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My first book, The Cold Start Problem. Plus Clubhouse, and more. It’s 2021, and I’m back!

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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,


San Francisco, CA

Written by Andrew Chen

February 4th, 2021 at 8:48 am

Posted in Uncategorized

The Adjacent User Theory

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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.

Written by Matt Groff

July 20th, 2020 at 9:53 am

Posted in Uncategorized

My top essays/tweetstorms in 2019 on product/market fit, investing, KPIs, YouTubers, and more

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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:

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!

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

– 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 😎



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

Written by Andrew Chen

January 9th, 2020 at 9:06 am

Posted in Uncategorized

“Is your startup idea taken?” — and why we love X for Y startups

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☝️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.

Written by Andrew Chen

November 6th, 2019 at 8:00 am

Posted in Uncategorized

The Passion Economy (Guest essay by Li Jin)

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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.



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.

Written by Andrew Chen

November 4th, 2019 at 8:00 am

Posted in Uncategorized

10 lessons from a serial entrepreneur – Justin Kan, Atrium, YC, and Twitch

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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!



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.”


“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:

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!

Written by Andrew Chen

July 25th, 2019 at 11:19 am

Posted in Uncategorized

28 ways to grow supply in a marketplace — by Lenny Rachitsky, ex-Airbnb

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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.



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


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.


  • 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



  • 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


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


  • 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



  • 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



  • 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



  • 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



  • 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



  • 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



  • 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


  • 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


  • Airbnb: Crashpadder, Statthotel
  • Rover: DogBuddy, DogVacay
  • Eventbrite: Ticketfly, Picatic


  • 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



  • 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



  • 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



  • 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



  • 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



  • 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


  • 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


  • 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


  • 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



  • 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


  • 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


  • 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


  • An Airbnb host buying additional properties to manage.
  • A Hipcamp host adding additional campsites.
  • An Outschool teacher offering additional classes.


  • 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


  • 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


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



  • 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



  • 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


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



  • 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


  • Airbnb: Private homes, vacation rentals, luxury home.
  • Uber: Black cars, individual car owner, scooter.
  • eBay: Individual sellers, brick and mortar stores, wholesalers.


  • 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.



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. 🙏

Written by Andrew Chen

June 25th, 2019 at 8:00 am

Posted in Uncategorized

Why startups are hard — the math of venture capital returns tells the story

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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.



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

Written by Andrew Chen

June 3rd, 2019 at 10:54 am

Posted in Uncategorized

The Podcast Ecosystem in 2019 – a16z’s 68 page analysis

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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!




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 AllStartUpCrimetown, and others), acquired by Spotify in early 2019; and Wondery (Over My Dead BodyGeneration WhyDr. 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 BrainHow I Built ThisPlanet 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 Dailyshared 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 thisthis, 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 JohnDr. 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, ExplainedSean 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!

Written by Andrew Chen

May 28th, 2019 at 7:17 pm

Posted in Uncategorized

The Dumb Idea Paradox: Why great ideas often start out by sounding dumb.

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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?


Written by Andrew Chen

May 27th, 2019 at 8:49 am

Posted in Uncategorized

Announcing Pietra and a16z — my first ex-Uber investment!

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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.



Marketplaces, Pietra, and the Network Effects of Next Startup Talent

One of my focus areas as an investor is marketplaces, because I’ve seen firsthand how they can transform an industry — especially when they also have network effects that can lead to huge scale and impact. And while marketplaces have been evolving into new areas for a while — including services — I especially love how marketplaces show up in interesting and sometimes unexpected places, places where technology has not gone before.

One such area is jewelry (yes, jewelry!). Even though gemstones and jewelry have been at the center of art, commerce, and culture since the dawn of human civilization — going from stone jewelry created 40,000 years ago in Africa to the trade routes between East and West to Fifth Avenue in New York to the Instagram feed on your phone — the technology for discovering, designing, and purchasing jewelry online hasn’t evolved much at all. Yet jewelry is one of the categories that could benefit most from modern trends such as social media, mobile, and mass personalization. This is especially true for the incredible variety of artisans and boutique jewelry vendors out there who currently can’t access bigger markets, or the deep technology expertise and stacks of bigger players.

That’s why I’m excited to announce Andreessen Horowitz’ seed investment in Pietra, a new startup focused on a marketplace for the jewelry and especially the diamonds industry. If you wanted to buy a diamond engagement ring, the process goes something like this: “Do you know where I can buy a diamond?” “I might know a guy.” That “guy” (more often a family business, an aggregator, or other player) then sells you a diamond with very little transparency into supply, pricing, or other things. That kind of exchange is ripe for technology to come in between and mediate things — not only efficiently connecting suppliers to buyers, but also expanding supply and demand for both sellers and buyers beyond local limits.

Jewelry represents $200B+ of annual spend, but remains a highly fragmented and opaque market… it’s yet another way marketplace businesses can provide more transparency, variety, and even education for consumers. So Pietra aims to fully modernize the jewelry buying experience across every touchpoint by offering beautiful, mobile-first product discovery; chat and collaboration tools to better engage, negotiate, and purchase jewelry; and vetted suppliers, along with curated product lines from boutique jewelers, influencers, and celebrities.

The team comes with decades of deep expertise in fashion, luxury commerce, and marketplaces. Co-founders Ronak Trivedi and Pan Pan are two of my former colleagues from Uber, where they led key efforts on UberPOOL and grew it from a new product only available in San Francisco to a global product supporting hundreds of millions of trips per year and billions in gross bookings. That kind of scale matters in a market like this. In fact, many of the core marketplace lessons and mindsets from Uber — combined with the team’s experience in the jewelry industry, deep customer insights, and passion for design — led to their starting Pietra.

I’m also personally very excited about the new wave of “network mafias” coming from people trained at Bay Area startups who go on to do new and different things, often borrowing from lessons learned in their previous startups. Classic examples include Paypal, and more recent examples include Square and others. For Uber alumni in particular — which I can personally speak to since I worked there for three years — there are three mindsets that are compelling to me and that I love seeing in startup founders are: (1) an entrepreneurial mindset that’s baked in at all levels; (2) specialized expertise that can transfer across industries; and (3) technical challenges coupled with networks of talent.

Because rideshare grew city by city at Uber, it led to an entrepreneurial team structure where each city had a General Manager (GM) who served as the de facto CEO of the city, acting like a mini-startup in the context of the larger organization. Surge pricing and driver incentives were first manually implemented by local teams with SQL queries and spreadsheets, and only later widely implemented in code by the software teams at headquarters. When I first joined Uber, each product team was also set up to be full stack, without dependencies into other teams, allowing them to build fast and iterate quickly to solve challenges. This kind of mindset — everyone’s the CEO of their own mini-startup unit — is key to fast cycles of innovation.

To make rideshare work as a global product, folks at Uber had to solve challenges in areas as diverse as Jakarta to Portland to ridesharing and food delivery. Whether it was solving the cold-start problem in a new market, or figuring out the best pricing and incentives, or growing network effects in a highly competitive market, those insights can be translated to new industries. Starting any new company requires founders to turn a series of insights into actions and products.

To be clear, it’s not just marketplace expertise that’s important here — it’s also about solving deep technical challenges at scale in areas such as machine learning, data, infrastructure, mapping, automation, and much more. But the social aspect of the Uber alumni network is also appealing, with a rich ecosystem of folks advising and angel investing in companies, paying it forward and creating a new generation of startups.

I’ve said it before: technology changes, but people stay the same. Whether it’s applying new behaviors and technologies to evergreen things — like jewelry! — or the evergreen turnover of a new wave of entrepreneurs founding the next generations of startups (in developer APIs, video streaming, SaaS, etc.), I’m excited to see what everyone does next. And I’m looking forward to investing in more companies like Pietra.

Written by Andrew Chen

March 27th, 2019 at 10:18 am

Posted in Uncategorized

What do you look for an investment? How long should a founder be without salary? And other Q&A

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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?




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?

These are the obvious offenders:

  • Cumulative charts for anything. These can only go up and to the right
  • Registered users. Totally useless, although sometimes I like to ask about this as a ratio to active users to get a sense for how efficiently the user acquisition is happening
  • Any retention metrics that aren’t standardized into cohort curves. Sometimes people will give a single snapshot number, like a “3 months later, X% still use the app!” and that’s not that helpful
  • Install numbers, without signups or activated signups or something more meaningful
  • For marketplace companies, “revenue” that’s actually “gross bookings” or GMV. Or GMV that counts in weird things, like security deposits or one-time setup charges
  • ARR meaning, “annual revenue run rate” as opposed to “annual recurring revenue.” Please, let’s just stick to ARR for recurring, not run rate. Thanks.
  • Taking the peak revenue of any single day and annualizing it as the headline number
  • Unlabeled X and Y axes in charts
  • Cohort curves that are some complex subset of users that make the retention look better
  • Showing “CAC” that’s actually blended CAC, and when you just look at the Paid CAC, it’s way above LTV
  • Actually LTV. Because who really cares about the lifetime of a user — startups should just manage to margin earned by a customer you acquire over the first 6–12 months, not the lifetime. That’s how you will make your ad spend decisions
  • Any misleading ratios where the denominator and numerator are totally non-obvious. Stick to actives, please.
  • Active user definitions that are complicated (must have visited 3 sessions in the last week, and done one action out of a list of 5). It makes all the downstream calculations on retention, engagement, etc., misleading since you’re throwing away all the data for the less active users
  • If you have a desktop app, and web, and mobile, break down the metrics for all three. Don’t combine, please

There are many, many more… but that’s a quick start.

6. What is your advice for startup CEOs?

I have a lot of advice, but maybe I will share the top 10 that come into my head:

  1. You’re not doing this alone. You have friends, family, your investors, and employees rooting you on. Talk to them
  2. Everything seems like it sucks — metrics go up and down. Customers leave. An employee quits. Product/market fit could be a lot better. But this is how it feels even if it’s a rocket ship. Important to put into perspective
  3. Your job changes dramatically over time. Your first job is to build the machine — the product that attracts the customers, and generates the revenue. But eventually it turns into a job where you’re building the machine that builds the machine. It’s all about hiring, leading, managing, etc., etc. Prepare for this to feel weird when it transitions — especially spending 25%+ of your time hiring
  4. Everyone’s gotten very data-driven these days, which is great, but you should set your strategy, and then your metrics should follow. It’s to verify that your strategy is working — having a lot of dashboards is no substitute for strong product insight and strategy.
  5. Some people say to stay off Twitter and forget the distraction. I say the opposite – find interesting, knowledgeable people from social media, and DM them to meet in person. Stay outbound. Use it for recruiting, networking, fundraising and more.
  6. Raising money is a really, really important thing. It can feel like a great milestone, but it’s just the beginning.
  7. Ben Horowitz’s book The Hard Things About Hard Things is the best book about being a CEO and managing your own psychology as you set out to do this crazy hard thing. It’s fantastic. Read and re-read it.
  8. Also read and re-read High Output Management by Andy Grove.
  9. Build long-term relationships with your employees, investors, and people in the ecosystem. Hopefully your startup thrives, but maybe it won’t — and you’ll still want to build a long-term network because there will be more to do in the future
  10. Don’t worry about generic startup advice — including lists like this one :) Make sure you find advice that’s tailored to your startup’s stage, industry, and specific situation. Talk to experts who are willing to dig in. Lists like this are fun to read but there’s a big gap in applying them

OK that’s my first 10 :)

Written by Andrew Chen

March 18th, 2019 at 10:30 am

Posted in Uncategorized

2018 essay collection on growth metrics, marketplaces, viral growth in the enterprise, and more (PDF included)

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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.



Download a PDF with 2018 essays

📥 Get this collection PDF, plus new updates and essays in the future by subscribing to my newsletter.

(If you’re already a subscriber, just stick in your email and it’ll work automagically)


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.


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.

Written by Andrew Chen

December 26th, 2018 at 9:00 am

Posted in Uncategorized

Silicon Valley network effects, OKRs for your personal life, and more: Podcast Q&A with Product Hunt

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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


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.

Written by Andrew Chen

December 13th, 2018 at 10:00 am

Posted in Uncategorized

Consumer startups are awesome, and here’s what I’m looking for at a16z (70 slide deck)

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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!

San Francisco, CA

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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.

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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.

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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).

Written by Andrew Chen

December 10th, 2018 at 8:00 am

Posted in Uncategorized

What’s next for marketplace startups? Reinventing the $10 trillion service economy, that’s what.

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[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.

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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)

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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!

Written by Andrew Chen

November 26th, 2018 at 6:45 am

Posted in Uncategorized

How to build a growth team – lessons from Uber, Hubspot, and others (50 slides)

without comments

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.


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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.


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.

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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.

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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


Written by Andrew Chen

November 13th, 2018 at 7:30 am

Posted in Uncategorized

The red flags and magic numbers that investors look for in your startup’s metrics – 80 slide deck included!

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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.

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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.

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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.

Get this deck as a PDF, and get new updates and essays in the future:

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.

Written by Andrew Chen

November 1st, 2018 at 9:00 am

Posted in Uncategorized

a16z Podcast: When Organic Growth Goes Enterprise

without comments

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.)

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?


Hi and welcome to the a16z Podcast. I’m Hanne, and today we’re talking about another aspect of growth. This episode is about the growth typically attached to bottoms up consumer companies, but that’s now more and more showing up in enterprise. So what does that more bottoms up growth for enterprise look like? How does it affect company building, how does it change how we evaluate growth, and what do we look at?

Joining us to talk about the tactics and questions we should be thinking about in this kind of hybrid scenario are a16z General Partners Martin Casado and Andrew Chen, and Russ Heddleston, CEO and co-founder of DocSend.  

Hanne: Let’s start with the super basic question, which is what exactly are you starting to see happen with this shift in enterprise?

Martin: So traditionally in the enterprise, you’d build a product, and that product would be informed by your knowledge of the market. And then once that product was ready, you’d go ahead and sell it by hiring salespeople and the salespeople would go directly engage. You’d probably do some sales-led marketing where maybe the salespeople would go find the customers or you’d have some basic marketing to do it. But the majority of the go-to-market effort in the early days was this kind of direct sale.

And we’re seeing kind of this huge shift, especially in SaaS and in open source where companies establish massive market presence and brand and growth using these kind of more traditional consumer-ish growth motions. And then that very seamlessly leads into sales, and often a very different type of sale. And so I think a lot of people in the industry are on their heels, both investors and people that have started companies in the enterprise before, they’re trying to understand exactly what’s going on.

Hanne: Is it actually seamless? Is it a seamless transition there?

Martin: Well, I mean, that’s often the question, right? So we’ve seen companies moving on either sides of this. Some companies are like, “You know, listen, we’re just going to do organic growth.” And they don’t actually do sales. And in our experience, these tend not to be kind of hyper growth on the revenue side. Right? So they’ll continue to kind of growth customers, but it’s hard for them to get these nice, hyper linear revenue growth.

On the other hand, we see companies that will just do sales. And for them, it’s actually very difficult to grow quickly because they don’t have the type of funnel that you’d get from the growth metrics. And the ones that seemed to have figured it out the best, what they’ll do is they’ll create kind of a brand phenomenon. They’ll get this growth, they’ll get that engine working and then they do kind of tack on some sort of sales on the backend and then those two motions work in tandem.

Russ: So if you’re a small startup, breaking into that big ACV sale is tough. You’ve got to have a really high annual contract value and everything is going to be more crowded. And it happens occasionally but it doesn’t happen as often. And if you’re trying to target a specific buyer, just getting access to them can be very challenging and that’s just a huge hurdle to overcome. Like, how on earth could anybody break into that? Consumer understands a lot of different tips and tricks because you have to be really frugal to acquire a customer that you’re just supporting with advertising to get someone who you make six bucks a year off of. You can’t spend any money to get that person. So there are a lot of tactics there that are really interesting. If you apply those to some of the B2B value propositions, you can actually break in in a way that no one else was really thinking about before.

Hanne: Well, let’s get into those. What are some of those?

Russ: The way we broke into the market is we took a relatively simple workflow which is sending content from one business to another business. And so we said, “Okay, a better way of doing that is to allow the person sending it to create 10 different links to the asset, send them off to 10 different companies and see what happens to them.” How long do they look at each page? Who do they forward it to? You can see what people care about.

And so the first version of DocSend was just free. That actually just gets people using the product, and it’s cheap enough that they can keep everything else in their stack. So we’re not replacing anything, we’re purely additive at that point. And that’s really how we got our toe hold in the market.

Andrew: Russ, how did you get your first 100 users?

Russ: I think the first revenue we got was in the form of a bottle of whiskey that someone gave me as a thank you for giving them a account that they used for their own fundraising process.

Hanne: What kind of whiskey?

Russ: You know, I don’t actually remember it. I think the office consumed it relatively quickly so I don’t think it was around for very long.

Andrew: But from a top of funnel standpoint, where did you get the first…

Russ: It was all word of mouth. Forty-two percent of our signups are still word of mouth. Twenty-eight percent of our signups are from someone viewing a link and then getting interested and coming into the product.

Andrew: when you look back at Dropbox the first thing they did to get traction was to announce on “Hacker News” and also “Dig” at the time was such a big deal, right? These days, maybe the actual platforms have changed, like, maybe you go to “Product Hunt” instead, maybe you go to Twitter. But ultimately, doing a big announcement but then kind of getting the all sort of viral word of mouth means that a lot of your first users end up experiencing it because one of their friends wants to show them the product, or they just decide they want to try it. As opposed to having somebody sort of email you or call you up.

Hanne: Is there a certain kind of company that this works for better than others?

Andrew: I think that there are certain kinds of products that can be all the way pegged to completely self-serve, bottoms up versus maybe what’s kind of in the middle. Is the product a horizontal enough product that literally you can bring almost all of your coworkers things like Dropbox, Asana, Slack, these are all things that everyone in your company can use, and so naturally is going to spread much faster because at every moment, each node in the network is going to be able to have access to all 15 to 30 people around them where it can spread.

The second thing is products that are actually really front and center in your workflows, all the acquisition that we see, especially virally, happens because of engagement. They’re deeply, deeply linked with each other. Because as you engage and as you’re using the product more, inevitably then you’re sharing links, you’re assigning tasks to people, you’re commenting on people’s files. These are all things that bring people back and bring new people into the product. there’s a whole class of products that aren’t completely horizontal that maybe only apply to a particular job title or function. And so that all of a sudden gets harder because maybe it can spread within the department, within the function, but it’s not going to go really broadly. And eventually you get to the set where it’s like, maybe there’s only a couple buyers in the entire company. And for that, you don’t go bottoms up at all. It’s just literally impossible.

Hanne: So this middle zone is what we’re talking about, where there’s some indication but it’s not completely horizontal and viral. It needs a little bit layered on.

Andrew: The new thing is that the fact that users can then bring these products into their workplace, and you might get a large company of 20,000 people with a patchwork of folks using a whole bunch of different products before IT actually makes a decision. Like, that’s new and very interesting.

Russ: Every company tends to have some form of super power that’s available to it based on just what their business is and what their product does. So we typically add features in one of three buckets. One is to increase the spread of a business to another business. One is to get more lock-in within a company itself, so getting that spread within the company. And then the third is just making our customers more engaged. because the more they’re using it, the more they’re sending it outside the company. Our top request at one point was, “I need to send a folder of content.” And you’re like, “Okay, that makes sense.” But what they really wanted was this kind of deal room thing. So we ended up building Spaces. And that just really increased engagement of our customers.

Andrew: That is why with the investor hat on, one of the really interesting things that, Martin, you and I end up talking about with these bottoms up companies is evaluating the engagement on the products using consumer metrics. Because often, it’s the engagement that’s really the leading indicator for growth, but from an acquisition standpoint as well as retention, which then is sort of the leading indicator for, like, are they actually going to renew their subscription over time?

Martin: So to me, this is one of the key questions. We see these companies that fall in between this kind of consumer-ish growth in this enterprise thing. And actually a question I’ve been meaning to ask you that I haven’t yet but this is a good opportunity, so is it the right thing to evaluate these things purely from a consumer lens? Are the growth patterns the same as you would see in consumer XX? Let’s even just put aside the question of sales. Should the growth metrics be the same as a consumer company?

Andrew: When you’re evaluating even purely consumer products, you have to really look at what the expected behavior is. And so I would kind of turn the same question for the kinds of things we’ve been working on, which is obviously if you have users that are trying out some new email security product, let’s say, hopefully they’re not interacting with it that much. But if the whole pitch of the company is, “Hey, this is going to be the system of record for everything that your team’s going to work on for all of their projects, or whatever, and they’re going to use it every day,” then it’s like, “All right, then let’s actually start using, you know, daily active metrics in order to evaluate if that engagement is actually there.

Hanne: What about from your point of view, Martin? Are there metrics that you…

Martin: Well, yeah, I think it starts to get a little complicated. So there are a number of consumer metrics you track. One of them is engagement which gives you a sense of how often it’s used, and maybe that’s something that you can proxy to value. There also is just simply top of funnel growth, right? How many people know about it, what is the brand? The world I come from is nobody knows about the product when you start. There is no organic growth. Marketing is, at best, linear with the dollars you put in, the number of customers that are top of funnel, it’s probably sub-linear. All the value and monetization is driven my direct sales and so you’re…

Russ: It’s account-based sales.

Martin: It’s account-based sales. So your ACV has to be high enough to cover the marketing cap. So that’s one bookend. The other bookend is all of this growth stuff you do acquires tons of customers and then the product will monetize itself, right? So my big question is, is there a slider bar here? If you slide the slider bar all the way to the left, there’s the Atlassian model, and there’s very little sales, And if you slide your slider all the way to the right, then it’s just direct sales and no marketing. And then the question is, what does it look like in the middle? Because you look at it like the slider bar is all the way to the left, and I look at like the slider bar is all the way to the right. But more and more, we’re seeing companies that actually they’re very interesting on both sides, but they’re not classic on either.

Andrew: Totally.

Martin: So let’s assume we take the case of the slider bar as all the way to the organic growth and it’s purely horizontal and it’s growing like crazy. So the question is does it still make sense to build a direct sales force? As in, will it increase the unit economics if you do? I think our experience here Slack and with Hub and with many companies is…

Andrew: It’s definitely yes, right?

Martin: Yeah, the answer is yes.

Andrew: Because definitely yes.

Martin: Because that’s how you maximize ACV per customer, because there is a procurement process and just finding the budget and maximizing that is something a human can do much better than a product at this point in sales.

Andrew: Right, and in fact, I think actually even the virally spreading products end up going tilting towards enterprise over time for a really simple reason, which is that with larger companies your cohorts will look better because there’s revenue extension. Because no matter what, when you’re working SMBs, I find it very hard to get better than, let’s say, a 5% per month churn rate. All these little companies keep going out of business all the time, they’re fickle, they have small budgets, etc. And so what you quickly find is you have to go to the big guys, all the budget’s there. And so then that inevitably leads you, even when you’re completely bottoms up, to start building stickier new products for enterprises and add the sales team, add customer service, and all of that. So I think that is the natural trend.

Hanne: my question is when is that happening? Is that happening in tandem all along? Are they sort of naturally that hybrid from the beginning or do they slide along as things change in the company’s cycle?

Martin: Specifically were you thinking about sales when you started?

Russ: No. Not at all.

Martin: The common refrain.

Russ: When we launched DocSend, we didn’t have any background in B2B. So it kind of caught us by surprise and we got a lot of interest that we weren’t able to convert into dollars because we weren’t even charging people. If we could do DocSend over again, I think we could build it in half the time. Because I think this is a new type of company that there aren’t that many examples for.

Hanne: if you were to put that very broadly as like the type of company you mean what is that type of company?

Russ: If you create a business value, like a B2B value for something, you build some product and you release it for less money than you should or free, you’re going to get some usage of it. if you’re creating a B2B value, you kind of picked your target audience, you get your 100 accounts you want to sell it into, and you have people just pound on their doors to get in there.

Martin: You literally start at the top of the list, you go to the bottom, and then you go back to the top of list.

Andrew: And I think when you compare it to consumer…I mean, for most consumer audience-based plays, you really defer monetization for a really long time. Because you have to aggregate this huge audience and then you start talking about, like, okay, let’s look at ad-based models. And so, and you contrast that to these B2B products where you can actually monetize from early on. And in fact, when you monetize it actually unlocks a bunch of paid acquisition channels, and it’ll unlock sales, and it unlocks a bunch of stuff. I think that’s very confusing for people who, you know, they get started and they’re kind of in this consumer products mindset. And so they often end up kind of like, “Oh, how I do grow? How do I increase acquisition?”

Hanne: What are the signs that that’s the right time when it begins shifting, the sort of tipping point where you’re like, “Okay, should I need to pay attention to this?”

Russ: We were just selling some small deals on the side. So I was like, “I think we should hire a salesperson.” So we hired our first SMB AE, and in our first month we’re like, “We don’t think she’s going to sell anything.” And she sold twice what the quota was supposed to be. There was just a lot of money laying around where if you actually talked to someone on the phone and explained it to them, they might have bought one seat before but now they’re going to buy 15.

Martin: Didn’t you have a support collecting checks?

Russ: We had a support person selling a lot of DocSend for quite a while.

Martin: That’s a pretty good indication it’s time to do sales.

Russ: Yeah, that’s another really indicator. Also, now that we’re going a little bit more up market, you actually need someone who’s able to run a good sales process even though they’re not doing the outbound part of it once you get them in the door, running a good sales process, having good sales hygiene, really understanding who your buyer is, you need to do all those things too. So you really need to marry both sides of it.

Martin: Another shift I’ve seen, which is important from a company building perspective, so if you think about direct enterprise sales, the actual lead up to the sale can take nine months to 18 months. You’re working the account, you’ve got an SE in the account and you’re educating them, etc. So with these new companies, often the customer is education themselves, they’re already trying, and so much of the actual total value of the account comes after they’re users of the product. So it’s about expanding the account. So now there’s this very interesting relationship between sales and customer success where a lot of the value is actually being driven by customer success. I don’t think the direct enterprise is used to this model.

Russ: Yeah, we always say, “You win the renewal when you do the onboarding.” And getting everyone engaged quickly with an account really helps with expansion and renewal. When we do onboarding, we have a little raffle. So if you’ve got 50 salespeople at your company and if you send a certain amount of DocSend links externally in the first two weeks, then you’re eligible in this raffle and you get one of three different prizes. It’s like a $200 bottle of whiskey or tequila or Amazon gift card. And that’ll actually…

Martin: What kind of whiskey?

Russ: I also don’t know. But that’ll actually get everyone using the product really quickly, and then they look at that and they say, “Oh, we bought the product for our sales team. Man, we should use this for our customer success team or our support team.” And so they build faith in it and then it naturally expands. Sometimes you need a salesperson involved, but more often than not, customer success is just saying, “Yeah, you can use it for that too.” And then they expand.

Hanne: So I want to get into the timing question of when, when this starts happening. When you happen into this moment, when all of a sudden you realize, this would be helpful, how do you begin to actually make that happen? What are the signs and signals that are telling you now is the time?

Andrew: Well, I think one really important one is what kinds of companies and people are signing into your service? Where you’re starting to see both prominent tech companies as well as Fortune 1000s just signing up to try it. Even on a purely bottoms up basis, you create the funnel from signing to using a contact enrichment service and starting to score all of these new users that are coming in. And if you find out that a large proportion of them are actually enterprises, that’s actually pulled demand from the market that you should actually be up leveling faster.

Russ: One of the things we actually did to spread that awareness faster is we decided that marketers will send off tons of things to people, so why don’t we just support the marketing use case? Not because we make more money from that. If we power, for instance, a researcher port for a company, they’re sending that to tens of thousands of people that then get exposure in lots of areas that we weren’t even in before. So it really kind of allows it to hop into other places, and then we generate more of that demand coming in. You need to take a look at who’s signing up for your product and you need to think about what might they be looking for and what problems might we be able to solve for them?

Andrew: Another thing I might add is what kinds of feature requests folks are having. If you’re building something that’s like an email client, something that is really horizontal or it’s a new document editor, everything’s great and all of a sudden, you start getting these future requests for Salesforce integration, and you’re like, oh, okay, this is like a different…

Russ: Another request we’ve always gotten has been DocSend, you can’t actually send anything from DocSend and it’s really nice to be able to send from email and customize it, and there’s a different philosophy around that but we were thinking, like, “Man, just let people send stuff right from DocSend. Because then it’s got a DocSend email that they get.” And so it’s actually a good growth thing, as well. So you can, kind of, reprioritize your product list based on how much it’s going to spread awareness about your product outside of the company, which is a great lens for every company to use when thinking about trying to make these viral loops go faster.

Hanne: That’s interesting. Okay, so say ideally you do have this kind of blended model going on. Are there conflicts ever in the types of information that you’re getting from the different sources?

Martin: At the highest level, I think there actually are a lot of conflicts in these motions and in a number of areas. And the most obvious one and this is something that’s so prevalent in open source is, a good way to get organic growth is to give something away for free. And if you give it away for free, it may be hard to monetize it because a lot of the assumptions here are predicated on organic growth, there’s always an open question of how much do you give away versus how do you monetize it? Enterprise really is all about monetization because there is no conversion between eyeballs and dollars like you do in kind of more advertising-like domains. And so there’s a real tension there.

Hanne: So how do you think about that balance?

Andrew: It’s sort of funny because it sort of implies that you can go one way and not the other. Meaning, if you have a product that’s making a bunch of money and you have a highly functional sales team, and then a product person in the org is like, “Hey, let’s have a free offering,” that is not going to happen. Versus the other way where you have something that’s product led and it generates a lot of users and then you build this whole pipeline off of that and you build the sales org. If you do it in that order, all of a sudden the freemium product actually feels like it’s actually very helpful. Nevertheless, eventually free tends to go away or become pretty crippled as the whole business evolves. But freemium can be so disruptive in these industries because if you’re a large enterprise, B2B software company, you’re not going to be able to do this kind of low end free offering.

Russ: Yeah, a lot of what we’re talking about is just pricing and packaging which is something that’s so hard for everybody. because you’ll look at a company and you’ll look at their pricing and packaging, and you’ll be like, “Congratulations. You’ve done it.” But then when you look at a new company and be like, “What should their pricing be?” Everyone’s like, “I have no idea.” And it’s hard because you can’t AB test it. And so you have examples of what’s worked but it’s really hard to predict what will work for any given business and so you could say on the low end, we got a free thing. On the high end, we got an enterprise thing. And then maybe there’s something in the middle.

We actually just increased the pricing and added a couple new plans. And we thought the conversion would come down but we’d make more money. What happened was that conversion went up and we made way more money.

Hanne: And why do you think that was happening?

Russ: We moved some features around and then we talked about the plans differently and who they’re for. And so people also trusted it a bit more because they’re paying more for it. People then value it more and actually use it more because they’re paying for it.

Andrew: Right. Well, I mean this is the difference between also when Netflix increases their monthly subscription by $2, everyone’s screaming bloody murder. And B2B is obviously less elastic.

Hanne: “Oh, it must be good.”

Andrew: There’s some price signaling as well.

Martin: But it’s also important to compare it to traditional pricing and packaging. the general model used to be when you first come to market, you are as expensive as possible and you know you’re going to go for a limited set, but ACV is high enough to cover it. And the sales cycles are long anyways. And then after you feel like you’re saturating that, you offer lower priced units so that the aggregate market is larger net cannibalization. So you don’t want to cannibalize yourself. And the way you do this is market research of existing customers, you know the target customer base, and you can AB test. You can actually do fairly small rollouts because it’s not marketing led.

That motion is lost in this world because basically, as soon as it’s publicly available for free, everybody knows about it and it’s very difficult then to kind of retract that. So you have to be very thoughtful about pricing and packaging upfront because any experiment basically is reality now. And that’s very, very different from the traditional enterprise motion. I mean we experimented with pricing so much in the early days and the only thing you had to hold sacrosanct was price very expensive early on because you’re only going to get 10 customers anyway and you just can’t do that motion now.

Andrew: Even the way that you do pricing, it can potentially impact engagement. Where do you put your pay wall? Is it a time-based trial, is it a usage-based thing? those things become really important because, especially when you have a product that is growing virally, it’s building a network inside these companies, you don’t want to cut off the network prematurely, because the network is what makes the whole thing sticky. So for example, it would not make sense for a product like Slack —
if they were like, “Well, we’re going to cap the number of people that can join the channel to five,” that doesn’t make sense because the entire network effect is based on having all of your colleagues there. So what you end up wanting to do is you’re gaining these features that the IT admins want, and those are the things that end up being how you differentiate the enterprise customers from purely the consumer ones.

Hanne: When you start thinking about forecasting or planning, do you ever get competing signals and information from this blended model where you’re doing two different kinds of growth and sales?

Martin: Well I think this is a really interesting question of…for wherever you are in the lifecycle of the company, let’s say you have $1 to spend on go to market, how much of that $1 goes to brand and marketing, versus how much of that $1 goes to sales? And that is a question I don’t think anybody knows the answer to.

Hanne: But what are some of the ways you start figuring it out?

Martin: The traditional view in the enterprise is you spend it all on sales, basically, until you’ve got a working pipeline or a repeatable sale. Then you have economics you understand and then you start increasing the top of funnel. That’s the traditional model. But now, we’re marketing led. And so, how do you know how to split those dollars up and when to do it?

Russ: A lot of it has to do too with the DNA of the founding team. my two co-founders and I are all engineers and product people. And so we’ve basically used our product as the marketing engine for the company so far. We haven’t done any paid acquisition, we haven’t been doing a lot of marketing stuff that’s been driving a lot of the top of the funnel. The product itself is driving the top of the funnel.

Hanne: But that would be what most of these companies are doing kind of? In this kind of company, that would be common?

Martin: Well, okay, I mean there are a number of companies that will actually just buy their users. I’m totally not used to that. Andrew’s totally used to that. And so this is kind of…

Andrew: …Yeah, and I hate it. Yeah, there’s folks that they’re spending tons and tons of money on Facebook, on Google, etc. That’s very common. The other one as well is a huge focus on content marketing as being one of the primary channels I think that is really different.

Russ: It’s kind of going back to what we said earlier where, should companies invest in sales? And my view on that would be, if you show me a company that’s growing organically, I’ll show you a company that’s performing better if you also add a sales team to it. If you can get it working with the product, you can actually probably get a good baseline of growth, but you should probably spend more on marketing and sales on top of that. And if you can get the unit economics anywhere near reasonable for a paid acquisition, you should probably put everything you can into that channel, knowing it’s just a component of your overall strategy.

Andrew: The thing that makes it hard to normalize a bunch of these efforts is they happen on very different time scales. You can literally increase your paid acquisition budget and see a spike in signups and self-serve conversions within a 24-hour period. If you’re going to go and hire and build out your sales team, it’s going to take you months to build the team, and then months to recruit them. But when the revenue hits from these really large contracts, it’s huge. Hopefully, you have multiple systems that are mutually reinforcing each other as opposed to feeling like they’re in conflict. But that certainly happens if you are trying to figure out, where do I put the next dollar?

Hanne: I mean, what are some ways around dealing with that discrepancy between timeframes and planning and forecasting when you’re trying to match up these two very different chronologies?

Martin: I don’t think there’s any recipe. There’s never a recipe to doing a startup anyway. There’s no recipe to find product market fit. I don’t think there’s any recipe to knowing what’s the right balance between growth and sales and when to do it. But here are things that a founder should think about that has traditional enterprise expertise in the new world. The first one is brand. You normally don’t think about brand, but brand does drive viral growth. Product focus, right? The product itself actually creates virality. The enterprise very rarely thinks about, believe it or not, product. They think more about solving problems.

Hanne: Really? That’s so surprising.

Martin: It’s not about making the product “delightful” or easily consumable. It’s solving a real problem and adding business value and less about consumability, right? Now you have to think a lot more about consumability, like single-player mode, like self-service mode. Right? Very different than traditional enterprise. You need to design your company for bottoms up growth whether you’re open source or you’re doing SaaS or whatever, because this is the new method of consumption. And I do think that the one most important is if you’re doing bottoms up growth, I think you have to expect a lower ACV which is a different way to build a sales team. And so you just have to be more comfortable with your inside, inside/outside models and then you have to be more comfortable with focusing in on expansion rather than upfront ACV.

So these are all very, very different than the traditional enterprise.

Hanne: They’re sort of mind shifts.

Martin: They’re all mind shifts.

Andrew: There are new organizational structures that end up being built within these companies that sit alongside sales because all of a sudden, you can have multiple revenue centers, right? And that’s a very different approach. Then the people that you hire for this end up being designers and PMs and engineers that are kind of this business-y, metrics focused folks. Going back to Dropbox, I know the most recent incarnation were sort of biz ops people turned PMs that were previously working oftentimes in consulting or banking.

Hanne: So it’s a new hybrid kind of role in organization as well that comes down from this?

Andrew: Right, exactly.

Hanne: That’s interesting.

Andrew: Do you want to hire the nth engineer into this team that can run a whole bunch more of these AB tests? Or do you build out your sales team more?” These are the kinds of decisions that these companies have to make these days.

Hanne: Russ, did you see that as well that kind of hybrid role?

Russ: Yeah, there are a lot of things that aren’t just salespeople calling and getting contracts signed. Enterprise sales is like a playbook that makes sense. For the bottoms up company, you’ll see this perfect curve and kind of the outside view of that is they did something brilliant at the beginning and then everyone went on vacation and it just kept growing. But in reality, behind the scenes is a series of every smart things you did to keep that growth going. And what got you from A to B is not going to get you from B to C. So you often have to do redo your organization, you have to add in new roles, and you have to recognize when you’re going to hit points of diminishing return for a type of investment. And you have to get ahead of that and say, “Well, what’s the next type of investment we’re going to be able to do to get us to the next stage of things?”

Hanne: Add on another layer, right?

Russ: Right.

Hanne: As Jeff would say.

Russ: Yeah, it’s different for every company. There’s no one right answer.

Andrew: The really important key thing is the importance of not just a great product but literally great user experience and design, and all the fit and finish that you would expect with a completely modern consumer-facing application.

Hanne: Now that’s coming to this world too.

Andrew: Right, exactly. Like, Envoy, that is an amazing B2B viral story. They’re very rare, But the reason why people use that now is because offices are part of the brand experience. And then after they use the thing, then they’re kind of like, “Oh, yeah, we’re using pen and paper back at the home office. We need to upgrade to this too.” These examples crossover both the consumer sort of design world, all the way to sales, all the way to performance marketing. You really have to leverage a lot of skills in order to execute these strategies.

Russ: The expectation for the usability of software I think is going up in enterprises. Larger companies expect more polish and more usability. And if it’s not there, they start to really worry about it being shelf-ware or not the value proposition. And shelf-ware is a pretty big problem at a lot of big companies.

Andrew: One of the funny anecdotes at Uber was that for a long time, we were officially on Hip Chat but there were so many teams across the company that would have their little secret Slack team chat going because they just didn’t wanna…

Hanne: Illicit Slacking?

Andrew: I feel comfortable saying that now that Hip Chat’s been shut down. employees will literally rebel and use whatever they want. And so as a result, as companies selling into these, your products have to be really good to compete with everything else that’s out there.

Martin: I didn’t understand how powerful actually just growth tactics were. independent of product. Actually independent of sales. Andrew, you and I were looking at a company which was amazing. Like the growth was amazing. Like all of these numbers were amazing. The engagement, they were monetizing, like everything looked great and the conclusion we came to was, like, it’s because they just had, like, such an amazing growth team that was almost independent of the product that they were selling.

Hanne: Oh my gosh.

Martin: We literally came to the end and we’re like, “Wow, this could be anything. This could be, like, you know, dog food. This could be, like,

Hanne: Doughnuts.

Martin: Yeah, whatever if you figure out how to do it right, it’s a very, very powerful thing. And by the way, that used to be what you said about sales. What you used to say about sales is if you have a very good sales team that understands the buyer, you know, it’s kind of independent of product.

Hanne: Awesome. Thank you guys so much for joining us on the a16z podcast.

Group: Thank you.

Written by Andrew Chen

September 24th, 2018 at 9:51 am

Posted in Uncategorized

a16z Podcast: Why paid marketing sucks, Network effects, Viral Growth, and more

without comments

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 :)

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…

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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.  



Written by Andrew Chen

September 4th, 2018 at 10:10 am

Posted in Uncategorized

Why “Uber for X” startups failed: The supply side is king

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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

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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!

Written by Andrew Chen

August 27th, 2018 at 10:24 am

Posted in Uncategorized

The Power User Curve: The best way to understand your most engaged users

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[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.

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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 the core activity that maps closely to how your business is monetized… or that better represents whether users are getting value from your product. This is important because it forces you to think about what really matters to measure.

The above chart for a content publishing platform shows the total number of days in the month users posted content. A lot of products have smile-shaped core activity Power User Curves, because while most people tend to contribute lightly, there is a small contingent of users who are power users. Think of the distribution of Youtube creators, or Ebay sellers, or even how often you post on Facebook.

As the CEO or product owner of a platform like this, it’s important to design the platform such that the everyone has a chance to succeed. On Facebook, the news feed algorithm makes sure that if you feel strong affinity to a person or organization, you’ll still see their posts even if the sheer volume of other content (for instance, from more prolific media companies) would otherwise drown it out. On OfferUp, even if I seldom sell items, when I do list something, their algorithm makes sure that it’s surfaced to the relevant potential buyers.

Why does this all matter?
Not everything is a daily use product, and that’s okay.

Power user analysis allows you to get a better understanding of how users are engaging with your product, and make more informed decisions using that data. That might mean choosing an appropriate business model that works for your pattern of engagement, or designing better re-engagement loops for lower-engaged user segments, or doubling down on use cases that your high-engagement user base is already getting value out of.

The beauty of the Power User Curve over DAU/MAU is that it shows heterogeneity among your user base, reflecting the nuances of different user segments (and therefore what drives each of those segments). Creating versions of Power User Curve by various user segments can also be particularly insightful. For instance, for a business with local network effects (like Uber or Thumbtack), showing Power User Curves by market can reveal which geographies are developing density and strong network effects.

Power User Curves show if your product is hitting a nerve among a super engaged core group of users, even if perhaps the overall blended DAU/MAU is low. It also doesn’t have to just reflect app opens or logins — you can hone in on an action that maps closely to users getting specific value out of your specific product and plot the Power User Curve for that action. The key for founders is to know that there isn’t a single silver bullet to measure perfect engagement — rather, the goal is to find the set of metrics that are appropriate for their businesses. Comparing the Power User Curve of a social app vs. a work collaboration app doesn’t make sense, but looking at your own Power User Curve over time, or finding benchmarks for your product category, can tell you what’s working… and what’s not.

[Thanks again to Li Jin for pulling together this essay! Another plug for her Twitter account here. -A]

Written by Andrew Chen

August 6th, 2018 at 9:45 am

Posted in Uncategorized

DAU/MAU is an important metric to measure engagement, but here’s where it fails

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How DAU/MAU got popular
DAU/MAU is a popular metric for user engagement – it’s the ratio of your daily active users over your monthly active users, expressed as a percentage. Usually apps over 20% are said to be good, and 50%+ is world class.

How did this metric come into use? DAU/MAU has been a popular metric because of Facebook, which popularized the metric. As a result, as they began to talk about it, other consumer apps came to often be judged by the same KPIs. I first encountered DAU/MAU as a ratio during the Facebook Platform days, when it was used to evaluate apps on their platform.

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This metric was always impressive for Facebook because it’s always been high. It’s historically been >50%. In fact, I was curious at one point whether or not it’s always been that good. And it has! I found this from a Facebook 2004 media kit showing crazy high numbers even with a small base of 70k users:

Assessing product/market fit with DAU/MAU
It’s an important metric, to be sure, but it’s often misused to say that “XYZ isn’t working” when in fact, there’s a slightly less frequent usage pattern that’s still equally valuable.

For consumer and bottoms up SaaS products, this metric is super useful, but seems to mostly exclude everything besides messaging/social products that are daily use. These are valuable products, but not the only ones.

Products that aren’t daily, but still hugely valuable
Not everything has to be daily use to be valuable. On the other side of the spectrum are products where the usage is episodic but each interaction is high value. DAU/MAU isn’t the right metric there.

  • At Uber, our most profitable rides are to airports, via Black Car for a special night out, business travel, etc. These don’t happen every day, and although there are folks using us to commute, that’s not the average use case. So our DAU/MAU wasn’t >50%. The driver side has clusters of “power drivers” who are active >30hrs/week, but as it’s been widely published, our average driver is actually part-time. (Pareto Principle!)
  • Linkedin is another interesting example which is low frequency – only recruiters and people looking for jobs use it in daily spurts – but it throws off so much unique data that you can build a bunch of vertical SaaS companies on top of this virally growing database.
  • Products in travel, like Airbnb and Booking, are only used a few times per year by consumers. The average consumer only travels ~2x/year. Yet there are multi deca-billion dollar companies built in this space.
  • In fact, for SaaS, it seems to be the exception not the rule. While email and business chat can be nearly daily use, a lot of super important tools like Workday, Google Analytics, Dropbox, Salesforce, etc. might only be used 1-2x/week at most.
  • Much of e-commerce looks like this too, of course. You buy mattresses, new sunglasses, watches, etc fairly infrequently. Yet there are $1B+ wins in the category.

You may notice a pattern here. If you’re low-frequency/episodic, then you have to generate enough dollars or data that it’s valuable. If you’re high-frequency, you have a higher chance of growing virally and building an audience business that monetizes using ads.

Nature versus nurture
To extend this idea further, you can argue that messaging/social products with high DAU/MAU is actually the extreme case, and in fact most product categories don’t index highly. A few years back I shared this interesting diagram from Flurry which compared different app categories and their retention versus frequency of use:

In this chart, a couple categories jump out:

  • Social games have high frequency (“I’m getting addicted!”) but once you burn through the content, you tend to churn
  • Weather is interesting too – you don’t often check, maybe only on cloudy days, but you will have a need to check throughout your entire life- so it maxes out on highest retention rate over 90 days
  • Communication, for all the reasons discussed before, is both high frequency and high retention. That’s awesome!

What I’d love to see on this chart would be another overlay, monetization. There, I bet Travel, Dating, and Gaming would tend to stand out for different reasons. Travel because each transaction is big, and Dating/Gaming because it’s frequency combined with a focus on monetization because you won’t have the user for long.

So you want to increase DAU/MAU? It’s hard
So let’s say that you want your DAU/MAU to increase – so what do you do? Funny enough, a lot of people seem to implement emails and push notifications thinking it’ll help. My experience is that it tends to increase casual numbers (the MAU) but not the daily users. In other words, it’ll actually lower your DAU/MAU to focus on notifications because you’ll grow your MAUs more highly than your DAUs.

I’ve also not seen a 10% DAU/MAU product, through sheer effort, become 40% DAU/MAU. There seems to be a natural cadence to the usage of these product categories that doesn’t change much over time.

Increase, measure your hardcore users, network effects, monetization
If your DAU/MAU isn’t super high, this is what I like to see instead: Show me your hardcore userbase. What % of your users are active every day last week? What are they doing? How are you going to produce more of them? Showing this group exists goes a long way.

Similarly, show how the freq of use increases in correlation to something. Perhaps size of their network – showing network effects – or how much content they’ve produced or saved. Then make the argument that by increasing that variable, DAU/MAU will rise in cohorts over time.

Finally, maybe DAU/MAU is just not for you. Sometimes you don’t have to be a foreground app to be successful. Maybe you just need to build something awesome that does something valuable for people, makes enough money, and they use it twice a year! Also great.

DAU/MAU is useful, but has its limits
In conclusion, if your product is a high-frequency, high-retention product that’s ultimately going to be ads supported, DAU/MAU should be your guiding light. But if you can monetize well, develop network effects, or quite frankly, your natural cadence isn’t going to be high – then just measure something else! It’s impossible to battle nature… just find the right metric for you that’s telling you that your product is providing value to your users.

Written by Andrew Chen

July 23rd, 2018 at 10:00 am

Posted in Uncategorized

Required reading for marketplace startups: The 20 best essays

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The current generation of marketplace startups has been incredibly successful. Airbnb, Lime, Uber, Lyft, Instacart, etc. I’ve been doing a broad survey of the best writing on this topic and wanted to share my list of 20 best links I’ve seen.

Marketplaces at Andreessen Horowitz
We look at a lot of marketplace startups at Andreessen Horowitz @a16z – and we fund a lot of them! – so it’s great to compile all the best thinking.

To lead off this list, my colleague @jeff_jordan has an awesome preso that covers everything from the marketplace “wheel” – network effects, and how they’re different than ecommerce products. Amazing, thoughtful preso. Must watch.https://www.youtube.com/watch?v=n57UaE08h7A

Solving the Chicken and Egg problem of marketplaces
Now let’s get to the links. First, here’s a series of links on the “Chicken and Egg” problem of marketplaces. How to do you get the initial liquidity to get the flywheel turning? Here’s a few links on the topic.

1. Josh Breinlinger (early oDesk) on “Liquidity Hacking.” Couple ways to do it: Provide value to one side: offer portfolios, community, tools. Find aggregators: Physical aggregators (like campuses), enterprise clients, supply aggregators, or scrape listings. Narrow the problem: geo, niche, vertical. Curate one side. Read the whole thing here: https://pando.com/2012/11/20/liquidity-hacking-how-to-build-a-two-sided-marketplace/

2. Here’s a nice podcast from Casey Winters (ex-Pinterest/Grubhub/etc) and Brian Rothenberg @bmrothenberg (VP Growth at Eventbrite) who talk about: The “chicken and egg” problem for marketplaces. Horizontal vs vertical. Online to Offline. https://news.greylock.com/paving-the-way-to-marketplace-liquidity-76c8e7854cad

3. Eli Chait (ex-OpenTable) on all the ways to boostrap a chicken and egg problem. Single player, Fill seats for suppliers, Create a marketplace where the buyers are sellers. Read the whole thing here: https://blog.elichait.com/2018/04/09/how-the-100-largest-marketplaces-solve-the-chicken-and-egg-problem/

4. Anand Iyer (ex-Threadflip) writes about using trust throughout the product: Ratings, Curation, Customer service, Mobile first, Good onboarding, Frictionless Payment, Social proof. http://firstround.com/review/How-Modern-Marketplaces-Like-Uber-Airbnb-Build-Trust-to-Hit-Liquidity/

5. Jonathan Golden (ex-Airbnb) on bootstrapping liquidity, adding host guarantees, reacting to competition, user experience. https://medium.com/@jgolden/lessons-learned-scaling-airbnb-100x-b862364fb3a7

Current trends in marketplaces
Next topic, the current crop of marketplaces has gotten huge for a reason. They’re doing a lot different, but going more “full-stack,” building deeper tools, etc. One important label is the new “market network” concept

6) Another by Casey Winters (ex-Grubhub) on how new marketplace companies are evolving: 1) connect buyers and sellers, 2) own the delivery network, 3) own the supply (managed/verticalized). http://caseyaccidental.com/three-stages-online-marketplaces/

7. Anand Iyer (Trusted) again, talks about the evolution from leadgen/search-based marketplaces to full-stack where the platform helps manage: 1) customer UX, 2) supply software tools, 3) retention/frequency, 4) transactional model, 5) trust/safety/risk, 6) pricing mgmt + guidance. Read the whole thing here: https://medium.com/@ai/the-evolution-of-managed-marketplaces-3382290963b2

8. James Currier (of NFX) pens one of the classics of the last few years, defining the term “Market Network” – multiple participants, SaaS tools, with transactions at the center.

Key differences: 1) Market networks target more complex services. 2) People matter – complex services mean each client is unique and not interchangeable. 3) Collaboration happens around a project. 4) There’s unique profiles of people involved. 5) Long term relationships between participants. 6) Referrals flow freely. 7) Increases transaction velocity and satisfaction. Re-read the whole thing here: https://www.nfx.com/post/10-years-about-market-networks

9. Andrei Brasovean (Accel) gives a comprehensive list of Marketplace metrics. https://medium.com/@algovc/10-marketplace-kpis-that-matter-22e0fd2d2779

Here’s the list: GMV, net revenue, gross margin / contribution margin, MoM growth rate, Market share, Liquidity, AOV, Items per basket, Messages, NPS, User reviews, Cohort retention, Repeat orders, Whale curves, Sector/Geo/Product concentration, Fragmentation, CAC, Channel scalability, Channel mix, LTV, LTV/CAC, Unit economics, Burn rate. A lot more detail in the essay.

10. Borja Moreno de los Rios, ceo of Merlin, writes one of my favorite articles where he has a bunch of graphs/concepts on measuring liquidity: https://techcrunch.com/2017/07/11/marketplace-liquidity/

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11. Angela Tran Kingyens (VersionOne) on a Marketplace metrics dashboard. GMV, revenue, Seller/supply metrics (engagement/overall), Buyer metrics (engagement/overall). https://versionone.vc/marketplace-kpi/

Product strategy for marketplaces
Finally, I wanted to add a section for overall marketplace strategy – how do you know you’re in the right vertical? What is a network effect exactly? How to think about frequency and retention?

12. Me! @andrewchen (ex-Uber). A few years back, I wrote this about Uber’s virtuous cycle around acquiring more drivers, keeping the marketplace in balance, and how to think about the hyperlocal nature of the product. http://andrewchen.co/ubers-virtuous-cycle-5-important-reads-about-uber/

13. My colleague Jeff Jordan again (a16z, on the Airbnb/Lime/Instacart boards) on how marketplaces must nurture and manage perfect competition. Gives a sense on why B2B marketplaces often don’t work: https://a16z.com/2015/01/22/online-marketplaces/

14. a16z has also put together two amazing resources on Network Effects. Defining them, case studies, strategies for building them, etc. https://a16z.com/2016/03/07/all-about-network-effects/

15. More from Jonathan (ex-Airbnb) on defining a marketplace, global network effects (versus root density), homogeneous/heterogeneous supply, two-sided incentives, size and frequency of interaction, unit economics: https://medium.com/@jgolden/four-questions-every-marketplace-startup-should-be-able-to-answer-defb0590e049

16. Another from Casey on 4 strategies to win on low frequency marketplaces: 1) SEO (expedia model), 2) Better/cheaper (Airbnb), 3) Insurance (HotelTonight), 4) Engagement (Houzz). http://caseyaccidental.com/low-frequency-marketplaces/

17. Two writeups on TaskRabbit which are worth reading. The first, from Leah (founder of TaskRabbit, now an investor at Fuel) visualizing the building blocks: https://www.fuelcapital.com/stories/2017/12/7/the-anatomy-of-a-marketplace

Also, the Reforge team collects key learnings from TaskRabbit as a case study: 1) Fixed pricing. 2) Faster txns, 3) Going vertical, 4) Raising enough VC , 5) Reputation systems, 6) Gig economy verticals are a power law. https://www.reforge.com/blog/taskrabbit-marketplace-growth

18. Bill Gurley (Benchmark) has a classic: 10 factors to evaluate with marketplaces: 1) New Experience vs. the Status Quo, 2) Economic Advantages vs. the Status Quo, 3) Opportunity for Technology to Add Value, 4) High fragmentation, 5) Friction of Supplier Sign-Up, 6) Size of the Market Opportunity, 7) Expand the Market, 8) Frequency, 9) Payment Flow, 10) Network Effects. http://abovethecrowd.com/2012/11/13/all-markets-are-not-created-equal-10-factors-to-consider-when-evaluating-digital-marketplaces/

19. Josh Breinlinger (early oDesk) on the ingredients for a successful marketplace: 1) recurring 2) episodic 3) standardized work 4) little trust required 5) non-monogamous. http://acrowdedspace.com/post/73232464154/the-ingredients-for-a-successful-marketplace

20. Worth a mention – not an essay, but The Perfect Store is a behind the scenes look at eBay that I read a long time ago that is great. https://www.amazon.com/Perfect-Store-Inside-eBay-ebook/dp/B001MYJ3VA

Re: Uber, I’ve read everything out there about Uber but there’s nothing good yet. @mikeisaac’s upcoming book is the one to watch.

I’m still collecting/curating my list! So if you have clues for other great pieces, please let me know. Also interested in books if I’m missing anything.

More ideas/thoughts welcome! I read every reply :)

[Originally tweetstormed, with some edits, at @andrewchen. Follow me there for more!]

Written by Andrew Chen

July 10th, 2018 at 10:00 am

Posted in Uncategorized

Conservation of Intent: The hidden reason why A/B tests aren’t as effective as they look

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When a +10% isn’t really a +10%
OK, this is an infuriating startup experience: You ship an experiment that’s +10% in your conversion funnel. Then your revenue/installs/whatever goes up by +10% right?

Wrong :(

Turns out usually it goes up a little bit, or maybe not at all.

Why is that? Let’s call this the “Conservation of Intent” (Inspired by the Law of the Conservation of Momentum 😊)

The difference between high- and low-intent users
For all your users coming in, only some of them are high-intent. It’s hard to increase that intent just by making a couple steps easier – that’ll just grow your low-intent users. Doing tactical things like moving buttons above the fold, optimizing headlines, removing form fields – those are great, but the increases won’t directly drop to your bottom line.

In other words, the total amount of intent in your system is fixed. Thus the law of the conservation of intent!

This is why you can’t add up your A/B test results
If you’re at a company that A/B tests everything and then announces the great results – that’s wonderful, of course, but just run the thought experiment of summing together all of those A/B tests. And then look at your top-line results. Rarely does it match.

The most obvious way to see this is to test something high up on a funnel, for example maybe the landing page where a new user hits, or an email that a re-engaged users opens – you can see that a big lift on the top of the funnel flows down unevenly. Each step of friction burns off the low-intent users that are flowing step-by-step.

Be skeptical of internal results, but more importantly, external case studies too
If you’re at a big company and another team publishes a test result, make sure you agree on the actual final metric you’re trying to impact – whether that’s revenue, highly engaged users, or something else. Make sure you always review that.

Similarly, this is a reason to be skeptical of vendors and 3rd parties who have case studies that’ll increase your revenue by X just because they increase their ad conversion rate (or whatever) by X. In these kinds of misleading case studies – often presented at conferences – not only do vendors have the ability to only cherry pick the best examples that reinforce their case, but also the metric that’s highest impacted! Be skeptical and don’t be fooled.

Unlock increases to the bottom line
First, understand what’s really blocking your high-intent users. Those are the ones who’d like to flow all the way through the funnel, but can’t, for whatever reason. For Uber, that was things like payment methods, app quality (for Android especially!), the forgot password flow, etc. If you can’t pay or can’t get back into your account, then even if you use the app every day, you might switch to a different app that’s less of a pain in the ass.

Also, you can focus your experiments. You obviously get real net incremental increases on conversion the further down the funnel you go. By that point, the low-intent folks have burned off. You’re closer to the bottom line. Look the steps right around your transaction flow – for ecommerce sites that might be the process to review your cart and add your shipping info, or the request invoice flow for SaaS products, etc. Think about high-intent scenarios, for example when you hit a paywall or run out of credits/disk space/resources/etc. All of these can be optimized and it’ll hit the bottom line quickly.

Make sure your roadmap reflects reality
When it comes to your product roadmapping, yes you can definitely brainstorm and ship a bunch of +10% increases, but you need to add a discount factor to your spreadsheets to reflect reality. Can’t just add up all your results.

When you focus on low-intent folks, you’ll have to get creative to build their intent quickly. Things like being able to try out the product, having their friends into the product – these are the “activation” steps that generate intent. Here’s a great place to start – a highly relevant essay on getting users more psych’d, guest written by Darius Contractor from the Dropbox growth team.

Conservation of Intent
Many of you have directly experienced the “Conservation of Intent” but now you have a name for it! It’s tricky.

This is really a reflection of how working on product growth is really a combo of psychology and data-driven product. You can’t just look at this stuff in a spreadsheet and assume that a lift in one place automatically cascades into the rest of the model.

[Originally tweetstormed at @andrewchen – follow me for future updates!]

Written by Andrew Chen

July 2nd, 2018 at 9:45 am

Posted in Uncategorized

The Startup Brand Fallacy: Why brand marketing is mostly useless for consumer startups

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Brand marketing is mostly useless for consumer startups. Startups build a great brand by being successful, finding product market fit and scaling traction, etc. But it’s not a real lever. Let’s not mix up correlation with causation!

If this seems contrarian to you, it’s because there’s a vast ecosystem of consultants, agencies, and other middlemen who are highly incentivized to have you spend $ and effort on non-ROI/non-performant activities. Early startups should opt out of all of this

It’s easy to confuse correlation and causation: If you’re starting a consumer startup, you see successful late stage cos with fawning media coverage, amazing conference speaking slots, celebrities on the cap table, etc., and think that’s what caused their success: Great brand.

But great brand is the lagging indicator of success. The buzz is created by the hard work that the entrepreneurs put in: Finding product/market fit, hiring a great core team, finding acquisition channels that scale. Brand marketing is great, but it should be layered on later.

The greatest consumer products in recent years slogged through years of obscurity. The overnight success of Uber, Airbnb, Instagram, etc were actually multi-year successes driven by hard work and multiple pivots.

Working on press mentions, conferences, etc can be a good way to get an initial hit of traffic. It’s great! But it’s not enough. Here’s an article from a few years back: After the TechCrunch bump, there’s life in the trough of sorrow.

Anyone who’s been on the homepage of TechCrunch, AngelList, Hacker News, or even in the NYTimes knows that it’s a increase to your dopamine but not so much your customer acquisition :) It’s great for the early days, but you need a lot more to scale.

Furthermore, the metrics-driven argument is obvious. Ultimately, the engagement in every product can be deconstructed into a series of user cohorts that join and decay over time. How does brand help these cohorts? My observation: They don’t help much.

One argument is that brand marketing can create buzz and word of mouth. OK if that’s the case, why does every brand-driven commerce company have >60% of their customer acquisition happen through paid marketing? Why do they have to buy all their customers?

If brand marketing helps make acquisition ultimately cheaper, then why does every startup’s paid acquisition become less efficient over time, even as the company becomes more well known? The same arguments apply to startups’ re-engagement efforts.

It’s true that a strong brand can confer defensibility in a noisy space – but it’s brittle, hard to create, and hard to sustain. Hard to bet on that in the early days of a startup.

Where brand marketing does matter, especially outside of consumer: Recruiting a great team. Raising money. Partnerships. These are all small targeted audiences where you can reach them with more touchy feely efforts, and it can work! So put your emphasis there.

For early consumer startup efforts, it’s better to focus on the basics. Understand your users, deliver a great product to the market that grows by itself, built moats, monetize in a user-aligned way. Grow your team, work with the best advisors/investors/etc. The basics.

Do all that, and your product’s brand will take care of itself – and then you can layer on more brand marketing efforts to 10x the effect. Just don’t do the steps out of order!

[Originally tweetstormed at @andrewchen]

Written by Andrew Chen

June 21st, 2018 at 10:00 am

Posted in Uncategorized

The Scooter Platform Play: Why scooter startups are important and strategic to the future of transportation

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(📷 lime)

The scooter startups are way more important than you think, or in emoji-speak: 🛴+📱=🤖🚗🚁. Let me explain.

Right now, scooters are a lot of things – fun, cute, adventurous – but here’s a couple words I rarely hear about them: Strategic. Important. Platform play. And yet they are.

Chris Dixon has written that the next big thing will start out by looking like a toy. Scooters are literally derived from kids’ toys. It’s the perfect example. (Btw, here’s the perfect moment to re-read his essay arguing that the next big thing will start out looking like a toy)

Like a toy, a scooter seems underpowered vs other transportation options. It only takes you on short trips – a few blocks at a time. It’s cheap and makes less money than a highly profitable Uber trip to the airport. They are placed all over the place in cities, annoying many

These all seem like weaknesses, but in fact they’re strengths. Because scooters are cheap, short-range, and ubiquitous, it means consumers are adopting them as an alternative to walking

SCOOTERS COMPETE WITH WALKING! What’s the market size on that?? :)

As a result, the scooter apps are being downloaded in the millions by consumers – the adoption has been incredible. But now we have another starting point to capture the intent to go from Point A to Point B. That intent is valuable

These scooter trips are short, frequent, and cheap, driving high engagement in the app. In fact, if you live in SF they become a home screen app. You might check it all the time, before you walk a couple blocks

Combine those factors – millions of consumers, high frequency, and strong intent – and all of a sudden it’s obvious why this is a big deal

When you’re the first look and the highest frequency place to start your trip, it’s the pole position in consumers’ minds. Everything else is downstream

Google has one of the best business models ever. It’s the starting point. It has a search box, maps user intent to URLs, and charges everyone downstream if they want to be promoted in any way

Scooter cos like Lime are also the starting point. High frequency and high intent. It has a search box for where you want to go, and maps user intent to a trip

Scooter apps could be the starting point for a lot of kinds of trips. Alongside Apple/Google Maps, rideshare, etc – the place where you’d go to book your autonomous vehicle rides. Or your VTOL / flying car trips. It could even upsell rideshare trips from Uber and others

Scooters look like a toy, but in fact they are something else: Strategic. Important. Platform play.


(Originally tweetstormed at @andrewchen)

Written by Andrew Chen

June 18th, 2018 at 10:00 am

Posted in Uncategorized

The IRL channel: Offline to online, Online to offline

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(📷 dmagazine)

We’ve heard about Facebook ads, Google adwords – but today let’s talk about the “IRL Channel.”

The IRL channel is an underappreciated advantage of companies that exist in the real world – Amazon Echo, Envoy, Lime, Uber, etc – that use constant in-real-life reminders to try out and use the product

The viral acquisition benefits are pretty obvious. If you’ve never seen/tried a product, but you see them swarming around your city (or your workplace), then naturally you’ll want to try it out

More importantly, some product usage patterns are naturally viral. Couple examples:

  • Transportation fits into this bucket, which is why Uber’s rider acquisition mostly viral/WOM Traveling and going out 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.
  • The new scooter/bike trend is another obvious example. Lots of brightly painted Lime scooters all over SF makes a splash. Put some pricing and instructions on the actual hardware, and riders who have big smiles on their faces, and you have a natural acquisition channel.
  • With Amazon Echo, the physical presence gets you an retention/engagement benefit. Sitting on your kitchen counter naturally encourages you to use it. The newest one, the Show, has a display which invites you to interact. Sometimes the Amazon Echo thinks you’re talking to it when you’re not. I’ve always thought that Amazon is unlikely to ever fix this since it probably increases engagement when it occasionally gets things wrong :)
  • Envoy is a B2B example. I’ve signed in with the system at the lobbies of dozens of companies, which means if I ever have to make a purchasing decision in the category, they’ll be the natural choice.

There’s not a ton of entrepreneurs who are brave enough to build new consumer hardware cos, but if you are, I think this has to be a key consideration!

The IRL channel is about a physical experience that drives you into a digital one. But the other way around is pretty profound as well.

When you see your social feeds populated with photos from highly instagrammable retail experiences like Boba Guys or the Museum of Ice Cream, like below…

(📷 Boba Guys fb page)

(📷 laweekly)

… you can’t help but pull up Yelp to figure out the closest place to go!

The other mega trend here is esports and the fandom community, of course. One day you’re playing League of Legends and reading Star Trek fan fiction, and the next day you’re going to esports arenas and checking out vidcon. It’s a thing.

The IRL channel is real. It helps you with acquisition, retention, and more. It’s starting to go both ways – from online to offline, which has been a force in retail for the past few years – but also offline to online, where IRL products remind you to interact with their digital sides.

Super fascinating, and I’m excited to see where this will all go!

[Published with some modifications, originally tweetstormed at @andrewchen]

Written by Andrew Chen

June 12th, 2018 at 11:57 am

Posted in Uncategorized

How startups die from their addiction to paid marketing

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[Originally tweetstormed at @andrewchen, Follow me for more!]

Many of the biggest implosions in recent history – especially ecommerce – have been due to startups getting addicted to paid marketing while fooling themselves on Customer Acqusition Costs. As spend scales, it always gets more expensive and harder to track – never less.

A familiar story: New product launches. Nice spike, but it dies down. The product is low freq – gotta spend to grow. Marketing spend increases, it’s profitable! More is spent, more money is raised via VCs. OMG this is working! Party!

Suddenly top line hits a ceiling. Payback period goes from 9 months to 12, then more. Unit economic profitable, but not with staff + HQ. Without top line growth, more investment dollars can’t be raised. Budgets get slashed, then layoffs.

Even slower growth means a pivot is in order. Try something else, also powered by paid marketing. Maybe subscription? Premium? Try another thing. Then another. Irrelevance – or maybe bankrupcy.

This happens enough that y’all should be nodding your heads now – it’s tough, but there’s a pattern. This is the Paid Marketing Local Max.

The key insight here is that Paid Marketing is tricky to grow, at scale, as the primary channel. It’s highly dependent on both against external forces – competition and platform – as well as the leadership team’s psychology when things get unsustainable.

The first mistake is to start by thinking of everything as Blended CAC – dividing all your acquisition against dollars – as opposed to understanding CAC of each channel (Facebook, Google display, Google AdWords, etc.). The former is misleading.

Because your initial organic users are your biggest fans, your Blended CAC and per-channel CAC can often by off by 2-5X. As you scale your paid, your organic won’t follow 1:1. So as you grow, your Blended will approach your dominant channel’s CAC.

Scale effects mostly work against you in paid marketing. The longer your campaigns run, the less effective they become – people start seeing your ads too often. The messaging becomes stale, and novelty effects are real. Market performance has a reversion to the mean.

Saturation is also a thing. As you buy up your core demographic, the extra volume comes from non-core, who are less responsive. The first US-based ad impression on a property is the most responsive, but you eventually run out of those.

Competitive dynamics are real. They’ll come in to copy not just your product, but also ad messaging and creative. It’s not hard to fast follow, especially if you can start the test just with a experiments on millennial-friendly ad copy and landing pages.

Contrast that to viral channels, folder sharing in Dropbox or team channel creation for Slack – these are highly situational and only a few folks can copy. Whereas in ads you’re competing with everyone going after your same demographic.

Addiction to paid marketing can get you into a local maximum. It’s much harder to fix the underlying issues – creating real moats, product differentiation, doing deeper adtech integrations. Easier to just spend more and push the LTV window from 9 months to 12 to 18.

There’s a few scenarios where paid marketing is justified, but it’s situational. If your product has network effects that kick in after an activation point and really scale, you can use paid to help bootstrap that. Facebook uses paid to build out new regions, for example.

If you are really going to invest a ton of time from engineering/growth to integrate with all the APIs, try out a ton of things algorithmically, then you can develop a lasting edge. I’ve heard Wish does this well, but it’s not common.

The new generation of ad platforms makes it possible to scale revenue to new heights, but without profitability. Make sure you don’t get addicted. Build out new channels. Fix churn and frequency. Don’t congratulate yourself too early. And calculate LTV/CAC correctly :)

So what do you do about it? One of the best case studies of this is from @drewhouston’s Dropbox presentation from the early days. Lots of great stuff in this deck and it’s worth paging through, now nearly 10 years later. Here it is.

On slide 18, Drew talks about early experiments they did on paid search. They executed the industry best practices at the time – go to trial-based pricing, hide the free option, optimize landing pages. Slide:

What they learned was that, in the mature market for cloud storage, there was already a lot of competition. All the paid marketing channels were unprofitable. Hiding the free option wasn’t user aligned. Etc etc.

The obvious move would have been to continue to grind on the problem! Tweak pricing, optimize more ads/funnel/landing pages, etc. And many would have been tempted to do that, because it’s worked for others

The interesting thing, and you can see in the deck, is that grew virally instead – via folder sharing, the give/get disk space program, etc. It seems obvious now, remember that back in the day, “cloud storage” was the space, and it’s not clear that you can go viral there.

Dropbox has done well since then, of course!

As an aside, isn’t it interesting that exponential growth curves always look linear instead? Here’s Slack’s as well:

In some ways, you could argue that Dropbox is lucky that their initial forays into paid marketing didn’t work. That made it easier for them to stop their efforts there, and to focus on the viral channels that are now their bread and butter.

On the other hand, it takes a lot of insight and reflection to go away from the current industry “best practices” – even if they erode profitability, cause shark fins, etc.

So for those of you who are thinking about going all-in on paid marketing, I challenge you to go deeper on that strategy. Perhaps cap your paid acquisition at 30-40% of TOF. Instead, where can you innovate?

In addition to Dropbox, I sometimes use the story of @Barkbox, which created a whole media property, Barkpost (http://barkpost.com ) as a viral content sharing engine that can cross-sell the subscription product.

Or at Uber, although they never became significant channels, we were keen to work on sharing viral sharing features like Share ETA, Fare Split, and Location Sharing to potentially drive acquisition.

The point is, knowing that Paid Marketing is highly addictive and hard to scale down, all of us in the industry should always be thinking about the 2nd or 3rd channel, in addition to organic/WOM, to give us a way to wean off an ever-increasing ad budget.

To do that, you’ll need empower your creative team to attack the problem from all angles- new viral product features, really investing in your referral program, building out your content/SEO strategy even though it’ll take years. It’s worth the investment!


Written by Andrew Chen

June 4th, 2018 at 9:45 am

Posted in Uncategorized

Podcast Q&A: Dropbox’s viral growth, Uber’s tricky funnels, and future growth channels

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[Hi readers: I wanted to share a podcast interview I did with Adam Risman of Intercom, who interviewed me on a wide array of topics including Dropbox’s viral growth methods, my time at Uber, and future growth strategies. This was originally published on Intercom’s blog here. Hope you enjoy! -A]

Listen to the podcast here »

tldr; Here’s 5 quick takeaways

  • Collaborative tools like Dropbox and Slack benefit from built-in virality, where teams adopt them together – and they represent a tidal wave of software products that truly understand the relationships between people.
  • When your users go through a high-consideration, high-intent signup funnel, like Uber drivers, the key to growth is understanding where folks fall off along the way and finding ways to simplify or shorten that process.
  • In high-profile cases where growth peaks and crashes, there are often two problems working in concert: The acquisition model might be a single channel, and/or the product might serve an infrequent need, like a mattress or a car. This creates an acquisition treadmill with built-in natural churn.
  • “The Law of Shitty Clickthroughs” posits that successful channels will become less efficient over time, thanks to a crowding effect that exhausts potential users. Those working in growth and retention must continually seek “fresh powder.”
  • Growth teams commonly make the mistake of picking random, off-the-shelf KPIs without thinking about how they all fit together. First zero in on a strategy for achieving your desired outcome, and then pick high quality metrics to validate your tests.

Full Interview with Adam Risman (Intercom)

Adam Risman: Andrew, welcome to Inside Intercom. You just started your new role at Andreessen Horowitz, and it’s a homecoming for you in that you were in the VC world previously. How are you settling in?

Andrew Chen: I’m wrapping up my fourth week at the firm, and it’s been incredible. The people are really great. It’s such a positive and happy job to have, with some of the best entrepreneurs out there coming to tell you about all the ways they’re going to change the world.

Adam: What drew you back? Was there a particular challenge or an itch you wanted to scratch?

Andrew: Definitely. Figuring out how to grow your business – how you acquire new customers, how you retain them, and how you engage them – is such an important topic for entrepreneurs. I found that after a couple of years at Uber, where I was laser-focused on ride sharing, it really excited me to bring all the knowledge and skills I’ve built over my career to actually help a lot of different entrepreneurs make a big impact across the ecosystem.

Secondly, Andreessen Horowitz is the firm that, for me as an entrepreneur, I’ve always wanted to work with. I’ve known Marc and Ben for a long time, and they originally seed-funded a startup of mine many years ago. It was just such an attractive thing to work somewhere where you have an awesome group of entrepreneurs who are in it to help other entrepreneurs.

Adam: A lot of our listeners are going to know you best through your writing; isn’t that how Marc originally found you back in 2007?

Andrew: That’s right. I moved to the Bay Area about 10 years ago, and I was writing down everything I was learning in my first year. At the time, everyone was like, “Are you crazy? This is your competitive advantage. Why are you writing everything down?” But one of the things that got me excited was saying, “I’m going to give this all away because I’m going to meet really amazing, interesting people.” My first year in the Bay Area, I actually got a cold email from Marc, who was working on his own stuff at the time. It kind of went from there.

What Dropbox can teach us about virality

Adam: You’ve gotten to work with a slew of interesting companies over the years: Gusto, Product Hunt, Angel List, even Boba Guys. You’ve also worked with Dropbox, who just had their very successful IPO. When I think about growth and Dropbox, Drew Houston’s classic talk from the 2010 Startup Lessons Learned Conference immediately comes to mind. He shares the story of how they were spending $200 or $300 to acquire a customer when the product was worth $99, and as a result, they shifted their approach toward virality. How did you get connected with Dropbox, and what can we learn from their story?

Andrew: Drew and Arash Ferdowsi started the company and put it through Y Combinator. I had gotten to know a lot of the folks within the YC community, including Drew. During that period of time he was working with Sean Ellis, who’s a close colleague of mine and coined the term “growth hacking.” We would spend time together and talk about a lot of these interesting challenges.

Dropbox is super unique and innovative today because of this thread they’ve been following over a long period of time, which is to take something that’s just part of your workflow – storing files – and making it spread because of the way people are working with each other. Those early experiments you’re talking about happened during a time when they knew that storing and syncing files had very high retention. Switching to a different service is something that takes a lot of effort.

The interesting early story there is that they had amazing retention but not a lot of top-line growth. The team’s remarkable insight was adding folder sharing. All of a sudden, you’re taking your storage product and then you’re sharing these folders with other people to create built-in, intrinsic virality. I think that’s a missing part of the story: they’re more recognized for the ‘give and get’ disk space, when it fact it’s that intrinsic virality that really powers things. They did an amazing job bringing that all the way up to hundreds of millions of users and then their products for the enterprise, like Paper, are all extensions of that core idea.

Adam: Those products do jobs associated with what Dropbox is built for, and they’re finding ways to grow into those spaces.

Andrew: Right, and that is one of the most exciting parts about products that are happening in the workplace. With B2B, bottoms-up SaaS companies, even Intercom, there is a lot of viral spread because so many people are busy collaborating with each other. Rather than spending years working on a social graph, there’s an interesting workplace graph based on all the people you’re working on projects with and documents you’re editing together. I think that Dropbox, Slack and these other collaborative tools that are emerging are the start of a tidal wave of software products within the enterprise that really understand the relationships between people.

Adam: Another one of those early learnings from Drew that sticks with me is when he talks about the realization that people weren’t really looking for a way to replace the USB drive in those early days. That seems to be when they changed their strategy.

Andrew: Totally. When I’m analyzing the growth strategy of a new product, I skip the homepage. The homepage is sort of what the company thinks it should be, but people often experience new products through some kind of a side door – like an invite or a shared folder. In the case of YouTube, I very rarely go to the homepage, because most of the time it’s a detail page where a video is playing, and that’s the beginning of your experience. So, when you’re in a world where no one is looking for a shared USB drive, it’s not a compelling pitch. However, if you get an email from a close colleague that says: “Hey, for this critical project we’re working on, here’s a shared folder with all the things that you need to look at. Let’s use this to keep up to date.” Obviously that’s an insanely compelling value proposition and has nothing to do with a shareable USB drive.

Navigating supply and demand at Uber

Adam: Shifting focus from your consulting and advisory roles, you spent the better part of three years in-house at Uber. You joined on the supply side, correct?

Andrew: I started on the driver side of the business, and as everyone knows about marketplaces, the supply side is often the trickiest, hardest side. The reason is very simple: there’s a professionalization that tends to happen. A small number of folks figure out they can make a little money, and then think, “Oh, I might as well make even more money.” These are the eBay power sellers and the folks on Uber who are driving 40-plus hours a week. That group is very finicky, because they’re using the driver app for 10 hours a day. Growing that base is incredibly valuable, so when I joined the company Travis Kalanick and Ed Baker put me on the drivers’ side of the problem, asking: “How do we grow our driver base? How do we acquire more and more folks?” Then, my last year and a half at the company was spent growing the riders’ side. I saw both sides of the marketplace, which was a lot of fun.

Adam: You joined Uber in 2015, so the company and user base were already extremely large. When you have a market that’s so big, where do you start? With established systems already in place, how did you prioritize all the different problems you could have solved?

Andrew: When you look inside any of these hyper-growth companies, what you find – and this is a good signal – is they’ve grown so fast organically they actually haven’t really needed to go super deep on the data, churn models or all the nuances. The first step for anybody coming into one of these teams is to focus on understanding what the hell is going on. The second piece is to then identify some of the key opportunities you want to then execute. Then, you want to measure, iterate and execute that loop as fast as you can.

On the drivers’ side, there were a couple obvious things that needed help. First, anyone who tried to sign up quickly found out that it’s a long process. You have to give a lot of information, you have to give a copy of your driver’s license, and you have to get a background check. In some places, like in Europe, you have to get licensed. So, it can actually take several months to become an Uber driver. This high-consideration, high-intent signup funnel is similar to the problems fintech companies like Wealthfront might face, or a B2B company facing a long, complicated API integration.

A lot of this is really trying to understand the places where folks are falling off. What’s the order of operations in terms of how much you need to ask people? Do you need to ask them for their email? Is a phone number okay? Do you need to actually have their full address up front? Or can you defer that and get them excited about the opportunity before you try to pull them through?

Adam: When you then transitioned to the demand side and concentrated on growing riders, was that a different muscle for you? How did that compare and contrast to the driver side?

Andrew: Drivers are almost like small businesses. They’re very motivated by earnings. They have a long, complicated funnel to get all the way to the end. One example that really works on the supply side is referrals: drivers referring other drivers. Because drivers are in it for earnings, referrals are awesome, and they actually select for drivers that are even better. Now, let’s compare that to the riders’ side, which is usually much simpler because you just put in your phone number and install the app.

Adam: You want them to have that “ah-ha” moment: the car shows up, they get in, and it’s seamless.

Andrew: Exactly. You still need a credit card in many cases, but in other parts of the world Uber goes with cash, so that lowers the friction even more. You’re talking about a different order of magnitude in terms of the complexity of the funnel, right? So, that’s different.

The other thing is that the channels become different. I was just talking about how referrals work so well for drivers because they’re trying to earn more. Think of it this way: if you have a rider who’s in it to get a discount, what kind of rider are they going to be? Probably one who doesn’t spend as much money. So, referrals actually bring slightly lower quality riders. You find a bunch of nuances in there that are very interesting.

One of the obvious observations about Uber these days is that the drivers’ side has more churn than the riders’ side. The riders start by taking rides to the airport, and they think, “Oh, this is pretty cool. I should take it when I’m out and about.” There’s more of a habit, whereas the drivers are always comparing their earnings with Uber to other opportunities like picking up a part-time job.

Why you need a mechanism for free acquisition

Adam: We’ve seen a lot of high-profile startups (particularly in the ecommerce space) raise hundreds of millions of dollars and go all-in on acquisition. Then, they end up crashing back to earth because they don’t have strong retention. Why do we keep seeing this, and what’s the big lesson there?

Andrew: This is one of the reasons why B2B SaaS companies have a recurring revenue model. It’s also why a transactional marketplace like Uber, where you have more riders who can actually use it every day for commuting, is nice. That regularity and habit formation means you have better lifetime value. It also means the engagement can power organic acquisition, because you naturally tell your friends about it. Going back to the Dropbox example, or looking at Slack, a natural network forms where every user has the opportunity to acquire one of their coworkers. Another example is DocuSign, where folks who are collaborating within a workflow involve other people from across companies. That’s going to be even more viral than something that only exists within a company. How many folks have discovered Intercom because they saw the little window on the bottom right and thought, “I want that too”? You get all of this free acquisition.

When I look at some of the high-profile cases where it didn’t work, I see a couple of things that work in concert to make it more difficult. First, you have an acquisition model that is a single channel. Maybe it’s Facebook ads, maybe it’s Google ads, maybe it’s SEO – but you don’t have any natural virality. Second, specific to ecommerce, if you’re buying something like a mattress or a car, that happens very infrequently. Because of that, you end up in an acquisition treadmill, where you’ve got to run really, really fast and then – if you’re on a single point of failure on your acquisition channel – there’s an arbitrage for a period of time. If you hit it at exactly the right moment, you can build a pretty decent company. But eventually you should just plan on losing it, right? This is another reason why a lot of gaming companies are hard to fund from a venture perspective: there’s built-in natural churn. Dating apps are also like this. You have that combined with the need to actually buy the traffic because it’s very hard in a dating app to say, “Oh, you should download this too.” That doesn’t make sense.

If you’re building something in fintech or healthcare, these are all things you have to be very careful with and make sure you understand how those dynamics are going to play out long-term.

Fighting channel fatigue

Adam: You wrote a great piece in 2017 outlining an economy where startups are getting cheaper to build but more expensive to grow. Your core thesis was that virality is naturally a channel that is peaking. What should listeners consider as a result of that?

Andrew: The idea is that, especially in pure consumer products, there was a period of time where we had address book importers: you got an invite to a product from a friend, and you were like, “Oh my god, what is this? This is so cool. I want to use this.” And people just got used to that. Eventually, we got to a point, especially now that we’ve gone to mobile, where we don’t have contact importers that work as effectively as the ones before. This is also because email spam and text spam are very different things. There are lots of laws around the latter with the Telephone Consumer Protection Act, and intermediaries like Twilio have a very strict stance on that stuff. What this means is that virality is much harder, and the spammy kind of virality we saw during the Facebook days is not there any more.

So, you have a few options: you could work in a different area where these channels haven’t been exhausted yet. My calendar has all the information about whom I’m meeting on a day-to-day basis. The documents I’m editing and everyone else’s edits on those documents tell me who’s interested in the topics I’m interested in. My email inbox is completely obvious. Even some of the other tools like Slack and Asana give great signals on whom I’m collaborating with. But I’ve actually seen very few products that are built on that idea. It’s this workplace graph that’s just sitting there. So, I’m really excited to see how people take consumer ideas, bring them into the workplace and then adjust them. For instance, in a workplace you don’t need to ‘follow’ your coworkers; you’re on teams automatically, you know you’re on the same email domains, and it’s much easier in many ways.

The other way, within consumer products, is you have to figure out how to make a lot more money and then use different forms of paid acquisition. If you are a product that figures out an awesome consumer subscription business – or you’ve figured out a high-ticket item like housing or cars – all of a sudden you can innovate within paid acquisition. You can do paid referrals or paid ads. You can figure out different kinds of incentives. On a total side tangent, we’re very early on a lot of the crypto applications, but if we fast-forward a couple of years, people are going to play around with a lot of really innovative approaches, whether they’re referrals or a different kind of incentivized engagement.

Adam: Looking at this from a higher level, eventually there will always be diminishing returns on these channels. That’s an idea developed in one of your most famous essays, “The Law of Shitty Clickthroughs.” In the time since you wrote that, how have you seen that observation materialize in new channels that have emerged?

Andrew: To summarize the idea, the very first banner ad was for HotWired, and it had a clickthrough rate of more than 70%. Now 20 years later, you look at the average clickthrough rate and it’s like .05%. It’s very low, and anyone who has worked in the industry long enough has seen this happen with email, SMS and all sorts of things for a bunch of reasons. You have competition, and you have the platforms themselves saying, “Hey, we need to clamp down on this.” There’s literally habituation from end users who are thinking, “Oh, it used to be fun to get a invite from my friend, but now I’m getting it all the time.” It’s just less effective, because you have a crowding effect.

The reason why I call it “The Law of Shitty Clickthroughs” is that it’s something that has been with us for a really long time and will continue to be. For all of us in marketing and growth, that means we have to continually find the fresh powder, because inevitably whatever worked in the past will no longer work. By the time a case study has been published on Medium about something that works, it’s probably done. Everyone still has to do it, but then you have to move beyond that.

A lot of the interesting work happening out there ends up on these “frontier platforms.” These are areas where maybe some of the big companies haven’t quite wised up yet; maybe they haven’t started experimenting; maybe the channel is a little too small. These are things like Alexa Skills.

One big area I have found really fascinating is the ecosystem that’s being built around gaming right now. You can livestream things, you can do voice chat, you can do all of these different things around ephemeral networks of players who are getting together over a short period of time to play one game. You’re not going to want to add all these folks to your Skype or Google Hangouts because you are literally just coming together for one game. However, a product that understands that ephemeral network can then build a whole ecosystem around it, and that’s what we’ve seen with Discord and Twitch.

It behooves all of us in the industry to stay on top of these trends and to see what’s working, because otherwise we’re in constant competition where all of our stuff stops working over time.

Unlocking the best insights in growth

Adam: One place where you’ve done an admirable job of trying to communicate those higher ideas is through Reforge with Brian Balfour. You just finished the Retention Series, and you’ve also got the Growth Series. What educational void is the team trying to fill with these programs?

Andrew: Brian Balfour was previously the VP of growth at HubSpot, which invented inbound marketing and a bunch of other important concepts. Brian and I have known each other for a long time. We write the same kind of long-form content, and we tend to be as thoughtful as possible. We try not do the “quick tips and tricks” thing. We really have come to relate on that, and we talk often about how the current form of executive education is kind of broken. It needs to be augmented, because especially in technology, we need to learn frontier skill sets constantly. We need to become lifelong learners, because if you master something, and then two years later there’s a new platform and a whole new ecosystem of startups, you just have to do it over and over again.

Brian and I have started with growth as the first vertical. Brian’s the CEO, I’m on the board, and we basically try to gather folks who are masters of the frontier skill set. We literally ask people,“Hey, who’s the smartest person you know on retention? Who’s the smartest person you know for viral growth on bottoms-up SaaS?” We gather all of those folks and patch them together so you can get real-life interaction with them and pick their brain. Within these frontier skill sets, many of the most amazing practitioners haven’t written their ideas down, because it’s changing all of the time. Brian and the team are capturing all of that.

Andrew’s lightning round

Adam: To close out, we’ve got a lightning round of questions we’ve been asking our growth guests. Short answers are totally fine, but feel free to expand on anything you want. What’s your favorite underused growth tactic?

Andrew: One of the most important things – especially when it comes to consumer and these days in the work place – is that your product has to be fun. We’ve gotten into a world where we’re so busy measuring and optimizing everything that we forget what a delightful, fun experience and a human voice can do to these response rates.

Adam: What book has most influenced your thinking?

Andrew: I love recommending this book. It’s called “My Life in Advertising,” and it’s the biography of Claude Hopkins, the man who actually invented coupons. He invented stunt marketing back in the day where he’d put things in the middle of malls, like the world’s largest cake. The reason I find it so compelling is that he’s a guy who invented a lot of new channels and strategies that people have built on for many decades since.

Adam: Speaking of people you admire, whom in the growth community do you think we have the most to learn from?

Andrew: First, obviously: Brian Balfour, Casey Winters and Shawn Clowes. Those three guys are involved in Reforge with me for a reason. They’re the most intelligent, thoughtful people from different corners of the growth ecosystem. I also have learned a ton working with Ed Baker and Aaron Schildkrout at Uber, and funny enough, they both started previous companies in the online dating world. Online dating, of course, is a two-sided market that’s hyperlocal, and they had just amazing instincts coming into another two-sided, hyperlocal marketplace with Uber.

Adam: Favorite recent onboarding experience?

Andrew: We’re so used to the world of digital experiences, but the problem with consumers is that when you send a push-notification, you have to compete with all the other notifications, right? One of the most amazing new trends is the way internet-connected physical objects interrupt your real life experience as you’re walking around. One example is all the new LimeBikes that are now in San Francisco, where the onboarding experience is walking around the city and seeing a green thing sitting there – and watching people on their scooters and bikes, having so much fun with big smiles on their faces. That is an amazing onboarding experience. I think as we see more internet-connected devices and products, we’re going to see more of this phenomenon where we figure out how to optimizing things and make them more interesting and presentable.

Adam: You’ve consulted with a lot of growth teams; what’s one common mistake you keep seeing them make when it comes to running experiments?

Andrew: A lot of folks spend their time picking the metrics first and then trying to increase them as much as possible. That’s a good place to start, but the problem is that you have to be so careful about picking your metrics. And in fact, the thing you should pick first is your strategy: “Hey, I’m going to make money on these business customers who are going to pay me, and then I’m going to use that money to buy more business customers.” Or some kind of loop like that. Then, you pick the metrics that validate whether the strategy is working, and you run the experiments afterward. It’s very easy to get caught up in picking random, off-the-shelf KPIs, like MAU or MRR, without really thinking through how it all fits together.

Adam: Where can our listeners go to follow what’s next for you here at Andreessen Horowitz?

Andrew: I am finishing my first month at the firm, and I’m really excited to dedicate a lot more time to writing. Through all of my time at Uber, I maybe wrote half a dozen essays. So, I’m going to try to get into a cadence of posting a couple of times a month cadence on my blog, which is andrewchen.co.

Adam: Careful, we’re going to hold you to it. Andrew, this has been awesome. Thanks so much for inviting us over to your new digs and the coffee and warm hospitality.

Written by Andrew Chen

May 14th, 2018 at 10:00 am

Posted in Uncategorized

Update: I’m joining Andreessen Horowitz!

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Hi readers,

Big update: I’m joining Andreessen Horowitz as a general partner!

Starting in April, I’m returning to my roots to invest in and help grow the next generation of startups. I’ll be focused on consumer startups, bottoms up SaaS, marketplaces, and more – utilizing my expertise in growth to launch and scale new companies. Incredibly excited.

How this came together tells you a lot about Marc and Ben, and how Silicon Valley works. I moved to the Bay Area in 2007, as a first time founder with a lot of energy and a lot of questions. I spent the first year meeting everyone I could, reading everything about tech, and writing down all that I was learning. A few months in, I was shocked to get a cold email from Marc introducing himself. Who knew that sort of thing happened? My blog was pretty much anonymous and I could be anyone – but he reached out to talk ideas, which made a big impression. I learned a lot about Silicon Valley that day.

Marc soon introduced me to Ben, and together, they provided a regular stream of advice/ideas/frameworks over breakfasts at the Creamery, Hobee’s, Stacks, and other assorted Palo Alto diners. I was a first-time founder, and the real-life entrepreneurial experiences they relayed – on fundraising, finding product/market fit, hiring, and much more – proved to be insanely helpful. My startup ultimately didn’t work out and the team soft-landed at Uber, but I always remembered the incredible support from Ben and Marc.

Andreessen Horowitz is a firm built on the same core values I saw first-hand. The investing team has a deep empathy for entrepreneurs that reflects their extensive operating experience. The partners on the operating team are incredible and enable the firm’s unmatched support of founders and their companies. For all of these reasons and more, I’m thrilled to join the a16z team.

A decade ago, I learned how impactful it can be when a couple experienced entrepreneurs reach out to a new founder. I can’t wait to close the loop by doing the same – working with new founders and their startups, and helping build the next generation of tech companies.

As excited as I am about the next step, I’m also sad to leave an extraordinary team and experience behind at Uber. I have nothing but admiration for the talented, passionate people who are working hard to ship all the amazing innovations that are coming down the queue. To all my friends and colleagues at Uber, thank you for the amazing two and a half years.

Finally, to the readers of this blog: I’ll be writing much more! The new job will let me put down a lot of what I’ve learned over the past few years. I’m excited to share ideas and stories from across companies and industries with all of you! Looking forward to it.


San Francisco, CA

Written by Andrew Chen

February 15th, 2018 at 7:00 am

Posted in Uncategorized

Hello 2018! Books, essays, and more from the past year.

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Dear readers,

2017 was a big year, where we got Trump’s first(!) year in office, a renaissance in interest around cryptocurrencies, Brexit, Puerto Rico, and oh yeah, things got a little crazy at Uber too. I want to take a moment to share some of my writing from the past year, a few books I’ve read recently, and also include stuff from the last year just for completeness. One of my 2018 goals is to spend more time writing – stay tuned for that – and am looking forward to sharing some incredible learnings I’ve gotten from Uber over the past few years.

As always, thank you again for reading!

Hayes Valley, San Francisco, CA

Essays from 2017

Startups are cheaper to build, but more expensive to grow – here’s why
Lots of important trends – cloud computing, open source, etc. – are making it cheaper to start a company. However, growth is getting harder and more expensive because of consolidation, making paid acquisition one of the few channels that still work. Startups are responding by raising more money, monetizing earlier, trying paid channels, and experimenting with referrals instead of virality.

VIDEO: Three things you need to know to raise money in Silicon Valley
I spoke to an audience of French entrepreneurs and tech folks, and explained some of the key lessons from watching startups raise money in San Francisco versus elsewhere. This means focusing on a big story, growth trajectory (versus today’s metrics), respecting differing investor motivations, etc. This is a short video and hope you enjoy it!

How to build a billion-dollar digital marketplace – examples from Uber, eBay, Craigslist, and more
Marketplaces are magical because they both have network effects as well as clear monetization. This means that often when a niche marketplace works, it can grow into adjacent niches quickly. To grow to beyond an initial vertical, startups have to think about expanding geos, adding new products and price points, decrease friction, and grow demand+supply stickiness. I use examples from the major marketplaces to make my points. More to come on this topic!

10 years of professional blogging – what I’ve learned
Expanding on a tweetstorm, this essay breaks down the key lessons I’ve learned from running a professional blog over the last 10 years. This includes how to write content – opinion-driven, please! – and why writing is the best possible networking activity ever.

Books I started reading in 2017

I originally titled this section “Books I read in 2017” but I probably started more books than I actually finished :) Here’s a collection.

Whenever you read a New York Times political column with a bunch of predictions – Trump is gonna do this! Saudi Arabia is gonna do that! – it’s entertaining, but who’s keeping track of these forecasts? This book covers the academic work of Philip Tetlock from UPenn, who puts together a forecasting competition and tracks who’s good at making these predictions. Lots of interesting learnings and relevant to those making startup investments also! Here’s a NYT article on the foxes versus hedgehog strategies for prediction, btw.

Venture Capitalists at Work: How VCs Identify and Build Billion-Dollar Successes
I read this awhile ago, but picked it up again and read more of the stories. It’s a series of interviews with many of the top venture firms – Floodgate, Founders Fund, First Round, Softbank, CRV – and the companies they’ve invested in. Each interview has a nice discussion and amount of detail. I found this much more compelling than many of the other books I’ve read on VCs, which remain a bit too high-level and adulating.

Ellen Pao’s story of her time at Kleiner Perkins, Reddit, and more. So much to learn from this experience.

The Ascent of Money
Sapiens for money :) Traces the history of money, the role it’s served over time, and the development of some of the major aspects of our modern financial system. Can’t wait for this to get revised for all the crypto stuff that’s happening now.

The One Device
History of the iPhone. Didn’t read this yet, but I love these recent tech history books.

Reading this because everyone else is too :) Lots of insights/lessons from Ray Dalio, one of the world’s best hedge fund dudes.

Stories of Your Life and Others
The recent film Arrival was based on this short story.

Area X
The director of my recent favorite movies – Ex Machina – is making a new movie starring Natalie Portman and a bunch of badass ladies exploring a strange, genetic-mutating world secured by the military. Reading the book ahead of time, before I see the movie! Here’s the trailer to the upcoming film.

Featured essays from 2016

10 years in the Bay Area – what I’ve learned
I’ve lived here for the last decade, and have learned a ton of about this region’s entrepreneurial drive, the unique culture, and wonderful folks. I wanted to share a couple lessons learned here.

The Bad Product Fallacy: Don’t confuse “I don’t like it” with “That’s a bad product and it’ll fail”
Your personal use cases and opinion are a shitty predictor of a product’s future success.

Growth is getting hard from intensive competition, consolidation, and saturation
It’s the end of a cycle, and we’re seeing headwinds on paid channels, banner blindless, competitive dynamics, and more. And it’s much harder to compete with boredom than with Facebook/Google/etc.

What 671 million push notifications say about how people spend their day
Here’s a study, based on Leanplum’s data, on how people spend their days – on sports, leisure, phone calls, and otherwise – in addition to what tech platforms they’re using.

Startups and big cos should approach growth differently (Video)
Here’s a video interview breaking down how startups evolve and change their strategies as they gain initial traction, hit product market fit, and eventually start to scale.

What’s next in growth? (Presentation at Australia’s StartCon)
Last year I presented this talk on how marketing has evolved over the last century, and how many of the ideas we think of as “growth” today are actually based on concepts from decades ago. I use this to talk about future platforms and where this might all go.

Uber’s virtuous cycle. Geographic density, hyperlocal marketplaces, and why drivers are key
In my last two years at Uber, I’ve learned a ton about the flywheel that makes Uber’s core business hum and grow incredibly fast. In this essay I draw from Bill Gurley’s essays on network effects, the labor market for part-time workers (aka drivers, “the supply side”), and how surge works within the company. A lot has evolved/changed since I’ve written this, but it’s a good overview from my first year of learnings.

Featured essays from 2015

The Next Feature Fallacy
“The fallacy that the next new feature will suddenly make people use your product.”

New data shows losing 80% of mobile users is normal, and why the best apps do better

This is the Product Death Cycle. Why it happens, and how to break out of it

Personal update- I’m joining Uber! Here’s why
“I’m joining Uber because it’s changing the world. It’s one of the very few companies where you can really say that, seriously and unironically.”

More essays from 2015

This is what free, ad-supported Uber rides might look like. Mockups, economics, and analysis.

The most common mistake when forecasting growth for new products (and how to fix it)

Why we should aim to build a forever company, not just a unicorn

Why investors don’t fund dating

Ten classic books that define tech

The race for Apple Watch’s killer app

Photos of the women who programmed the ENIAC, wrote the code for Apollo 11, and designed the Mac

Written by Andrew Chen

January 31st, 2018 at 10:00 am

Posted in Uncategorized

10 years of professional blogging – what I’ve learned

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Building your personal bat signal
I want to cross-pollinate a tweetstorm on lessons I’ve learned from a decade of professional writing. In a way, it’s a followup to some more general life lessons from 10 years of living in the Bay Area. Writing has been enormously impactful from a professional standpoint, and I continue to recommend to everyone – especially folks who are new to the Bay Area – to do it as a way to send out the “bat signal” on their aspirations, ideas, and interests.

It’s awesome, but insanely hard to get started. Of course, everyone knows the mechanics of setting up a blog – but the hard part is finding your voice, figuring out topics that are interesting for other folks to read, and building a long-term habit.

The lessons
Without further ado, here are a few opinions I’ve developed up along the way:

  • Titles are 80% of the work, but you write it as the very last thing. It has to be a compelling opinion or important learning
  • There’s always room for high-quality thoughts/opinions. Venn diagram of people w/ knowledge and those we can communicate is tiny
  • Writing is the most scalable professional networking activity – stay home, don’t go to events/conferences, and just put ideas down
  • Think of your writing on the same timescale as your career. Write on a multi-decade timeframe. This means, don’t just pub on Quora/Medium
  • Focus on writing freq over anything else. Schedule it. Don’t worry about building an immediate audience. Focus on the intrinsic.
  • To develop the habit, put a calendar reminder each Sunday for 2 hours. Forced myself to stare at a blank text box and put something down
  • Most of my writing comes from talking/reading deciding I strongly agree or disagree. These opinions become titles. Titles become essays.
  • People are often obsessed with needing to write original ideas. Forget it. You’re a journalist with a day job in the tech industry
  • An email subscriber is worth 100x twitter or LinkedIn followers or whatever other stuff is out there. An email = a real channel
  • I started writing while working at a VC. They asked, “Why give away ideas? That’s your edge.” Ironic that VCs blog/tweet all day now ;)
  • Publishing ideas, learnings, opinions, for years & years is a great way to give. And you’ll figure out how to capture value later

But let’s talk about each one of these in more detail.

The lessons, but with more detail!

Titles are 80% of the work, but you write it as the very last thing. It has to be an compelling opinion or important learning

Titles are often written as a vague pre-thought, but in fact, it’s the most important creative decision you’ll make. Titles are the text that’ll be featured prominently in every tweet, Facebook share, and link – and people will refer to it by name. Titles are best when they can pass the “naked share” test – imagine some text that’s so compelling that even if it’s not linked to anything, people will want to share it.

The best example of this in my work is “Growth Hacker is the new VP Marketing” which started out as a tweet with 20+ shares, and then was developed into an essay afterward. To pass the naked share test, this means a title should be an opinion on its own. Or be a factoid (like push notifs being 40%+ CTR) that’s fascinating and shareable. Or if that’s just too hard, the common “curiosity gap” pattern of a listicle can work too. Just avoid vague titles like “Here are my thoughts on XYZ.” No one cares. As a result, in the course of my work, I often write a placeholder title, write the essay, and then at the very end, spend a good chunk of time iterating on titles until there’s a good one.

There’s always room for high-quality thoughts/opinions. Venn diagram of people w/ knowledge and those we can communicate is tiny

You might think that there are too many blogs on tech, startups, whatever. There’s always room though, when you think of the whitespace as Knowledge x Communication x Medium. People with real knowledge are busy, especially when that knowledge is under a huge amount of demand. And even when an expert can poke their heads up and do something besides executing their craft, they often can’t communicate! It’s hard to make professional content – often dry, boring, technical – into something that’s compelling and accessible to a wide audience. And furthermore, I’d add the medium into the mix as a third dimension, which is the idea that the knowledge can be shared via video, long-form essays, podcasts, presentation decks, etc. Even when there are experts writing long-form content about cryptocurrencies, let’s say, there’s still room in the market for a highly visual version. Just figure out the whitespace and dive in!

Writing is the most scalable professional networking activity – stay home, don’t go to events/conferences, and just put ideas down

When I first moved to the Bay Area, I was spending at least one afternoon/evening a week at a launch party, a conference. Plus hours and hours of 1:1s as I was meeting a ton of people. After an entire year of hard work, I had met something like 1000 new people for one-off conversations. But it took hundreds of hours. At the same time, I was dedicating about the same amount of time to writing, but quickly unlocked 5,000+ people, and started reaching into their inboxes on a weekly basis.

Speaking at conferences is the worst time suck. You spend hours prepping a deck, speak to a group of perhaps a few hundred people, and retain very few them in any meaningful relationship. It can feel good to be recognized, but at the same time, it just can’t compare to writing a piece of content that lives forever. I’m still getting traffic – and email feedback – on essays I wrote ten years ago, which is insane! But that’s the power of scale – nothing can beat content as a bat signal.

Think of your writing on the same timescale as your career. Write on a multi-decade timeframe. This means, don’t just pub on Quora/Medium

Building your network, your audience, and your ideas will be something you’ll want to do over your entire career. Likely a multi-decade thing that will last longer than any individual publishing startup. That’s why I refuse to write on Medium or Quora. Instead, I prefer to run open source software that I can move around, prioritize building my email list (more on that later) and try to keep regular backups. I used to write on Blogger and watched them slowly stop maintaining the platform after the Google acquisition. Then I switched to Typepad, only to watch the same thing happen. I learned my lesson.

Focus on writing freq over anything else. Schedule it. Don’t worry about building an immediate audience. Focus on the intrinsic.

I get it- the activation energy to start publishing your professional ideas and thoughts are high. Nevertheless, because initially no one will read your work, the key is just to get started. Your initial topics and format should be whatever you can do easily and maintain some sort of frequency. Maybe that’s 500 words a month on a new product you’ve tried, and whether you hate or it not. Just get started, find out what you like, and you’ll have a lot of time to figure out the intersection of what you want to write, and what others want to read.

To develop the habit, put a calendar reminder each Sunday for 2 hours. Forced myself to stare at a blank text box and put something down

Several years in, writing remains hard. It’s something that still – to this day – requires time to be set aside. I turn off the music, stop checking email, and write over a few hours to crank something out. Some parts get easier, but the core activity stays difficult. Since starting a normal job (haha) it’s gotten harder to write on Sunday evenings, since that’s when the work email starts. But a good chunk of the writing on this blog happened over Sunday evenings, a few times a month, blocked out with no distractions.

Most of my writing comes from talking/reading deciding I strongly agree or disagree. These opinions become titles. Titles become essays.

After a lively lunch/dinner discussion where a provocative opinion is blurted out – say, that cryptocurrencies are going to be widely adopted and ultimately cause a global recession – I usually write it down. If it’s fun and memorable, it’s an easy thing to write 3-4 supporting points as paragraphs, and turn into an essay later.

People are often obsessed with needing to write original ideas. Forget it. You’re a journalist with a day job in the tech industry

Thinking of yourself as a journalist that’s covering interesting ideas, trends, products, and everything that’s happening around you leads to much better/stronger content. It means you can write often and build on others’ ideas, without feeling like everything has to be completely new. Just as startup ideas are rarely new, but rather twists on older ideas, the same goes for your observations and ideas on tech.

An email subscriber is worth 100x twitter or LinkedIn followers or whatever other stuff is out there. An email = a real channel

For a professional audience, at least, email is the only KPI I care about. Nothing has more engagement. And importantly, to a previous point, it’s independent/decentralized and will clearly be around in a decade – it’s hard to say that about any of these other subscriber metrics. Given that, I focus on my blog’s UI on collecting emails – both on the homepage, at the bottom of essays, plus those annoying popups that are (unfortunately) super effective.

I started writing while working at a VC. They asked, “Why give away ideas? That’s your edge.” Ironic that VCs blog/tweet all day now ;)

It took a long time for VCs to figure out how to market themselves and their ideas :)

Publishing ideas, learnings, opinions, for years & years is a great way to give. And you’ll figure out how to capture value later

The first year of writing, I had an audience of hundreds, including friends/colleagues from Seattle, my sister, etc. It wouldn’t be until a year later that I figured out it was a helpful asset when you’re going out and trying to raise money for a startup! And years after that, to help get your company acquired. And a great launching pad for market research and side projects too!

Creating is the thing – writing is a subset
For me, writing on this blog has been a real gamechanger in terms of building relationships, a professional reputation, etc. But it’s just one potential method of creating and putting content out there. Maybe your version of this is through videos, photography, or podcasts. Or maybe you’re a developer and want to keep shipping open source projects. All of it can work. The important part is just to start giving out your knowledge and ideas – and over time, to build that into a platform for other activities.

Just get started and I doubt you’ll regret it. And to those who’ve been reading my work for the last decade, thank you! I appreciate it.

PS. Bonus lessons
To close, I’ll point you to some bonus ideas from an old essay, How to start a professional blog: 10 tips for new bloggers, written when I was just starting:

  • Carpet bomb a key area and stake out mindshare
  • Take time to find your voice
  • Stay consistent on your blog format and topic
  • Just show up
  • Go deep on your topic of expertise
  • Meatspace and the blogosphere are tightly connected
  • Embrace the universal reader acquisition strategies for blogs
  • Come up with new topics with brainstorms, news headlines, and notes-to-self
  • Look at your analytics every day
  • Don’t overdo it

More details here.

Written by Andrew Chen

December 18th, 2017 at 9:30 am

Posted in Uncategorized

How to build a billion dollar digital marketplace – examples from Uber, eBay, Craigslist, and more

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Marketplaces are easily underestimated
When marketplaces get big, they can get really big. Some of the biggest tech successes ever – eBay, Airbnb, Alibaba, Uber – are marketplaces worth tens of billions of dollars each.

And yet marketplaces often start small, in niches and weird corners of the Internet. As we all know, when eBay got started in 1995, it was focused on collectibles. The venerable venture capital firm, Bessemer Venture Partners, famously passed on an early investment:

“Stamps? Coins? Comic books? You’ve GOT to be kidding,” thought David Cowan, a partner at Bessemer. “No-brainer pass.”

An early investment in eBay would soon yield a 50,000% return from Series A to after the IPO, as the company started to help transact on everything from electronics, cars, homewares, and more.

Two decades after eBay was founded, a similar story unfolded itself, this time over Uber (my current employer!) and the taxi market. NYU Professor Aswath Damodaran asserted that Uber was overvalued after a 2014 investment round. Based on data points from the global taxi and car-service market, he concluded the real number should be $5.9B. Since the 2014 article, Uber has blown past his estimate by 10X, with top line revenues to support it. Not bad. The reason the estimate was so off, as investor Bill Gurley pointed out, is that Uber goes beyond taxi use cases and grows the market substantially by unlocking many new categories of transportation. Another example of going from niche into more use cases over time.

(As an aside, a slightly different flavor of the expansion of audiences and use cases leading to wild underestimates – this time my mistake: Why I doubted Facebook could build a billion dollar business, and what I learned from being horribly wrong)

Starting small, and what to do next
In both the eBay and Uber examples, we see that you can start with a niche – whether that’s a geography or product line – and then quickly scale into a huge network of buyers and sellers. It turns out that there are a couple key moves to make this happen, and today I’ll highlight some of the main strategies with examples across the past few decades:

  1. Expand into new geographic markets
  2. Add new products and price points
  3. Decrease friction from signup to successful transaction
  4. Grow supply + demand stickiness

Let’s dive into each one.

1. Expand into new geographic markets
Marketplaces like Uber, OpenTable, Craigslist, and others are hyperlocal in nature, and a critical mass of supply/demand must be quickly built within a constrained geography. If a customer is trying to book a restaurant in the Hayes Valley neighborhood of San Francisco, you don’t care much how many restaurants are also on the platform in Manhattan.

As you might imagine, breaking into each new local market can be incredibly painful. Marketplace companies often end up employing teams of “launchers,” a specialized ops role focused on cracking new cities.

Here’s a great Quora writeup on Uber’s Launcher team from Chris Ballard (these days, GM SoCal):

The “Launcher” role at Uber is one of the most physically, emotionally, and mentally challenging roles that an individual will come across.  It is also one of the most rewarding. […]

Once in a city, the Launcher must simultaneously:

  • recruit, hire, and train a local team
  • develop partnerships and manage relationships with local hire car operators (NB: Uber does not own any vehicles.  We work with existing accredited, licensed, and insured hire car owners)
  • create a marketing strategy to scale the client base and increase visibility
  • explore biz dev opportunities (sponsorships / partnerships / co-promotions)
  • form relationships with local press
  • throw a legendary launch event to officially kick off the city!

The travel is intensive.  Launchers are on the road over 300 days per year.  We live out of suitcases, and our most important possessions are our MacAirs and our Passports.  If you tend to get homesick after a few days or don’t sleep well unless you’re in your own bed, this is definitely not the position for you.

Launching is hard work, but the good news about these hyperlocal marketplaces is that if it works in one market, then it will probably work in hundreds more. Sometimes there will be stronger cross-network growth across geographies than you initially imagine, enabled by factors like Airbnb’s global travel use case, which can supercharge your addressable market.

Furthermore, if you are a new startup, you can go after hyperlocal markets where your competitors are weak, and build a local network effect that will be hard to dislodge.

2. Add new products and price points
The next variable that marketplaces can play with is expansion of product lines and price points. Both of these directly unlock new use cases and addressable market, and there are strong examples of how this happens. Craigslist, the mother of all free marketplaces, started with events and then expanded to jobs and apartments.

In an Inc interview in 2016, Craig Newmark reminisces on the early form of Craigslist – literally just an email list – and how he intuitively added product categories over time:

Craigslist began with a single email in 1995–you simply shared interesting things going on in San Francisco. What was in that first email? The first ones had to do with two events: Joe’s Digital Diner, where people would show the use of multimedia technology. It was just emerging then. Around a dozen of us would come and have dinner–always spaghetti and meatballs–around a big table. And a party called the Anon Salon, which was very theatrical but also technology focused.

How many people did that first email go to? Ten to 12.

And then? People just kept emailing me asking for their addresses to be added to the cc list, or eventually to the listserv. As tasks started getting onerous, I would usually write some code to automate them. And I just kept listening. At first, the email was just arts and technology events. Then people asked if I could pass on a post about a job or something for sale. I could sense an apartment shortage growing, so I asked people to send apartment notices, too.

Today, Craigslist in over 57,000 cities, generating $700M in revenue per year (on job listings fees!) with just 50 employees. Amazing.

A related move is to offer new price points to the market, which can unlock new use cases and grow the addressable market as well. A good example of this is Airbnb, which provides a much wider set of offerings to guests – from super cheap to super expensive – as compared to their hotel competitors. The low-end of this enables new, higher-frequency use cases to emerge, like weekend getaways. The high-end allows for large family gatherings, like weddings or reunions, to all share a huge house together.

Pricing is a key strategic move because it’s often the main factor for customers, as seen in this Morgan Stanley survey of Airbnb customers:

And of course, we’re also seeing direct product expansion from Airbnb, via their new Experiences product that can be an upsell in addition to accommodations.

3. Decrease friction from signup to successful transaction
The dual levers of geographic and product expansion are powerful, and decreasing the friction of conducting transactions on the marketplace amplifies both. This grows the TAM in two ways: 1) First, directly growing the market because lower friction transactions mean more sales. 2) But also, more subtly, it unlocks more transactions when your marketplace can be incorporated into new use cases that require reliability and ease of use.

For example, few people use taxis to commute, because the service can be expensive/flaky, whereas many folks use Uber POOL to commute because it’s reliable and affordable. You’re bound to use OpenTable more to snag last minute reservations when restaurant inventory is up to date, making it convenient for even casual get-togethers.

There are many ways to decrease friction, but in particular we should look at this from the perspective of the customer (both buyer/seller) through their journey from signup to transaction:

  • Reducing friction from signup to first transaction
    • Signup and onboarding
    • Setting up payment
    • Finding the desired transaction
    • Trust infrastructure (depending on product: Reviews/photos – or ETA – or availability calendar)
  • Reducing friction from the transaction to receiving the product/service:
    • Reliability and consistency – driven by both market liquidity and UX
    • Determining the right price
    • Timing and logistics on completing the transaction
    • Resolving post-transaction issues

Focusing on reducing the friction on the above doesn’t just generate more revenue for the marketplace, but it’s also just a much better customer experience.

4. Grow supply + demand stickiness
Transactions require strong retention of both demand and supply, and if a marketplace can improve that stickiness, more activity can be generated on the platform. In many ways, this is just a classic retention problem, except with multiple players within the ecosystem. Just as you would on a social network product, you can tackle using traditional growth methods:

  • Notifications: Creating a strong notifications platform to engage buyers/sellers at the right time
  • Use cases: Understanding use cases and how to up-sell and cross-sell the stickiest ones
  • Offers/promotions: Using offers and content throughout the calendar cycle to engage
  • Optimization: A/B testing growth levers – from email/SMS/push copy – to when/how to reach out

However, beyond the traditional techniques, we’ve also seen a recent trends towards deeper productization of workflows for buyers and sellers within a platform. This solution, coined in recent years by James Currier and the NFX crew, is to build a “market network” that’s part SaaS tooling and part marketplace.

As a reminder, Market Networks provide useful tools to each side of the market – for instance, OpenTable’s seating system, you get stickiness purely through utility. Combine that with a marketplace, and you get even stronger effect.

Here’s a diagram illustrating the ecosystem:

And below are some examples based on AngelList and Honeybook – showing how multiple players on a market network ecosystem might interact with each other.

As one can see, sometimes these relationships between ecosystem players happen via money, and sometimes it’s through content/community. These rich interactions, facilitated by a great product UX, can retain multiple players and generate a rich stream of transactions. It’s still early years for market networks, and I’m excited to see this sector develop.

Marketplaces can start small, and end up big. Very big. 
To build a billion dollar marketplace, you have to build expansion into your model from day 1.

For some, this will look like focusing on geographic growth and building your team of launchers. For others, it’ll be about adding new product lines and price points quickly, to create new use cases for your market. Or you can improve the core platform, by increasing efficiency – whether that means onboarding or the friction of each transaction. Others can double down on retention, by building utility and workflow automation, to set a foundation for more transactions.

Each of these moves can be valid, and different marketplaces will do each. Or perhaps all of them!

Written by Andrew Chen

October 17th, 2017 at 10:00 am

Posted in Uncategorized

Startups are cheaper to build, but more expensive to grow – here’s why

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Startups should be getting cheaper to build. After all, the industry’s created several waves of innovation that’s supporting this across multiple layers in the stack:

  • Open source software instead of paid developer tools
  • AWS instead of your own datacenter
  • Per-click ads instead of Superbowl commercials
  • Off-the-shelf SaaS tools versus building your own
  • App stores for efficient global distribution

Not only do a number of these trends make building new products cheap, in many cases it’s about driving the costs down to zero. If we zoom just into AWS / cloud computing, you see how a massive amount of competition is leading to significantly lower costs – even some vendors giving away their services pro bono:

As cloud providers rush to build new data centres, and battle for market share, businesses are finding that the cost of putting their computing and data storage into the online cloud is getting ever cheaper. In the past three years prices are down by around a quarter, according to Citigroup, a bank; and further significant falls look all but inevitable. Some providers, such as Microsoft, have started providing their services free to startups, in the hope of turning them into paying customers as they grow. (Economist)

However, this is opposite of what’s happening. Instead, startups are raising more capital and burning more capital to get to their Series As. It might be cheap to build the v1 of your app, but getting traction is a whole other story. Compared to a decade ago, it’s getting more expensive to get traction, while at the same time, growth is getting harder from intensive competition, consolidation, and saturation.

Why costs are rising
There are two underlying reasons for the increasing costs: Salary/comp for your team, and growth has shifted more towards paid acquisition. While the former is obvious (especially to those paying rent in San Francisco), the second is more nuanced, since it’s driven by a number of industry trends.

As we’ve said, growth is getting harder, and as a result, companies building new products are evolving their strategies away from counting on traditional channels like virality, SEO, and organic, and more towards paid acquisition to scale. Even though traction is difficult to achieve in today’s climate, venture capital is plentiful for those who hit a solid growth curve. This means that companies have an advantage when they execute well also have a natural product/channel match for paid acquisition channel. (Think high LTVs, lack of ad competition, being good at fundraising.)

What’s happening as a result
As a result of this pivot towards paid acquisition to scale, we see four trends that go along with rising costs:

  1. Startups are raising more money to get to traction
  2. Companies are trying paid marketing earlier
  3. There’s an increase in emphasis on paid referral programs rather than virality
  4. Companies are going for deeper monetization in order to open up paid channels

Let’s look at each of these trends.

1. Startups are raising more money to get to traction
More focus on paid acquisition means startups need to raise more money to raise money only once they can prove out their traction. We’re seeing more companies raising more money to get more traction before they raise, and when they do take the new round, it’s often to fund bigger and more expensive paid acquisition efforts.

The median seed round tripled from $272K to $750K between 2010 and 2016 according to analysis from Tom Tunguz over at Redpoint, and that growth extends to later rounds too. Companies across the board are raising bigger rounds, often from non-traditional investors, to drive growth for the next fundraise or for an exit (source: Quartz):

In the initial stages, this extra money enables buying early growth through testing and sub-scale campaigns to compliment organic growth. As a company scales, these bigger rounds buy you time and acquisition resources to build a defensible but expensive flywheel.

2. Companies are trying paid marketing earlier
The good news about more companies trying paid acquisition is that it’s easier than ever to experiment with paid marketing early. Self-serve ad systems are now the norm, which we can see from recent self-serve ad launches from newer platforms like Snap and Quora. Companies can test and master paid spend much earlier and run meaningful experiments with budget as low as $50. This allows an earlier and better understanding of unit economics and how to optimize the other steps in the funnel.

“Today, advertisers of all sizes expect platforms to offer them a number features as basic built-ins: self-serve, hyper-targeting, analytics, dynamic pricing. The way ad platforms are now structured with these features allows you to run small tests with sub-scale campaigns. It takes minimal time to make the creative, and it’s super easy to do testing for startups and new products.”

Sriram Krishnan, ex-Revenue Products at Snap, Mobile Ad Platform at Facebook.

The internet advertising industry continues to grow across all channels. The number of advertisers on Facebook alone recently hit 5 million, up from 4 million just 7 months ago.

There are a couple of implications to this. First, more competition (in total spend and in number of spenders) increases the global focus on paid acquisition. As a result, everyone’s spending more.

3. More emphasis on paid referral programs rather than virality
Viral channels aren’t working as well as they used to because of the natural lifecycle that affects all acquisition channels. Today, 10 years after the introduction of biggest social networks, most viral channels have peaked:

Perhaps we’ll see the return of these social channels, as messaging platforms mature, but in the meantime, many companies are utilizing referral campaigns to juice their acquisition. Paid referral programs also help build user engagement and get companies to faster network effects because on top of bringing in more users, they bring in more users who are already connected to each other.

Dropbox’s give/get disk space was one famous early example of referral, but these days, the largest companies from Uber to Airbnb all utilize referral programs.

4. Monetize more deeply to open up channels
To support the increase in paid spend, companies need to either raise more money, or make more money. As a result, we’re seeing companies optimize for better LTVs to justify higher CAC and increased competition across the board.

Companies like Wealthfront, Breather, Credit Karma and Gusto have all hit high LTVs early in their lifecycles, and that profitability has bought them a competitive edge in acquisition as those stronger LTVs afford them higher CAC. Anecdotally, it’s been said that many Fintech companies have CACs over $1000+ to acquire a single customer.

All acquisition channels are an efficient market at some point, and this means that companies that monetize better than their competitors (either with higher LTVs or because they enjoy shorter payback periods) will be able to afford a higher CAC and subsequently out-invest those competitors. In short, better monetization is a competitive advantage for growth.

As you build your company, don’t underestimate the rising cost of distribution. Yes, everything’s getting cheaper from the growth of cloud computing, off-the-shelf SaaS, open-source code, and more granular and accessible performance marketing. But, growth is also getting tougher from channel saturation, better competitors, and consolidated winner-take-all platforms.

To keep growing in this type of landscape, you’ll need to think carefully about paid acquisition, deeper monetization, and how to compete in this new environment:

  • New products are often sub-scale on unit economics, so they have negative LTV:CAC. Show and carve out a clear path to monetization so you can afford growth.
  • No one can afford to put off paid acquisition anymore, and it’s easy to test ads on small budgets, so start as early as possible.
  • Think of referral programs are another form of paid spend. You have the same CAC, but instead of giving the money to Facebook or Google, you give value to your users and their friends.
  • Finally, consider ways to deepen differentiation by solving hard(er) problems and building your moat with tech.

Good luck out there!

Written by Andrew Chen

July 19th, 2017 at 10:00 am

Posted in Uncategorized

This year’s top essays on growth metrics, consumer psychology, Uber, push notifs, NPS, and more

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As you can tell, I’ve been a bit more active writing in the last few months. I wanted to do a quick roundup of my essays over the last year, in case you’ve missed any of them. I’ve published a number of guest essays and original writing on topics like growth metrics, consumer psych, the startup ecosystem in the Bay Area, push notifications, and much more.

If you want future updates, you can always subscribe to get the newsletter.

For your convenience, I’ve written a couple blurbs underneath each essay so you can get a sense for each article.

Finally, I wanted to note – can you believe I’ve been writing for almost 11 years now? Who knew I’d be able to keep it up for so long?! Appreciate all the folks who’ve been with me for years. Thank you for reading!

Andrew Chen
San Francisco, California


Original essays

10 years in the Bay Area – what I’ve learned
I’ve lived here for the last decade, and have learned a ton of about this region’s entrepreneurial drive, the unique culture, and wonderful folks. I wanted to share a couple lessons learned here.

The Bad Product Fallacy: Don’t confuse “I don’t like it” with “That’s a bad product and it’ll fail”
Your personal use cases and opinion are a shitty predictor of a product’s future success.

Growth is getting hard from intensive competition, consolidation, and saturation
It’s the end of a cycle, and we’re seeing headwinds on paid channels, banner blindless, competitive dynamics, and more. And it’s much harder to compete with boredom than with Facebook/Google/etc.

What 671 million push notifications say about how people spend their day
Here’s a study, based on Leanplum’s data, on how people spend their days – on sports, leisure, phone calls, and otherwise – in addition to what tech platforms they’re using.

Startups and big cos should approach growth differently (Video)
Here’s a video interview breaking down how startups evolve and change their strategies as they gain initial traction, hit product market fit, and eventually start to scale.

What’s next in growth? (Presentation at Australia’s StartCon)
Last year I presented this talk on how marketing has evolved over the last century, and how many of the ideas we think of as “growth” today are actually based on concepts from decades ago. I use this to talk about future platforms and where this might all go.

Uber’s virtuous cycle. Geographic density, hyperlocal marketplaces, and why drivers are key
In my last two years at Uber, I’ve learned a ton about the flywheel that makes Uber’s core business hum and grow incredibly fast. In this essay I draw from Bill Gurley’s essays on network effects, the labor market for part-time workers (aka drivers, “the supply side”), and how surge works within the company. A lot has evolved/changed since I’ve written this, but it’s a good overview from my first year of learnings.

Guest essays

How To (Actually) Calculate CAC
Brian Balfour, ex-vp growth at Hubspot, talks about how to calculate cost of acquisition and all the practical difficulties involved.

A Practitioner’s Guide to Net Promoter Score
Sachin Rekhi, ex-director product at Linkedin, breaks down how to measure and utilize Net Promoter Score and its relation to viral growth.

Growth Interview Questions from Atlassian, SurveyMonkey, Gusto and Hubspot
Lots of amazing interview questions from the growth leads at some of the best SaaS companies on the market.

Psych’d: A new user psychology framework for increasing funnel conversion
Darius Contractor at Dropbox describes a framework on pushing users through conversion funnels by getting them psych’d (via value prop, clear CTAs, etc). Nice framework that speaks to reducing friction and increasing value.

Top essays from 2015
This roundup, but from two years ago :) Includes writing about Uber, online dating, push notifications, Apple Watch, and more.

Written by Andrew Chen

July 10th, 2017 at 9:30 am

Posted in Uncategorized

Growth is getting hard from intensive competition, consolidation, and saturation

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The end of the cycle
One of the best essays written last year was Elad Gil’s End of Cycle? – referencing our most recent 2007-2017 run on mobile and web software, and the implications for investing, startups, and entrepreneurs. Although he doesn’t directly talk about it, the end of a tech cycle has major implications for launching new products, growing existing product categories, because of a simple thing:

It gets much, much harder to grow new products or pivot existing ones into new markets

The reason for the above is that there are multiple trends – happening right now – that impede growth for new products. These trends are being driven by the biggest players – Google/Facebook, et al – but also by the significant leveling up around of practitioners in design/PM/data/growth.

We’ll look at a couple trends in this essay, including the following:

  1. Mobile platform consolidation
  2. Competition on paid channels
  3. Banner blindness  = shitty clickthroughs
  4. Superior tooling
  5. Smarter, faster competitors
  6. Competing with boredom is easier than competing with Google/Facebook

These trends are powerful and critical to understanding why all of a sudden, entrepreneurs/investors are starting to get into many new fields (genomics, VTOL cars, cryptocurrency, autonomy, IoT, etc) in order to find new opportunities. After all, if you can’t grow in the existing markets, you very quickly need to get into new ones, as Elad describes:

One sign that technology markets often exhibit at the tail end of a cycle is a fast diversification of the types of startups getting funded. For example, following the core internet boom of the late 90s (Google, Yahoo!, eBay, PayPal), in early 2000 and 2001 there was a sudden diversification and investment into P2P and mobile (before mobile was ready) and then in 2002-2003 people started looking at CleanTech, Nanotech etc – industries that obviously all eventually failed from an entrepreneurial and investment return perspective.

Nanotech, cleantech, etc was the last cycle, and now we’re talking about the next one.

#1 Mobile platform consolidation
The new Google/Apple app duopoly is more concentrated, more closed, and far less rich (from a growth standpoint) as compared to web – which means that mobile is far more stagnant and harder to break into. App Store functionality like top ranking charts, “Essential” bundles of apps, editorialized “Featured App” sections, all help drive a winner-takes-all mobile ecosystem.

No wonder app store rankings have ossified over the years. Facebook and Google now control most of the Top 10 apps in the mobile ecosystem:

Source: Nielsen, Dec 2016

If you’re introducing a new app – whether unbundling a more complex app or launching a new startup – how do you break into this? There’s not a ton of organic opportunities. And the paid acquisition channels are getting saturated too.

#2 Competition on paid channels
Paying for acquisition is one of the key channels still available, if you can find the right untapped audience segments with high ROIs. This only works when prices aren’t bidded up and you don’t face too much competition for the same ad inventory. Unfortunately that’s not what’s happening.

For example, let’s look at some of the dynamics of Facebook increasing their revenue per DAU over the last few years:

This is driven by a number of factors, of course – relevance, targeting, ad unit engagement, etc. – but it’s also because competition is getting fiercer on Facebook ads, not less, which is evidenced by the rapid increase in the advertiser count as well as the increase in revenue per user. In 2017, Facebook counts over 5 million advertisers on its platform, up from 4 million in Q3 of last year and 2 million in 2015. During its Q1 2017 earnings call, Facebook told investors that it expected ad revenue was approaching a saturation point, despite major growth in Q1 2017 earnings as compared to 2016. It’s currently at 2 billion users, with 17% YoY user growth, and its ability to add more inventory depends increasing its user base, or increasing users’ time spent on Facebook.

#3 Banner blindness = shitty clickthroughs
Additionally, everyone’s getting smarter about growth, including consumers. Today, most invite systems no longer have the same novelty value or efficacy as they did 10 years ago (Dropbox’ give/get was novel when it launched), and consumers’ “banner blindness” extends far beyond actual display advertising to encompass referral systems and virality programs.

In Mary Meeker’s latest internet trends report, she reports that up to 1/3 of some countries are using ad blocking, and we’re quickly on our way to 600M internet MAU who can’t be reached by ads:

This is just the 2017 version of The Law of Shitty Clickthroughs, which I wrote about a few years ago, where I showed some stats indicating that email marketing open rates are on the decline:

… and that traditional banner CTRs seem to be asymptotically approaching zero:

These trends are troubling, and mean that these channels are getting less engagement per user, and we haven’t found amazing new channels to replace them.

#4 Superior tooling – which levels the playing field
At the same time as advertising is getting more crowded, there’s also increasingly widespread availability and adoption of tools like Mixpanel, Leanplum, Optimizely and others that close the gap on being data-driven at companies.

Ten years ago, we used to look at total registered users. Cohort analysis was a sophisticated approach, and we also didn’t have a sense for MAU, DAU or other more granular metrics. One of the killer features of Mixpanel is that it made understanding cohort-based retention turnkey. It used to take a real investment of engineers, data scientists, and know how to be able to create simple graphs like this:

Now, it’s pretty much turnkey. You can get this chart from Mixpanel (and may others!) practically for free, as soon as you implement your analytics tracking.

In B2B, we’re seeing the same phenomenon. Outbound used to be painstaking and manual. Today, there are many sales tools that make outbound more accessible (Mixmax, Outreach, insidesales.com etc), which automates part of the process but also generates more noise and competition. Tasks that used to be more manual and higher friction are automated and easier, which leads to more people jumping in.

The result is that it makes everyone better. You and all your competitors understand your/their acquisition and retention bottlenecks. Everyone has an equal, data-driven shot at improving LTV, and as a corollary can spend more on ads.

#5 Smarter and faster competitors
It used to be that startups could count on their competitors to be big, dumb, and slow. Not anymore. We’ve all gotten smarter and faster, and that includes your competitors. It used to be that you could wait a few years before competitors would respond. Now the Facebooks, Hubspots and Salesforces of the world can and will copy you right away.

Most famously, we’ve seen Facebook fast follow Snap within their Messenger, Instagram, Whatsapp and core product:

But it’s not just consumer where this is happening:

  • Dropbox <> Google Drive
  • Slack <> Microsoft Teams
  • YesWare <> Hubspot Sales

… and many more examples too.

#6 Competing with boredom is easier than competing with Facebook + Google
When the App Store first launched, competition was easy: Boredom. Mobile app developers were taking time away from easy, ‘idle’ activities like waiting in line, commuting etc. But today, acquiring a new app user means stealing a user’s time from their favorite existing app.

As we’re near the end of the cycle, companies have moved from non-zero sum to a zero-sum competition.

Instead of competing with boredom, we’re now competing with Silicon Valley’s top tech companies, who already have all your users (back to number 2 above). This also applies to the consumerized workplace, where new entrants will be competing to steal users’ time from Slack, Dropbox and other favorite apps. This is much, much harder because the incumbents have pretty great products! And proven distribution models to respond if needed.

How the industry is evolving, in response
The above trends are troubling for new products, and especially for startups. All 6 of these trends are scary, and they’ve emerged because we’re at the end of a cycle. There’s a variety of natural monopolistic trends (like app stores, ad platforms, etc), where everything with related to growth and traction is getting harder.

If companies want to stay in the mobile/software product categories, they need to evolve their strategies. I’ll save a deeper discussion for a future essay, but here are some observations on what’s happening:

  1. More money diverted to paid acquisition
  2. Deeper monetization to open up channels – especially paid
  3. Creation of paid referral programs to complement ad buying
  4. Personalization features that rely on lots of data to amp up targeting
  5. Products trying to deepen differentiation by solving hard(er) problems/tech

There seems to be a deepening in both monetization, differentiation, and personalization to help open up growth. This happens by solving more fundamental customer problems – especially those that help generate real $ value for people – but also helps open up paid channels, whether that’s advertising, referrals, or promos.

More discussion on this in a future writeup!

Written by Andrew Chen

June 26th, 2017 at 9:30 am

Posted in Uncategorized

Psych’d: A new user psychology framework for increasing funnel conversion (Guest Post)

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[Hi readers, my good friend Darius Contractor (currently growth eng at Dropbox) has a brilliant new framework how user psychology has driven growth at companies like Bebo, Tickle, PhotoSugar and of course, Dropbox. Thanks to Darius and the folks at Reforge for putting this together. Hope you enjoy the writeup here! -Andrew]

Increase your funnel conversion by getting users Psych’d – by Darius Contractor

Have you ever wondered why people are bouncing from your nearly-frictionless onboarding flow? Why the same change can result in a lift on one page and cause drop-off on another? Or why people who find you via search bounce away after a few moments?

Having spent years focused on building experiences that got millions of users sharing, onboarding and inviting their friends, I’ve learned 2 things:

1. Every element on the page adds or subtracts emotional energy
2. Inspiring users is as important as reducing friction

A secret of the top growth experts in tech is to think about every UX interaction as an emotional event. But far from being random or beyond our control, emotion-driven interactions can be broken down into components, optimized at each step and replicated to get better results for onboarding and conversion.

The Psych Framework

Today I’m sharing the Psych Framework I’ve used to help grow companies like Tickle, Bebo and Dropbox. It is a systematic way to detect and improve the way an experience affects user emotional energy, which we call “psych”.

User Psych Framework by Darius Contractor

Every UX interaction increases or decreases Psych, the unit of measure for motivation to complete an action.

Every element of a webpage either inspires us by giving us more units of Psych or overwhelms us by depleting our existing store of Psych.

Once you understand what elements are adding to or depleting users’ energy, you can then start to manage that energy: adding inspiration and minimizing overwhelm to help users take your core actions.

Psych Units

We measure user energy in units of “Psych”, from 0 to 100.

User psych fuel gauge measures unitsA user at 100 Psych is maximally committed to their current experience, does not need further motivation, and will overcome most challenges. For example, a person who needs to file their taxes tonight will do whatever it takes to download their W2 from their company’s payroll site. They’ll complete a forgotten password step, suffer through a poor interface they’ve never used before, and read through confusing numbers in order to get their taxes done in time.

A user at 0 Psych is exhausted and disinterested, to the point of abandoning their current experience. For example, a person accidentally clicking on an ad who realizes they’ve ended up on a scam site will have no motivation to continue and will bounce.

Psych Elements

Being aware of +Psych in your UI can massively drive user excitement/growth:

Tinder: “Discover new and interesting people nearby” → Yes, let’s!

Likewise, being unaware of the -Psych in your product can massively decrease success:

Global Entry site: “Fill in 40 form fields about yourself” → Ugh, maybe later… <closes browser>

We call elements that inspire users and add to their emotional energy +Psych.

If a car rental site pitched “Get your car for $15/day” that might be a +Psych, inspiring users to try to get this good deal. Inversely, -Psych are items that tire or overwhelm users, such as long sign up forms, unclear UX, too much text, and unclear next actions.

Let’s test drive this concept with Match’s home page. After that, we’ll talk about what to look for in your flows and examine what Airbnb gets right in their host sign-up experience.

Example 1: Match’s homepage

Match homepage user psychology
How do we evaluate the Psych score of this page?

1. Determine your starting Psych

To understand how much Psych a user has when arriving to a site, consider how they got there.

For example, users who arrive on Match through a Google Search are high-intent and have intrinsic motivation since they’re explicitly searching for dating. So they’re around a 60 Psych.

By contrast, visits from banner clicks would likely be low-intent since their clickthrough came in response to an external trigger. They’re perhaps a 30 Psych.

A referral from a friend might result in a clickthrough that’s low intent but has high social validation. So, they might be at 50 Psych.

2. Follow the user’s attention from top-left to bottom-right

In left-to-right languages like English, we consume content from top-left to bottom-right. As we follow our natural path across the page, our Psych will either go up or (more likely) down as we encounter elements that excite us or elements that are obstacles.

On the Match homepage above, these are the elements we encounter from top-left to bottom-right:

  • Logo
  • Photos of singles
  • “#1 in dates, relationships and marriages”
  • Demographic form
  • “View Photos »” button

As you can see, right after the logo assuring us it’s a real company entity that we can trust, we see appealing photos of smiling people. Then we see the byline “#1 in dates, relationships and marriages,” which assures us that we’re going with the best site and that it’s there to help us achieve dates, relationships, or even marriage.

Next, there’s a form, which requires action that potentially depletes Psych. But we’re spurred on by the “View Photos” button — which is exactly the thing a user interested in “dates, relationships, and marriage” wants to do at this point.

3. Which elements are +Psych for you? Which are -Psych?

Let’s run through the Match homepage again and tally up Psych, element by element.

Match homepage user psychology each step animated
These are the + Psych elements:

  • “Ooohh, these people look attractive!” → +10 Psych
  • “They’re #1? And I can get dates/relationships/marriages?” → +3 Psych
  • “I like that it defaults to Woman seeking Man.” → +3 Psych
  • “Nice, can’t wait to View Photos” → +8 Psych

These are the – Psych elements:

  • “Hrm, what age am I looking for?” → -5 Psych
  • “Why do they need my zipcode? Argh, keyboard…” → -10 Psych

4. Sum it up!

Tally up all the Psych elements to see where users are by the time they get to your call-to-action.
Greater-than-zero Psych means the user got through the flow.

  • Starting from a Search: 50 Psych
  • +Psych: 10+3+3+8 = +24 Psych
  • -Psych: -5-10 = -15 Psych
  • Result: 59 Psych → They made it!

Next, we’ll go through some of the top +Psych and -Psych factors across common pages.

Maximizing Psych on each of your pages

1. Assess initial Psych

People come to your site with an initial quantity of Psych.

If you’re hungry at noon and haven’t eaten all day, then your Psych level for a sandwich will be very high. That will help you power through the friction of standing in line, deciding between options, and pulling out your wallet to be saved by an $8 hero.

Therefore, the first step to evaluating Psych is to look at factors determining how much Psych people have when they enter your funnel:

2. Psych on the landing page

Once a visitor hits your landing page — great! You now have multiple chances to increase their Psych to get them to continue to signup or, if you’re not careful, decrease their Psych and cause them to bounce.

3. Enter personal info

At some point, you’re going to have to ask your visitor to enter some personal information, even if that’s just their email address.

Asking for personal information usually creates a negative Psych moment, whether it’s because people are wary to share their information or because they’re simply feeling lazy and don’t want to complete an input action.

4. Interact with product

If you have a freemium or free trial model, your user will get a chance to interact with the product before it’s time to pay. This is a chance to increase Psych before the user gets to the Psych-depleting payment action.

5. Enter payment information

For most businesses, eventually it’ll be time for users to enter their payment information and complete a transaction.

This is the ultimate Psych-depleting action because of the psychological phenomenon of the pain of spending money.

While we might think that the purchase action should be Psych-increasing because of the anticipated pleasure of acquiring something we want, it actually triggers the same area of the brain as for physical pain. So, spending money = pain.

But, MIT researchers found that credit cards increase detachment from purchases. In other words, credit cards help decrease the pain we feel from spending (cash) money.

Aside from the inherent pain and negative Psych of spending, there are still things you can do to improve Psych at the payment step.

Decreasing cognitive load is more than just short signup forms

The above is a basic framework anyone can go through for figuring out the ups and downs of Psych within an onboarding flow.

But optimizing Psych isn’t just a matter of removing clicks and reducing text. In some cases, more detailed forms or copy can help by decreasing the cognitive load on making the decision — even though it’s technically more effort. For example, including more information about security and money-back guarantees can overcome trust barriers and alleviate fears for big purchases.

Now let’s look at another live example.

Example 2: Airbnb’s hosting flow

Let’s go through a more complex example — becoming a host on Airbnb.

How do we evaluate the Psych score of this page?

1. Determine your starting Psych.

To figure out the ups and downs of Psych for someone who’s thinking about hosting on Airbnb, let’s start with their mindset before they hit the landing page.

  • They might have heard some stories about making a lot of money on Airbnb → +20 Psych
  • They might have heard about it being lots of work or a negative hosting story → -10 Psych

But ultimately their positive starting Psych is greater than their doubts and they are motivated enough to check it out.

2. Follow the user’s attention from top-left to bottom-right

Airbnb offers three different ways for becoming a “host” on the platform:

  1. Renting a house or apartment
  2. Helping neighbors with their Airbnb listings
  3. Leading a tour or other travel experience

To keep it simple for this post, we’ll just look at the core Airbnb “host” action — renting a house or apartment.

As you follow the user’s attention from top-left to bottom-right, it’s pretty clear that Airbnb knows what its hosting users care most about — making extra money by renting out their places.

Here are the immediate elements we encounter above the fold, from top-left to bottom-right:

  • Logo
  • “Earn money” value proposition
  • An interactive calculator to see how much you could earn
  • A “Weekly Average” box with an impressive earnings estimate and a dollar sign

From there, the page displays more content aimed at increasing Psych and reducing doubt:

  • Tactical instructions that demystify how Airbnb works
  • Reassuring details about safety and security, like their host protection insurance and their $1M guarantee for hosts
  • Social proof in the form of a video featuring a diverse handful of hosts talking about how income from Airbnb has helped them to stay in their homes, pay for medical bills, or fund a retirement.

Because users have to log in to continue the host flow, Airbnb’s goal on this first page is to drive up Psych high enough to carry them through the subsequent, more tedious steps in the flow — logging in or creating an account and setting up their first listing.

3. Which elements are +Psych for you? Which are -Psych?

Now let’s run through and tally up Psych, element by element.

Airbnb does a good job of loading up the page with +Psych elements because they know they’re asking a lot of the user:

  • “Earn money? Yes please.” → +10 Psych
  • “I can rent out an entire place, or a room, or just a couch? Then there probably is something for me.” → +5 Psych
  • “$741 weekly average… higher than I thought.” → +10 Psych
  • “Host insurance, and a $1M guarantee that protects my stuff… ok phew.” → +4 Psych

4. Sum it up!

Once again, you can tally up all the Psych elements to see where users are by the time they get to a call-to-action. Remember, greater-than-zero Psych means the user clicked the “Sign me up” button.

This is a simple tally of Psych on just one of the pages of the Airbnb “become a host” flow. There are obviously many more steps before a user becomes a successful host that’s contributing to the marketplace. With that in mind, you can run through the Psych Framework at each step of your onboarding or conversion flow to find opportunities to reverse -Psych and increase +Psych.

So that’s the psych framework in a nutshell. Tell me what you think in the comments and if you’d like to hear more.


Disclaimer: I’m a Dropbox employee, but I’m not posting on behalf of Dropbox or in an official capacity as a Dropbox employee. This post was originally published here.

Written by Andrew Chen

June 12th, 2017 at 10:00 am

Posted in Uncategorized

Startups and big cos should approach growth differently (Video)

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I recently did a video interview on the topic of how your growth strategy changes from being a small startup versus becoming a larger company. It’s hard to compete when you’re launching a new product and you have to think asymmetrically for your growth efforts to work. I walk through each stage, step by step, and talk through some of the strategic dynamics to think about. (Thanks to the Reforge folks for setting this up!)

You can watch the full video here, and check out the notes below.



New startups have to focus on underrated acquisition channels for early growth efforts [0:00]

  • Look for channels that are too small for the big companies to worry about — this is how smaller companies can gain an asymmetric advantage
  • Examples: niche communities, sub reddits, mailing lists, offline, blogs, linkedin groups, facebook groups
  • The best underrated channels are all small but high-intent

Find underrated channels that directly match your product’s target market [1:45]

  • Look at what are the channels that match your product the most.
  • It has everything to do with finding lots of little channels with high relevance.
  • Regardless of where you start, you need to quickly be able to cobble together a bunch of small channels to get going — not just one or two.

Learn with very small channels by focusing on qualitative feedback to start [2:48]

  • Any small channels that allow interaction with the audience, will allow you to do testing.
  • Even small groups for customer development are worth exploring
  • Test for the responsiveness of your audience

Scale up to the next, bigger channels, by starting with tests to optimize your performance [3:58]

  • First path: Go after the bigger channels that the bigger co’s are going after, siphon off a small amount of traffic, at minimum it’s a means of testing
  • Second path: trying to find a new channel / platform, that others haven’t considered that’s unique to your product. Example: Dropbox or Slack integration if you’re doing workplace productivity.
  • Those channels are “medium sized” right now but have the chance to scale up bigger as their APIs develop.

Big companies approach acquisition by building a portfolio of channels that can scale [5:32]

  • Once you’re big, it’s about building a portfolio
  • All the little things will hit their ceiling and won’t scale
  • You’ll end up with a few established, large channels, and your strategy will be about aggregation. Grow your portfolio, not replace channels
  • Big opportunity is around attacking existing channels (or new ones) in a unique way that’s tied to your product
  • Product-channel fit is key (Pinterest + workplace productivity tool = dissonance)
  • Examples for b2b: the calendar, the browser
  • For consumer, there’s YouTube

The most exciting channels “right now” need to tie uniquely/directly to your product [9:02]

  • “Right now” is the wrong way to think about it; it’s about the trajectory
  • Stage 1: Map the user lifecycle:
    • What are all the other tools, apps, offline experiences that your users are also coming into contact with?
    • Find out what your product is most adjacent to
  • Stage 2: Sizing / understanding the trajectory of all these things that are out there (at an MAU level).
    • What kind of integration can you get?
    • Some platforms are more conducive to virality or being used as a growth channel (instagram doesn’t give you a way to link out, so it’s less good of a channel VS youtube, which does allow cross linking and linking out)
  • Channels have to match what people are trying to do with your product

You have to pick the right social channels for virality [11:30]

  • Social isn’t just digital experiences; it’s also offline experiences
  • So, “social channels” are any ways that your users / customers talk to each other to convey that the other person should try a product
  • Direct recommendation or invite: “I’ve invited you to Facebook, you should use this.”
  • Indirect recommendation or invite: “I’ve taken this cool Instagram photo, do you like it?”
  • Same applies to B2B; Dropbox is an example of indirect.
  • Digital is important because it’s attributable, but the broader takeaway is to create a product with a bunch of touchpoints in a user’s life; those touchpoints trigger natural recommendation or invite opportunities

How do products spread on social channels? [16:06]

  • Extrinsic vs intrinsic motivation, and their accompanying rewards systems
  • Extrinsic — get a direct reward for referring someone
  • Intrinsic — my friends using this makes the experience better for me
  • They’re not mutually exclusive, and can work well together
  • Extrinsic is easier to bolt on after the fact, but intrinsic (built in to the product early on) creates deeper defensibility by creating network effects

“Going viral” doesn’t mean just building something cool [21:19]

  • “I’ll just make something really cool, and people will talk about it” is not as sustainable as acquisition channels that are based on combined intrinsic and extrinsic motivations
  • Example: Slack going viral

Not every platform is created equal (for virality). How do you build for the right one? [22:48]

  • Anything that’s spreading from user to user is spreading on a platform that already exists.
  • Platforms are built on top of each other (Facebook on top of .edu, many companies built on top of the Facebook platform) and certain ones are more suitable than others.
  • Key questions to ask when evaluating platform potential:
    • How easy is it for customers to communicate with one another?
    • How much control do you have (via APIs or anything else) to customize the invite experience?
    • Do you have the ability to add a link?
    • Can you get ahold of an address book or social graph, in order to generate invites?
  • You have to assess not only size and trajectory, but also how open is that platform? What are the hooks the it supplies you to build growth?

Written by Andrew Chen

June 6th, 2017 at 10:00 am

Posted in Uncategorized

The Bad Product Fallacy: Don’t confuse “I don’t like it” with “That’s a bad product and it’ll fail”

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Benedict Evans at a16z recently tweeted the following:

There’s so much truth in this tweet. And it resonates so much, I think it deserves a name:

The Bad Product Fallacy
Your personal use cases and opinion are a shitty predictor of a product’s future success.

I’ve been in the Bay Area for 10 years now, and nothing stings more than whiffing on the prediction of whether a product will be success. Getting this wrong can hurt the ego and sometimes the checkbook too – just ask the dozens of investors who’ve passed on Facebook, Google, Uber, and so on! Personally, I missed completely on Facebook’s potential, and that’s just one of many bad predictions over the years.

The Bad Product Fallacy happens because the trajectory of a product evolves quickly – it’s just software, after all – and a simple set of features can quick grow into a rich, complex platform over time.

Let’s look at some of the comment root causes of the Bad Product Fallacy:

It all starts with a toy
The first and most well-studied root cause of the Bad Product Fallacy is from the theory of disruptive innovation. Many products can look like toys before they become successful. Just take Instagram as an example – it was just a photo filters app at the beginning, and is now one of the largest media properties in the world. Or personal computers, which was initially meant for hobbyists since they were underpowered and weren’t useful for business applications.

This whole phenomenon – widely studied as disruptive innovation theory by Harvard’s Clayton Christensen – is nicely summarized in this blurb:

Disruptive technologies are dismissed as toys because when they are first launched they “undershoot” user needs. The first telephone could only carry voices a mile or two. The leading telco of the time, Western Union, passed on acquiring the phone because they didn’t see how it could possibly be useful to businesses and railroads – their primary customers.

– Chris Dixon, gp at a16z

Here’s now you know you might be falling for this trap: If you use a new product for the first time and say, “huh, is that all there is?” then you may just be whiffing. Or if you complain about a lack of features, even as the underlying technologies are being upgraded extremely rapidly.

Just wait a couple years- by then, the product will have improved so much that you’ll realize you got it all wrong.

Moore’s Law for everything
The inverse of disruptive innovation is that products can start out super premium, but then quickly fall in price to find success in a large, mainstream market. The iPhone is the classic example, but Tesla, Uber, and others are pulling this off too. Sometimes there’s a Moore’s Law kind of effect, where things are getting enormously better and cheaper over time.

Let’s look at the iPhone, the classic example. Steve Ballmer made a very bad prediction – when asked about the new device, he laughed! Not a threat! Instead, he explained why the iPhone would fail:

500 dollars? Fully subsidized? With a plan? I said that is the most expensive phone in the world. And it doesn’t appeal to business customers because it doesn’t have a keyboard. Which makes it not a very good email machine.

-Steve Ballmer, Microsoft on the iPhone

Funny, right? Hindsight is 20/20. Or speaking of phones, here’s another funny example, but about mobile phones in general:

In the early 1980s AT&T asked McKinsey to estimate how many cellular phones would be in use in the world at the turn of the century. The consultancy noted all the problems with the new devices—the handsets were absurdly heavy, the batteries kept running out, the coverage was patchy and the cost per minute was exorbitant—and concluded that the total market would be about 900,000. At the time this persuaded AT&T to pull out of the market, although it changed its mind later.

– The Economist, Oct 1999

But of course, mobile phones as a luxury was quickly fixed. By making the cost per minute cheap and fixing the other technical issues, the mobile phone has become the most ubiquitous computing device in the world.

Here’s how you know you’re about to commit this flavor of the Bad Product Fallacy: If you try a product and ask “Why would anyone pay so much for this?” then you need to think through what happens if the service/product becomes much, much cheaper. Or if it turns out that consumers don’t mind the price. Thinking through these trends can change the game.

I’ve myself missed here when looking at Uber in their early years. When Uber first came out, I thought, wow – why would anyone need an app to call a limo? This is a fancy person’s problem. But of course, if you can get the pricing down from a limo to a taxi, then cheaper to a taxi, and one day cheaper than owning a car – well that’s potentially a trillion dollar company. It turns out there’s some kind of Moore’s Law effect for the cost of transportation over time, and now I’m working there :)

S0me products start by selling stamps, coins, and comic books
Marketplaces have their own flavor of this fallacy because they often start with a vertical niche where buyers/sellers gather, and slowly need to grow to new verticals to be relevant. If these initial niches aren’t your jam, then you may miss on the marketplace’s potential, even if its on a trajectory to ultimately grow into areas that you’ll find useful too.

The classic example of this is eBay: Bessemer Ventures had the chance to invest, but at the time, the marketplace had a lot of collectibles. Here was their evaluation:

“Stamps? Coins? Comic books? You’ve GOT to be kidding,” thought Cowan. “No-brainer pass.”

– Bessemer Venture Partners, Anti-Portfolio page

Of course, eBay went on to add many new verticals, from cars to electronics to much more, eventually returning 700X to their original investors.

The tricky thing here is that you may not want to buy products that are in the marketplace’s initial verticals, which means the product won’t serve your use cases or you won’t love it. However, if you wait a couple years, the marketplace may eventually grow into product categories that you care about.

Social networks and content platforms need density, penetration, to become useful
Finally, let’s look at social/communication/UGC networks which have their own issues. These platforms can be super tricky because similar to marketplaces, they need time to mature as the networks form.

The often cited 1/9/90 rule for digital communities fundamentally drives this dynamic:

The 1% rule states that the number of people who create content on the Internet represents approximately 1% of the people actually viewing that content. For example, for every person who posts on a forum, generally about 99 other people are viewing that forum but not posting. (Wikipedia)

This means that, similar to marketplaces, you need the right balance of content creators and consumers in every vertical of content to have a functioning network. If a social communications product like Snapchat is only useful when you have >5 friends using it, you’ll inherently misunderstand it if the core market is teens and not 40 year old venture capitalists. If you tried using the Internet back in 1990, you may have decided that it’d never work since it’s all academic researchers.

Today, you may be skeptical about VR because it’s mostly games and the apps you’d really like to use haven’t been developed yet. But just wait, it might all click once the right dynamic of content creators, consumers, developers, and other constituents are at the table.

Similar to marketplaces, social networks, communications tools, and user-generated content platforms need critical masses of both creators and consumers to make things work. Sometimes this starts with a niche – like college students or San Francisco techies. But if a product can nail an initial vertical and start hitting up other ones, it may be on its way to mainstream success. Don’t judge too early!

Avoiding the Bad Product Fallacy
In the end, we all love to use our own personal judgement to quickly say yes or no to products. But the Bad Product Fallacy says our own opinions are terrible predictors of success, because tech is changing so quickly.

So instead, I leave you with a couple questions to ask when you are looking at a new product:

  • If it looks like a toy, what happens if it’s successful with its initial audience and then starts to add a lot more features?
  • If it looks like a luxury, what happens if it becomes much cheaper? Or much better, at the same price?
  • If it’s a marketplace that doesn’t sell anything you’d buy, what happens when it starts stocking products and services you find valauble?
  • If none of your friends use a social product, what happens when they win a niche and ultimately all your friends are using it too?

It’s hard to ask these questions, since they mostly imply nonlinear trajectories in product innovation. However, technology rarely progresses in a straight line – they grow exponentially, whether in utility, price/performance, or in network effect. Ask yourself the above questions to stay centered, and if you use it to find the next Uber or Facebook, give me (and Ben!) a holler :)

Written by Andrew Chen

January 30th, 2017 at 9:30 am

Posted in Uncategorized