some are slightly engaged, whereas others are power users. Contrast this with DAU/MAU: it’s a single number and so blurs this variance.
• When mapped to cohorts, Power User Curves let you see if your engagement is getting better over time — which in turn helps assess product launches and performance of other feature changes.
• Power User Curves can be shown for different user actions, not just app opens. This matters if the core activity that matters for your product is deeper in the funnel.

In other words, while the DAU/MAU gives you a single number, the Power User Curve gives entrepreneurs several avenues of analysis to assess their product’s engagement to the most addicted users — in a single snapshot, over time, and also in relation to monetization. This is useful. So how does it work?

The Power User Curve will “smile” when things are good
The shape of the Power User Curve can be left-leaning or smile-like, all of which means different things. Here’s a smile:

The Power User Curve above is for a social product, and shows the characteristic smile shape that indicates there’s a group of highly engaged users using the app daily or nearly daily. Social products with frequent user engagement like this lend themselves well to monetization via ads—there’s enough users returning frequently that the impressions can support an ad business. Remember that Facebook would have a very right-leaning smile, with 60%+ of its MAUs coming back daily.

What matters is that, over time, the platform is able to retain and grow its power users: successive Power User Curves should ideally show users shifting over more to the right side of the smile. As the density of the network grow, and with stronger network effects, it’s expected that there’s more reason for users to return on a daily basis.

The Power User Curve can show when strong monetization is needed
Let’s look a different example, which doesn’t smile:

This Power User Curve of a professional networking product looks quite different than that of a social product. It’s left-weighted with a mode of just 1 day of activity per month, and decays rapidly after those few days. There’s no power users. But this light engagement can be okay — not every company needs to have a smile-shaped Power User Curve, just as not every product category necessarily lends itself to an ultra-high DAU/MAU.

When there’s low engagement, what matters is that the company has a way to extract enough value from users when they are engaged. Think about an investing product like Wealthfront or networks like LinkedIn — few users are likely to actively check it on a daily basis, but that’s ok, since they have business models that aren’t tied to daily usage.

CEOs of such companies should therefore,think about: Is there a way to create revenue streams where the business can still monetize effectively despite users’ infrequent engagement? Or, who are the users using this product more frequently, and how can I get more of them? Is there something about the product — e.g. onboarding, the core experience, etc. — where a significant chunk of the user base isn’t experiencing the ‘aha moment’ that makes them “get” the product, and therefore not getting value from it right now (and if so how to get there)?

Some products should be analyzed in a 7 day timeframe – like SaaS/productivity – and others on 30 days
Another flavor of the Power User Curve is a histogram of users’ engagement for a 7-day period, also commonly called L7. The 7 day Power User Curve shows weekly actives, not monthly actives. Plotting this version can make sense if your product naturally follows a weekly cycle, for instance, if it’s a productivity/work-related product that users engage with Monday through Friday. B2B SaaS products will often find it useful to show this version, as they want to drive usage during the work week.

Note that using DAU/MAU wouldn’t be the appropriate metric for this product as it’s not designed to be a daily use product. You can also see there’s actually a smile curve through 5 days, but fewer users are using it 6-7 days, which makes sense for the power users of a workweek product like this.

CEOs of such product companies should therefore want to understand: Who are the users engaging just 1 or 2 days each week? Are there certain teams or functions within an organization that are getting more value, and how can I build out features to capture the teams with less engagement? Or, if the product is really driving a lot of value for specific departments — how can I understand their needs better and make sure we continue building in a direction that supports their daily workflow (and that we can upsell new features)?

The trend of over time can show if the product is getting more engaging over time
Plotting the Power User Curve for different WAU or MAU cohorts can also be very insightful. Over time, you can see if more of your user base are becoming power users, by seeing the shift towards higher-frequency engagement.

Here’s an example:

The Power User Curve for MAU cohorts from August through November shows a positive shift in user engagement, where a larger segment of the population is becoming active on a daily basis, and there’s more of a smile curve.

You can see when the line starts to inflect in order to see when a critical product release or marketing effort might have started to bend the curve.  This might be a place to double down, to increase engagement. For a network effects product, you might expect to see newer cohorts gradually improve as you achieve network density/liquidity.

On an ongoing basis, you can measure the success of product changes or new releases by looking at different cohorts’ Power User Curves. If a product unblocks a bunch of features for power users, you might see a gradual increase in power users.

The Power User Curve can be based on core activity, not just app opens or logins
The frequency histogram can be keyed on actions beyond the visit — did someone show up or not — you can also go with deeper user actions. For instance, you may want to plot t