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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.

Onwards!

Andrew
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!

Andrew
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.

Superforecasting
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.

Reset
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.

Principles
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.

Conclusion
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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Readers,
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!

Regards,
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

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

What’s next in growth?

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[I recently gave the keynote at the largest startup conference in Australia, StartCon. Many awesome growth folks were there, including Elena Verna at SurveyMonkey, Nate Moch at Zillow, Sean Ellis at GrowthHackers, etc. My talk is below, with links to my talk, preso PDF, etc at the bottom. If you want to see all the conference talks, they’re here. Thanks! -A]

In tech, we’re always thinking about the future.

This is why it’s no surprise that one of the most common questions I get is: What’s next in growth? As practitioners in growth, marketing, entrepreneurship, and tech, we’re looking for the edge that’ll give our products a chance to succeed in an extremely competitive and dynamic environment.

The answer to this isn’t simple – there isn’t an obvious closed form solution, so I won’t try to give a “tips and tricks” kind of answer. Instead, let’s talk about how to systematically answer this in a way that’ll be relevant today as well as 10 years from now.

First, we have to zoom out.

Technology, products, growth, and marketing don’t exist in a vacuum. There’ve been many products, and lots of smart folks thinking about this problem for a long time. If we look back at what’s come before us, we can try connecting the dots to see if we can spot any patterns.

The first thing we spot when we zoom out is that the pace of innovation has been speeding up:

Here’s a graph of various technologies and how fast they’ve been adopted over the last hundred years. It used to be that products like the telephone or electricity took on order of 50 years to go from 0% of the US population to 90%. In the last recent cycles, things have been going much faster – the internet, cellphones, and the computer have all hit massive penetration within just 10 years. Amazing.

This also means that strategies for growing your products are becoming even more competitive, dynamic, and tricky.

Today, we’re going to look at three common techniques for growth:

  • Getting customers to refer friends
  • Spreading viral content
  • Bootstrapping marketplaces

Let’s look at what these problems looked like 100+ years ago, and how we’re thinking about them now. I think we can learn a lot by connecting the dots.

We’ll start with customer referrals:

OK- classic move. How do we get customers to refer their friends? Some of the biggest and most successful products grow this way.

You might think of it conceptually like this:

From a fundamental path, it’s all about building a tree – getting one person to refer many friends, of which a small percentage will go on to refer the next generation of friends, and so on.

The idea here is that if we can make this happen in a chain, then we get a viral loop, which grows and grows awareness for your products.

No matter what kind of marketplace, the questions you have to answer – in any configuration here – are quite simple.

We need to figure out why you refer people. Is it monetary? Is it because the product gains in utility as more people join? Is it because you want to galvanize a collective response – like voting on a poll?

Who do you invite? Is a small network of close friends and family? Or is it a wide group, like a YouTube video where you want millions of views from people you may not even know?

What channel do the invites happen in? Is it real life word of mouth? Or do you ask people to import their email addressbooks? Or is it through a new platform like messaging/SMS on mobile?

And finally, is this referral process successful – exponential?

We have to answer these questions for any new referral system in a digital product, but what’s surprising is, folks had to answer this question in the past as well, for anything they want to spread between friends.

Here’s a simple example: Chain letters.

Here’s an example of one of the oldest chain letters. It promised something pretty simple – if you send this to a bunch of your friends, and include some money to the folks on the list, while simultaneously adding yourself to the beneficiaries list, then you’ll get rich.

The call to action is even super specific: Within 3 days, make 5 copies, and send to 5 of your friends. Doesn’t that remind you a little bit of Facebook’s “7 friends in 10 days” internal guidance, in fact? :)

The point here is that they had to answer all the questions. You invite people to make money. The channel is email. You invite friends you want to be prosperous.

Ultimately, how different is this kind of call to action to the referral programs we see in tech products?

For Airbnb, you invite your friends so that both of you can get $35 in travel credit.

Sure, there’s major advancements. First, there’s an awesome product behind this loop, not just an unsustainable chain letter :)

Also, there’s Messenger and Facebook integration. You can invite your Gmail, Yahoo, and Outlook contacts so they tap into a massive social graph via the various platforms. Airbnb also uses a personalized link so that they can track attribution and personalize the landing pages and flows for the invitees.

Much more sophisticated in some ways, but the basics are similar to what existed 100 years ago.

Same with Dropbox, where you can give and get space. Same with many other referral programs.

The behaviors that cause people to do this are the same as what existed in the past – there’s a personal incentive for the sender, but also an incentive for the recipient. There’s a clear call to action, and a channel these messages travel upon. We can do so much more these days by integrating into platforms, and by tracking, and our sophistication in metrics like viral factor and cohort analysis, but the same general trust is there.

Next, let’s talk about spreading viral content:

Imagine this: The world gets a disruptive new technology, democratizing publishing. All of a sudden, it’s much easier for people to publish whatever content they want. The cost to publish media goes way down. A big boom proliferates, with many new kinds of media being created. Some of it is fancy, some of it is drivel. This drives societal change across many dimensions.

Is this the social media revolution in tech from the last 10 years?

No, this is the penny press revolution from 150 years ago.

Turns out, 150 years ago, we dealt with many of the same forces that affect us today in social media.

The newspaper industry went from hand-crafted to steam-powered printing. The daily press became accessible to everyone, as you could buy papers for 1 cent rather than 6 cents, spawning a brand new industry including papers like The New York Daily Times, which eventually became The New York Times.

But we’re not going to talk about the NYT, we’re going to talk about one of their cross-town rivals, The Sun of New York.

Let’s give a flavor of how they were defining news at the time:

This is great, right? News for everyone. Much cheaper. Spread all that information – it wants to be free!

Sounds amazing in theory, but we also quickly faced a problem: Fake news.

In 1895, a 6-part essay series was published in The Sun, talking about how astronomers had discovered life on the Moon. It includes vivid descriptions of an entire civilization – with buildings, amazing animals, and winged humans living in sophisticated cities.

The Sun claimed that these stories were so compelling it dramatically increased their circulation.

They didn’t retract the story for weeks!

We may laugh at this – but then surely we must also laugh at ourselves.

After all, we’ve created the next next level printing press: The Internet. Combined with social media and blogging platforms like WordPress, we’re suddenly in an environment where we went from 6 cents to publish a paper to 1 cent and now down to 0.0001 cents.

And with that, we’ve created fake news:

The Great Moon Hoax of 1835 isn’t much different than the various hoaxes and fake news of 2016. Not great.

This is an example of how technology has enabled successive generations of viral media content that’s of questionable truthiness. And even as we ponder what we’ve created recently, we have to start thinking about what technologies like VR might ultimately let us create. And how far photo and video manipulation will go, given the continued democratization of those technologies.

OK, the last growth topic we’re going to talk about is bootstrapping marketplaces:

This is an important and very old topic because humans have been trading since time immortal.

In fact, there’s evidence from prehistoric periods that there’s been long distance trade for over 150,000 years. Amazing!

So let’s dig into marketplaces:

This is a diagram is showing a two-sided marketplace, where you have buyers and sellers meeting together to exchange goods.

No matter what the marketplace looks like, you have to answer a couple simple questions:

One side will always be constrained. Usually, it’s the supply side that’s hard to get, and as long as you have folks with goods/services, you can find buyers. After you understand that dynamic, you also have to figure out how to grow buyers and how to grow sellers. Both sides of the market are typically pretty hard.

Then there’s the middle piece, which is to help both sides find, price, and transact. If you just help do one step in the process but not the whole thing, this causes problems since the experience will be disjointed. That’s why it was natural for eBay to buy Paypal.

The central growth problem with marketplaces is, how do you get them spun up? They are wonderful businesses once the marketplace network effect is going. There’s strategies to solve that now, but as you might have guessed, we had pretty sophisticated tools to solve this back in the day.

Let’s start this story with grocery stores:

Back in the day, local grocers were one major side of the marketplace that needed to be bootstrapped. If they didn’t carry your goods, you couldn’t get them in front of your customers. So how do you solve this? One of my favorite books is called My Life in Advertising by Claude Hopkins, written 100 years ago, talks about how they tackled this problem in a very clever way.

The first move was to invent the coupon:

Now, the coupon creates many important incentives. Obviously, it creates demand because then customers want to buy a product and try it out. The more nuanced effect is that it also stimulates the middlemen, the grocery stores, to carry the goods. The reason is that before you run a huge coupon marketing campaign, you can go to all the grocers and telegraph what’s going to happen

Here’s what to do: Just say, “a bunch of customers are coming to your stores for this new product, and if they don’t find it, they’re going to go to your competitors to find it instead.” Voila! By using money to generate some short-term momentum, it creates a Prisoner’s Dilemma for grocers to carry your goods. Then if your product is good, and customers keep coming back, then grocers will want to keep stocking, and your bootstrap problem is solved.

Now this is clever, but how clever is Kickstarter?

Isn’t Kickstarter and the various crowdfunding platforms really doing the same thing?

They help creators publish their products, drumming up demand, and with all the pre-orders, they’re able to convince a whole slew of ecosystem partners to help them: investors, manufacturers, retail partners, etc. The analogy is even more spot-on when you consider that they often give discounts to early backers of every product. In a way, this is just a coupon on something that doesn’t exist yet, to help bootstrap a market that would otherwise exist.

OK, so I hope you are enjoying all of these stories.

Truthfully, there are a ton of examples.

We can go on and on with examples like this. I also have some great notes on content marketing from 100 years ago – what else is the Michellin Guide for? And of press stunts. And may other examples.

After all, we’ve been at this game for a while.

The reason is that we’ve been convincing folks to buy stuff for a long, long time. Remember, humans have been trading with each other for 150,000+ years! Pretty sure that the first prehistoric cave-dwelling ancestor that could advertise their wares to their nearby communities got a major edge. And likely was the first to invent word-of-mouth marketing alongside fire!

But I want to make a broader point here:

Technology changes, but people stay the same. Human beings and our brains have been unchanged for a long time, which is why behavioral economics is so interesting – we’re susceptible to the same techniques whether it was delivered to our caveman brains or our modern brains.

And by the way, there’s a lot of techniques:

There’s so many marketing techniques that have been invented over time, and why they worked back in the day, work today, and will always work. While the platforms may have been different, many of these ideas were invented over 100 years ago. Even more, it’s obvious that these techniques will be relevant even 100 years from now! Or even 1000 years from now! Until we upload our brains to the cloud, we’ll still fundamentally refer friends for the same reasons, and find the same salacious articles compelling. Fake news will always work. :(

So when people ask “What’s next in growth?” they are often expecting an answer about technology when we should really be talking about people.

I can say with full certainty that growth opportunities will come from taking classic strategies – the stuff that’s been around for 100 years – that are fundamentally anchored on human behavior, and anchoring them on new platforms while executing well.

When it comes to new technologies, we’re talking about IoT. Wearables. Video. VR/AR. Smart TV. Autonomy. And much more…




And what a time to work on this problem. After all, technology is increasing faster and faster!

The rapid pace of technology adoption means that more and more platforms will be coming our way. And with each new platform, new opportunities will arise if we apply classic strategies and execute well.

So hats off to everyone betting their careers on VR/AR, drones, and all the new platforms coming out! It’ll take times, but there will be winners there.

In the end, if you find yourself still asking about what’s next, here’s some closing thoughts.

My challenge to everyone in the industry is simple. First, explore what’s come before us. Study the classics.

Ignore all the random noise out there in the world – all the tips and tricks that seem fun, but are actually the potato chips for our brains and careers. They taste good, can fill us up, but make us feel gross and aren’t nutritious.

Instead my guidance is simple:

 

If you think about new platforms, where they are in their maturity cycle, and are early when it counts, that’s great.

Much of the success of social products are due to them being first to email virality. And many of the winners within mobile were early to the smartphone platforms, which have now started to ossify.

Once you figure out the platforms, here’s where tactics can count. Execute thoughtfully and iteratively, so that you are learning about your product and platform. If you are prepared, and have mastered the tactics of the trade, then this is when you can shine.

This is the realm of A/B testing, funnel optimization, viral loop construction, building viral content, and creating sticky products. When you combine this with a new platform that’s all fresh powder, the results can be explosive.

And I want to also give a warning about growth hacks:

Nothing in this industry is ever easy. Tips and tricks aren’t enough. We have to think strategically and execute very well, over years and years to be successful.

What’s next in growth? It’s what’s come before, but combined with a dash of new tech :)

So again, the future of growth will combine a few factors:

  • Classic strategies that are all about people
  • New platforms that present big new opportunities
  • Combined with iterative, thoughtful execution

We can learn from the past, because the things that worked 100 years ago, or even 1000 years ago, still work today.

Here’s another quick example:

After all, technology changes, but people stay the same.

Thank you! :)

Finally, as promised, here’s a video of this talk and here’s a PDF to download. The video is just under 30 minutes, so it’s a quick watch. If you want to learn more about the conference go here: Startcon. Thanks to Cheryl and Matt for having me there!

Written by Andrew Chen

January 23rd, 2017 at 10:00 am

Posted in Uncategorized

10 years in the Bay Area – what I’ve learned

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January 2007
Ten years ago this week, I took a long, cloudy drive from Seattle to Silicon Valley on Highway 1 to start a new job and new life. I was barely 24 years old, in a hurry to change the world, and eager to begin my first day at MDV, a Silicon Valley venture firm, as their new Entrepreneur-in-Residence. It was 2007, and the iPhone hadn’t been released yet, YCombinator was just getting started, and MySpace was still bigger than Facebook. And who’s this Obama guy that folks are talking about?

That was a long time ago :)

A decade later, the world is incredibly different. And I’m different too, because the Bay Area has profoundly and fundamentally changed me. Along the way, there’s been good decisions and plenty of bad. I want to share some high-level observations/thoughts, focused on mostly career/professional stuff but a little bit of personal too.

Let’s dedicate this essay to all the new folks starting out in the Bay Area. Welcome!

People are the secret sauce
First, what makes the Bay Area special for tech is the people. I barely knew anyone when I first arrived here, so I had a simple goal in 2007:

Meet 5 new people per day, every day.

It helped that working at a venture firm is all about networking, so I picked aggressive goal! I started by emailing my tech friends to intro me to smart people working on cool products. Upon grabbing coffee with them, I followed up to ask for more intros, and more. I kept this insane pace for 6 months, which created an incredible introduction to the SF tech community.

Years later, while I’m nowhere near this volume anymore, I’m still going! This is one of the most fantastic ways to learn. Most importantly, while this started out as a work thing, many of the folks I met in 2007 are now close friends.

Think long-term
Everyone you meet here will likely still be here in 10 years. This changes the professional dynamic so that we can all help each other, build relationships, and give real time/effort, without feeling like things have to be transactional. It starts to make sense to invest in activities that pay off in years or decades, not months.

My writing has also been a microcosm of this, where in the first few months, there was a grand total of maybe a few dozen readers – mostly friends and family, forcibly subscribed! It’s been a slow/steady ramp that’s taken thousands of hours of effort and many years to grow into a real following. So for the folks who are struggling to build audiences for your newsletters or blogs, keep going! It’s worth it.

Vuja De
The more years of experience you accumulate in tech, the easier it gets to become negative and closed off to new ideas. It’s easy to say “No, that’s never going to work” because experience usually generalizes towards everything failing!

And yet every couple years, there’s a new cycle – social, smartphones, ridesharing – that’s counterintuitive and huge, and blows away prior assumptions. I’ve written about why I doubted Facebook could be a billion dollar business, and why I was wrong. In my years in the Bay Area, that’s one of that’s just one out of many wrong calls :)

It takes real effort to stay open-minded, even as you learn more and get comfortable in your own expertise. The IDEO folks sometimes talk of “vuja de,” a twist on the familiar term:

Deja vu is when you see something new, but feel like you’ve already seen it before.

Vuja de is when you’ve seen something a million times, but see it like it’s the first time.

It takes a lot of openness and humility to try and understand weird new companies/categories, especially when there’s bad historical datapoints. Like how search engines were a terrible business until Google. Same with social networks. Or how mobile was always the next new thing, but actually perpetual vaporware, until the iPhone.

Missed chances
The longer you live in the Bay Area, the more missed chances you’ll have. I met the Facebook founding team when they were 11 people, and thought for a split second that I should try to get a job there, before deciding it could never be big. Hilariously wrong. I have friends who could’ve invested in Uber’s seed round back when it was valued at $4M, but passed because it was “just a taxi app” – oops. An early Googler told me about a guy who joined as one of the first ten employees, but quit on his first day, forgoing $1B of stock, because the founders’ mannerisms annoyed him.

These missed chances will weirdly haunt you, even when you know better.

Startup romanticism
From the outside looking in, I thought that doing a startup was a magical, rare experience that you only got a few shots at in your life. Kind of a romantic notion that I held on to for many years. But once you’re in the Bay Area for a few years, what you quickly realize is that starting a company and getting investors funding you, actually isn’t rare at all. It’s commonplace, because actually mediocre startups and tech companies are plentiful! And it’s unfortunately very easy to start a mediocre startup of your own.

Bill Gossman, a long-time mentor who’s lived in both SF and Seattle, gave me some advice early on:

Don’t think that Silicon Valley has better entrepreneurs. They don’t. But they have more people trying. They have more crappy companies and mediocre entrepreneurs, but also they have more great companies and people too.

For me, this meant my first years in the Bay Area were spent on trying to get the “rare” chance of building a startup. Over time, I came to believe that the rare thing is actually building the Amazon, Google, Facebook, Uber-type companies that come around only every 5-10 years.

Last year, I decided to jump onto the rocketship of a great company rather than continuing with the mediocre. This is a core reason I’m at Uber these days – to have a special experience that I’ll remember, years from now.

To another ten years!
Finally, I want to thank everyone who’s been reading for the past few years. As I mention above, writing has been an enormously fulfilling thing. I’m hugely appreciative for you to have come on the journey – thank you for reading and for your comments/feedback over the years.

Here’s to a happy new year and a few more decades in the Bay Area for me :)

Written by Andrew Chen

January 3rd, 2017 at 10:00 am

Posted in Uncategorized

What 671 million push notifications say about how people spend their day

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Push notifications are a cornerstone of every mobile app’s engagement and retention strategy, yet we know so little about them. Previously I’ve written about why 60% of users opt-out of push notifications and why some pushes are getting 40% CTRs.

Today, we’ll look at some push notification data from Leanplum, a mobile marketing automation tool, which breaks down 671 million pushes to uncover some interesting trends, particularly on time of day targeting for push notifications.

Average weekday push notification activity in North America
Let’s first look at when marketers are sending push notifications, by hour, and how users are interacting with these pushes. The graph below shows the metrics for push notification sends and opens for the average weekday in North America, on a sample of millions of notifications. The data is normalized by local hour, and represents the raw sends and opens for that given hour. The blue line shows the raw number of sends and uses the left axis. The red line shows the raw number of opens and uses the right axis.

Leanplum Chart for Andrew

You can see an interesting trend here- you can see pushes sent and opened trending upward throughout the day, with a small peak around noon, a slightly larger one around 3pm, and the largest in the evening. The post-evening trend is interesting – after 6pm, on a relative basis, Pushes Opened starts to trend higher, relative to previous hours, and Pushes Sent is lower. This indicates that while mobile apps are delivering a ton of pushes leading up to evening, it might be more effective to time them post-evening, when engagement seems high.

Either way, this curve is super interesting, and we should look at a typical day to understand the behavior patterns on how people spend their days.

Studies on the average day reinforce this trend
The American Time Use Survey visualization below shows why the aforementioned push notification graph makes sense. The video simulates the minute-by-minute average day of activities for 1,000 people, and what they do- some phone calls to sports to shopping to work.

View the visualization video here or click the image below:

4Bnkg6g16x

 

In the morning hours from 7-9am, people are waking up and kicking off their morning rituals – eating, personal care, commuting to the office, and beginning to work. These consistent morning tasks that get you to the office and productive could justify why push engagement is low before 12pm. Around 3pm there is an interesting activity shift seen in the video that also correlates with a higher push send and open rate. This could be people taking a break to grab a coffee or get outside. Great time to take a look at your phone. By 6pm, most people have left work and transitioned to leisure activities.

It makes sense then why opens of push notifications are so high from 6-9pm. Work is done and people are likely to be on their phones, browsing apps and catching up on social media. The shift to leisure activities lasts for a few hours and by 10pm, most people have moved to personal care and sleep. The peak of push notification engagement follows a similar shape of post work leisure time.

Media consumption by medium
Ok, so we see that leisure activity correlates with higher opens of push notifications. What exactly are people doing during these leisure hours? The Flurry graph of daily media consumption by channel shows some interesting trends:

flurry_tod

Couple obvious notes:

  • Internet usage has two main peaks: one at 8am and a larger one at 7pm.
  • iOS and Android app usage kicks up in the morning around 7am, gradually builds throughout the day before falling off its peak at 9pm.
  • TV is clearly an after work deal, with a huge peak between 7 and 11pm.

You can match this up to this similar chart analyzing media consumption in the UK, via Ofcom:

Proportion-of-media-activity-during-the-day

With both charts, you can see that, TV appears to be a significant portion of the leisure hours, especially after work. Voice communication tends to be constrained to the day, while online comms (SMS/email) happens throughout the day.

Push notification engagement versus media consumption
To tie this daily pattern to the Leanplum push notification engagement data, we can speculate why people are engaging with pushes in the evening. I bet we’re seeing the effect of mobile as the second screen, where people are engaging with push notifications while casually watching TV. They likely have their phones in their pocket or nearby, and can easily catch up on apps.

Thanks to David Grotting and Leanplum for their help on this essay.

 

Written by Andrew Chen

May 16th, 2016 at 10:00 am

Posted in Uncategorized

The state of growth hacking (Guest post)

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[Hi readers, I recently met some amazing folks at Mixpanel – Justin Megahan and Amelia Salyers – who interviewed me for their “Grow and Tell” series. This was originally published on the Mixpanel blog, and I’m excited to re-publish it here too. Thanks to Suhail, founder/ceo of Mixpanel, for helping set this up. Hope you enjoy the interview. -Andrew]

2016-03-16 00-58-47.416402-andrew-chen-on-the-state-of-growth-hacking-ctt

After navigating a few winding hallways at Uber HQ to find a tucked away conference room, I’m chatting with Andrew Chen about one of his favorite topics: growth hacking.

Across the table, he’s telling me about the importance the product plays in growth hacking, all as he taps away on his iPhone. On the other side of the glass is a growth team of engineers, data scientists, product managers, and who knows what else. They are, Andrew assures me, one of the best teams in the game.

He would know. When it comes to growth, there are few names that carry the weight of Andrew Chen. In a relatively new field, Andrew is an elder statesman. He’s been at this for a while. His early posts on growth hacking helped put the term on the Silicon Valley map. Thousands subscribe to his newsletter to get articles explaining the viral loop or the Law of Shitty Clickthroughs.

And currently he’s explaining to me why my observation—that the popular growth hacking tactics seem to be less effective as they go mainstream—isn’t indicative of any slowing in growth hacking as a movement. All without looking up from his iPhone. What is he doing? Is he returning an urgent text or something?

“The folks that are doing growth very successfully start out with an amazing product, right?” he continues.

Andrew has a habit of ending his statements with “right?” and it makes it almost impossible to not come around to see from his perspective. Growth does need a great product. Right.

“Growth is a magnifying glass. If you have a tiny diamond and you put it under a magnifying glass, then you’ll make something big and great. But if it’s just kind of a tiny piece of shit, then it’s just going to be a big piece of shit, right?”

At its core, a growth hack is a way to get more people into your product by using your existing user base. It’s marketing by engineers. Or engineering by marketers. Growth hacks turn your user base into a channel to pull in more users, compounding your previous success.

After another minute of back and forth on the importance of a bullet-proof product, Andrew looks up from his phone with a wry smile.

“But you asked me if growth hacking was slowing down,” he says and hands over his phone, showing me what he’s been tapping away at.

“Does it look like it?”

On the screen is a Google Trends report for “growth hacking.” The graph is exactly what you want to see in growth, dramatically up and to the right.

growthhacking_trends

Understandably, Andrew is a bit evangelical about growth hacking. He knows the value in it, the huge upside it can bring. But, more importantly, Andrew knows that growth hacking is not something you can learn by just reading a bunch of blog posts (like this one). To do it successfully, you have to understand why it works, when it does.

The way Andrew sees it, growth hacking is a philosophical approach to the problem. It’s about creating a growth system around your product, not just applying a universal set of tactics to it. If your product is any good, it’s unique. And if it’s unique, it deserves a novel set of growth solutions.

Where it came from

To understand how and why growth hacking came to be, you first have to go back to January 30th, 2000.

“During the dot-com boom, there was a bunch of traditional marketers who believed they could use normal consumer marketing techniques to grow websites,” Andrew remembers. “It didn’t take long for them to figure out that it doesn’t work.”

January 30th, 2000 was the day of Super Bowl XXXIV. Football fans will remember it for its dramatic finish, with the Titans’ Kevin Dyson falling a mere yard short of tying the Rams on the final play of the game. The tech world remembers it for an entirely different reason. 19 tech companies spent an average of $2.2 million each for Super Bowl advertising spots. Commercial breaks were full of names like Pets.com, Computer.com, and HotJobs.com.

It was the peak of dot coms going mainstream. Just 40 days later the NASDAQ hit an all-time high. And then it all came crumbling down. By the end of the year, more than a handful of the dot coms that had spent small fortunes to get their names in front of the 88.5 million Super Bowl viewers had gone bankrupt.

There were many contributing factors to the dot-com bubble and its burst. The failure of traditional, and expensive, marketing campaigns was only a piece of that puzzle. But in the aftermath, those ads and billboards were a mistake that few tech companies looked to repeat. Marketing became a dirty word. And that is what set the scene for the origin of the term “growth hacking.”

Andrew remembers hearing it for the first time sitting at brunch with Sean Ellis.

“Sean was already working with startups like DropBox and EventBrite. But whenever anyone tried to connect him with entrepreneurs, they didn’t know how to describe him. They’d say something like, ‘This is Sean, he’s kinda like a VP of marketing.’ And that wasn’t going over very well. The entrepreneurs thought he was going to buy them Super Bowl ads.”

Sean’s background was in direct response marketing. He wasn’t buying billboards; he was creating marketing campaigns to get people to take specific and measurable actions.

“So Sean says, ‘Let’s not call it marketing. That sounds horrible. Introduce me as a growth hacker.’”

And that’s how the term came to be. The term and the philosophy are a reaction to the failed practices of the dot com boom.

“The folks that pioneered growth hacking were those that kept building products after the dot com bubble crashed. The people that built companies like Paypal, Yelp, and YouTube.”

When times got tough, startups became lean. There wasn’t room on the balance sheet for an expensive marketing spend.

“What you’re left with is engineers who know how to build products. And when they look at the problem of getting customers, or users, or whatever you want to call it, they come at it from a product and engineering point of view. They looked at the problem through a quantitative lens, the way that an engineer would.”

That approach is the one Sean Ellis was taking on, and, as marketers do, he gave it a good name.

Start with the product

Alas, just adding the skill to your LinkedIn profile doesn’t make you a growth hacker. And just because you want to hit some number doesn’t mean that you can follow step-by-step instructions to easy growth success. It all starts with the product, and it’s not easy.

“I get emails saying, ‘Hey I’m building this start-up and my investors tell me I really need to be at x million users to get the next round of funding. How do I do that?’,” Andrew tells me.

“But they’re conflating traction and growth with just getting a number because that’s what they’re supposed to do. Growth is an after-effect of strong product market fit and great distribution.”

You can’t just growth hack a mediocre product to success. It’s not something you tack on when you’re looking to “go viral.”

“My usual response is, ‘Well, is your product working? How many people are coming back? Are you getting a lot of word of mouth?’ First and foremost, you need to dig into what’s actually going on with the product and how people are using it.”

You can’t skip the product and go right to distribution.

2016-03-16 01-30-30.035939-growth-is-after-effect-ctt

“It’s challenging because if your product isn’t quite working, but you have to hit these really aggressive targets, you end up forcing it,” Andrew says.

“Even if you hit the numbers, they won’t be real. You spent a lot of money to get there. And what is the point in acquiring all those users, if they leave once they see the product?”

It all starts with the product. All of the best growth hacks, from Facebook to Dropbox and Airbnb, have roots in the actual product. And while you can learn from those successes, too many would-be growth hackers look to them as recipes for their own product. Just google “growth hacking”, and you’ll find page after page of tips and tricks. That’s how these things get packaged by people like me. It makes them easy to read and even easier to share.

“People love factoids. They’re fun. You feel just a bit smarter after you’ve read them. And it doesn’t seem that hard. You just have to do ‘this one thing.’”

Unfortunately, as anyone who has ever been on a diet knows, reading about it is the easy part. And anyone can say they’re going to eat better and exercise more, but actually making it work for you and putting in the time and effort to execute on your weight loss plan is hard and takes commitment. Don’t let any blog post tell you differently.

“When I talk to companies I never offer tips and tricks. By themselves, they’re irrelevant. You have to look at the company, understand their context and what customers are trying to do to understand what the right channels are.”

Tactics decay

Andrew recalls the story from My Life in Advertising by a man named Claude C. Hopkins, the man who invented the marketing tactic called “the coupon.”

“It’s crazy to think about, but there was a point where coupons were an innovation. After Hopkins created the coupon he had this ridiculous competitive advantage for years.”

It was an incredibly novel concept. Since consumers were going to buy milk anyways, all they had to do was bring this piece of paper to the store and they would save money buying that brand of milk. And from the shop’s perspective, their shoppers were coming in and asking for a specific brand of milk, which would lead to more shops stocking your milk.

In its time, it was ingenious. Of course, today, the coupon is old hat. It ran its course. That’s what happens to individual tactics.

“Email marketing used to be amazing. Banners used to be amazing. Now they’re almost irrelevant. That’s natural decay. You can’t focus on the tactics, because eventually they become useless. To really reap the benefits, you have to be on the bleeding edge and do the things that no one else is doing,” Andrew says.

“What’s problematic about the tactics is that as more people adopt the same tactic, they tend to not work as much because they become fatigued.”

Make no mistake, by the time someone is sharing a successful growth hack in their company blog, the specific technique has already been milked for all it’s worth.

“It’s not that I’m against tactics,” Andrew explains. “If you want to be a great chef, you need to know all the recipes, to have the ingredients, to have the knife skills and all that other stuff.”

I haven’t any idea what the other stuff is, I’m not great in the kitchen. But so far this is making sense.

“But you can’t become an amazing chef just by reading recipes or watching cooking shows. To make your own amazing dish, you need to bring it all together, and to make educated attempts at trying different and new things.”

The value is in knowing why all these other recipes work and then applying those learnings to your own situation.

Andrew recalled sitting down with David Sacks to talk about how he learned from the growth tactics of Facebook and applied it to Yammer.

Now that all your aunts and uncles are on Facebook, it’s hard to remember a time when it wasn’t completely ubiquitous. But Facebook found early traction on university campuses by requiring a college email address to create an account. For most products, limiting your potential audience would be a hinderance. For Facebook, which thrived in high-trust networks, it allowed them to divide and conquer. They created a groundswell and rode it through one university and then on to the next.

Yammer was also trying to make ground in a completely different type of high-trust network the workplace. And, it just so happened, people here also had their own email addresses. By adapting and applying the approach to their product, Yammer allowed you to join with your work email and automatically be in a closed-off network with your co-workers.

“Facebook had proven it could gain traction for their product, but no one had applied it to the corporate world, yet. It was a mix-and-match of innovation.”

Growth isn’t about 2x. It’s 10x or 100x

“Growth accelerates what’s already working. It requires a lot of buzz, and getting word of mouth. That’s why a lot of this stuff ends up being pretty magical.”

The way that Andrew sees it, there’s an art in the science to of growth. And it’s the product, user experience, the market, luck & timing, and a bunch of other variables all wrapped up together. And when it all works, it’s like magic, and products explode onto the scene.

“What the folks who are really great at growth do is package up this thing that is already is going well, and they magnify it. They blow it up. Really running up the score and achieving the highest possible upside.”

That’s a level of growth that isn’t reached just by moving the needle on the conversion rate of your signup flow. You have to take big swings.

“You can’t get there purely by optimization. You may even need to reinvent pieces of the product in order to accommodate a new growth strategy,” Andrew says.

“I’ll talk with people about their road maps and I’ll ask them, ‘You have this great thing that’s working, but how do you 10x it?’ Maybe they’ll have some ideas, but then I’ll say, ‘Okay, now what would you do to 100X it?’ It gets to a point where you need to take really big swings to make that happen.”

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When a product is approaching that level of growth, it’s not from one particular tactic. It can’t just be great SEO or a clever push notification campaign. Growth on that scale is a result of a system of growth built in and around the product.

“You can’t look at things and say, ‘Well, we’re doing a ton of SEO already, let’s just do more.’ It would take another level to be like, ‘Great that’s one channel. Let’s build the next three channels, and this is what you’d have to do and this is what the product would have to be.’”

And that’s not a philosophy limited to acquisition. Startups are starting from zero, so they are obviously acquisition-focused. For teams at more mature products, who are building that growth system, it goes far beyond acquisition and activation.

“For growth teams that are later in their cycle and are operating with millions of MAUs, there are more saturation effects. After you’ve built those acquisition channels, reducing churn become the main focus around growing your user base.”

Many of these growth teams end up managing acquisition, activation, engagement, and retention, and then all the product features and analytics that support those, like onboarding, funnels, and notifications.

“It’s really more about a growth team than it is about an individual. These things are a collaboration between product managers, engineers, designers, and data scientists. You have all these people working together.”

Growth at Uber

That’s the type of team Andrew came to at Uber when he joined last fall.

“Uber has built the best growth team in the industry, period,” he asserts. “Growth is not an afterthought, it is one of the most important focuses of the company.”

He can’t really say much about Uber, he told me over email. And then once again in person, when I, of course, still ask him about Uber. The fact that, after a handful of years bouncing around investing, consulting, and speaking, Uber was able to lure him into the office says all that needs to be said about the opportunity Andrew sees there.

“What can I say about Uber?” he asks aloud and pauses.

“Uber has the potential to be the biggest company ever. Uber has the potential to be the biggest company ever. It has that potential.”

How close Uber can come to hitting that ceiling will depend on how well they execute on their ambitious growth strategy.

In Uber, it’s clear that Andrew sees the potential to take all his growth experience and see what it can do at what is already one of the biggest tech companies in the world. It’s also clear that he genuinely believes in the product.

After going down a tangent and talking a bit about the “Uber of Xs” startups that are borrowing the tech giant’s delivery model, Andrew stops in his tracks. “Except we’re going to be Uber for everything,” he jokes.

“I mean, theoretically that sounds like a fun pitch, right?”

Spoken like a true growth hacker.

[Again, thanks to Justin Megahan and Amelia Salyers for writing up this great interview. -Andrew]

Written by Andrew Chen

March 21st, 2016 at 9:50 am

Posted in Uncategorized

Growth is a system, not a bag of tricks – Manifesto Conference Q&A video/transcript

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I recently was interviewed by Tim Chang, Managing Director of the Mayfield Fund at the Manifesto conference. We talked about growth, what I has learned from trying different tactics, and how to evaluate the success of growth initiatives. (By the way, SC Moatti, the co-producer of Manifesto, also a tech entrepreneur and ex-Facebook PM, is writing a book on the business mobile called Mobilized. I had a chance to read it and find her insights really valuable. And the next Manifesto is dedicated to mobile.)

You can watch the full video of my talk here.

 

 
Here’s the key points of the Q&A below. (Thanks to David Grotting for helping with these)

How Did You Get Started?

  • Started in Venture Capital as an entrepreneur in residence at Mohr Davidow Ventures.
  • One of my most important experiences was working in Ad Tech
    • You spend all of your time thinking about quantitative customer acquisition for tens of thousands of customers.
    • You learn all the interesting nuances of different business models and gain a keen sense of customer funnel optimization and key metrics like cost per customer and LTV.
    • One example being working with a dating site and realizing that the average time for a dating “match” was about 4 months so it’s important to get users to sign up for a 12 month subscription. This is very different than a gaming company strategy, as a contrast.
    • I was one of the few people who had all this quantitative experience with customer acquisition and wanted to apply it in a consumer context.
  • At this time in ‘07 the IQ around consumer products was was relatively low – and investors were making investments off number of hundreds of thousands of registered users. It’s been amazing to see over the the last couple years how much the industry has upgraded its thinking.

Lessons To Share About Growth:

  • Growth is not a bunch of small, tactical growth hacks. (Make a button this color, etc.)
  • Growth is a full model and system with linked parts and loops that align with your business
  • Tactics can be used to optimize loops, but the entire model must be well understood first.
  • If you’re going to focus on virality – it’s not about just building something cool, but actually thinking how one batch of users end up inviting the next batch of users and optimizing that entire loop with with tactics.
  • You must put in the hard work to build a framework that is situationally right for your product.

What is a Growth Hacker?

  • Sean Ellis coined the term while working with Sequoia companies. It was the embodiment of a direct response marketer combined with a product manager that had a deep focus on analytics.
  • Growth is fundamentally a product thing: Requires true team effort of PMs, designers, developers, etc. that utilize software to drive KPIs
  • Growth Hacking has started to become a negative signal: Next generation of consultant keyword/title

Metrics should be a reflection of the strategy you’ve decided

  • Be careful about focusing on vanity metrics like daily installs
  • Churn is one of the most defining aspects of whether or not you have a sustainable business.
  • Have your metrics prove that you are creating a sustainable growth model
  • In the beginning, entrepreneurs build a product from the heart and have the ability to be hyperattentive, listen to each customer, and get all the details right. You then get to a point where there is a phase transition where you have to truly scale, and that tends to be the most difficult.
  • Finding product/market fit is key before scaling growth efforts
  • Mobile is in a dark time and it is very difficult to grow a mobile app now. It is daunting for new startups as successful apps are prioritized in rankings and it becomes more difficult to gain new-app visibility. You must continue to look for new platforms.

Messaging apps are going to create the next platform beyond app stores

  • Messaging is where social was in ‘07/’08
  • Just like adding social to other categories of products has been successful (Social Reviews = Yelp, Social Videos = YouTube, etc.), messaging will have opportunities to be paired with other categories of products with success.
  • Messaging and communication is the killer app for your phone.
  • I’m hopeful that messaging will create the next generation platform for mobile app distribution.
  • Working backwards when you have a new idea or new product and thinking about how it relates to messaging is a very important question to ask if you want to be hitting scale 2-3 years from now.

Who are your influencers and what are some of your favorite books?

Written by Andrew Chen

February 15th, 2016 at 10:00 am

Posted in Uncategorized

A Practitioner’s Guide to Net Promoter Score

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[Dear readers, this essay is about the practical aspects of measuring Net Promoter Score, an important metric that often correlates strongly with word-of-mouth virality. Sachin Rekhi, the author, has a blog and can be found on Twitter at @sachinrekhi. He’s was most recently Director of Product at Linkedin, leading the Sales Navigator product, and previously, an Entrepreneur-in-Residence at Trinity Ventures. Most importantly, Sachin married my sister Ada after meeting her in college at UPenn :) -Andrew]

Sachin Rekhi (ex-LinkedIn):
A Practitioner’s Guide to Net Promoter Score

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Over the past year at LinkedIn I developed a strong appreciation for using Net Promoter Score (NPS) as a key performance indicator (KPI) to understand customer loyalty. In addition to the standard repertoire of acquisition, engagement, and monetization KPIs, NPS has become a great additional measure for understanding customer loyalty and ultimately an actionable metric for enhancing your product experience to deliver delight.

I wanted to share the best practices I’ve learned for implementing an NPS program within an organization to get the most out of this KPI for driving more delightful product experiences.

The Origin of NPS
Net Promoter Score (NPS) is a measure of your customer’s loyalty, devised by Fred Reichheld at Bain & Company in 2003. He introduced it in a seminal HBR article entitled The One Number You Need to Grow, which I highly recommend anyone serious about NPS to read in detail. Fred found NPS to be a strong alternative to long customer satisfaction surveys as it was such a simple single question to administer and was able to show correlation between NPS and long-term company growth.

How NPS is Calculated
NPS is calculated by surveying your customers and asking them a very simple question: “How likely is it that you would recommend our company to a friend or colleague?” Based on their responses on a 0 – 10 scale, group your customers into Promoters (9-10 score), Passives (7-8 score), and Detractors (0-6 score). Then subtract the percentage of detractors from the percentage of promoters and you have your NPS score. The score ranges from -100 (all detractors) to +100 (all promoters). An NPS score that is greater than 0 is considered good and a score of +50 is excellent.

Additional NPS Questions
In addition to asking the likelihood to recommend, it’s essential to also ask the open-ended question: “Why did you give our company a rating of [customer’s score]?” This is critical because it’s what turns the score from simply a past performance measure to an actionable metric to improve future performance.

It’s also helpful to ask how likely they are to recommend your competitor products or alternatives, so you can establish a benchmark for how your NPS score compares to others in your industry as there are substantial differences in scores by product category. Keep in mind though that these results are biased since you are sampling your own customers for these benchmarks instead of a random cross-section of potential customers, including those who have chosen competitive solutions.

Many ask additional questions to understand additional drivers of the customer’s score. These are optional as while they add value in understanding the results, they add complexity which reduces the response rate, so you need to consider the trade-off of doing so.

Collection Methods
NPS scores for online products are typically collected by sending the survey via email to your customers or through an in-product prompt to answer the survey. To maximize response rates, it’s important to offer the survey across both your desktop & mobile experiences. While you could create such a collection tool in-house, I encourage folks to use one of the NPS survey solutions out there that support collection and analysis across a variety of channels and interfaces, such as one offered by my wife Ada’s employer SurveyMonkey.

One challenge with both email and in-product based survey methodologies is they tend to bias responses to more engaged customers as less engaged users are likely not coming back to the product nor answering your company’s emails as frequently. We’ll talk about potentially addressing this below.

Sample Selection
It’s important to survey a random representative sample of your customers each NPS survey. While that may sound easy, we found cases in which the responses weren’t in fact random and it became important to control for this in sampling or analysis. For example, we found strong correlation between engagement and NPS results. Therefore it was important to ensure your sample in fact reflects the engagement levels of your actual overall user base. Similarly, we found a correlation between customer tenure and NPS results as well, thus another key factor to ensure the customer tenure in the sample similarly matches that of your overall user base.

Survey Frequency
When thinking about how frequently to administer an NPS survey, there are several key considerations. The first is the size of your customer base. The smaller your customer base, the larger sample you need to survey each time or even wait longer for more responses to achieve a higher response rate, which limits how frequently you can administer future surveys. The second consideration is associated with your product development cycle. Product enhancements end up being one way to drive increases in scores and therefore the frequency of score changes depends on how quickly you are iterating on your product to drive such increases. NPS tends to be a lagging indicator so it takes time even after you’ve implemented changes to the customer’s experience for them to internalize the changes and then reflect such changes in their scores. On my team at LinkedIn we found it best to administer our NPS survey quarterly, which aligned with our quarterly product planning cycle. This enabled us to have the most recent scores before going into quarterly planning and enabled us to react to any meaningful observations from the survey in our upcoming roadmap.

Analysis Team
If your goal is to use NPS to drive more delightful product experiences, it’s important that you have all the key stakeholders involved in product development as part of the NPS analysis team. Without this, the NPS survey rarely get’s used as a meaningful part of the product development lifecycle. For us at LinkedIn, this meant including product managers, product marketing, market research, and business operations in the core NPS team. We also broadly share the findings with the entire R&D team each quarter. While it will certainly depend on your own development process, it’s critical to ensure the right stakeholders are involved right from the beginning.

Verbatim Analysis
The most actionable part of the NPS survey is the categorization of the open-ended verbatim comments from promoters & detractors. Each survey we would analyze the promoter comments and categorize each comment into primary promoter benefit categories as well as similarly categorize each detractor comment into primary detractor issue categories. The categories were initially deduced by reading every single comment and coming up with the large themes across them. We conducted this analysis every quarter so we could see quarter-over-quarter trends in the results. This categorization became the basis of how we came up with roadmap suggestions to address detractor pain points and improve their overall experience. While it can be daunting to read every comment, there is no substitute for the product team digging in and really listening directly to the voice of the customer and how they articulate their experience with your product.

Promoter Drivers
While oftentimes folks spend a lot of time looking at NPS detractors and how to address their concerns, we found it equally helpful to spend time on promoters and understanding what was different about their experiences to make them successful. We correlated specific behavior within the product to NPS results (logins, searches, profile views, and more) and found a strong correlation between certain product actions and a higher NPS. This can help deduce what your product’s “magic moment” is when your users are truly activated and likely to derive delight from your product. Then you can focus on product optimizations to get more of your customer base to this point. The best way to get to these correlations is simply to look at every major action in your product and see if there are any clear correlations with NPS scores. It’s easy to just graph and see if this is the case.

Methodology Sensitivities
We found NPS to be sensitive to methodology changes in the questions being asked. So it’s incredibly important to be as consistent in your methodology across surveys. Only with a fully consistent methodology can you consider results comparable across surveys. The ordering of the questions matters. The list of competitors that you include in the survey matters. The sampling approach matters. Change the methodology as infrequently as possible.

Seasonality
We found that there may be some seasonality at play in certain quarters that effect NPS results, correlating with engagement seasonality. We’ve heard that this is even truer for other businesses. So it may end up being more important to compare year-over-year changes as opposed to quarter-over-quarter changes to ensure the effects of seasonality are minimized. While this may not be possible, it’s at least important to realize how this could be effecting your scores.

Limitations of NPS
While NPS is an effective measure for understanding customer loyalty and developing concrete action plans to drive it up, it does have it’s limitations that are important to understand:

1. The infrequency of NPS results make it a poor operational metric for running your day-to-day business. Continue to leverage your existing acquisition, engagement, and monetization dashboards for tracking regular performance as well as for conducting A/B tests and other optimizations.

2. Margin of error with the NPS results depend on your sample size. It can often be prohibitive to get large enough of a sample to significantly reduce the margin of error. So it’s important to be aware of this and not sweat small changes in NPS results between surveys. More classic measures like engagement that don’t require sampling have a far lower margin of error.

3. NPS analysis is not a replacement for product strategy. It’s simply a tool for understanding how your customers are perceiving your execution against your product strategy as well as provides concrete optimizations you can make to better achieve your already defined strategy.

[This essay was first published here. Sachin Rekhi, the author, has a blog and can be found on Twitter at @sachinrekhi. He’s was most recently Director of Product at Linkedin, leading the Sales Navigator product, and previously, an Entrepreneur-in-Residence at Trinity Ventures. ]

Written by Andrew Chen

February 8th, 2016 at 10:30 am

Posted in Uncategorized

Uber’s virtuous cycle. Geographic density, hyperlocal marketplaces, and why drivers are key

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Uber’s virtuous cycle
Back in 2014, David Sacks (ex-Paypal, Yammer, Zenefits) tweeted the above diagram to explain why Uber’s geographic density is the new network effect. It’s an insightful diagram that’s been built upon by Bill Gurley (Benchmark Capital and on Uber’s board) in his essay How to Miss By a Mile: An Alternative Look at Uber’s Potential Market Size.

Bill Gurley sums up Uber’s network effect as three major drivers:

  1. Pick-up times. As Uber expands in a market, and as demand and supply both grow, pickup times fall. Residents of San Francisco have seen this play out over many years. Shorter pickup times mean more reliability and more potential use cases. The more people that use Uber, the shorter the pick up times in each region.
  2. Coverage Density. As Uber grows in a city, the outer geographic range of supplier liquidity increases and increases. Once again, Uber started in San Francisco proper. Today there is coverage from South San Jose all the way up to Napa. The more people that use Uber, the greater the coverage.
  3. Utilization. As Uber grows in any given city, utilization increases. Basically, the time that a driver has a paying ride per hour is constantly rising. This is simply a math problem – more demand and more supply make the economical traveling-salesman type problem easier to solve. Uber then uses the increased utilization to lower rates – which results in lower prices which once again leads to more use cases. The more people that use Uber, the lower the overall price will be for the consumer.

Ben Thompson says it differently, with a competitive lens, in his essay Why Uber Fights, which is also a great compliment to Gurley’s piece.

The point to the above articles is super interesting. From a UX experience, Uber is “hit a button and a car comes,” but from a business standpoint, it’s a vast collection of hundreds of hyperlocal marketplaces in nearly 70 countries. Each marketplace is 2-sided, with riders and drivers, has its own network effects driven by pickup times, coverage density, and utilization.

Understanding the above has been one of my biggest lessons since joining Uber. There’s a lot of nuances in the business that come out of deeply grokking this perspective, and the run-on implications – especially the importance of drivers – are fundamental in understanding Uber and on-demand companies in general.

“More Drivers”
If I were to simplify my role at Uber, it’s pretty simple – in the diagram above, it’s figuring out how to get More Drivers. This is one of the foundational elements of Uber’s business, because as I mentioned before, the company is a collection of hundreds of local 2-sided marketplaces. And while most in the tech scene have a pretty good understanding of how you might go about getting more people to install the Uber rider app, it’s harder to imagine what it takes to get more drivers onto the Uber platform. I know I certainly didn’t know much about it before starting to work at the company!

Uber’s platform has 1M+ drivers
So let’s dive into this topic, and we’ll start with a quote about why Uber’s platform is so important for drivers, using a quote from David Plouffe, who’s on the board of Uber and also ran Obama’s 2008 campaign.

In his essay Uber and the American Worker, he writes:

The Bureau of Labor Statistics estimates that 20 million Americans are forced to work part-time for “non-economic reasons” like child care or education. And 47 percent of people in the U.S. say they would struggle to handle an unexpected $400 bill, and a third of those said they would have to borrow to pay it.

In other words, tens of millions of people in America need work. The Uber platform has a lot of drivers on the platform – over a million – and we hope to get more. That’s real scale, and something that inspires me every day. Plouffe continues in his essay with some interesting statistics:

Uber currently has 1.1 million active drivers on the platform globally. Here in the U.S., there are more than 400,000 active drivers taking at least four trips a month. Many more take only a trip or two to earn a little extra cash. It adds up: in 2015, drivers have earned over $3.5 billion. And by the way, only about 40 percent of drivers are still active a year after taking their first trip.

You can see from the above why driver growth is a key to Uber’s success – you’re convincing a ton of people to drive who have often never driven before, and many try it out and leave. Or they are part-time.

If you want to read more about drivers, their demographics, growth rate, etc., here’s a great 30-page paper called An Analysis of the Labor Market for Driver-Partners in the United States. Great reading.

Surge pricing and lowering fares: Keeping the marketplace in balance
The lens of Uber as hundreds of 2-sided local marketplaces also helps explains the importance of compromises like surge pricing and fare cuts. These mechanisms keep the marketplace in balance, and help grow the network effects that Gurley and Sacks recognize in Uber. Without them, one side of the marketplace might outstrip the other, causing a downward spiral. So even though neither side is happy with all of the marketplace balance tools that Uber puts to use, it’s ultimately a foundational tool in Uber’s business.

Take surge pricing, for instance. It’s easy to hate it, as a rider, and there are legitimate cases where it should be turned off. But think about it from the driver’s point of view- it gives them a huge incentive to get out onto the road, and to come to the exact area in the city where they are most needed. In fact, surge is done on a hyperlocal basis- just check out the screenshot of the driver/partner app to get a sense for how tightly drivers are directed to come to high-demand spots.

surge

Each colored hexagon above is a different level of surge. If you want to go in-depth on surge pricing, there’s a medium-length case study here: The Effects of Uber’s Surge Pricing.

(You can see more about the driver/partner app here and a Wired article on its development).

The flip side of surge prices, which raise fares for consumers, are lowering fares. Uber has recently cut fares in about 100 markets. Like surge prices, these cuts are a marketplace balancing mechanism to increase demand and ultimately increase driver earnings.

This is done in a way described within Sack’s diagram above, where the “less downtime” arrow is the key. When drivers aren’t sitting and waiting for their next trip, they are more efficiently utilized, which increases their earnings. If you can get earnings-per-trip and trips-per-hour to go the right way, you can increase earnings-per-hour.

Uber has released some directional charts showing this as positive for drivers, as part of Price cuts for riders and guaranteed earnings for drivers. The essay describes an approach of lowering fares to boost demand, and pairing that with guarantees while the rider side of the market figures this out. In tandem, good things happen, such as these graphs of earnings in some of Uber’s largest markets:

uber_earnings

As you can see, the earnings numbers are moving up and to the right. Not bad.

In closing, a fun video
Ultimately, Uber is providing an important platform for both riders and drivers to interact, across hundreds of hyperlocal marketplaces around the world. When you start to think of it this way, and especially from the driver’s POV, rather than the rider, you’ll start to 10X your understanding of Uber’s business.

If you want to learn more about roles at Uber, here’s a link to get in touch or just look at the careers page.

And finally, I want to leave y’all with a fun video featuring Jonathan driving an Uber and singing Roses (The Chainsmokers) with his riders. Enjoy.

Written by Andrew Chen

January 25th, 2016 at 10:30 am

Posted in Uncategorized

My top 2015 essays on Uber, online dating, push notifications, Apple Watch, and more

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Good news- my vacation from writing is over!

A few months back, I joined Uber, and took time to get settled into the new role. As promised, I’m back, and will have more essays on growth, tech, and more for 2016. If you want to get my future essays via email – just fill this out:

In the meantime, below are a list of my top essays from 2015. It includes writing on a bunch of topics: Uber, Dating apps, Push notifications and retention, Apple Watch, Women in tech, and more.

Hope you enjoy them.

-Andrew
San Francisco, CA

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 5th, 2016 at 10:30 am

Posted in Uncategorized

Personal update- I’m joining Uber! Here’s why

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Hi readers,
Big news: I’m headed to Uber to join the growth team.

I’m incredibly excited to apply everything I know about growth and combine that with an explosive company on a historic trajectory. My new role will head up everything related to driver signups, referral programs, and top-of-funnel for the supply side. I’m lucky to join the incredible team at Uber to work on a product I use every day. Thank you to Ed Baker, Travis VanderZanden, and Travis Kalanick for bringing me on board.

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. I’ve gone from being aware of the potential for Uber transforming our lives – from reading all the same articles we’ve all been reading – to truly believing it, after some deep and thoughtful conversations with the team. It’s still very early for the company and its impact, and I’m excited to be a part of it.

Not surprisingly, this also means I’m going to stop writing for a bit and take a blog vacation – I’ll check back in when 2016 rolls around to write an update though.

Finally, those who know me well also know that this is actually the last chapter of a startup I had founded a couple years ago, that achieved some success but didn’t take off the way we all wanted. (Longer story there, that I’ll write about some day). Thanks to everyone who supported me, including Marc Andreessen, Ben Horowitz, Mitch Kapor, Akash Garg, Bill Gossman, Bubba Murarka, Rishad Tobaccowala, Jim Young, Stephen DeBerry, Jameson Hsu, Grant Ries, and Ron Conway. Also Chris Howard, Brad Silverberg, Danny Rimer, Xuyang Ren, Jianfeng Lu, Tom Bedecarre, Sheila Spence, and the folks at Docomo and Recruit. My family, Ada Chen and Sachin Rekhi, who listened to my constant hand-wringing as I made the final decision. And of course, my team, in particular my close friends John Richmond and Arnor Sigurdsson, and also Steve, Logan, Alan, and the other folks I’ve worked with over the years.

Thanks for reading! I’ll be touch after the New Year.

Regards,
Andrew

 

Written by Andrew Chen

August 24th, 2015 at 1:00 pm

Posted in Uncategorized

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

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The hardest part of any new product launch is the beginning, when it’s not quite working, and you’re iterating and molding the experience to fix it. It may be the hardest phase, but it’s also the most fun.

The Product Death Cycle
All of this was on my mind when I saw a great tweet from about a year ago, on the Product Death Cycle, when things go wrong. David Bland, a management consultant based in San Francisco, tweeted this diagram:

This is what I’m calling the Product Death Cycle
– @davidjbland


product_death_cycle

A year ago when I saw this, I retweeted this diagram right away, and a year later, it’s hit 1,400+ RTs overall. This diagram has resonated with a ton of people because sadly, we’ve seen this Product Death Cycle happen many times. We’ve maybe even fallen into it ourselves – it’s all too easy. I’ve written about this phase before, in After the Techcrunch bump: Life in the Trough of Sorrow.  As well as some thoughts and strategies related to getting to product/market fit sooner rather than later.

Let’s talk about each step of this cycle, why it happens, and present a list of questions/provocations that might allow us to escape.

1) No one uses our product
The natural state of any new product is that no one’s using it :) So that’s not a problem in itself. However, the way you react to this problem is what causes the Product Death Cycle.

2) Ask customers what features are missing
One of the big early mistakes is to be completely user-led rather than having a product vision. This manifests itself in asking users “What features are missing?”

Here are the problems with this approach:

  • Users who love your product now may not represent the much larger market of non-users who’ve never experienced it. So the feedback you get might be skewed towards a niche group, and the features they suggest may not be mainstream
  • User research is great for coming up with design problems, but you can’t expect users to come up with their own design solutions. That’s your job! They may be stuck in a certain paradigm and won’t have the tools/skills to come up with their own solutions. Faster horses and all that
  • “What features are missing?” assumes that just adding more features will somehow fix the problem. But there are many, many other reasons why your product may not be working- maybe the pricing is wrong. Or it’s not being marketed well, the activation is broken, or the positioning sucks, etc.

Even the Simpsons know that slavishly listening to feature requests is a bad idea. Thus the episode about The Homer, a car that tried to appeal to everyone:

the-homer-inline4

Instead of asking for what’s missing, instead the solution is to ask- what is the root cause of users not using the product? Where’s the fundamental bottleneck? In a world where 80% of daily active users are lost within 30 days, there’s a lot of reasons why users are bouncing before they even get into the “deep engagement features” you’ve built out. Asking engaged users what features they want won’t help much- instead you’ll likely get a laundry list of disorganized features that will push you towards your competitors.

One book recommendation on this topic: Harvard Business School professor Youngme Moon’s book on competitive differentiation precisely describes the process in which customer research quickly leads to muddled differentiation, it’s worth reading.

3) Build the missing features
The second jump in the Product Death Cycle is to take features that customers have suggested, and just build the missing features. However, this quickly falls into the Next Feature Fallacy, which is the mistaken belief that just adding one more new feature will suddenly make people want to use your whole product.

As I discussed in that essay, every product has an amazing dropoff of usage from when people first encounter it:

Screenshot 2015-05-31 19.50.54

 

I’ve also published some real-life data at Losing 80% of mobile users is normal. The point is, most interaction with a product happens in the first few visits. That’s where you can ask the user to setup for long-term retention and to present the user with a magic moment. Building a bunch of “missing” features is unlikely to target the leakiest part of the user experience, which is in the onboarding. If the new features are meant to target the core experience, it’s important that they really improve the majority workflows within the UI, otherwise people won’t use them enough to change their engagement levels.

To break out of this part of the Product Death Cycle, ask yourself- is this enough of a change to influence the experience? Is it far enough “up the funnel” to impact the leakiest parts of the product experience? Is this just another cool feature that only a small % of users will experience?

Breaking out
The Product Death Cycle is tricky because it’s driven by the right intentions: Listen to customers and build what they want. People in the Product Death Cycle naturally believe that they are doing the right things, but good intentions don’t translate to good traction. Instead, ask “why?” over and over, to understand the root cause for lack of growth.

The response to these root causes should consist of a large toolkit of responses- maybe marketing: Pricing, positioning, distribution, PR, content marketing, etc. Maybe it has to do with the strategy: Going high-end instead of low-end. Building a utilitarian product instead of a network-based one, or vice versa. The point is, the solution should be tailored to the root cause, rather than to be explicitly driven by the desire of every product team to build more product.

Thanks again to David Bland for sharing the Product Death Cycle diagram with all of us.

Written by Andrew Chen

June 16th, 2015 at 9:45 am

Posted in Uncategorized

Quick update: Quoted in WSJ on dating apps, recent podcast interview, plus recent essays

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Screenshot 2015-06-10 18.13.50

Couple quick things that I wanted to batch up in a single post.

A quote from me in the Wall Street Journal today
First, there’s a quote from me in the Wall Street Journal today, for an article covering the opportunities/challenges of dating apps. I’ve been told the story will be on page B1 of the paper edition, but here’s the link for everyone who loves trees: The Dating Business: Love on the Rocks by Georgia Wells. The quote was pulled from a recent essay of mine on why investors are often skeptical of dating startups, which you can read: Why investors don’t fund dating.

Podcast interview with Codenewbies
Last week, I did a fun, casual interview with Codenewbies, a podcast targeted at people learning to code. In the interview, I talk about how taking a year of Computer Science in college, plus internships as a Software Engineer, helped me break into my first post-college job, at venture capital Mohr Davidow Ventures. And I also have a short story about the first real program I ever wrote, in GW-BASIC back in fifth grade, where I managed to blow up one of the Mac Plus computers in class.

Let’s meet up in person (San Francisco)
I’m kicking off a series of small-group gatherings to grab drinks/food in SF – something like ten people, in SOMA. If you’re based in the area, I’d love to catch up and meet. I plan to include friends/guests from top startups and tech companies in the Bay Area to join us- here’s how to register.

Follow me on Twitter
Just a reminder- if you’re not already following me, here I am: @andrewchen

Recent essays, if you missed them
Finally, I wanted to include a list of some of my recent essays in case you missed them. I’m pretty happy with how the last couple have turned out, so I hope you enjoy.

Thanks for reading!

Written by Andrew Chen

June 11th, 2015 at 9:45 am

Posted in Uncategorized

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

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Exclusive data on retention curves for mobile apps
In a recent essay covering the Next Feature Fallacy, I explained why shipping “just one more feature” doesn’t fix your product. The root cause is that the average app has pretty bad retention metrics. Today, I’m excited to share some real numbers on mobile retention. I’ve worked with mobile intelligence startup Quettra and it’s founder/CEO Ankit Jain (formerly head of search+discovery for Google Play) to put together some exclusive data/graphs on retention rates** based on anonymized datapoints from over 125M mobile phones.

Average retention for Google Play apps
The first graph shows a retention curve: The number of days that have passed since the initial install, and what % of those users are active on that particular day. As my readers know, this is often used in a sentence like “the D7 retention is 40%” meaning that seven days after the initial install, 40% of those users was active on that specific day.

The graph is pretty amazing to see:

retention_graph_average

Based on Quettra’s data, we can see that the average app loses 77% of its DAUs within the first 3 days after the install. Within 30 days, it’s lost 90% of DAUs. Within 90 days, it’s over 95%. Stunning. The other way to say this is that the average app mostly loses its entire userbase within a few months, which is why of the >1.5 million apps in the Google Play store, only a few thousand sustain meaningful traffic. (*Tabular data in the footnotes if you’re interested)

Ankit Jain, who collaborated with me on this essay, commented on this trend:

Users try out a lot of apps but decide which ones they want to ‘stop using’ within the first 3-7 days. For ‘decent’ apps, the majority of users retained for 7 days stick around much longer. The key to success is to get the users hooked during that critical first 3-7 day period.

This maps to my own experience, where I see that most of the leverage in improving these retention curves happen in how the product is described, the onboarding flow, and what triggers you set up to drive ongoing retention. This work is generally focused on the first days of usage, whereas the long-term numbers are hard to budge, no matter how many reminder emails you send.

Note that when we say that these DAUs are being “lost” it doesn’t mean that users are suddenly going completely inactive – they might just be using the app once per week, or a few times per month. Different apps have different usage patterns, as I’ve written about in What factors influence DAU/MAU? with data from Flurry. Just because you lose a Daily Active User doesn’t mean that you’re losing a Monthly Active User, yet because the two correlate, you can’t sustain the latter without the former.

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How do the best apps perform? Much better.
The second graph we’ll discuss is a comparison of retention curves based on Google Play ranking. The data shows that there is a very clear and direct correlation:

android_retention
The top apps have higher D1 retention rates, and end with much stronger absolute D30 numbers. However, interestingly enough, the falloff from D1 to D30 is about the same as all the other apps. Another way to say it is that users find the top apps immediately useful, use it repeatedly in the first week, and the drop off happens at about the same speed as the average apps. Fascinating.

Bending the curve happens via activation, not notification spam
To me, this is further validation that the best way to bend the retention curve is to target the first few days of usage, and in particular the first visit. That way, users set up themselves up for success. Although the data shown today relates to mobile apps, I’ve seen data for desktop clients and websites, and they all look the same. So whether you’re building a mobile app or something else, the same idea applies:

  • For a blogging product, you might want users to pick a theme, a name, and write their first post, to get them invested.
  • For a social service, you might want users to import their addressbook and connect to a few friends, to give them a strong feed experience and opt them into friend notifications
  • For a SaaS analytics product, you might want users to put their JS tag on their site, so that you can start collecting data for them and sending digest emails
  • For an enterprise collaboration product, you might want users to start up a new project and add a couple coworkers to get them started

Each of the scenarios above can have both a qualitative activation goal, as well as quantitive results to make sure it’s really happening. Whatever you do, sending a shitload of spammy email notifications with the subject line “We Miss You” is unlikely to bend the curve significantly.

I hate those, and you should too.

(Thanks again to Ankit Jain of Quettra for sharing this data and assisting me in developing this piece. More from them here, which examines app-by-app retention rates for messaging apps)

*Tabular data

0 1 3 7 14 30 60 90
Top 10 Apps 100 74.67 71.51 67.39 63.28 59.80 55.10 50.87
Next 50 Apps 100 64.85 60.31 54.13 49.48 44.81 39.60 34.50
Next 100 Apps 100 48.72 42.96 35.93 30.79 25.45 21.25 18.98
Next 5000 Apps 100 34.31 28.54 21.64 17.43 13.62 10.74 8.99
Average 100 29.17 23.42 17.28 13.11 9.55 6.82 3.97

 

**Methodology
Some notes on methodology below, shared by Quettra:

Quettra software, that currently resides on over 125M Android devices worldwide, collects install and usage statistics of every application present on the device. For this report, we examine five months of data starting from January 1, 2015.

Since we exclusively consider Android users in this study, we exclude Google apps (e.g. Gmail, YouTube, Maps, Hangouts, Google Play etc.) and other commonly pre-installed apps from our study to remove biases. We also only consider apps that have over 10,000 installs worldwide.

A note on privacy, which is very important to us: All data that we collected is anonymized, and no personally identifiable information is collected by any of our systems. From our understanding, this is the first time ubiquitous mobile application usage has been analyzed at such large scale. Quettra does not have a direct relationship with any of the apps or app developers mentioned in this report.

Written by Andrew Chen

June 9th, 2015 at 9:45 am

Posted in Uncategorized

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

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Ada Lovelace, an early computing pioneer, featured prominently in Walter Isaacson’s book The Innovators

Innovation is messy, and too easy to oversimplify
After finishing Walter Isaacson’s biography of Steve Jobs, I eagerly devoured his followup book, The Innovators. This book zooms out to focus not on an individual, but on the teams of collaborators and competitors who’ve driven technology forward, and the messiness of innovation. The stars of the story turn out to be the often unappreciated women who contributed to computing at key moments, and the book fittingly begins and ends with Ada Lovelace, an 19th century mathematician who defined the first algorithm and loops. I recommend the book, and here are the NYT and NPR write-ups which you can check out as well.

Isaacson’s book resonated with me because once you know how messy the success stories are, it’s obvious why the media always chooses to simplify things down to just a few characters with simple motivations. As a result, we know what kind of shoes that Steve Jobs wears, but forget the names of the people around him who worked for years to make the products we love.

The stories from the book have been floating around in my brain for a few months now, and coincided with two other pieces that went viral on Twitter/Facebook. First, there’s been some great photos of Margaret Hamilton who led the software development for Apollo 11 mission. Second, there was a discussion on Charlie Rose on the women who worked on the original Macintosh. I did some research and put together a few photos of these key pioneers from computing history. Seeing their faces and names help make them more real, and I wanted to share the photos along with some blurbs for context.

If you have more photos to send me, just tweet them at @andrewchen and I’ll continue to update this article.

Women of ENIAC
One of the most interesting backstories in Isaacson’s book is the fact that women mostly dominated software in the early years of computing. Programming seemed close to typing or clerical work, and so it was mostly driven by women:

Bartik was one of six female mathematicians who created programs for one of the world’s first fully electronic general-purpose computers. Isaacson says the men didn’t think it was an important job.

“Men were interested in building, the hardware,” says Isaacson, “doing the circuits, figuring out the machinery. And women were very good mathematicians back then.”

In the earliest days of computing, the US Army built the ENIAC, the first electronic general purpose computer in 1946. And women programmed it – but not the way we do now – it was driven by connecting electrical wires and using punch cards for data.

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Left: Patsy Simmers, holding ENIAC board Next: Mrs. Gail Taylor, holding EDVAC board Next: Mrs. Milly Beck, holding ORDVAC board Right: Mrs. Norma Stec, holding BRLESC I board.

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Left: Betty Jennings. Right: Frances Bilas

ca. 1940s --- Computer operators program ENIAC, the first electronic digital computer, by plugging and unplugging cables and adjusting switches. | Location: Mid-Atlantic USA.  --- Image by © CORBIS
Jean Jennings (left), Marlyn Wescoff (center), and Ruth Lichterman program ENIAC at the University of Pennsylvania, circa 1946.

More photos here.

Margaret Hamilton and Apollo 11
Twitter has been circulating this amazing photo of Margaret Hamilton and printouts of the Apollo Guidance Computer source code. This is the code that was used in the Apollo 11 mission, you know, the one that took humankind to the moon. This is how Margaret describes it:

In this picture, I am standing next to listings of the actual Apollo Guidance Computer (AGC) source code. To clarify, there are no other kinds of printouts, like debugging printouts, or logs, or what have you, in the picture.

There are some nice articles about this photo on both Vox and Medium, which are worth reading.

Here it is, along with a few other photos of her during this time:

Margaret_Hamilton

f2aa4ddd9cd5800df5983790163517cf

Margaret_Hamilton_in_action.0.0

A side note to this that I found pretty nerdtastic is that the the guidance computer used something called “core rope memory” which was weaved together by an army of “little old ladies” in order to resist the harsh environment of space.

To resist the harsh rigors of space, NASA used something called core rope memory in the Apollo and Gemini missions of the 1960s and 70s. The memory consisted of ferrite cores connected together by wire. The cores were used as transformers, and acted as either a binary one or zero. The software was created by weaving together sequences of one and zero cores by hand. According to the documentary Moon Machines, engineers at the time nicknamed it LOL memory, an acronym for “little old lady,” after the women on the factory floor that wove the memory together.

Here’s what it looked like:

Screenshot 2015-06-03 21.21.02

Susan Kare, Joanna Hoffman and the Mac
Megan Smith, the new chief technology officer of the United States, was on Charlie Rose recently and referenced the fact that the women who worked on the Macintosh were unfairly written out of the Steve Jobs movie – you can see the video excerpt of her speaking about it here. I tracked down the photo she was referring to, and wanted to share it below.

Screen_Shot_2014-01-17_at_8.13.59_AM
Macintosh team members: Row 1 (top): Rony Sebok, Susan Kare; Row 2: Andy Hertzfeld, Bill Atkinson, Owen Densmore; Row 3: Jerome Coonen, Bruce Horn, Steve Capps, Larry Kenyon; Row 4: Donn Denman, Tracy Kenyon, Patti Kenyon

On the top of the pyramid photo is Susan Kare, who was a designer on the original Mac and did all the typefaces and icons. Some of the most famous visuals, such as the happy mac, watch, etc., are all from her.

01_macicons
One cool thing to add to your office: Some signed/numbered prints of Susan’s most famous work. I have a couple – here’s the link to get your own.

Here’s a May 2014 talk. Susan Kare, Iconographer (EG8) from EG Conference on Vimeo.

Another key member of the original Mac team, Joanna Hoffman, isn’t in the pyramid photo but I was able to find a video of her talking about the Mac on YouTube, and embedded it below. Here’s the link if you can’t see the embed. She wasn’t in the Steve Jobs movie from Ashton Kutcher, but it looks like she will be played by Kate Winslet in the new Aaron Sorkin film coming out.

Here’s the video:

 

Bonus graph: Women majoring in Computer Science
Hope you enjoyed the photos. If you have more links/photos to include, please send them to me at @andrewchen.

As a final note, one of the most surprising facts from Isaacson’s book is that early computing had a high level of participation from women, but dropped off over time. I was curious when/why this happened, and later found an interesting article from NPR which includes a graph visualizing the % of computer science majors who are women over the last few decades.

The graph below is from the NPR article called When Women Stopped Coding – it’s worth reading. It speculates that women stopped majoring in Computer Science around the time that computers hit the home, in the early 80s. That’s when male college students often showed up with years of experience working with computers, and intro classes came with much higher expectations on experience with computers.

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Written by Andrew Chen

June 4th, 2015 at 10:00 am

Posted in Uncategorized

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

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A few weeks ago, I read this tweet, and found myself nodding my head in vigorous agreement.

For people who love to build product, when something’s not working, it’s tempting to simply build more product. It leads to the launch-fail-relaunch cycle that I alluded to in a previous essay, Mobile app startups are failing like it’s 1999. However, this rarely works, and when you look at the metrics, it’s obvious why.

The metrics behind the Next Feature Fallacy
Let’s go into the numbers. The reason why the Fallacy is true can be described by one simple diagram, which might be described as the most tragic curve in tech:

 

Screenshot 2015-05-31 19.50.54

 

The above diagram shows the precipitous drop-off between initially attracting a user versus the difficulty of retaining them over the first month. Perhaps it reminds you of the diagram, popularized by Edward Tufte, of the destruction of the Napoleon’s Grande ArmĂ©e during his disastrous invasion of Russia. The curve drops off fast- very fast. I’ve seen a lot of real data around this, and believe me, there are very few cases where things look pretty.

Some example metrics for a web app with average (but not great) numbers:

  • 1000 users visit your homepage to check out your product
  • 20% sign up
  • 80% finish onboarding
  • 40% visit the next day after signup
  • 20% visit the next week after signup
  • 10% visit after 30 days after signup
  • After 30 days, 20 users (out of 1000!) are DAUs

This is pretty typical, and you can see the steep drop-off.

And yes, occasionally I’ve seen better numbers than this, for apps that have built a great brand, or are getting most of their traffic through high-conversion referrals. Or the D1/D7/D30 for certain categories, like messaging apps, are often 2-3X higher than what I’m publishing above. But in the main, everyone is a bit depressed about their numbers. I’ve written about the routine mediocrity of these metrics in more detail here: Why consumer product metrics are all terrible.

The vast majority of features won’t bend the curve
These metrics are terrible, and the Next Feature Fallacy strikes because it’s easy to build new features that don’t target the important parts. Two mistakes are often made when designing features meant to bend this engagement curve:

  • Too few people will use the feature. In particular, that the features target engaged/retained users rather than non-users and new users
  • Too little impact is made when they do engage. Especially the case when important/key functions are displayed like optional actions outside of the onboarding process.

These mistakes are made because there’s often the well-intentioned instinct to focus on features that drive deep engagement. Of course you need a strong baseline of engagement, but at its extreme, this turns misguided because features that aren’t often used can’t bend the curve. A “day 7 feature” will automatically be used less than an experience tied to onboarding, since the tragic curve above shows that fewer than 4% of visitors will end up seeing it.

Similarly, a product’s onboarding experience can be weak if there isn’t a strong opinion on the right way to use (and setup) the product. In the early Twitter days, you’d sign up and immediately be dropped onto a blank feed, and a text box to type in your status. While this might let you explore the product and do anything, ultimately this is a weaker design then asking you to follow a bunch of accounts, which is the current design. Understanding that Twitter is meant to be mostly used as a reader, potentially without tweeting much, is a deep insight and a strong opinion that has paid dividends for the product.

Another frame to think about is to make sure a new feature doesn’t assume deep engagement/investment in your product. Let’s introduce the concept of an engagement wall, which exists at the moment that your product asks the user to deeply invest in their product usage, where “behind the wall” means that the feature can only be experienced once the users buys into a product, and engages. An example might be a high-effort, low % action like posting a photo, creating a new project, or dropping files into a folder. In front of the wall means features that create value without much investment, such as browsing a feed, rating some photos, or clicking a link. If you build a bunch of amazing features that are behind the engagement wall, then chances are, only a small % of users will experience the benefits. Adding a bunch of these features won’t bend the curve.

How to pick the next feature
Picking the features that bend the curve requires a strong understanding of your user lifecycle.

First and foremost is maximizing the reach of your feature, so it impacts the most people. It’s a good rule of thumb that the best features often focus mostly on non-users and casual users, with the reason that there’s simply many more of them. A small increase in the front of the tragic curve can ripple down benefits to the rest of it. This means the landing page, onboarding sequence, and the initial out-of-box product experience are critical, and usually don’t get enough attention.

Similarly, it’s important to have deep insights on what users need to do to become activated, so that their first visit is set up properly. For social networks, getting them to follow/add friends is key, because that kicks off a number of loops that will bring them back later on. For a SaaS metrics app, it might be getting a JS tag onto the right pages. For a blog, it might be for them to pick a name, theme, and write the first post so they get invested. Isolating the minimum onboarding process lets you keep the initial steps high-conversion, yet set up their experience for success.

When a product is still early, when you’re searching for and building game-changing features, the resources those features eat through can be massive. The risk that your company takes in building them might be too high, and your team might overestimate the  probability that a feature will meet your expected growth goals for it. There is always a chance that the next feature will bend the curve, but it requires being smart, shrewd, and informed. Good luck.

Written by Andrew Chen

June 1st, 2015 at 10:00 am

Posted in Uncategorized

Why investors don’t fund dating

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I’ve been listening to the excellent Season 2 of the podcast Startup, which gives an inside look at YCombinator startup The Dating Ring (NYT coverage here). The episodes are all great. They talk about many important topics, but I had some specific comments on fundraising for dating products.

Here’s a simple fact: It’s super hard to get a dating product funded by mainstream Silicon Valley investors, even though it’s a favorite startup category from 20-something entrepreneurs. There’s a large swath of angels/funds who categorically refuse to invest in the dating category in the same way that many refuse to invest in games, hardware, gambling, etc. Perhaps they’d make an exception for a breakout like CoffeeMeetsBagel (I’m an advisor) or Tinder, but in the main, it’s an uphill battle for dating apps to attract interest. Here’s some data on the few dating cos that have raised.

Obviously, anyone starting a new company in dating should try to understand investor biases in this sector. This essay also compliments a previous one on operating, from HowAboutWe co-founder Aaron Schildkrout, now at Uber, who also wrote about his experiences.

Here are the reasons usually given for why investors don’t do dating:

  • Built-in churn
  • Dating has a shelf-life
  • Paid acquisition channels are expensive
  • City-by-city expansion sucks
  • Hard to exit
  • Demographic mismatch with investors

Let’s break it down.

Built-in churn
Churn sucks, and the better your dating product works, the more your customers will churn*. Every churned customer is a new customer you’ll have to acquire just to get back to even. When you look at a successful subscription service like Netflix or Hulu, you might find a churn rate of 2-5% per month, and you can calculate the annual churn through the following:

Annual Churn = 1-(1-churn_rate)^12
2% monthly churn = 1-(1-0.02)^12 = 21% annual churn
10% monthly churn = 1-(1-0.1)^12 = 70% annual churn

If you have an 70% annual churn rate, you have to have a strategy to replace almost your entire customer base each year, plus a bunch of percentage points to drive topline growth. You can imagine why successful public SaaS companies try to keep their monthly churn under 2%.

So what do the churn rates look like for a dating product? I’ve heard numbers as high as 20-30% monthly. Let’s calculate that:

20% monthly churn = 1-(1-0.2)^12 = 93% annual churn

You read that right. And that means at 20% monthly churn, it gets very hard to retain what you have, much less fill the top-of-funnel with enough new customers to grow the business. Scary.

With most subscription products, the more you improve your product, the lower your churn. With dating products, the better you are at delivering dates and matches, the more they churn! As you might imagine, that creates the wrong incentives. A product focused on casual dating, like Tinder, might escape this dilemma, but dating products generally have built-in churn that’s unavoidable.

Dating is niche and has a shelf-life
All this churn is especially complicated by the fact that the dating market at any given time is pretty niche. Similar to buying a car, refinancing your student loans, or moving into a new house, the reality is that being “in the market” as a single person looking to meet others has a limited time window. Another way to say this is the dating has “intent” the same way that shopping might, especially when you are talking about a paid subscription service. This limits the market size as well as restricting the types of marketing channels you can use to read those consumers.

A similar challenge is that these products aren’t “social” in the same way that Skype or Facebook might be. Although the stigma is quickly passing, it’s not like consumers want to sign up for a dating site and then invite their friends+family to join them on the site. In that way, it’s more similar to a financial or health product, where some privacy is required.

Again, one of the ways that the new generation of mobile dating products solve this is that they are free plus focus more on casual dating. Both factors open up the market to a wider audience, reduce churn, and create opportunities for viral growth.

Paid acquisition channels are expensive
Dating products have historically depended on paid acquisition channels to build their customer base, and other subscription products have generally done the same. In order to make the ROI work, you have to calculate your customer acquisition cost (CAC) versus your lifetime value (LTV) and make sure you are making enough money to support both the marketing as well as operations. In SaaS, you’d try to get a 3X ratio for CAC:LTV but that’s building in some profit for the company – a dating startup might be able to run it closer to the metal to get their initial growth.

Here’s a couple scenarios for products that buy their customers:

  • Make a ton of money all at once (example: car/insurance/loan/mortgage leadgen)
  • Make a little bit of money over a long period of time (storage, streaming music, etc.)
  • Make a little money at first, then grow the revenue over a long period of time (SaaS)

Here’s a visualization of this:

77kthIJwcPC_a1LFuUoDcA6x3oy9mNP7r7CIh23guQ4

 

When you start to fill in this chart, you can see a couple things:

-Re5F75xIToCuGtpvgpYdQBO8gfIL0uQikuX4UhLn-k

First, you’ll observe that of course the “ideal” case might look like a super low churn business that also generates a ton of revenue from each customer. However, the market size might be much smaller than the others. Christoph Janz, a venture capitalist and initial investor in Zendesk wrote a great essay on this topic, called Five ways to build a $100M business that talks about market size as an issue for this.

But back to dating- where does it go? The trouble is, it has some of the same economics for consumer subscription products priced at <$50/month, but at the same time, super high churn that looks like a one-time product. It’s hard to build a high LTV off of that, and so paid channels turn tricky.

Again, this is an area where the new mobile dating apps excel. They have the ability to tap into organic viral/word-of-mouth installs, are super sticky due to their messaging features, and free installs mean infinity return on ad spend (ROAS). At the same time, their focus on casual dating lowers churn and they can monetize via microtransactions. It’s a different model that’s more attractive.

City-by-city expansion sucks
Dating products inherently rely on a local marketplace, and bootstrapping a series of marketplaces is very hard, and expensive. People are willing to travel to meet each other, but only so much. And there needs to be the right mix of male/female participants (or whatever permutation makes sense). To make this work, each city needs to get spun up the same way that on-demand services are spun up, which is one of the reasons why local expansion has remained expensive and unscalable.

This is why we often see dating products continually hold a series of unscalable events/parties/etc in a city to get things going. Until there’s word-of-mouth, and enough people to generate a quality experience, the marketplace will suck. But it’s slow going.

Demographic mismatch with older, married investors
Dating solves a problem that’s most universal and acute for unmarried 18-35yos. Most investors who can write checks (as opposed to associates) are older, married, with kids. Oftentimes they haven’t had to date anyone in decades, unless you’re talking about Ashley Madison. Given this demographic mismatch, it’s a lot harder to get investors to put in the time to really understand the nuances of how one dating product is superior to another. This isn’t just a problem for dating, but also for women’s fashion or startups targeting international markets. It’s tricky, and investors would often rather sit back and wait for traction rather than investing on the merits of a product, something they’re willing to do in other categories. (Thanks to my friend Jason Crawford for adding this point)

Hard to exit
In the end, we’ve seen that dating products often end up being owned by IAC. They own Match, OKCupid, Tinder, HowAboutWe, and others. They have deep experience in local and dating, and the deep pockets to squeeze profit from the category. In contrast, we’ve seen recent dating cos like Zoosk withdraw their IPO plans. I don’t have any inside info, but I’m sure it’s because the churn was high, the channels were degraded, and it was hard to replace lost customers.

In the end, the lack of exits might be more the result, not the cause, of investor disinterest in dating. After all, given the challenges above, it’s very hard to maintain a steady customer base much less grow it consistently year-after-year.

A final note on this: Investor pattern-matching is lazy and often sucks. Google wasn’t the 1st search engine and it wasn’t clear the category was a good investment. Lots of failures. Same with Facebook or Whatsapp. But the investors who won were able to look at the specific characteristics of some of these new companies, rather than looking at the category, and bet that they’d figure it out. Very possible within dating as well, and I wish everyone who’s working in the category that they will be the ones to figure it out.

UPDATE: Fixed some math, thanks Hacker News commenters.

 

Written by Andrew Chen

May 26th, 2015 at 11:19 am

Posted in Uncategorized

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

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unicorn1

“Unicorn company.” It’s the latest bit of jargon that’s infected our conversations here in the Bay Area, to the point where both WSJ and Fortune have clever infographics and lists of the top companies. In pitches, entrepreneurs are asked to explain how their new company will become the next unicorn startup, and the tech press routinely debates if a hot new team will build the next unicorn. And yet, this term could not be a more meaningless goal for entrepreneurs.

After all, what’s the definition of a unicorn startup? Just one that reaches $1B in valuation? Who cares? I wish we’d just go back to saying “billion dollar startup” rather than unicorn, to reflect the real nature of the term, not one with a cutesy veneer.

It’s the ultimate vanity metric, because $1B of shareholder value is merely the lagging indicator that we’ve created something useful for the world. This should never, in itself, be the goal of starting up a company. So let’s all stop talking about unicorns. I’m calling peak unicorn. Let’s focus on the inputs for building impactful, lasting companies, where wealth creation is a side effect of doing a great job.

Instead, let’s talk about how to build our forever companies.

Low-attention spans in tech
When I first started out, as a young techie with a low attention-span living in Seattle, I had an irrational admiration for self-described “serial entrepreneurs,” the ones who build and sell a bunch of startups in their careers, even when they are quick flips. The variety of starting up multiple companies seems dreadfully exciting, especially when you are young and lack purpose. However, the more time I spend in the industry, the more my admiration shifts to those who start and run their companies for years, decades, and perhaps their whole lifetimes. Warren Buffett, Richard Branson, Jeff Bezos, Mark Zuckerberg, and others all fall into this camp.

These folks have started and built their forever companies. These companies also happen to be incredibly successful, but more importantly, as entrepreneurs they’ve found their life’s work.

After all, many of us in tech idolize Steve Jobs for his sense for design, and his vision. Some even emulate his fashion. But you know what’s hard to emulate? The fact that he started working on hardware/software products as a teenager, and built on those ideas for the next 40 years of his life, until he ran out of time. How many of us can profess a lifetime of dedication towards our work like that?

Counterbalance
The forever company is an entrepreneur-focused counterbalance to the financially-motivated goal of becoming a unicorn. Hopefully we can build both! Of course we all want our companies to be valuable, and make a big impact, but while a unicorn concerns itself with the output of entrepreneurship, the goal of a forever company starts with the inputs and the right intentions.

This is different than a lifestyle company. Bezos runs Amazon as his forever company, but it’s certainly not just to support his lifestyle, it’s to make a much bigger impact than that. And Amazon took millions in venture capital money on their way to becoming a public company, to fully capture the opportunity. There’s a different kind of problem when the desire for a lifestyle interferes with the “forever” part of the goal. Those who underinvest in their products create the danger for a smarter/bigger/funded competitor to put them out of business, which is a lifestyle company that doesn’t last forever! This distinction is subtle, but important.

I didn’t coin the term. It’s the kind of idea that could only come out of a deep, late-night conversation with my sister and bro-in-law Ada and Sachin, who also work in tech. They mentioned it in passing as a worth goal for themselves, one day, and the term really resonated with me. It’s stuck like few things have, and I hope it sticks with all my readers too.

When forever companies scale, and when they don’t
Sometimes forever companies scale and become a multi-billion dollar company. In many cases, a forever company and a unicorn are the same, when the market is big, the team is talented, and there’s some good luck. These are the companies we all want to start and want to fund in Silicon Valley. These companies are easy to embrace.

But sometimes, a forever company just implies a lifetime of dedication towards something that may never get big. There is great honor in that as well, and I can’t help but admire those who pursue this goal. By now, we’ve all seen Jiro Dreams of Sushi, and his passion and skill for sushi is incredible. Closer to tech might be someone like David Kelley of IDEO, who founded his firm decades ago, and while they’ll never be a unicorn, I imagine he must be very proud of the work they’ve done over the past 25+ years.

Finally, I want to leave you with a great interview with Jiro I saw recently. He talks about feeling like a master only after reaching 50 years. The discussion on handmade versus automation fascinating, as well as the work ethic of younger generations. Everyone who’s working in design or engineering in software will relate, I’m sure.

I’ve embedded it below but if it doesn’t show, here’s the link to Vimeo. Enjoy.

Written by Andrew Chen

May 4th, 2015 at 12:51 pm

Posted in Uncategorized

Ten classic books that define tech

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library

Today, I was asked for the definitive list of books that I’d recommend as the classics for tech products and business. It’s hard to pare things down to such a short list, since there’s so much great stuff that’s been written.

In addition, a lot of new exciting books have been published in recent years, but they haven’t stood the test of time. My lists solely consists of books that skew oldish, but have aged well and continue to provide value today. This leaves out amazing contributions such as The Hard Thing About Hard Things, Zero to One, and The Lean Startup, which will undoubtedly make a list like this in future years.

With that in mind, here’s my list. I’m sure you’ve read many of them, but hopefully you find a gem or two that you haven’t already read. In no particular order:

There’s also a number of great books that just tell the narrative of one company, such as The New New Thing, Startup, eBoys, Only the Paranoid Survive, Breaking Windows, but I’ve left those off the list even though they are very fun to read.

I’m sure I’ve missed some great ones! If you want suggest a replacement and/or share your own list, tweet me at @andrewchen.

 

Written by Andrew Chen

April 30th, 2015 at 2:50 pm

Posted in Uncategorized

How I first met Eric Ries and also why I’ve ordered his new Kickstarter-exclusive book The Leader’s Guide

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20150321_Dinner-0590
Taken a few weeks ago at dinner, at Mission Rock in Dogpatch

tldr
It’s the last week to order Eric Ries’s new book, called The Leader’s Guide. In a very innovative experiment, it’s being published exclusively on Kickstarter. It’s the only way to buy a copy. I’ve already ordered a signed version and encourage you to support his work too. Here’s the link.

Making entrepreneurship mainstream
Like many of you, I’m a huge fan of Eric – he’s created a compelling, cohesive framework for thinking iteratively and entrepreneurially about products. The ideas are so powerful that the ideas in the book – such as “pivoting” and “MVP” – are now part of industry jargon and have even been featured on HBO’s Silicon Valley.  (Also, isn’t it inevitable that he makes a cameo at some point?) The ideas in Lean Startup are amazingly powerful, and I continue to reference them all the time.

How I first met Eric
Last month in March 2015, I hosted a dinner at Mission Rock in Dogpatch where he was a special guest, and we talked about how we first met back in 2008. Eric and I had our first coffee before The Lean Startup was a real thing, when he was between jobs and hanging out at Kleiner Perkins. (PS. if you’re interested in attending events like this in the future, subscribe to my newsletter and you’ll get email updates if I do this again sometime)

Anyway, here’s how I first got to know Eric- at that point, I was writing a niche, mostly unread blog about tech and products, at the end of my stint as an Entrepreneur-in-Residence at Mohr Davidow Ventures. I had maybe 100 readers total. It was thrilling to find another niche, mostly unread blog full of content I was interested in :) Looking at my referer traffic, I noticed I was getting a tiny bit of traffic from a blog called “Startup Lessons Learned.”

I clicked through, and my mind was blown…

First off, the blog was anonymously written. There were a ton of essays across a bunch of meaty topics – picking products, continuous deployment, landing pages, and metrics. It was obviously from someone who was on the front lines. I couldn’t tell who was writing it, but whoever it was, the content was amazing. I bookmarked it and added it to my Google Reader, and would diligently check for updates every week.

I just couldn’t believe that someone was writing this much great stuff without taking credit for it :) Eventually I found a cryptic email address from the blog, decided to write in to figure out who the hell was writing this amazing content. Soon after, I got a quick reply, and that’s how we first met.

A few months later, Eric told me a few months later he was going to leave Kleiner to work on a book. I was very confused :) Turns out that book he was working on was The Lean Startup, and it ended up doing pretty well!

Fast forward
The success of The Lean Startup has changed the culture of entrepreneurship across the world. And many new ideas, frameworks, and refinements have been built on top of the ideas presented in the original book. There’s a ton of lessons learned from trying to implement these ideas across a wide variety of industries – for example, at the aforementioned dinner event we heard from Eric on how people have tried to apply the ideas to non-tech industries like manufacturing, aerospace, as well as some of the nuances of applying MVPs in a design-centric world of consumer mobile apps.

Because of this, I was thrilled to hear that a lot of these case studies and ideas have been collected into a new book, The Leader’s Guide. I ordered my copy immediately upon hearing about it.

Here’s the link if you want to check it out too:
The Leader’s Guide, by Eric Ries.

Written by Andrew Chen

April 7th, 2015 at 4:01 pm

Posted in Uncategorized

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

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intro

Cheaper and cheaper rides
Free, ad-supported Uber rides are inevitable, and if Uber doesn’t do them, a different competitor – perhaps Google! – will do it. It would be the next step in the industry’s trajectory towards lowering prices. Uber started in the high-end of the market, as “everyone’s private driver” which emphasized quality, but the early pricing was out of range for most. Dropping prices will increase demand, grow the market, and nothing will do that faster than going free.

Starting with UberX and now UberPool, each tier of service has dropped the price, tapping into more and more latent demand. Today, the figures are staggering – in San Francisco, Uber’s revenue is 3X bigger than the city’s entire taxi industry. Worldwide, the company now does 1 million rides per day and is adding 50,000 new drivers per month. Not bad at all.  I wrote a tweet about this concept a few weeks ago and wanted to expand more on this topic.

Free!
The next step is free. Lowering prices drive all sorts of goodness- more rides, busier drivers, shorter wait times – all which powers the company’s network effects. So how would you push the prices even lower than UberPool?

Well, here’s a crazy idea:

UberZero. A free, ad-supported tier of service that’s cheaper than UberPool and UberX.

That’s right, ads. I think it can be done well. And no, I don’t mean the cheesy in-taxi TVs that play ads nonstop, because those are broken for a variety of reasons I’ll cover. Instead, my proposal would be to put the ad units on your smartphone, in the dead time while waiting for your car and when you’re on your ride. And then to connect these ads with the big existing markets for app installs, lead generation, video ads, and local ads.

After all, advertisers are already comfortable paying a couple bucks for a variety of direct response actions from users. Individually, or together, you could imagine the above budgets making a big dent into the price of a single ride. In some cases, it might just be a discount of $1 or $2 off. In other cases, you might make the ride completely free.

Mockups for four potential ad units
Here’s four potential ad units that might work. These mockups were done in collaboration with the talented Chris Liu (@machinehuman), who’s done UX at Mercedes Benz, Alexa/A9, and IMVU. It was fun to throw together a couple designs.

Without further adieu, some of the concepts:

1) App Installs
The first and most obvious opportunity would be to tap into the ecosystem of paid app installs. Most of the $10B+ market for mobile ads is from driving app installs.

The user experience would be something like this- today, right after you book a ride, you often just sit there, staring at your phone, wondering how many minutes it takes for an Uber to travel a few blocks in San Francisco. Instead of just twiddling your thumbs waiting, how about getting a buck or two off your ride just from downloading and playing with an app?

Or similarly, imagine your current in-car experience. I often get into an Uber, pull out my phone, and do a little email during the ride. It would be easy to simply redirect my attention to an app, or a video ad, or some other activity that creates value for both myself and an advertiser.

It might look something like this:

app-download

As mentioned earlier, app developers are often paying $3-10 per install, especially in the games and commerce categories. This is a meaningful % when UberPool currently lets you go anywhere in San Francisco for $7 flat fee.

The biggest problem with this is that this is an incentivized install, which performs worse and Apple doesn’t like it. The plus side is that the folks who are really spending money in this area direct marketing-oriented and will back out the proper Cost-Per-Install to bid given all of that. As for Apple’s dislike of incentivized installs, you could certainly combine it with other ad units, and make the install a completely optional thing, which brings us to the next idea…

2) Video ads
One of the most compelling new ad formats for mobile is video, which are commanding high rates, especially when combined with a call to action to install an app. This is one of the many reasons why ad-supported companies like Twitter and Facebook are doubling down on in-stream videos.

So while you wait for your car to arrive, or once you get into the car, just watch a short clip that does a full-screen takeover of your phone. Then after you watch, the ad presents an optional action to download the app, which is a double dip on value. This packages both a brand ad with some a direct response ad unit, making for a powerful combination.

In Snapchat’s early tests on video advertising, Adweek and others have reported they are asking $750k, equivalent to a $100 CPM to participate. There’s no reason why Uber wouldn’t be able to command similar rates, especially for endemic advertisers in travel, shopping, and local.

It might look something like this:

movie-trailer

The key problem here would be to provide context and targeting. A classic TV ad against a show like, say, Oprah, provides both context, targeting, and a brand halo effect. An ad to an anonymous passenger isn’t compelling until you know a little bit more about them.

This begs the question, what do we know about a passenger? What would make sense from a targeting standpoint? Targeting is interesting because it’s an anonymous segment of people, which you could make off a number of interesting values:

  • Name
  • Contact info (email/phone)
  • Location
  • Where they’re coming from and where they’re going
  • Work or personal credit card

Of course from a targeting perspective, you’d never pass on the personally-identifiable information to an advertiser, unless there was opt-in (more on that later!). But how compelling would it be to for example target people in a segment like the following:

  • Passengers heading to a movie theater
  • Passengers coming from SFO airport, heading to SOMA
  • Passengers with email addresses like @google.com and @fb.com
  • Passengers traveling in a group of 2+ going out on a Friday night
  • Passengers heading home from work, or vice versa
  • Passengers heading to the Dreamforce conference
  • … and many more combinations

Now ask yourself, who else is able to deliver targeting ad inventory like this? There’s a very short list of companies that could even contemplate it. Of course it’ll be up to Uber as well as passengers to decide whether or not this kind of targeting criteria can be aggregated anonymously for ad targeting, but history has shown that ad targeting options tend to increase rather than decrease over time.

If you found the examples above compelling, imagine an instant rebate on your ride that relied on opt-in from the passenger to share this contact information directly. There’s a whole world of leadgen that’s willing to pay big bucks for accurate contact information for people who are in the market for targeted services and products.

3) Lead gen, especially for SaaS/B2B
I find leadgen an especially intriguing possibility because it’s commonplace for advertisers to pay something like $10 for an email address. For a SaaS/B2B advertiser, that number is even higher, as much as $30-50 in some cases. And if you’re talking about getting a credit card alongside an app install, that might be as high as $200 or more.

Of course you could never pass along a passenger’s contact info + credit card number without their consent. But what if you made it frictionless to consent? Then that previous list of attributes could all be sent in a series of confirmations: Name, contact info, payment information, etc.

At the heart of this is the fact that segmentation/targeting makes some audiences much more valuable than another. Today, a service like Uber charges a C-level executive the same price as the Average Joe. Imagine if you could have a leadgen campaign where an advertiser  decides that someone with a “name@google.com” address is pretty valuable given that they’re a Google employee. Or perhaps it’s interesting to know that they are coming from a Salesforce conference.

You might provide a call to action like this:

free-ride

This would make obvious sense for the high stakes world of SaaS/B2B leadgen, but it might also make sense to target a wide consumer base, especially around commerce. For example, targeting travelers who are arriving from the airport, to target them with highly personalized hotel/tour offers. Or targeting hardcore movie or concert-goers for their next night out.

Similar to the video ad scenario, the targeting capabilities would be key. Targeting based on location and context is key. And as I think about this, the most compelling part of the whole thing is that Uber/Lyft have data and context that is unique in the industry. They know where you are going, and where you are coming from, and enable you to go from Point A to Point B.

You know who else is like this and has created the most profitable and targeted advertising ever? Google, of course.

Google’s superpower is the Power of Redirection. When you search for something, Google can understand that intent and gently redirect you to the right place. Sometimes this is a destination that they deem relevant based on their algorithms, but sometimes, they just take you somewhere that’s paying them a bunch of money. This superpower is very valuable.

4) The Power of Redirection for local ads
Uber also has the redirection superpower, albeit in a different flavor. Similar to Google, when you plot a destination, there’s intent built into that. All the examples I’ve used previously indicate this- heading to Facebook’s HQ means something. Leaving from SFO means something. Heading to the 49ers stadium on a Sunday means something. And it would be easy for Uber to take you to your destination, but maybe they can suggest somewhere else to go, or perhaps they can suggest a complimentary product/service.

And using the analogy of organic versus paid search, perhaps there are organic destinations and there’s also paid destinations. In the case where a ride can be redirected to a paid destination, then Uber can literally, actually drive foot traffic. Pretty amazing!

For a trivial example, you might imagine something like this, that redirects you to a nearby Starbucks as a detour on your ride:

starbucks

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(The second mockup that bundles in restaurant reservations was sent in by Marc Köhlbrugge, founder of BetaList – thanks!)

You could imagine a clear application for travelers of course. Heading to the W Hotel? Why don’t you try an Aloft hotel and get $X off your stay. Think about how compelling this is compared to other forms of getting local foot traffic. Billboards? Radio? Well, in this case you can literally get someone to your footstep.

I think this could be fundamentally a new market, although it would make sense to tie this back to the $4.5B mobile/local ad market. Or you could tie it back to the unit economics of something like pay-per-call, where local vendors are willing to buy a live phone call with a customer for $10. Nevertheless, still super compelling when you are talking about tens of billions of local ad dollars moving to mobile over the next decade.

Lots of practical challenges
But wait, could you really make all rides free all the time? Maybe not. After all, there’s aren’t infinite drivers. Won’t this make the ride UX horrible? Maybe, which is why you’d have to be thoughtful with the design. Another issue is that advertisers won’t pay for passengers to download the same apps over and over again – they’ll want frequency caps. And Apple doesn’t like incentivized installs, or maybe passing PII to advertisers will be frowned upon by the press. All very real issues that will have to be worked out over time.

And in the end, maybe it’ll turn into free* with an asterisk, limited to 1 per day within your city and only in the morning. Or just another way to decrease fares by another 25%. Whichever way it’s done, I’m convinced it will be done, because the benefits are too great.

Isn’t this perfect for Google?
If Uber doesn’t do this, perhaps Google will. There’s been rumors that they’re working on some kind of Uber-competitor, and they already have all the advertisers to make the above ad units work.

Providing free, ad-supported rides would certainly be a unique way to enter the market. Even more so, there’s network effects. Ad networks are fundamentally marketplaces, and they have tremendous network effects at scale. You could see the following virtuous cycle:

  • Free rides = way, way more rides
  • More rides = more advertising reach
  • More reach = more interesting targeting options, and drivers
  • More advertisers = more profitable yet free rides
  • Which leads to more drivers, customers, and more

At scale, a network like this would be very hard to attack, especially when combined with existing ad businesses like the type that Google possesses. Thus, if we see Google launch an Uber-competitor, I think it’d be a matter of time before they experimented with this.

Why old in-taxi advertising systems suck
The final point I want to make is to contrast all of these ideas with the old in-taxi ad systems we used to see. If you need your memory jogged, they used to look something like this:

IMG_5315

These systems suck. And they can’t generate the kind of economics to power the subsidy we’d want to see to make a dent on the actually fare price. The point isn’t to actually make money via advertising- the point is to drive down the cost of a ride so that the market is more efficient and liquid, creating network effects.

These old advertising displays are archaic:

  • Passive advertising content
  • Doesn’t know anything about you or your trip
  • Can’t get you to download an app
  • Limited internet connectivity

These systems are ineffective because ultimately, this display belongs to the taxi and not you. As a result, it lacks some of the key ingredients that an advertiser finds valuable: an easy way to get your contact info, or to get you to install an app on your phone. It’s good for display untargeted video ads and not much else.

That’s why Uber sits on a special opportunity to build an ad platform that’s never existed before. It’d be an ad platform that has unique access to context, intent, and built on your personal device. If it’s done well, advertisers will love it and consumers will be grateful to have free/discounted rides.

Yet another reason to be bullish about the company, in addition to all the great momentum they already have.

(Special thanks to Chris Liu collaborating on these great mockups that really make the discussion in this essay pop!)

Written by Andrew Chen

March 30th, 2015 at 9:56 am

Posted in Uncategorized

Personal update: I’ve moved to Oakland! Here’s why.

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Oakland-JLS-2
My new neighborhood, in Jack London Square, Oakland, CA.

Where’d you move?
I moved to a tiny neighborhood called Jack London Square in Oakland. Yes, it’s named for that guy that wrote about the gold rush. I’ve only been here 3 months but I really like it so far. Previously, I lived in Palo Alto for 5 years, then about 2 years in the Lower Pac Heights neighborhood of San Francisco, but had never really spent much time in the East Bay. I had sort of heard that people were moving from SF to Oakland, but didn’t really have a reason to check out the neighborhood until a few people I know moved here.

Here were some of the articles I read while doing research:

PS. if you call Oakland “the next Brooklyn” to people who’ve lived here for a long time, they don’t like it :)

I live near there too! / How do I find out more about it?
If there’s interest, I’ll host a tech get-together or two.

Sign up here to get updates on an upcoming brunch/drinks/dimsum/whatever in Oakland.

There aren’t too many tech people here, so it’d be fun to get the small community that is out here together.

Where is it relative to San Francisco? How’s the commute?
I travel to the city pretty much every day. I usually take the BART, and sometimes the ferry (it has wifi!).

There’s a couple ways to get to the city:

  • BART (10min walk + 20min BART)
  • Ferry ride (25min ride + walk from Ferry building)
  • Car (30min without traffic, 60min+ with traffic)

I used to live in the Mission, and going from 24th+Mission to SOMA is about comparable to my current commute. However, I occasionally do have the morbid fear that there’ll be an earthquake while I’m underwater in the train.

Screen Shot 2015-02-24 at 2.07.42 PM

Where’s all the good food?
Right now, the Uptown neighborhood is opening the most new/amazing restaurants, where you can eat before you go to the Fox Theater or the Paramount for a show. Jack London Square has great food as well – there’s an eclectic mix of fancy pizza shops, vegan, and southern. A quick walk into Chinatown provides an endless supply of cheap eats, and the Oakland Chinatown is huge – about 2x the size of the city’s, without the tourist stuff.

A quick map search shows you where all the food is- pretty much in the Broadway/Telegraph corridor, but Rockridge, Temescal, and Grand Lake do well too.

Doesn’t Oakland have a ton of crime?
Crime was one of my top concerns moving to Oakland, but if the spectrum in San Francisco is aggressive/disturbed people in the Tenderloin to the nicest part of Presidio Heights, I think Oakland is about the same. You wouldn’t want to walk around Market St at 3am and you wouldn’t want to do that on Broadway in Oakland either. (Palo Alto / Menlo Park / Atherton are on a whole other planet, of course)

The biggest lesson I’ve learned from exploring the long list of East Bay neighborhoods is that Oakland is very diverse, and while the crime factor is a big one, it’s an acute problem for some neighborhoods and less of a problem for others. So, it all depends (just like SF, btw).

Houses in the Oakland Hills look like the kind of fancy houses you’d see while driving on 280 in the peninsula. Some neighborhoods like Rockridge, Grand Lake, and Adams Point are small and upscale, not unlike University Ave in Palo Alto. Uptown/Downtown feels like Market Street in San Francisco, but inexplicably cleaner. My new neighborhood, Jack London Square, feels a bit like South Beach in SOMA.

On the other hand, neighborhoods in deep East Oakland don’t feel very safe. That’s where you can find the car sideshows on YouTube.

Is it cheaper to live there?
For now, buying or renting seems to be about 50-75% the cost of San Francisco. Maybe as low as 30% if you are adventurous.

Is Oakland really warmer than San Francisco?
Yep. Sort of like the peninsula, up to 10 degrees warmer. Sometimes I miss the fog.

Here’s a typical day on the waterfront in Jack London Square.

Jack-London-Square_Bldg-F1-20-20-1200x556

Where do you go for coffee?
The headquarters for Blue Bottle Coffee is here. Yes, there’s usually a line.

But there’s also a bunch of other coffee places too:

Interestingly enough, the density of tech in Oakland is still relatively low. Cafes aren’t full of tech bros with terminal open. Coworking spaces are more likely to be nonprofits, writers, and sales, rather than unpronounceable names of startups. I’m sure a bit of this might change over time, and there’s been rumors of one of the big cos taking over the old Sears building in downtown.

bluebottlejacklondonoutside

How do I dress when I visit Oakland?
Like this video.

What’s the best way to visit Oakland?
The first step is to come out here by car/BART/ferry and check it out. I’d encourage you to do it, I think you’ll be surprised by how nice it is. And as I said above, if you’re interested in attending a casual get-together in the new neighborhood, or if you already live around here, just sign up on this mailing list and I’ll post some future updates.

Written by Andrew Chen

February 24th, 2015 at 3:42 pm

Posted in Uncategorized

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

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weather
Forecasting weather is hard, and so is forecasting product growth.

Startups are about growth
Paul Graham’s essay in 2012 called “Startup = Growth” makes a big point in the first paragraph:

A startup is a company designed to grow fast. Being newly founded does not in itself make a company a startup. Nor is it necessary for a startup to work on technology, or take venture funding, or have some sort of “exit.” The only essential thing is growth. Everything else we associate with startups follows from growth.

The other important reason for new products to focus on growth is simple: You’re starting from zero. Without growth, you have nothing, and the status quo is death. Combine that with the fact that investors just want to see traction, and it’s even more important to get to interesting numbers. In fact, later in the essay, pg talks about how important it is to hit “5-7% per week.”

Getting to this number while trying to show a hockey stick leads to a bad forecast. Here’s why.

The bad forecast
The most common mistake I see in product growth forecasts looks something like this:

Image 2015-02-18 at 7.16.38 PM

In this example, the number of active users is a lagging indicator, and if you multiply this lagging indicator of a growth curve, it’s a truism that the growth will go up and to the right. If you do that, the whole thing is just a vanity exercise for how traction magically appears out of nowhere.

And of course these growth curves look the same: They all look like smooth, unadulterated hockey sticks. The problem is, it’s never that easy or smooth. In reality, you’re upgrading from one channel to another, and in the early days, you do PR but eventually that doesn’t scale. Then you’ll switch to a different channel, which takes some time but also eventually caps out. Eventually you’ll have to pick one of the very few growth models that scale to a massive level.

The point is, incrementing each month with a fixed percentage hides the details of the machinery required to generate the growth in the first place. This disconnects the actions required to be successful with the output of those actions. It disassociates the inputs from the outputs.

In other words, this type of forecast just isn’t very useful. Worse, it lulls you into a false sense of security, since “assume success” becomes the foundation of the whole model, when entrepreneurs should assume the opposite.

Create a better forecast by focusing on inputs, not outputs.

How to fix this forecast
A more complete model would start with a different foundation.

It would:

  • Focus on leading indicators that are specific to your product/business – not cookie cutter metrics like MAU, total registered, etc.
  • Start with inputs not lagging vanity metrics
  • It’d show a series of steps that show how these inputs result in outputs
  • And, how the inputs to the model would need to scale, in order to scale the output

In other words, rather than assuming a growth rate, the focus should be deriving the growth rate.

If you plan to 2X your revenue for your SaaS product, which is done by doubling the # of leads in your sales pipeline, and those leads come from content marketing – well, then I want to know how you’ll scale your content marketing. And how much content needs to be published, and whether that means new people have to be hired.

That also means that if you want to 2X your installs/day, and plan to do it with invites, I want to understand the plan to double your invites or their conversion rates.

Or better yet, say all of this in reverse, starting with the inputs and then resulting in the outputs.

Inputs are what you actually control
Focus on the inputs because that’s what you can actually control. The outputs are just what happens when everything happens according to plan.

One helpful part of this analysis is that it helps identify key bottlenecks. If your plan to generate 2x in revenue requires you to 5X sales team headcount when it’s been hard to find even one or two good people, you know it’s not realistic. If your SEO-driven leadgen model assumes that Google is going to index your fresh content faster and with higher rank than it’s ever done, then that’s a red flag.

In the end, it’s also true what they say:

No plan survives contact with the enemy.
smart prussian army guy

Keep that in mind while you fiddle around with Excel formulas, and you’ll be in good shape.

Written by Andrew Chen

February 23rd, 2015 at 10:30 am

Posted in Uncategorized

The race for Apple Watch’s killer app

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The upcoming race
As the release of the Apple Watch draws near, we’re seeing press coverage hit a frenzied pace – covering both the product, the watch’s designers, sales forecasts, and the retail displays. That’ll be fun for us as consumers. But for those of us who are in the business of building new products, the bigger news is that we have a big new platform for play with!

The launch of the Apple Watch will create an opportunity to build the first “watch-first” killer app, and if successful, it could create a new generation of apps and startups.

Why new platforms matter – the Law of Shitty Clickthroughs
Regular readers will know that I’m endlessly fascinated by new platforms. The reason is because of The Law of Shitty Clickthroughs, which claims that the aggregate performance of any channel will always go down over time, driven by competition, spam, and customer fatigue.

When you have a big new platform, you avoid all of this. So it’s not surprising that every new platform often leads to a batch of multi-billion dollar companies being minted. With mobile, it was Uber, Whatsapp, Snapchat, etc. With the Facebook platform, we saw the rise of social gaming companies like Zynga. With the web, we had the dot com bubble. It’s very possible that wearables, led by the Apple Watch, could be that big too.

With the Apple Watch, we have fresh snow:

  • Right after the launch, there’s a period of experimentation and novelty, where people are excited to try out new apps, no matter how trivial
  • A barrage of excitement from the tech and mainstream press, which will publicize all the big apps adding integration
  • A device built around interacting with notifications and “glances” which, along with the novelty effects, will cause engagement rates to be ridiculously high
  • The app store which will promote apps that integrate with the Watch in clever ways
  • Unique APIs and scenarios in health, payments, news, etc., leading to creative new apps in these categories

At the same time, there will be less competition:

  • Many apps will take a “wait and see” approach to the platform
  • Some teams won’t try at all Apple Watch, since it won’t be easy to jam their app’s value into a wearables format – for example, you can’t just cram any game on there
  • The best practices around onboarding, growth, engagement still have to be discovered – so there’s a higher chance someone new will figure it out

The above dynamics mean that the Watch launch will lead to some exciting results. Apple has been thoughtful and extraordinarily picky about bringing out new products, so with the Watch, we know they’ll put real effort and marketing prowess behind it. Combine that with the rumored ramp up to millions of units per month, and you can imagine a critical mass of high-value users forming quickly.

What kinds of apps will succeed? It’s hard to answer this question without looking at what you can do with the platform.

The Human Interface Guidelines is worth a skim
Beyond the ubiquitous buzz stories that have been released, it’s hard to have a nuanced discussion about the Apple Watch until you really dig into the details. Here to save us are two documents:

Both documents offer some tantalizing clues for the main uses for the Watch, as well as the APIs offered by Apple for developers to take advantage of. The HIG document is particularly enlightening. Going through the screenshots, here are the apps that are shown via screenshot:

  • Visual messaging
  • Weather
  • Stock ticker
  • Step counter
  • Calendar
  • Photo gallery
  • Maps
  • Time, of course :)

personal_digitaltouch_2xlightweight_weatherglance_2x

For the most part, this is exactly what you’d expect. These are all apps that have existed on the phone, and the Watch serves as an extra screen. I’m sure this will only be the start.

The more interesting question is what the new Watch APIs will uniquely allow.

Apple Watch will supercharge notifications
One of the biggest takeaways in reading through the HIG is the prominence of the notifications UI. Although you might find yourself idly swiping through the Glances UI to see what’s going on, it seems most likely that one of the most common interactions is to get a notification, check it on your watch, and then take action from there. This will be the core of many engagement loops.

For that reason, Apple has designed two flavors of notifications – a “short look” that is a summary of the new notification, and a “long look” that’s actually interactive with up to 4 action buttons. Here’s a long look notification:

longlook_calendar_2x

Because it’s so easy to check your watch for notifications, and you’ll have your watch out all the time, I think we’ll see Apple Watch notifications perform much better than push notifications ever have. Combine this with the novelty period around the launch, and I think we’ll see reports of much higher retention, engagement, and usage for apps that have integrated Watch, and these case studies will drive more developers to adopt.

Waiting for the Watch-first killer app
Succeeding as a Watch-first app remains a compelling thought experiment. We saw that after a few years of smartphones, the question “Why does this app uniquely work for mobile?” is an important question.

Apps that were basically ports of a pre-existing website ended up duds – crammed with features and presenting a worse experience than just using the website. Contrast that to the breakthrough mobile apps that take advantage of the built-in camera, always-on internet, location, or other APIs available. Said another way, many flavors of “Uber for X” have failed because it’s unique to calling a taxi to constantly need to consume the service in new/unknown locations, and with high enough frequency for this consumption. Not every web app should be a mobile app. In the same analogy, the majority of apps in the initial release of the Watch may take it to simply be a fancier way to show annoying push notifications, and drive usage of the pre-existing iPhone app.

The more tantalizing question is what apps will cause high engagement on the Watch by itself, with minimal iPhone app interaction? That’s what a Watch-first killer app will will look like. I’m waiting with a lot of excitement for the industry to figure this out.

Good luck!
For everyone working on Watch-integrated apps, good luck, and I salute you for working to avoid the Law of Shitty Clickthroughs. If you’re working on something cool and want to show me, don’t hesitate to reach out at @andrewchen.

Written by Andrew Chen

February 17th, 2015 at 2:48 pm

Posted in Uncategorized

My top essays in 2014 about mobile, growth, and tech

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Hello readers,

Happy 2015 and hope everyone had a relaxing holiday. As always, more essays are coming soon for the next year. In the meantime, I wanted to share some of my essays published here over the last year. If you want to stay up to date, just make sure to subscribe for updates.

Also, thanks to the SumoMe team (specifically Noah Kagan!) for supporting my hosting costs. I’ve used SumoMe since the product was in alpha and find it immensely helpful in building an audience.

Thanks,
Andrew

The essays
Without further ado, here’s a selection of essays published in 2014:

Make content creation easy: Short-form, ephemeral, mobile, and now, anonymous

How to design successful social products with 3 habit-forming feedback loops

How to solve the cold-start problem for social products

Why consumer product metrics are all terrible

There’s only a few ways to scale user growth, and here’s the list

Why aren’t App Constellations working?

New data shows up to 60% of users opt-out of push notifications

Early Traction: How to go from zero to 150,000 email subscribers

Why Android desperately needs a billion dollar success story: The best new apps are all going iPhone-first

New data on push notifications show up to 40% CTRs, the best perform 4X better than the worst

Mobile retention benchmarks for 2014 vs 2013 show a 50% drop in D1 retention

Why messaging apps are so addictive

If you want more, here’s the link to subscribe to future updates: http://eepurl.com/xY3WD.

Written by Andrew Chen

January 6th, 2015 at 12:30 pm

Posted in Uncategorized

Why messaging apps are so addictive (Guest Post)

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[Andrew: This guest post is written by my friend and former Palo Alto running partner, Nir Eyal. Messaging apps have been a fascinating area within mobile, and the big reason for it is that the metrics – especially engagement – have been amazingly strong. I asked Nir to write a bit about why this might be the case, using the habit model. He’s in a unique position to comment on this: After spending time at Stanford and in the startup world, recently he’s been writing about psychology – particularly habit-building – and applying that to the realm of technology and products. Check out his new book Hooked: How to Build Habit-Forming Products and Nir blogs about the psychology of products at NirAndFar.com.]

Nir Eyal, Author of Hooked:

Today, there’s an app for just about everything. With all the amazing things our smartphones can do, there is one thing that hasn’t changed since the phone was first developed. No matter how advanced phones become, they are still communication devices — they connect people together.

Though I can’t remember the last time I actually talked to another person live on the phone, I text, email, Tweet, Skype and video message throughout my day. The “job-to-be-done” hasn’t changed — the phone still helps us communicate with people we care about — rather, the interface has evolved to provide options for sending the right message in the right format at the right time.

Clearly, we’re a social species and these tech solutions help us re-create the tribal connection we seek.  However, there are other more hidden reasons why messaging services keep us checking, pecking, and duckface posing.

The Hook

In my book, Hooked: How to Build Habit-Forming Products, I detail a pattern found in products we can’t seem to put down. Though the pattern is found in all sorts of products, successful messaging services are particularly good at deploying the four steps I call, “the Hook,” to keep users coming back.

The Hook is composed of a trigger, action, variable reward, and investment.  By understanding these four basic steps, businesses can build better products and services, and consumers can understand the hidden psychology behind our daily technology habits.

1

Nir Eyal’s Hook Model

Trigger

A trigger is what cues a habit. Whether in the form of an external trigger that tells users what to do next (such as a “click here” button) or an internal trigger (such as an emotion or routine), a trigger must be present for a habitual behavior to occur.

Over time, users form associations with internal triggers so that no external prompting is needed — they come back on their own out of habit. For example, when we’re lonely, we check Facebook. When we fear losing a moment, we capture it with Instagram. These situations and emotions don’t provide any explicit information for what solution solves our needs, rather we eventually form strong connections with products that scratch our emotional itch.

By passing through the four steps of the Hook, users form associations with internal triggers. However, before the habit is formed, companies use external prompts to get users to act. For messaging services, the external trigger is clear. Whenever a friend sends a message via WhatsApp, for example, you see a notification telling you to open the app to check the message.

2

WhatsApp’s External Trigger

Action

Notifications prompt users to act, in this case tapping the app. The action phase of the Hook is defined as the simplest behavior done in anticipation of a reward. Simply clicking on the app icon opens the messaging app and the message is read.

When the habit forms, users will take this simple action spontaneously to alleviate a feeling, such as the pang of boredom or missing someone special. Opening the app gives the user what they came for — a bit of relief obtained in the easiest way possible.

Variable Reward

The next step of the Hook is the variable rewards phase. This is when users get what they came for and yet are left wanting more.

This phase of the Hook utilizes the classic work of BF Skinner who published his research on intermittent reinforcement. Skinner found that when rewards were given variably, the action preceding the reward occurred more frequently. When forming a new habit, products that incorporate a bit of mystery have an easier time getting us hooked.

For example, Snapchat, the massively popular messaging app that 77% of American college students say they use every day, incorporates all sorts of variable rewards that spike curiosity and interest. The ease of sending selfies that the sender believes will self-destruct makes sending more, shall we say, “interesting,” pics possible. The payoff of opening the app is seeing what’s been sent. As is the case with many successful communication services, the variability is in the message itself — novelty keeps us tapping.

3

You never know what you’ll see when you open Snapchat

Investment

The final phase of the Hook prompts the user to put something into the service to increase the likelihood of using the service in the future. For example, when users add friends, set preferences, or create content they want to save, they are storing value in the platform. Storing value in a service increases its worth the more users engage with it, making it better with use.

Investments also increase the likelihood of users returning by getting them to load the next trigger. For example, sending a message prompts someone else to reply. Once you get the reply, a notification appears and you’ll likely click through the Hook again.

4

Growing a “buddylist” on Snapchat is an investment in the platform

Through frequent passes through the Hook, user preferences are shaped, tastes are formed, and habits take hold. Messaging services are here to stay and we’ll most likely see many more iterations on the theme as technological solutions find new ways to bring people together. By understanding the deeper psychology of what makes us click by knowing what makes us tick, we can build better products and ultimately live better lives.

Written by Andrew Chen

November 4th, 2014 at 10:30 am

Posted in Uncategorized

Why Android desperately needs a billion dollar success story: The best new apps are all going iPhone-first

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jbstatue2

Why startups are all going iPhone-first
There’s been a number of articles over the last year that reiterate a simple fact: The best new apps are all going iPhone-first. Here’s three popular articles on this topic over the last few months:

At this point, going iPhone-first is a widely held best practice. It’s our generation’s version of “Nobody ever got fired for buying IBM.” While there’s some debate on the margins on how quickly you should follow up with an Android app, certainly no one is arguing for Android-first (meaning, don’t do iPhone). There’s a number of reasons for this consensus, which the above articles thoroughly explain- here’s the superset of the reasons they give.

  • Device fragmentation – both OS versions, carrier/handset add-ons, and hardware itself
  • Less advanced, less stable tools and documentation
  • Larger install base, but smaller addressable market (Feldman states that a 50% market share really translates to 12% if you support the most recent versions of Android, versus 30% for iPhone)
  • Less valuable audiences on Android, losing ground in the US in key demos
  • Cheaper iPhones may steal marketshare from Android in the future
  • Higher cost of development for Android (2-3X claims Steve Cheney)
  • $800k-$1.2M seed rounds leading to a “all-in on one platform” strategy

Note, I’m not saying I agree with above, just summarizing what’s been said.

In addition to this, I’d also like to add a couple more human aspects of the decision:

  • Many/Most startup founders and employees are Steve Jobs fanboys, carry iPhones, and want to design for themselves
  • Their friends carry iPhones, and they want to make something for their friends
  • There’s more hipster designery mobile developers who build for iPhone, and that’s the supply-side of talent in SF Bay Area
  • Investors (other than Bubba Murarka) usually carry iPhones, so it’s easier to pitch to them
  • More tech press outlets want to cover iPhone-specific news

Whether you agree if the above is sane or not, the reality is, there’s a lot of friction to going against the norms. In order for a whole class of developers to move en masse to the Android platform is going to require a big carrot. I’m going to argue that this big carrot is going to be that the best developers, the ones who are investing $1M+ into a single app, need to feel like there’s such a huge opportunity in Android that they can’t miss out.

What Android can learn from Microsoft Windows
This consensus towards iPhone-first is happening at a critical time for Android. In many ways, the platform has been a huge success, and many who lived through the Windows vs. Mac years could make some interesting comparisons.

Here’s a stab at it- here’s some of the key reasons why Microsoft Windows won:

  • Cheaper
  • Ubiquitous
  • More open
  • Better penetration into the workplace
  • Lots of applications that were exclusive to the platform

I’d argue that on almost all the point above, Android has achieved the same success as Windows. It’s cheaper, there’s more devices sold, it’s more open. But a critical component, of having more apps, isn’t there. Remember how there was always some key games that’d run on Windows that wouldn’t exist on Mac? Or how there were a bunch of business applications that would only run on Windows? That meant that the Windows platform had the virtuous cycle between developers and users to drive total domination.

But Android is not Windows. When you look at the current mobile ecosystem, iPhone has more apps. It has better apps. It gets the designery, well-funded startups to build iPhone-first.

Consumers and developers, together, will continue to choose the iPhone until that network effect is broken.

Today, Android is merely playing catchup – every time there’s a proprietary iPhone app, soon thereafter, they’ve done a good job convincing developers that they also need to release an Android app. Yet, to play to win, Android needs to convince many, many developers to create apps exclusively for their platform, just like Windows did a generation ago.

How does Google get there?
To me, the biggest thing that Google is lacking is a billion dollar tech startup story that’s exclusively about how Android is a better platform for developers. That story doesn’t exist, and as a result, people have focused more on the friction in going Android-first, rather than the opportunity.

IMHO, Android gets there by rewarding startups that are exclusively choosing its platform. I’m talking my book here, as I’m involved with a few Android-first products, but it’s also nevertheless from direct knowledge. Google should extensively feature apps that aren’t merely clones of iOS apps – it’s not enough to play catchup. Instead, Android should seek to really showcase apps that take advantage of very differentiated features and APIs. There should be a billion user success story around Android launchers and lock screens in the US, rather than acquihires/flips of Aviate, Emu, Cover, and others. These were solid companies led by good teams that wanted to go Android-first, but there was a missed opportunity in supporting them.

App store discovery for iOS is a glaring weakness. It’s editorially driven, and doesn’t give great new apps the chance to be successful, which is why four companies own 70% of the top apps – Google, Facebook, Yahoo, and Apple. They’ve created a winner-take-all environment, especially as Facebook has shown how to effectively use App Constellations to drive traffic between apps. Instead, Google could create a much more fluid ecosystem, which would reward and boost apps the way that search has driven traffic to millions of long-tail websites. Everyone does SEO because they know that yes, you can create a public company like Yelp, or a fast-growing startup like Genius, on the Google Search platform. There’s no story like this in mobile.

And yes, shifting installs from the “head” developers into the long tail might make it less attractive for some, but the biggest developers will always develop for both – they don’t have a choice. The battleground is for the hearts, minds, and product roadmaps of new, innovative apps that will drive adoption for their parent platform.

Android is an important platform, and it’s built more closely to the open nature of the internet, and so for that reason I’m rooting for it. But to make it the first choice for developers, it needs to do more than it is.

Do you work at Google?
Finally, if anyone at Google is reading this, email me: voodoo [at] gmail. Like I said, I’m happy to talk my book :)

Written by Andrew Chen

August 27th, 2014 at 10:27 am

Posted in Uncategorized

Why aren’t App Constellations working? (Guest Post)

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[Recently, I read this roundup of perspectives on “App Constellations” on the new social/professional news app Quibb. This is an emerging product strategy in mobile embraced by Facebook, Linkedin, Foursquare, Twitter, and others, and found it fascinating. Thanks to the authors below for sharing their opinions on this new approach. -Andrew]

Screen_Shot_2014-07-15_at_11.51.42_AM

Fred Wilson recently coined the term ‘App Constellations‘ to describe what he was seeing in the mobile app ecosystem with respect to distribution. Over the past few months, mobile companies have continued with this strategy, the idea being that standalone apps are better for mobile, versus the all encompassing platforms that dominate desktop experiences – and it’s a better position to own and control several of these key apps on any person’s device.

It has been interesting to watch as Dropbox, Facebook, and Foursquare experiment with this approach – but it doesn’t seem to be working, at least not yet. CB Insights shared some discouraging data last month, and things have only gotten worse over the past few weeks for the most recent apps to be put out by these big players:

Screen Shot 2014-07-11 at 2.12.32 PM

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It’s important to note that none of the companies using this strategy have promoted their unbundled apps aggressively – beyond Facebook Messenger, which is seemingly the only app where this App Constellation approach is paying off:

Screen Shot 2014-07-11 at 2.16.50 PM

Toufeeq Hussain (Senior Product Manager at Storm8)

The App Constellation strategy works when you have a core resource which can be shared across multiple apps. Slingshot and Poke are attempting to create a new resource (reply-to-view-images for Slingshot and disappearing images for Poke) and hence isn’t really leveraging whats core to Facebook (social graph and shared photos). In the case of Slingshot, even the social graph had to recreated from scratch. So even though these apps get huge media attention when they launch, they slowly slide down the charts as there is nothing holding them up.

One company that has done a great job of using core resources and creating a “basket of apps” is Evernote. It currently has Evernote Hello, Evernote Food, Skitch and Penultimate. Each of these are focused around helping users create more notes and thereby getting more usage of the core Evernote product. Of course, not all of Evernote’s apps are in the top-50 lists but they are targeted mainly at new users who are looking for more specific solutions to note taking. Carousel is on the same lines but my feeling is that Dropbox needs more apps that read data stored in Dropbox than contributing to it. If the goal is to contribute to the Dropbox then it needs to be something more than just syncing photo/videos as the default Dropbox app already performs that.

The Asian messenger apps use the contact list as the core resource across any apps integrated into LINE or WeChat. Invitations are sent through the core messaging app but specific functions are performed in the corresponding apps. Games, photo sharing, stickers all have specific apps but use the core LINE or Wechat identity and social graph to seamlessly work across multiple apps.

In conclusion, though its very valuable for a Facebook or Dropbox to shoot for “stars” and build constellations, what we have seen from companies like Evernote and the asian messenger apps (LINE, WeChat) is that a “basket of apps” approach that leverages a common core resource between other apps might actually be a more scalable strategy. Its very hard engineering “stars” – a lot of things need to fall in place to be a top-50 free app, a better strategy for Facebook might be to play to its strengths than alienating new apps from core FB resources.

Bubba Murarka (Managing Director at Draper Fisher Jurvetson)
App constellations are being deployed because the problem of distribution on mobile is “solved” in the sense that large incumbent app owner, and mobile marketers with sufficient resources, can predictably drive installs of their other apps via well known set of steps that includes cross promotion, cross linking, merchandising on mobile web & of course paid distribution. One of the best examples of this is how Facebook has managed to drive massive number of installs of the standalone Messenger app via the main Facebook integration. I would go so far as to say that if an unbundled app from a large incumbent does not to have a massive install base it is intentional to enable the app to mature before investing in driving distribution. FWIW, I am not saying distribution for younger companies without existing massive install bases, well known brand names or meaningful financial resources is “solved” yet
because, well it is not.

As for the value of this strategy it is easy to say it doesn’t work but that is not nuanced enough analysis of why companies pursue this approach. The multi app strategy allows more experimentation, different release cycles and tailored experiences to drive deeper engagement. This allows faster iterations for nascent product lines which is critical for finding product market fit (traditionally the key advantage startups have over large incumbents). Using Facebook Messenger, as an example again, FB only focused on driving installs 3 years after they had initially released the standalone app. I’d hypothesize that once Dropbox wants to drive installations of their app constellation they will have no problems – “Install Carousel and get 500 free MBs of storage” or “To sync your photos to Dropbox you need to install Carousel”. In summary, I’d suggest the best way to assess the value of this strategy is not to look at installs as the only measure of success.

Casey Winters (Growth Marketing Manager at Pinterest)
App unbundling or constellations are a nuanced strategy that I think needs a few, rare conditions to be effective. One is that your main app needs to be a top app already, with little room to improve. Then, it is advantageous to create a new app to see if you can take another top spot on the App Store/Google Play, and another icon on a user’s phone (see my blog post for more details on that strategy).

When you have this scenario, it’s little risk to create a new app, but it means that a very successful company needs to launch a new app and have it be an immediate success. You don’t have the advantage of iterating and starting small and working your way up to popularity like most apps do. It’s very tough to launch new apps this way and be successful because a ton of people try the app the moment it’s launched before you have any market feedback. Typically, people try it, discard it, and you don’t get another shot. So what ends up happening is that popular apps buy newly popular apps instead.

The only way I have seen the constellation strategy work is if the new app was already a core feature of the main app and is then unbundled. Facebook Messenger is an example of this. Since the feature is already popular inside Facebook, and the new app is now where that functionality lives (and that functionality hasn’t meaningfully changed), the new app is successful. Where foursquare erred is the the check-in was declining in popularity in their app, and when they unbundled it they changed the functionality meaningfully to upset those core users.

Alex Schiff (CEO and Co-Founder of Fetchnotes)
Unbundling into “app constellations” is understandably a compelling strategy. More real estate, more targeted products, and more mind share — hooray!

The thing is, outside the tech community most people just don’t download that many apps. Statista put out a report late last year that on average, US smartphone holders have installed 26 apps. To put that in perspective, that’s just over a page of apps on an iPhone 5 screen. Not only are most people not reading about or searching for apps, but when they do hear about one from a company they know, the default behavior is not, “Wowee! I already have Facebook – I should start using their new app!”

Putting the apathy of mainstream consumers aside, there’s a much deeper problem with the whole unbundling strategy. It only works if it’s a fundamentally distinct behavior being segmented into a stand-alone application. I think Facebook Messenger is a great example of unbundling that worked — messaging is very different from browsing stories and stalking people. Separating the two made both my messaging and social voyeurism (let’s be real, that’s what Facebook is for) experience better. Moreover, Facebook wanted to be your go-to messaging utility. That couldn’t happen, I believe, unless it was its own application.

Today, over 200M people use Messenger.

Now compare that with the launch of Paper. Paper, for all intents and purposes, has the same core features as Facebook proper – you browse stories, accept friend requests, view notifications, etc. More recently, they even added back in some of the features they left out, like birthdays and events. The major difference is UI (it’s beautiful) and a philosophical focus on stories over people.

In other words, it’s just a different approach to Facebook. Most users I’ve talked to use Paper instead of Facebook, not alongside it. Since Paper offers pretty much the same functionality as Facebook proper, most people just aren’t that motivated to try it out. As of June 11, Paper has only 119,000 MAUs. Frankly, I wouldn’t be surprised if Paper was just a test for a new approach they’ll be bringing to the core Facebook app. Unless Facebook strips Paper down to be stories and stories alone, I don’t see it surviving long-term as a stand-alone app.

There are certainly more granular product reasons Slingshot, Paper, Carousel, etc. haven’t taken off. However, across any app constellation effort, the products need to compliment — not cannibalize — each other.

Messenger does exactly that. Paper doesn’t.

Adam Sigel (Product Manager at Aereo)
App constellations, unbundling, whatever you want to call it, will ultimately yield a better experience for mobile users and better business practice for the companies making apps, but we’re going to have to work through some pain in the short-term. The trouble for now is that app constellations are ahead of the rest of the mobile experience.

It starts with app discovery. The App Store (for iOS especially) is largely leaderboard driven, and it’s hard to crack into the top ranks, especially for non-gaming apps. One “north star” app in a constellation makes it much easier to build satellites around, and we’re seeing that with Facebook, Dropbox, Google, LinkedIn, and Amazon. As Fred Wilson wrote, this creates a “rich get richer” scenario and creates enormous challenges for newcomers.

Having different apps optimized for different use cases is great, but managing all those apps is a pain. As I mentioned in a blog post about invisible apps, the mobile OSes need a way to have apps that exist outside the homescreen. “Winning the homescreen” just doesn’t make sense for lots of apps (finance and travel, to name a few). iOS is the worst offender here compared to Android and Windows Phone, but this could be addressed a number of ways including design changes, gesture controls, or anticipatory computing.

Even though more apps are building in deep linking capabilities—a very good thing for the mobile experience overall—app switching still stinks. It’s visually jarring to a lot of users, and app management is still a power user skill. To go with the typical early majority example, my parents don’t double-tap the home button to switch apps, nor do they put very much thought into which apps go on which screen or in which folders.

These are temporary imbalances, and recent announcements from Google and Apple suggest directional improvement. As app discovery improves, mobile OSes continue to evolve, and the market matures, we’ll get new, more sophisticated and seamless mobile experiences.

More:
There’s even more discussion in the comments, on Quibb.

Written by Andrew Chen

August 4th, 2014 at 10:00 am

Posted in Uncategorized

There’s only a few ways to scale user growth, and here’s the list

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Scaling growth is hard – there’s only a few ways to do it
When you study the most successful mobile/web products, you start to see a pattern on how they grow. Turns out, there’s not too many ways to reach 100s of millions of users or revenue. Instead, products mostly have one or two major growth channels, which they optimize into perfection. These methods are commonplace and predictable.

Here are the major channels that successful products use to drive traction – think of them as the moonshots.

  1. Paid acquisition. If your users give you money, then you can buy users directly through ads. Usually companies try to maintain a 3:1 CLV:CAC ratio to keep their margins reasonable after other costs. (eBay, Match, Fab, etc.)
  2. Virality. If your users love your product, then you can get major “word of mouth” virality driven by a high Net Promoter Score. If you can get your product to spread as a result of users engaging with the product, you can further optimize the viral loops using A/B tests to generate even more virality. People often measure “viral factor” to see how effectively existing users attract new users, and of course, you want your viral factor to exceed 1.0. (Facebook, Instagram, Twitter)
  3. SEO. If your product creates a ton of unique content, in the form of Q&A, articles, long-form reviews, etc., you might end up with millions of unique pages that can in turn attract hundreds of millions of new users who are searching for content via search engines. (Yelp, Rap Genius, Stack Overflow, etc.)
  4. Sales. For startups targeting SMBs or the enterprise, you’ll end up fielding a large sales org to handle both inbound and outbound. This is especially true for companies targeting local SMBs, where telesales becomes the only option. Of course, to make this work, you’ll need to generate a multiple in revenue of what you pay them.
  5. Other. There’s the odd partnership, like Yahoo/Google, that can help make or break a startup – but these are rare and situational. But sometimes it happens!

These channels work and scale, because of two reasons:

  • They’re feedback loops. Each of these channels creates exponential growth because when you make money from customers, you can use that money to buy more customers, which give you more money. Or in the virality scenario, a cohort of new users will invite even more users, who then invite even more on top of that.
  • They have a high ceiling on saturation. Part of why paid acquisition will always be around is because people like free products, which cause these products to monetize using ads. As long as people will love free products (which they will, forever), there will be advertising to buy. The biggest ad networks reach a billion users or more. Similarly, SEO works because almost everyone uses Google, so as long as you’re dealing with a high-volume base of searches (like music lyrics, or products) then you’ll be able to reach hundreds of millions of users.

It might seem like it’s best to crack one of these channels right away, and then ride then into glory. But that almost never happens, and instead startups have to work towards them – but it takes time. To figure out if your CLV and CAC match up, you need to buy some users, then wait 6 months to see how well they monetize. If you want to see if your product is viral, you need to build your app, then wait to see if you have the retention and frequency to support a strong viral loop. SEO is hard because after the content is built, Google has to index it and you have to build PageRank. This can take months and years.

New products often only have months, or a year, to live, so these strategies are often not a real option.

High-risk, high-reward
Attacking one of these scalable channels is high risk but also high reward. Every startup has to make sure they are able to slot themselves into one of channels in order to scale their business, but in the meantime, how do you show enough traction to not run out of money?

This essay by Paul Graham gives us a clue, as he writes about Startups = Growth:

A good growth rate during YC is 5-7% a week. If you can hit 10% a week you’re doing exceptionally well. If you can only manage 1%, it’s a sign you haven’t yet figured out what you’re doing.

Another way to say this is, growth is measured through a percentage and so early on, small things can drive a high % growth when the base is small. When you’re starting, there’s a whole list of other tools you can use which don’t scale at all but are nevertheless low risk.

Here are some low-risk, unscalable ways to get users:

  1. Getting your friends+family to use the product
  2. Emailing/posting among your local community, whether that’s college or an alumni mailing list or whatever
  3. Guest writing on niche blogs – you often see this with mommy blogs, etc.
  4. Cold e-mailing potential users and influencers
  5. Engaging with potential users over Twitter, Reddit, forums, and other communities
  6. Contests and giveaways, partnering with a blogger/YouTuber or something
  7. Getting covered in niche press outlets, like the tech press
  8. … etc., etc.

All of the above require hustle, but are low-risk and fairly high-percentage. And when a contest can generate a few thousand signups, on a small base that’s not bad at all. The other added benefit is that these methods put you in direct/close contact with your users. So in the early phase, when you are still working on product/market fit, this can be an important way to learn if you have the right product.

However, none of these methods scale well, which is OK, if you know when you need to move on. Even getting covered in the mainstream press, like NYT level, maybe only garners a few hundred thousand signups max. Getting featured by Google or Apple is about the same thing. That’s better than nothing, of course, but it’s still far below what you need to get on a rocketship trajectory. For the rocketship, you’d need to perfect one of the 4 main channels I listed earlier.

So ultimately, how do you balance these? Let’s talk about the barbell strategy.

The barbell
To answer the question of how to balance these growth projects, let’s talk about the barbell strategy. The barbell strategy is a way that investors can split their holdings between some high-risk/high-return investments as well as low-risk/low-return conservative investments. Investopedia describes it:

Put your eggs in two baskets. One basket holds extremely safe investments, while the other holds nothing but leverage and speculation.

In the context of these growth channels, the key is to balance a series of progressively more scalable growth projects, while keeping track of the big growth channels that will help you shoot the moon.

Do the methods that don’t scale
During the early days, by all means, sign up friends and family. And get those blog mentions, and do all the content marketing you can handle. That’ll help create a base of engaged users, while you hit product/market fit. At each point, as what works caps out, go after the next marketing channel that can drive incrementally more users. In the early days, perhaps a contest partnership with a niche blog would do, but after a while, maybe you’d hire a small team to author long-term content marketing pieces to circulate.

Invest in moonshots
The other end of the barbell, the high-risk/high-reward projects, should be taken with deliberate projects and analysis. If you need your userbase to generate a lot more unique content for SEO, start fiddling around with features that reward long-form content. And start tracking what % of users write great content. And start making the small changes needed for Google to index your site. After a few months of this, you can start to understand what it would take to create enough pieces of unique content to make an SEO strategy work. You can usually work this kind of thing out on a spreadsheet.

Balancing between the two
It’s important to balance these short-term and long-term efforts. If all you do is work on nonscalable marketing methods, then inevitably the channels will tap out and your growth will slow. When you see the startups that are highly dependent on press hits for their traction, but seem anemic otherwise, this is exactly what’s happening.

The barbell strategy helps products make progress on long-term goals while still creating short-term momentum – you’ll need momentum to attract investor interest, but you’ll need the long-term scalable growth channels to really build your business.

Good luck. And if you have a product that’s working well, has a nice base of traction, and now the only things that can move the needle are scalable methods, don’t hesitate to email me for advice: voodoo at gmail.

 

Written by Andrew Chen

July 8th, 2014 at 3:27 pm

Posted in Uncategorized

Why consumer product metrics are all terrible

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unicorn

The reality of consumer products
I’ve never met an entrepreneur who’s happy with their metrics.

Whether you’re talking about sign up rates, retention rates, or how often your users create content – on face value, the metrics always seem terrible. The secret is, almost everyone’s consumer product metrics are horrible, so once you start to compare them with everyone else’s terrible metrics – then at least we’re all in the same leaky boat together!

Other than the exceptional cases, consumers are impatient and disinterested in your product. Even the ones who sign up to try it out, only a small % are willing to stick around to use it more. As we discuss later, a typical product might see 90% refuse to sign up to a product. And then of the ones who do sign up, over 90% of users disengage and become inactive over time. These metrics are terrible, but they’re normal.

The purpose of this discussion isn’t to excuse mediocre engagement or retention, but rather, to have an honest discussion of what most companies are seeing in the market. This will help us plan better, give us more options for our Plan B, versus being total newbs on the issue.

This essay breaks down a few different metrics and the uphill slog we all face as consumer-focused entrepreneurs:

  • Signup rates
  • Retention and frequency
  • Social graph density

And before we start, it’s worth mentioning that every product is different. Mobile apps often have better engagement metrics, but have lower upfront conversion rates. SEO products have the lowest signup rates. But the intention of this essay is to add to the discussion for the kinds of social apps being built right now – social consumer products on web and mobile – and give everyone a baseline for discussion.

Signup rates as low as 1%
Average signup rates are surprisingly low. On homepages, it’s not so bad- sometimes 10 or 20% signup rates are possible. But they can be as low as 1%, or even lower, when you’re talking about non-homepage pages where people are coming in from SEO. In the extreme, when a user arrives on a content-filled landing page after typing in a query like “what is this growth I have on my back?” their primary interest is the content, not the product you’ve created. Similarly, there’s always pressure from Google’s robots to present as much content as possible, rather than hiding it behind a registration wall.

Thats why for SEO-driven products like Stackexchange, Yelp, and others, the conversion to a signed up user is extraordinary low on these content pages, sometimes much less than 1%. This leads to a pretty depressing metric in an era where most social products measure and report their Monthly Active Users, which consist of activity from users who have signed up. Unique users per month seems so 1998 :(

What if you want to raise the signup rates on these detail pages? Of course you can choose to raise this number by gating the content, as Quora has does, but perhaps at the expense of UX:

Screen Shot 2014-05-12 at 1.13.22 PM

 

But these are just the content pages. If we’re talking about the homepage, we’d expect signup rates to be much higher. The reason is that this traffic is usually based on word of mouth, which leads to sign up rates that are 10% or higher, since people are looking for your product in order to try it. Even better, you can send them to a minimal homepage that generates signup rates closer to 20% or 30%.

Over 95% of your users are inactive on any given day
Another metric that’s easily depressing is retention, where it’s common to see that the vast majority of your users, often over 90%, aren’t engaged on a daily basis. Instead, they’ve churned or are only active a few days per month.

The reasoning for this is simple. It’s become common to look at retention/frequency metrics in the form of D1 versus D7 versus D30 retention. Naturally, D1 means, “the number of users active on the day after signing up.” And usually retention curves look something like the below, where there’s fall-off pretty quickly with eventually stabilization around a mediocre number – often a single digit percentage:

Screen Shot 2014-05-12 at 3.11.52 PM

Usually there’s a very steep drop-off over the first week or two, and then it starts to stabilize. But you lose a ton of active users in the meantime, which is a result from multiple factors. This curve combines a few different aspects of your product:

  • First, how many users sign up and actually try out your product (onboarding)
  • And also, within a month, how many days are they active? (frequency)
  • Finally, how useful is your product over time (long-term retention)

Given that frequency is often low – 3 or 4 active days per month isn’t uncommon – when you pair that with crappy onboarding or retention, then very quickly you’ll see that getting 10% of your users to come back every day is an amazing feat. Anything more than 10% of your total users coming back every day is a success case! More often it’s 5%, or even lower.

So what if your metrics aren’t at this level? Sadly, this isn’t something that’s easily fixable with something superficial, like more email or push notifications. As I’ve noted before, a lot of these engagement metrics are more nature than nuture, and getting high usage every day has as much to do with the product category you’re building for as anything else. I’ve yet to see a product with horrible DAU/MAU get fixed using cosmetic changes. If your engagement or frequency sucks, figure out how to tie it into someone’s pre-existing behaviors, rather than asking them to do something new.

Changing engagement metrics might be the hardest thing to do with products, though. You can make your onboarding better, or get people to invite incrementally more friends. But getting them to come back over time, that’s not something that’s easy to solve using optimization techniques.

50% of your users are forever alone
So let’s say you build a new social product, whether it’s a new form of microblogging  or a new messaging app. Of course, the ideal is to have a nice feed full of personalized content. But it turns out, most products are very, very far away from that. How many people have, effectively, zero friends? You’d be surprised to know that often 50% or more of your users don’t know anyone else in the service, meaning that you need to backfill their feed with a bunch of content just from one person, or worse yet, impersonal content.

In fact, one of the most explosively viral products in recently history had a full 65% of their users disconnected from anyone else: Instagram.

Here’s a pie-chart by RJMetrics of how many Instagram users followed, while the product was in their first year:

6

 

And later in the article, this is what they say about it:

Interestingly, over half of Instagram’s users are following exactly one other user, with another 13% not following anyone. We checked into that, and it looks like the vast majority of users who follow only one other user are following the “Instagram Team” account, which was likely automatically added to their list at signup.

This means that 65% of users effectively follow no one.

(Emphasis added.) This is amazing, and ultimately didn’t stop them from becoming a very functional social network alternative to Facebook itself.

When you combine the fact that getting social graphs to fill is very hard, and the fact that only a few percentages of users will author content (known by the 1% rule), then you can imagine why creating a healthy, dynamic news feed is so hard.

Ultimately, density can be solved by more growth. More users mean a denser social graph. But also, a key component of getting people to follow more people is to connect their Facebook accounts, their email addressbooks, and other pre-existing graphs which help them bootstrap their relationships. Or do what Twitter does, in forcing users to follow before creating an account.

Mediocre metrics aren’t an excuse
The point of this essay isn’t to provide an excuse for mediocre metrics, but rather, to point out the harsh reality of the situation. There’s just a stark contrast between how much we as consumer entrepreneurs care about our products, versus our target audience who really doesn’t give a shit about how much effort we put in. As a result, people aren’t signing up, and if they do, they don’t use the product nor have a good experience. It’s very hard.

But even as the average product’s metrics suck, as an industry we’re looking for the unicorns. So just as I say that a 30% DAU/MAU is good, when you compare that to Whatsapp’s 70%, you can see the gulf between good versus great. We all want great, because the tech industry is all about building great products.

On the plus side, even though these percentages all seem small, great businesses have been built from a few percentage points here or there. How many paid subscription services monetize by convincing 2-3% of users to pay? Tons of them. Or who build billion dollar ad-supported businesses just based on getting a few % of users to click on ads? That’s everyone in the ads business. So it can work, but until it’s scaled and is growing faster, these metrics can look like a mess. Until then, keep at it.

Written by Andrew Chen

May 13th, 2014 at 10:26 am

Posted in Uncategorized

How to solve the cold-start problem for social products

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EmptySeats-m

Social products need mass before scaling growth
I often write on the topic of how social products can scale growth, resulting in inbound emails to the effect of “how do I get my product to go viral?” The problem is, until you have a strong baseline of engagement, it’s nearly impossible to have a metrics-oriented discussion on growth and virality. So you have to get that first, before you can talk about the next step.

The focus should be on creating that baseline – a small-to-medium sized network of highly engaged users in a big market, that’s growing. Maybe this is 10,000+ active users organically gaining hundreds per day, at a 20%+ DAU/MAU. If you can hit that, then it’s much easier to talk about how to scale it up. I’ve written Zero-to-Product/Market Fit in the past to talk about some of the steps you might take to reach this stage. Similarly, I have some slides for this topic. (And if you’re at this point, don’t hesitate to email me)

There’s a unique aspect to social products in getting to this baseline, which is how can you solve the dreaded cold-start problem? If your product is inherently social, but you don’t have a critical mass of users, then it’ll naturally fail. How do you get beyond that? This is different than productivity or SaaS products because you don’t just have to get the product right- you have to get your initial user network to be large enough and active enough too.

Here’s a few ways I’ve collected over time on how to approach that problem:

Single user utility
This is one of the most common ways to approach the cold-start problem. Give people a value-proposition that gets them creating/curating content within your network, and as a by product, it’ll help bootstrap the network around the user. I think of Pinterest as the quintessential example here, where you can use it as a tool to collect/gather/organize content around a particular project you’re pursuing- decorating a new apartment, planning a wedding, or switching to a new diet. As you’re doing this, then you use the common mechanisms around finding friends, Facebook sign in, etc., to build a network around the user. If you can get this to grow fast enough, and build the right social feedback loops, then users will find themselves blending a single user value prop with a network value prop over time.

Linkedin is another classic example here, where initially they could market themselves as a way to put your resume online. But of course, once you go through their onboarding flows, you’ll quickly find out that people are connecting and reaching out to you via your profile, thus cementing the network value proposition.

A blog network like Tumblr is another great example. People like making their own websites, and you can use Tumblr for that – plus you get the themes, tools, and domain for free. But once you’re set up, it becomes easy to get reblogged and followed and all of a sudden you’re part of the network product.

The trickiest part of using this strategy is that you’re asking users to switch their mindset from one value proposition into the other. Managing that transition isn’t easy. You may find that users actually want their single user value prop to be private, and nonsocial by nature. Or you might find that if you don’t get the social feedback loops right, you may not be able to convert your one-off users into network users fast enough, and it feels like you are maintaining two separate products. And it might feel like your product isn’t really working if the majority of your users aren’t involved in the network.

Publishing into an pre-existing network
A variation of the single user utility is one where the primary functionality of your product is to share into a pre-existing network. The classic example of this is Instagram, which provided the initial value prop of photo filters and sharing to Facebook, which can be used even if none of your friends are using the service. However, it spread virally over time, which brought more people to Instagram, and this was then used to bootstrap a separate network based on following celebrities rather than the bidirectional Facebook friend model.

(I published a guest post Social Products with with utility, not invites, as a longer exploration of this idea)

The main challenge with this model is twofold: One is the “two value proposition” problem as stated before: Initially, a large % of your users might view Instagram as “that app I use to post to Facebook” rather than a destination in itself. The second challenge is that you can have a platform dependency that may not end well if your “host platform” decides to cut you off.

Small network requirements
Not every product has to have a single user value proposition, and in fact, it can complicate things to feel like you have to design for multiple use cases at the same time. Instead, a different approach would be to focus on building a product that has a small critical mass requirement. In fact, you could look at the following categories and assess their critical mass requirements:

  • Skype: 2+ people
  • Group mailing list: 5+ people
  • Social network: 10+? 50+?
  • Social+mobile+location based: Lots :)

So one strategy is, how can your product be useful for just a small handful of people? That way, if you have a big launch, you can get lots of active pairs of users, like families or couples, and you can hold on to your audience. But if your product requires a very large critical mass of users, then maybe it will be very hard to get there.

Local network saturation
For products that require a large critical mass to get started, I’m already skeptical. But if you must, going after some kind of hyper-connected vertical is a good way to start. Rather than getting 1,000 users randomly and who don’t know each other, instead you focus on getting 1,000 users who are densely connected already. That way, you can saturate the network and hold onto that group of 1,000, and then go from there.

Although the following companies also often had attributes such as single user value prop and small network requirements, it’s useful to think about starting within a niche: Yammer did this within a company. Yelp did this within San Francisco. Facebook within Harvard, and Twitter within the tech community at SXSW. Snapchat within SoCal high schools.

Perhaps there’s a niche hyper-connected pre-existing network that matches with your product, and if you can retain those users, then you can build a much larger network from there.

Why big unfocused launches often fail
The above provides a clue on why big social product launches on Techcrunch or DEMO or whatever often fail. The problem comes down to the fact that for social products, you often need hit some metric of connection density to succeed. A “minimum network density” metric, if you will. And when you think about it like that, you’d much rather have 100,000 users with a density of 30 connections/person, than 1,000,000 who have a density of 2 connections. Because ultimately, those million users will churn out because they won’t have the content and feedback loops necessary to stay engaged.

Big launches fail because they might pump up the total users number, but don’t help much with the network density number. If anything, they might lower the average. So if you want to go with the big launch, make sure that it either targets a network that’s hyper-connected, and who will onboard nicely into your product. Or make sure there’s a very strong single user value prop, and even if they can’t find anyone they know in the product, that’s OK.

OK, good luck my friends!

Written by Andrew Chen

March 27th, 2014 at 11:34 am

Posted in Uncategorized

How to design successful social products with 3 habit-forming feedback loops

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TOMA_3loopsW2

Social products share a common ancestry and set of problems
It’s been a decade after Friendster popularized the notion of the social network, and we’ve seen hundreds of flavors of social products. Many of them are very different from each other, showing that success can come from many variations. I’ve come to believe there’s 3 main feedback loops that drive the success of these social product designs – here’s the trifecta:

  1. A feedback loop that rewards content posters when they push new content into the network
  2. A feedback loop that rewards passive content consumers with relevant and valuable content
  3. A feedback loop that rewards (and culls) connections within the network

It’s great when all three feedback loops act in harmony. As users act within each feedback loop, everyone’s happy, and the players in the ecosystem produce and consume valuable content for the network. When this happens on a daily or hourly basis, it creates habitual usage within your product- driving engagement and retention.

On the other hand, when even one feedback loop starts to fail, reverse Metcalfe’s Law goes into effect, leading to stagnation and ultimately, network collapse.

As an industry we’ve often talked about the distribution of content creators, curators, and consumers – it’s often known as the 1/9/90 principle. But that’s about the distribution of these different kinds of users, and not about fundamental motivations behind their actions. The feedback loops for social product aims to think in terms of why these feedback loops are able to create happy emotions and build up habits. Furthermore, by looking at each loop in isolation, it becomes more obvious where one could innovate- by adding a twist in content creation, consumption, or how people are networked. I’d argue that anonymity, constrained media types, algorithmic news, and other innovations all fit into these feedback loops in different ways.

Content posters that crave feedback (or utility)
First and foremost, let’s talk about the folks who post content – these are the 1% and 9% part of the 1/9/90. These users might post content by creating it in a textarea or uploading a photo, or it might be more curation oriented- simply retweeting a funny link or sharing a link. Either way, they take an action that writes new info into your network that impacts the content consumption experience. The feedback loop that’s important here is to reward content posters with social feedback. You publish content to your audience and then social feedback trickles in over time, drawing you back to the product. If content creation is easy enough, and the social feedback is compelling enough, then you do more. And so the loop continues.

It turns out that what type of content people post is important: Social products ultimately have some kind of content in the middle of it (sometimes called the social object), that determines the posting/consumption behavior of the content. This might be a tweet, a photo, a musical playlist, a restaurant review, or even a commerce page. It would be a mistake to assume that it’s as simple as wanting this content to be as simple as possible to create, because you also need to make it a frequent behavior. You also need the resulting content to be compelling as well – it’s these constrains that make this system tricky.

First let’s talk about what it means to make the posting “easy” – it’s not just that the tools are simple, but also:

  • You’re already creating it, so it’s not a new behavior (for example, almost everyone sends links, photos, etc.)
  • You can create it in seconds (sometimes via an artificial constraint)
  • You do it all the time, and over a long period of time
  • You don’t feel self-conscious publishing it
  • You can use new technology that lowers the bar (location sensors, camera, etc.)

A lot of the recent innovations in social products have focused on making this easier. One important tool is the use of constrained media types, where a tweet of 140 characters ensures a level playing ground for content so everyone can write a tweet in a few seconds. The 6-second Snapchat lowers the mental effort in taking the perfect photo. Foursquare uses our smartphones to make it easy to publish our location, whereas years ago, the effort on a feature phone would have been much higher. Similarly, the new trend of anonymity is another way to lower our inhibitions towards content creation. (I’m excited about the trend towards wearable and ubiquitous computing because they’ll be tools for all sorts of easy content creation.)

The tricky part of content creation is that the output has to be compelling to consumers, and over a long period of time. If your content is novelty (for example an avatar creator), then it may thrive for a period of time but ultimately the loop will weaken and stop. That’s fine for an ad campaign but not a product.

On the other hand, sometimes content can be very high cost but still be really compelling, for example long-form writing or high-production video production. You end up with a small % of creators who can actually author the content, but the end result is compelling enough that the whole thing keeps going. Yelp reviews, Stackoverflow, and others operate like this, with a push from SEO which help both creators and consumers find the site again over time.

Ultimately, the balancing act between content creation cost, the frequency/retention of it, and how compelling the output is – well that’s the magic of a new product design.

The health of the feedback loop around content consumption versus social feedback is based on a number of key variables, all of which are interrelated with each other:

  • What % of users create content
  • How much content is created (ease, frequency, retention)
  • Who this content is shown to
  • How compelling the content is
  • What % of consumers give feedback to the content creators
  • How compelling that feedback is
  • Whether the feedback brings back content creators to make more

The tricky part to the above is that many key variables oppose one another. You can increase how often content is shown to people just by blasting out content indiscriminately, but that decreases the relevance of the content. You can make it really easy to give user feedback, but at the cost of making the feedback less compelling. All of these tradeoffs ultimately manifest themselves in the design of a social product, hopefully in the right dosage and combination.

One footnote is that content posters can also be compelled by providing a single user utility, which produces compelling content as a byproduct. The classic example of this is bookmarking- Pinterest and Delicious help you organize content as your single user utility, but once the content is in the network, other folks can interact with it. This ultimately bootstraps the network as positive social feedback flows in, ultimately replacing the “organize stuff” value proposition with a “people tell you how much they love your stuff” benefit.

Content consumers want relevant content, updated frequently
Now lets think about the viewing experience. When it comes to content consumption, I think about the things that people want to look at every day. There’s not too many of them. News about their friends/family, news about the world. News about work. That’s one big chunk. Entertainment, which these days might look like YouTube videos, but even easy-to-create memes. For some demographics, maybe they want to see commerce content – shopping is always fun. And if you have hobbies, maybe you want to see a bunch of vertical content about that kind of thing – whether it’s about the arts, cooking, or programming.

The feedback loop for content consumers is simple: Every time they open your app or website, they see compelling content. That builds a habit for them to check in every morning, every time they’re standing in line, and every time they’re bored at work.

Yet the loop is easily broken – here’s the usual failure states:

  • Feeds that lack content
  • Feeds with stale content
  • Feeds with too much content
  • Feeds with irrelevant content

Lack of content and stale content comes from using a friending/following method of connecting content posters and consumers – but often, the network is underdeveloped or isn’t growing fast enough. Or maybe there’s not enough friend density to drive a full feed. Or even if there is a lot of users using the product, there isn’t the “right” users – for instance, an adult user stumbling into a website mostly filled by teens. These are some of the common reasons why it can be difficult to evaluate new social products – even if the mechanics and loops are well setup, if you don’t have the right users it’s hard to see the magic.

But once there’s a nice balance of new content coming into a feed at about the rate that content consumers want to see it, something great happens. Then the engagement can lead to people giving social feedback to the folks who posted it in the first place – via likes, comments, re-shares – and that stimulates the production of more content.

Connecting content posters and consumers to drive relevance
The way that content consumers participate in the feedback loop is that they give feedback to content creators. But before they do that, they need to have a method of picking what content is relevant to them on their home screens:

  • Picking people (Facebook, Twitter)
  • Picking topics (Quora, Stackexchange)
  • Leaderboards (Reddit, Hacker News)
  • Editorial curation (Medium)
  • Algorithmic curation (Flipboard, Prismatic)
  • Location (Foursquare, Highlight)
  • Anonymously matched (Secret)
  • … and more to be invented!

All of the above work, with different tradeoffs. Allowing people to customize their content consumption based on people and topics is the most scalable, but the hardest to get started. It’s a classic cold-start problem. To get to that, you need a critical mass of content creators who are making the kind of content that might attract a passive audience. Given that content creators are also consumers, that’s why oftentimes it’s the easiest to get started with a group of content creators.

The feedback loop about generating meaningful connections needs to reward the network when authentic connections are made. When you pick a new topic, or a new person, does that expose you to new content that then gives you new opportunities for people to follow? Do you have plenty of opportunities to unfollow or otherwise clean out your feed of irrelevant information? And are new people joining the product all the time, driving notifications, re-engagement, and ultimately new content into the network?

Editorial curation and leaderboards (like Hacker News) are easier to start, but have the drawback that they don’t scale well. Editorial requires you hire lots of people. Leaderboards create a single public space where it’s difficult to create a “one size fits all” experience that makes everyone happy.

It’s also difficult to mix the two. If you combine user generated content with editorial, within the same feed, then inevitably editorial content will “steal” the feedback from the UGC. That’ll weaken the loop. Instead, to really make sure that enough social feedback is being given, the goal is to make a feed with compelling assortment of content, and a lot of easy ways for consumers to interact with the content creators.

Another interesting issue on social feedback is the issue of quality. If you upload a video to YouTube, and then get 1000s of incomprehensible comments from teenagers, is that better than a smaller number of comments from thoughtful people? I suppose it depends on your own tastes, but over time, I’ve personally come to value the feedback of a small group of people I respect rather than trying to maximize the levels of pageviews or comments that I get.

This can be a difficult challenge because startups obviously face the pressure to grow, and one of the easiest ways to do that is to get your users to invite and add lots of meaningless connections. At the same time, if they follow too many users, or topics, then their feed will get busy and the product will lose relevance. So ideally, you have a system in place where users can add (and remove) connections to other users easily, and the system is able to suggest more relevant connections. This should also provide a better and more personalized experience around content consumption.

Building a checklist to ensure your loops are healthy
I leave you with a checklist for those of you who are designing social products, but find that your feedback loops aren’t quite working. The question I’d ask is, zoom into each of the feedback loops, starting with the folks who are posting content. Ask yourself, are they getting feedback on every action they take? Is it high quality feedback that makes them feel good? Are they making enough content to be interesting? If not, the feedback loop is broken and needs to be fixed.

For content consumers, are people getting high value, meaningful feeds? Or is it a random mishmash of popular content in your product? And if the feedback loops aren’t working, consider creating a small network where it all works, and grow that out, rather than forcing bad feeds on everyone that visits.

Or alternatively, consider taking a small part of another products’ feedback loops, and tweaking it a little. There’s many innovative products yet to be invented.

In a decade of social product design, we’ve seen many significant innovations around many components of these feedback loops. Facebook innovated with real names and a privacy model which helped drive closer-knit social feedback. They also invented the feed, a new way for posters and consumers to more efficiently transact on content. Twitter pioneered the follow model, which is yet another way to connect people. Instagram took advantage of much easier content creation methods on your smartphone, combined with plugging into existing networks, to bring something new to the model. And recently, anonymity apps like Secret are connecting people in yet another new way.

When I first arrived in Silicon Valley back in 2007, I remember a very smart B2B investor asked me, “Does the world need another social app?” implying that the category had been fully exploited. I think we see that in fact, given the years of solid innovation since then, there’s many new social products yet to come.

Written by Andrew Chen

March 17th, 2014 at 1:45 pm

Posted in Uncategorized

Congrats to my sis Ada Chen, who’s joining SurveyMonkey as VP Marketing

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I’m happy to congratulate my kid sister Ada Chen (@adachen) who joined SurveyMonkey as their VP Marketing this week. Awesome.

Ada is exceedingly modest for a successful entrepreneur. She has two successful exits and a deep background in marketing (yes, it runs in the family!). After attending UPenn and joining Microsoft, she soon moved from Seattle to San Francisco to join her fiance Sachin Rekhi (now husband). In the process, Ada met with a ton of different startups, ultimately joining as one of the first dozen employees at Mochi Media, an Accel-backed games+ads startup. Soon after, it was grown to over 100M uu/month and acquired for $80M by Shanda Games, a public games co in China.

Soon after, she started Connected with her husband Sachin, backed by Trinity and 500 startups, which aimed to manage all your professional relationships in one place. The technology behind the product was amazing, and was quickly picked by Linkedin to form their new Contacts product – the announcement here. And now, she’s moved on to one of the so-called “unicorn” billion dollar companies in Silicon Valley at SurveyMonkey.

It’s great to see friends do well, and even better to see my sister do well. I’m happy to share this good news.

And finally, a photo of us in our bowl cut days:

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Written by Andrew Chen

February 26th, 2014 at 12:18 pm

Posted in Uncategorized

How to make content creation easy: Short-form, ephemeral, mobile, and now, anonymous

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Image 2014-02-10 at 3.58.44 PM

Like many folks over the last couple days, I downloaded Secret and started playing with it over the weekend. The NYT even wrote an article about this category of apps. A few thoughts went through my head- is this a gimmick? Are the push notifications too much? Will I have secret fatigue? Yet after a couple days of using the app, I’ve come to believe that anonymity is a powerful new innovation that dramatically lowers the cost of creating content.

This is important because content creation is the heart of any social app. If it’s easy to create compelling content, then that content is quickly shared to other networks, driving viral growth back to its source. A fresh stream of compelling content brings the bulk of any social product’s primary audience – a large group of passive consumers who just want to flip through all the cool photos, videos, tweets, and more, maybe commenting or liking a few they really feel strongly about.

However, content creation is hard. That’s why, famously, only 1% of users will do it. In the last few years, social products have started to innovate around making it easier to create and curate content. I wrote about this previously before in the essay, Constrained media: How disappearing photos, 6 second videos, and 140 characters are conquering the world.

But to summarize, here are some of the ways content creation is being made easier:

Short-form content forces everyone on the platform to keep things casual. For example, Vine’s 6 seconds or Twitter’s 140 characters. This reduces competition and the feeling that you have to do something really long, well thought out, and intense.

Ephemeral content makes everything throwaway, like Snapchat’s photos. If you can’t save it, and look at it later, then it’s no big deal if you send an ugly/uncomposed photo, especially if it’s an ugly duck face selfie.

Mobile content takes advantage of the fact we’re now carrying a bunch of sensors with us at all times. Foursquare makes it easy to share a location by using your phone’s GPS – this is something that’s unique and wouldn’t have been easy 10 years ago. Photo-sharing social apps have exploded now that we all carry cameras 24/7. Wearable computing will lower the barriers even more, and enable ambient/ubiquitous content creation – Runkeeper maps are just the beginning.

Curated content can be great because you don’t have to create the content yourself- you just have to share the link. So whether it’s sharing shopping links on Pinterest or memes on Reddit, all you need to know is copy and paste.

… and now, it’s clear that Anonymous content may be it’s own thing:

Anonymous content makes it easier to share what you really think, without worrying about what other people will think of you. For the last few years, the trend has been towards real identities – but with that paper trail, it’s easy to self-censor. Anonymity removes the desire to self-censor, at least for certain kinds of content.

I think we’ll find that anonymity, just like the other above options, is just a design choice that has its own limitations and tradeoffs. The power of real identity is that your content becomes an extension of yourself. So as long as the content is positive – an accomplishment, or a fancy vacation – then you’re likely to want to share it publicly, attached to your friends, with your real name. But there’s also a world of content we’re all censoring from other people – our insecurities, authentic (sometimes negative) opinions, and so on. – and anonymity is powerful there.

It’s impressive to see entrepreneurs continue to reinvent social paradigms even 10 years after Facebook got started. I remember a few years ago, a smart VC asked me, “why does the world need another social network?” when in fact, it looks like we’ll see many new successful social products over the next few years. Excited to see what else people come up with.

Written by Andrew Chen

February 10th, 2014 at 6:17 pm

Posted in Uncategorized