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

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

Listen to the podcast here »

tldr; Here’s 5 quick takeaways

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

Full Interview with Adam Risman (Intercom)

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

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

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

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

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

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

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

What Dropbox can teach us about virality

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

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

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

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

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

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

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

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

Navigating supply and demand at Uber

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

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

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

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

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

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

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

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

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

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

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

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

Why you need a mechanism for free acquisition

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

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

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

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

Fighting channel fatigue

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

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

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

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

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

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

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

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

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

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

Unlocking the best insights in growth

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

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

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

Andrew’s lightning round

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

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

Adam: What book has most influenced your thinking?

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

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

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

Adam: Favorite recent onboarding experience?

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

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

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

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

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

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

Written by Andrew Chen

May 14th, 2018 at 10:00 am

Posted in Uncategorized

Update: I’m joining Andreessen Horowitz!

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Hi readers,

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Psych Framework

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

User Psych Framework by Darius Contractor

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

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

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

Psych Units

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

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

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

Psych Elements

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

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

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

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

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

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

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

Example 1: Match’s homepage

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

1. Determine your starting Psych

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

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

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

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

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

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

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

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

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

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

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

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

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

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

These are the – Psych elements:

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

4. Sum it up!

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

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

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

Maximizing Psych on each of your pages

1. Assess initial Psych

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

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

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


2. Psych on the landing page

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


3. Enter personal info

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

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


4. Interact with product

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


5. Enter payment information

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

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

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

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

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

Decreasing cognitive load is more than just short signup forms

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

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

Now let’s look at another live example.

Example 2: Airbnb’s hosting flow

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


How do we evaluate the Psych score of this page?

1. Determine your starting Psych.

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

4. Sum it up!

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

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

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

-Darius

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

Written by Andrew Chen

June 12th, 2017 at 10:00 am

Posted in Uncategorized

Startups and big cos should approach growth differently (Video)

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

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

 

 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Written by Andrew Chen

June 6th, 2017 at 10:00 am

Posted in Uncategorized

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

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Benedict Evans at a16z recently tweeted the following:

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

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

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

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

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

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

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

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

– Chris Dixon, gp at a16z

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

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

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

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

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

-Steve Ballmer, Microsoft on the iPhone

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

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

– The Economist, Oct 1999

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

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

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

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

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

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

– Bessemer Venture Partners, Anti-Portfolio page

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

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

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

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

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

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

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

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

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

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

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

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

Written by Andrew Chen

January 30th, 2017 at 9:30 am

Posted in Uncategorized

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

How To (Actually) Calculate CAC

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[Andrew: Paid marketing remains an integral part of many products’ acquisition channels, and one of the key metrics is Cost of Customer Acquisition, which is a nuanced calculation with lots of gotchas. My good friend Brian Balfour (ex-vp growth at Hubspot) put together this incredible essays with details on how to think about it.]

Brian Balfour (ex-VP Growth at Hubspot):

avatarIn everything from growth projections to company valuations, it’s common to use CAC and CPA interchangeably — but it’s wrong, and it can cost you. In this post, I break down growth’s most important jargon to demystify the true cost of acquisition, and dig into the most common mistakes leading growth teams off track.

Note: This is a big topic that’s best addressed with live examples and interactive frameworks. To that end, I’ve included a number of examples of real companies, plus interactive spreadsheets that you can access throughout the guide. To adapt any spreadsheet for your own calculations, click here, then go to File > Make a copy to create your own version.

Customer acquisition is not CPA – Three examples

To start off, let’s address a common myth. Customer acquisition cost (CAC) and cost per acquisition (CPA) are commonly conflated, and yet in reality they’re completely different metrics. Understanding the difference is the start to understanding CAC in depth.

CAC specifically measures the cost to acquire a customer. Conversely, CPA (Cost Per Acquisition) measures the cost to acquire something that is not a customer — for example, a registration, activated user, trial, or a lead. The two are related because CPA is usually used to measure the cost of things that are leading indicators to CAC.

Four examples of how CAC and CPA are different but related:

1. Dropbox

Since Dropbox is a freemium product, CAC would be the cost to acquire a paying user on either their pro or their team plan. CPA would be used for things such as the Cost Per Registration (of a free user), Cost Per Activated (free) User, and other important, but still non-paying, actions that signal that someone has moved from being a visitor to a user of the product.

image-1-dropbox-cac-768x576

image-2-dropbox-cpa-768x576

2. HubSpot

Since HubSpot is a B2B SaaS product, CAC would equal the cost to acquire a new customer on one of their Basic, Pro, or Enterprise plans. CPA would be used for leading indicators to CAC, such as Cost Per Lead, Cost Per Sales Qualified Lead, Cost Per Trial or other points in the marketing and sales funnel.

image-3-hubspot-cac-768x576

image-4-hubspot-cpa-768x576

3. Facebook

B2C companies supported by ad models are a little different. In Facebook’s case the paying customer are advertisers so CAC is the cost to acquire a new advertiser.  However if you just look at users, users/customers are the same. CPA is most likely used for things like Cost Per Registration, Cost Per Activated User, etc.

image-5-facebook-cac-and-cpa-768x576

The key point is the first thing you need to do to understand CAC is very simple:

Define who/what a customer is in your model.

Make that definition clear and simple and get the language between CAC and CPA consistent otherwise communication will be very confusing.

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The basic calculation of CAC and why it’s wrong

If you google ‘how to calculate cost of customer acquisition” you will get the basic formula below:

CAC = Total Marketing + Sales Expenses / # of New Customers Acquired

image-7-cac-equation-1-768x576

On the surface this is correct, but it is missing a lot of details and definitions around each variable in the equation to get it right. Even the best basic calculation can be very misleading. For example, using this formula you might look at the below spreadsheet and assume the following:

To adapt this spreadsheet for your own calculations, click here, then go to File > Make a copy to create your own version.

But what if I told you the following things:

  1. It takes on average for most customers 60 days from lead to become a customer.
  2. Not all customers are new customers, but some of them are returning.
  3. This is a freemium product and there are costs to supporting users while they are free before they become a paying user (customer).

These three tidbits of additional information should change how we look at the basic formula above. Instead of taking the equation at face value, we need to evaluate a few questions first.

How to really calculate CAC

There are three key questions we need to ask to define a more accurate calculation of CAC for a business. All of them dig into a level deeper around the variables in the equation.

Key Question #1:  How long between your marketing/sales touch points and when someone becomes a customer?

The first issue with the basic calculation is that it doesn’t take into account the time period between when you spend the marketing/sales money and when you actually acquire a customer. Here are two examples:

Example 1:  Freemium Product

Let’s look at Dropbox as an example. When you sign up for Dropbox you start using their free tier. You use Dropbox free for some time period until you hit your storage space limit, which you then might upgrade. For a lot of users that time period is months (and in some cases over a year). The story is the same with other freemium products such as Evernote, Buffer, etc.

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Example 2:  SaaS Company w/ Inside Sales

Most SaaS companies with inside sales models someone might become a lead this month (due to our marketing efforts this month) but will take 60+ days before they become an actual customer because they need to go through the sales process.

image-9-time-delay-2-768x576

If you don’t take these time periods into account, you could be overestimating or underestimating CAC and as a result making some terrible operating decisions.

Here is an example of how you could overestimate.In the below example, CAC is being calculated by taking the month’s marketing costs and dividing it by new customers in the same month.

To adapt this spreadsheet for your own calculations, click here, then go to File > Make a copy to create your own version.

In March we try some new channels which causes a large increase in marketing costs.  Under the simple calculation our CAC is $148.  If our target CAC is $125, we would likely make the decision that March was unsuccessful and we would turn off those new channels.

But, let’s say it actually takes 2 months for someone to become a customer.  Below is the same data but changing the calculation to account for this 2 month period.

To adapt this spreadsheet for your own calculations, click here, then go to File > Make a copy to create your own version.

This change tells a completely different story. In March we have a CAC of $84, and in April a CAC of $111. Instead of turning off the new channels we tried in March, we would probably make the decision to scale them.

The key question about time between marketing/sales expenses and acquiring a customer does not matter if you fall in one of two scenarios:

1. The time between marketing touch point and someone becoming a customer is very short. This is true for a lot of B2C companies that have very short decision funnels for users: Snapchat, Instagram, and others.

2. Your marketing/sales expenses are so consistent that it normalizes itself out over time. But even in this case it is best to be more accurate.

You need to figure out how the timing of the expenses correlate with the timing someone actually becomes a customer. Marketing expenses could correlate differently than sales expenses.

The simplest way to account for this is figure out your average marketing/sales cycle. In other words what is the average amount of time from first marketing touch point, to acquiring the customer?

Here’s an example. Let’s say we are a SaaS company where the average time is 60 days from lead to customer and we believe the sales expenses are spread evenly over that two month time period.

image-10-cac-equation-2-768x576

The CAC equation would be as follows:

CAC = (Marketing Expenses (n-60) + 1/2 Sales (n-30) + œ Sales (n)) / New Customers (n)

n= Current Month

Here is an example model with that calculation built in:

To adapt this spreadsheet for your own calculations, click here, then go to File > Make a copy to create your own version.

Key Question #2:  What expenses do you include in the Marketing + Sales?

The second question you need to answer to get an accurate CAC calculation is what expenses do you include in the numerator (marketing/sales)? Before we look at some examples of the answer to this question differs, here are the three most common mistakes I see:

Mistake #1:  Not Including Salaries

You need to include the salaries of all people working on marketing and sales. We see CAC numbers a lot where they aren’t included. This includes not just the individual contributors who are 100% dedicated to marketing/sales, but also those (often times managers) who spend part of their time on marketing/sales. A CAC number with salaries included is often referred to as “Fully Loaded CAC.” There are some scenarios where it is useful to separate Fully Loaded from Non-Loaded CAC which we’ll explain later.

Mistake #2:  Not Including Overhead

Similar to the mistake of not including salaries, you need to include the overhead (rent, equipment, etc) allocated to those employees working on marketing sales.

Mistake #3:  Not Including Money Spent On Tools

The marketing and sales tool space has exploded. Most teams are using 10+ tools to operate their marketing and sales machine. These tools can add up in cost and need to be included in the expenses  of your CAC calculation.

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If you include those main things, the answer to this question starts to get a little more complicated and range from company to company. A couple of examples:

Spotify and the Case of Freemium – Does that Include Product/Engineering/Support?

image-12-cac-spotify-freemium-768x576

Spotify is a freemium business. They have millions of users using the free version of their product which helps them acquire new users through sharing music and other viral channels.

In most companies, product, engineering, and support are not included in CAC (typically part of R&D). But if the free product is your primary method of customer acquisition, shouldn’t the expenses that support that free product be included in the expenses portion of your CAC calculation? There are different opinions to this question, but we fall on the side of yes.

If you had engineers, PMs, and other roles on the marketing or sales team for marketing/ops you would include those salaries and expenses in the CAC calculation. The engineers, PMs, or other roles might not technically be on the “marketing” or “sales” teams, but they are still expenses that are required to support new customer acquisition.

HubSpot and the Case of SaaS –  Include Customer Success Costs?

Most SaaS companies like HubSpot have sizable Customer Success teams. The definition and roles of these teams can vary widely. Some Customer Success teams are purely devoted to churn prevention. Some are devoted to helping onboard a new customer. Some are devoted to winning previous lost customers back.

All this begs the question, should customer success costs be included in CAC? Some of these responsibilities touch acquiring new customers and therefore make the case that they should be allocated to CAC.

Dollar Shave Club and the Case of Subscription Ecommerce – Support, Shipping for Free Trial?

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Dollar Shave Club is a subscription ecommerce business. They famously have a one month trial for $1. That one month trial has a number of expenses outside of marketing, including shipment costs for the initial package, support during the trial, and other costs.  Should those costs be included in CAC? The answer to that depends how you define a new customer.

In Dollar Shave Club’s case, we would make an argument that someone on a $1 trial is not a customer yet. A new customer is someone that extends beyond the trial.  Therefore all of those costs associated with supporting the free trial should be included in CAC. Those costs are partially offset by the $1 trial payment, but not fully offset.

A customer is a customer, right? Not necessarily. When it comes to calculating CAC, we need to distinguish between new and returning customers. In most organizations there are marketing and sales efforts focused on new customers, and there are marketing and sales efforts focused on retaining or getting customers back.

The mistake is only including marketing and sales expenses in the numerator for new customers, but including all customers (including returning customers) in the denominator. This will make your CAC look artificially low. You can solve this in one of two ways:

1. Include all marketing/sales expenses (including those focused on retention) and all customers.

2. Separate expenses for new customers from reactivating old customers and separate out new from reactivated customers in the denominator.

Recap

From the examples we’ve covered today, I hope it’s more clear that calculating true CAC involves a lot more than a simple, one-size-fits-all equation.

Instead, an honest assessment of customer acquisition cost looks at the length of your sales cycle, how many customers are truly new customers (as opposed to returning customers), and the total costs and resources required to support marketing efforts that lead to new customer acquisition.

Brian Balfour is CEO of Reforge, previously VP Growth @ HubSpot, EIR @ Trinity Ventures, Co-Founder of Boundless, Co-Founder of Viximo, Co-Founder reCatalyze & PopSignal. 

Written by Susan Su

December 20th, 2016 at 10:00 am

Posted in Uncategorized

Growth Interview Questions from Atlassian, SurveyMonkey, Gusto and Hubspot (Guest Post)

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[Andrew: Excited about today’s guest post! I was recently interviewed by the folks at Reforge, a new company started by my friends Brian Balfour and Susan Su focused on advanced professional education. They asked a great question – how do you interview for growth folks? I gave some my 2 cents based on my experience helping startups and growth folks. They pulled together a great essay below!]

Growth Interview Questions

Guest Essay by Susan Su, Reforge

Together, Shawn Clowes (Atlassian), Elena Verna (SurveyMonkey), Nick Soman (Gusto), Andrew Chen and Brian Balfour (Reforge, previously at Hubspot) have interviewed or screened over 1,000 individual candidates for growth roles – both for their current and previous companies, plus startups where they’ve invested/advised.

Growth is an emerging field, and there’s hardly a playbook on how to ace your growth interview (whichever side of the table you’re on), and yet hiring and team could be the most important “growth hack” of all. I recently asked a handful of folks from the Reforge Collective about the questions they ask when interviewing candidates for competitive positions in growth.

Here are some of the questions we’ll cover:

1. The Golden Gate Bridge
2. Growth Hacking a City
3. Are you an early adopter?
4. Live brainstorm an experiment backlog
5. What Are Your Setbacks?
6. Look for opportunity, not just risk
7. But
 why?
8. The Bullshit Test
9. The Trajectory of Growth
10. Probe on Metrics
11. And on Culture
12. 6-Month Roadmap
13. Growth Resources
14. Low Hanging Fruit

If you’re looking for your next job in growth, or you’re trying to fill one at your company, consider this a peek into how a few other growth marketing experts are structuring their growth interviews.

This 1 Weird Interview Question
Growth is as much about showing creative problem solving as much as quick metrics-based iteration. If a candidate is out there googling answers to “trick” interview questions, then they’re missing out on an opportunity to showcase their own unique way of combining creativity with numbers.

“Weird” interview questions let you out to roam beyond the constraints of the role in front of you. The best weird questions are designed to highlight original problem-solving, and if that doesn’t excite you, then a position in growth probably won’t be fun or fruitful.

1. The Golden Gate Bridge

Elena Verna SurveyMonkey growth interview

When she was still a candidate interviewing at SurveyMonkey, Elena Verna fielded an unusual interview question that she continues to use in her own growth interviews to this day:

Let’s say you were to come in tomorrow, and you got a new project to work on the Golden Gate Bridge and its toll collecting. But, you’re told that they’ve recently lost all of the tracking of both traffic flow and revenue collection, and your boss has just told you that you need to come up with a revenue estimate in the next 15 minutes. You don’t know anything about how much has been collected historically. All you have is a blueprint of the bridge.

How would you estimate how much revenue or the bridge generates on a weekly basis?

Elena explains that the question is meant to demonstrate your problem solving skills (not to get to the “right” answer necessarily).

For me it’s a sign that you’re willing to tackle an open-ended question in a creative way — without all of the data and answers. How am I going to approximate with only broad strokes of knowledge? What are the key variables (Bridge length? Car length? Car weight? Traffic volume?), and how far can I go in 15 minutes?

Elena insists there’s no right answer. Instead, it’s a window into your process of thinking with uncertain variables — and also how excited you get when you’re tasked with reasoning through an unsolved problem.

2. Growth Hacking a City

Nick Soman Gusto growth interview

Ultimately, hiring for your growth role is both high risk and high opportunity. The teams with the most at stake (and offering the best opportunities) will need to explore every last corner of a person’s thinking and style, and that sometimes means taking a lateral approach.

Nick Soman poses an unusual question designed to get at the way candidates think about growth beyond the templates and playbooks that circulate in the growth community:

How would you growth hack a city?

It’s not an immediately technical or product-based experience, and yet it’s an interesting question that might actually become more and more relevant. How would you attract residents to it? How would you attract the other people and elements that that ecosystem requires? What mechanisms would you employ to grow your city? It’s very revealing to see how people approach growth when they have no templates, when they start from zero.”

3. Are you an early adopter?

Shaun Clowes Atlassian growth interview

Have you ever thought about your own relationship with Snapchat? Have you broken down your own psychology of engagement with Facebook? How about for lesser-known, non-consumer products that are in your life?

We are what we eat, and the products and apps we consume (and how we interact with them) can say a lot about who and how we are when it comes to creating and growing our own products and apps.

Shaun Clowes wants to know what you’re using at work:

If you just got a new computer at work, what apps would you immediately set up?

I’m looking for their take on a piece of software that they care about, something that gets them excited, and then how they explain it to me.

What are the most recent apps you been playing with on your phone?

That gives me insight into how in touch you are with the industry, how much you’re seeking out things that are different or somewhat common, and whether you’re an early adopter of things.

4. Live brainstorm an experiment backlog

It’s the default to come prepared to a growth interview. You’ve looked at their core growth loops, you’ve analyzed their funnels, and you have an (externally informed) idea of where the business is going. But what if you were put on the spot to dig even deeper into how the business can grow?

Nick Soman wants to see candidates live-brainstorm an experiment backlog:

How many ideas can you come up with in 3 minutes?  Maybe it’s a handful, let’s say 5. Then, I really want to push for a 6th or a 7th.

I want to see the candidate beyond their comfort zone and extend beyond their pre-planned ideas and analysis, especially in real-time. I resist the urge to ask follow-up questions — even when I’m really curious — because I want to see you go for breadth, and then I want you to be able to follow that up with an objective assessment of those ideas.

5. What Are Your Setbacks?

If none of your experiments are failures, then you’re probably not testing the right things. Running growth means swimming in setbacks — and it helps if you’ve had some life experience with that.

Andrew Chen says the level of your setbacks say more about you than their specific details:

One of the questions that I like a lot that doesn’t have to do with growth but tells you a lot about a person is to ask about someone’s biggest setback, either personally or professionally. You can get a sense of if that’s a real setback and how they reacted to it.

For example, someone who’s very junior is going to come up with a small setback. They’re going to say, ‘Oh well at work I did a project and it sucked.’ That’s very different than something like, ‘I was at a company and I convinced the board to do something and it was a long decision and the company failed. And everyone got fired.’ Or, ‘I moved from country A to country B, left my family and friends and started over again.’

Understanding setbacks helps you understand what a person’s priorities are, and how much resilience they have to bounce back.

What Most Growth Interviews Are Missing

6. Look for opportunity, not just risk

Elena Verna surveymonkey growth interview excellence question

A lot of people try to understand a candidate’s weaknesses, but Elena Verna wants to know your superpower. Early to mid-stage growth is as much about doubling down on unique advantages as it is about fixing leaks (ie, identifying and addressing weaknesses). Later, the truly stand-out orgs are the ones defined by their unfair advantages, particularly in growth and product.

Building a growth team is a microcosm of the trajectory you want to see that team take, according to Elena:

Very few growth managers will look at an opportunity in a person and say, “Ok, these are your strengths and I’m actually going to tailor a roll around you to make sure that I’m playing to your strengths.

It’s good to know what people aren’t good at — where they’ll be a liability. But I want to dig into to what they’re excellent at as well. That’s what you really need to focus on, and to make sure that that strength aligns with the position at hand or that it’s possible to mold the position around it.

Too often, we identify a problem or a hole in the business and start looking for the person that will fit it. The person you find could be effective very early on, but evaluating too tightly against specific role can be very short-sighted. Yes, they might be able to sort out that immediate issue for you but in the same stroke you may end up hiring the wrong person long term.

The real opportunity is finding the person who will be happy (and make your business happy) as the definition of growth itself expands, and the immediate problem becomes obsolete. Where do you want them to be in a year? Look for the opportunities, not just the “urgent” holes.”

7. But
 why?

Most of us aren’t professional interviewers. We know our area, or we know growth marketing as a broader domain, and we stick to what we know. But, in building a growth team, you’re called to ask people about things that may not be your cup of tea.

As a result, many of us don’t dig into the details of a candidate’s experience, which is a bad idea for both the hiring organization and the candidate — the former misses out on opportunity and risk assessment (is this person a good fit?), and the latter may not get to tell their punchline.

Shaun Clowes asks a deeper layer of question where most interviews have called it a day.

Most interviewers will ask you a question about how in the past have you done X thing. You give them a surface-level answer like ‘We did Y and then achieved Z.’

A few ways to drill in more deeply would be to follow up with questions like:

  • When you say “we,” how much was you and how much was everybody else?
  • Were you really pivotally involved in this or was this really something that you just got carried along with?

Sometimes it feels like the answer has been rehearsed. It’s the correct answer, but when you drill into it, it’s clear that either the initiative isn’t all they said it was or it wasn’t as deep as they said it was or their involvement wasn’t as deep as they said it was. The best way to get through this is with a one-word question: ‘Why?’ ‘Why did you do that?’

You actually can reply with “Why did you do that?” to every subsequent answer, and it’s almost endlessly educational. This addresses the need for depth that growth roles need, but that many interviews often lack.”

8. The Bullshit Test

How do you know someone really knows growth and doesn’t just have a great handle on acronyms?

Growth is a critical role but not one that hiring teams can succinctly test for. That is, you can’t check out someone’s GitHub for growth or decouple what public evidence you find of their work from other situational factors, like a great team, a solid company, or a unique ecosystem opportunity whose bandwagon they jumped onto.

Andrew Chen runs a “bullshit test” to make sure that candidates aren’t merely fluent in blog posts and jargon.

I ask people to get on the whiteboard and draw out the whole thing. For example, I may ask someone “How does YouTube grow?

There, I want to watch you draw out the entire flow for how a user comes into YouTube and how you think it might all work — just from what you’ve observed on the outside. Then I’ll ask you where you might make improvements. I want you to do all of this in real-time with me in the room.

This exercise shows a level of detail and thinking that indicates that you’ve mastered what you’re doing, versus that you’ve read all the blogs. I want to get the sense that your capacity goes beyond knowing the concepts, and that you have a depth of process understanding that you can bring to anything you do next.”

What You Should Ask Your Interviewer (but Probably Aren’t)

“Do you have any questions for me?” is a common wrap-up to an interview session. It’s also its own covert test of your listening skills and the depth of your analytical abilities.

But aside from generalities about company culture, project overviews, and basic metrics, the top candidates for growth roles get under the surface of their opportunity with specific questions for their interviewers.

9. The Trajectory of Growth  

The definition of growth or growth team can differ significantly from one business to another. Some growth teams are focused only on driving acquisition into the business while others are making fundamental calls on product strategy and development.

Elena wants to see candidates who are taking a long, non-static view of growth.

Ask, ‘What does growth mean for this company, and what will it mean?’ You need to know whether they are responsible for driving metrics across the rest of the funnel or not, and how they may or may not evolve with the rest of the business.

Ask, ‘How does the growth team actually catch up with the structure of the business as it evolves?’ Many applicants simply want to understand where they’re going to be in a couple of months. This is very short-sighted. It’s not just about growth today, in this place and time; it’s about trajectory.”

10. Probe on Metrics

Brian Balfour growth interview

For growth roles you want to know are you coming in to fix the broken system, or are you coming in to make a good system great?

Understanding the answer to that fundamental question comes down to understanding both the metrics of the business and the culture of the team.

When it comes to metrics, most people simply don’t go far enough into the specifics. Brian Balfour says that’s where great growth candidates stand out.

What does retention look like? What does LTV look like? What are the biggest dropoffs?

If a growth candidate doesn’t ask me those basic question in an interview, I’m shocked.

But then you need to keep going. Keep asking questions about the metrics until your interviewer stops you and says, “It’s too detailed and we can’t give that out in an interview”.

You should get as much information as you possibly can. Not only will you know what type of situation you’re walking into, you’ll also show that you know how to think about growth for that company.”

11. And on Culture

The velocity of growth is determined by one part strategy, one part implementation. Great strategy and promising metrics can still be blocked by cultural issues within the organization; successful growth is technical, but it’s also fundamentally human.

Brian wants to see candidates who seek to understand the relationship between the growth team and other teams: core product, marketing, sales, executive.

Ask, ‘If I wanted to make X type of change in this part of the product, what would the process look like to make that happen?

Follow it with, ‘Would I or my team have our own resources and autonomy to be able to make that change?’

Changes can require negotiation, politics and navigating a number of other ‘people’ steps. You need to pose that same type of situational question to your interviewer that they’re probably asking you: ‘If I wanted to do this we thought if it was a good idea to do this, how would we get this done?’

That gives you a much better idea of how the team works, rather than simply asking them, ‘What’s the process around here?’

You’ll start to realize when they are describing how to get something done that there are certain points where they’ll show discomfort. Those are the areas where there’s friction within the company.

No company is perfect. But, it’s much better to know what the flaws are going into it, rather than being surprised after the fact. That way you can be better prepared and more effective from day one.”

12. 6-Month Roadmap

Being effective in flux starts with having a sense of what’s expected of you in the first six months in a new role. When you’re running weekly experiments or solving previously untackled problems, Andrew Chen says you should ask:

If I were to join, what would I be tasked with achieving in the first 6 months?’ You have to have a good sense for the 6 month roadmap — what you would actually do in terms of experiments and goals — so that you can come in with that from day 1.

13. Growth Resources

There are many different flavors of growth, and resourcing the growth function is a key variable from team to team. Growth has been interesting because there are different flavors of it.

Andrew wants to see candidates ask:

Are there are going to be dedicated engineers? Are there going to be dedicated designers? Or is this a situation where we need someone to kind of think about growth but they’re not part of the product?

14. Low Hanging Fruit, aka How Many Times Has the Homepage Been Optimized?

Andrew Chen uber growth interview

The best candidates want to understand the potential reaches of their own impact. How wide and deep are the outcomes that are under your purview? Andrew wants to know how much low-hanging fruit already been picked.

Have people been working on growth or really smart people thinking about it?

As a candidate, I would want to ask how many times has the homepage been optimized in the last 6 months, same with landing pages, same with everything. That gives you a sense of the kind of impact that you’ll be able to create.”

Susan Su leads marketing at Reforge providing training and connections for growth professionals, and is a venture partner at 500 Startups.  Special thanks to the Reforge Collective members who contributed to this post: Elena Verna, Nick Soman, Andrew Chen, Shaun Clowes, and Brian Balfour.

Written by Susan Su

October 31st, 2016 at 9:30 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.

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“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.

1-2-1531-25-ExplorePAHistory-a0l4o6-a_349
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.

computer.rosies2.t1larg
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.

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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:

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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

CBXZ5V3W4AArWJn

(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

IAC’s HowAboutWe co-founder: How to Avoid Delusional Thinking in Start-up Growth Strategy (Guest Post)

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[Andrew: Trying to build and launch dating apps is a favorite pastime of 20-something tech entrepreneurs. However, dating products are notoriously hard to grow because it requires people to be “in-market” and also they don’t necessarily want their friends to know they’re online dating. Today, we have a great piece from a veteran of the space. Earlier this year, IAC bought HowAboutWe, a new dating product that was trying to reinvent the entire experience so that it’d focus on activities rather than dating profiles. The cofounder, Aaron Schildkrout, contributed the following essay below, enumerating the difficulties of the various growth channels, and also more generally, how to be realistic about your growth strategy. You can follow Aaron at @schildkrout on Twitter.]

Aaron Schildkrout:
How to Avoid Delusional Thinking in Start-up Growth Strategy

howaboutwe_logo_310x206_color

So, you have a consumer internet idea you think could be big.

The statistics say you’re are almost surely wrong. There is a 95+% chance you will fail for one of the following five reasons: a) your product idea is shitty; b) your market is small; c) your execution or team is weak; d) you are undercapitalized; or e) your growth strategy belies a belief in magic.

In this piece I’ll focus on this fifth demon: Magical strategy for growth. My primary case study will be the online dating industry, a petri dish for delusional startup growth strategies.

I co-founded @howaboutwe in 2010 and was co-CEO until its recent acquisition by IAC. Like everyone who has ever built an online dating company, we started off with a growth strategy that looked a lot like a manual of magic tricks. Most online dating startups never escape this; the number of historical failures in the dating space is staggering — the vapidity of magical thinking coming home to roost. We succeeding to an extent in transcending these challenges; I’ll speak about our small victories against delusion.

Magical Thinking

When I ask pre-launch or very early stage founders about their customer acquisition strategies, they invariably think they have a plan. They might share a document or slide with a list of tactics like “press,” “word of mouth and friend invites,” “biz dev,” and “content.” They may even thoughtfully quote Andrew Chen.

But when you really dig into their ideas and predicted results, the defining characteristic of such plans is almost invariably an uncanny belief in magic.

Here’s a basic overview of why magical thinking is so pervasive in early stage online dating distribution strategies, organized by acquisition channel:

Virality: The only two dating sites in the world that have attained true virality are Badoo and Tinder. A few others have attained rapid exponential growth through some complex dynamic including a large advertising spend. But in every one of these cases, the result has been a massively degraded experience verging on soft porn, disturbingly spammy tactics, and a userbase with very low lifetime values relative to match.com. With the unicorn exception of Tinder, the only way to attain virality in dating (discovered thus far) is to aggressively (read: deceptively?) capture the user’s email address book and spam the entire list. Basically: block the feeling that the user might find love (or, more to the point: sex) with a tricky address book capture. If your goal is to create a dating site that isn’t solely about finding sex and that has the potential to become a well-respected national or international brand with high subscription revenues, virality has, to-date, been nearly impossible to achieve. I’ve met with two or three dozen people in the last few years thinking about starting dating sites. Of these, maybe 90% have believed in some magic virality system. Of these, none have achieved magic.

Press: About 3 months into launching HowAboutWe we had a full-page front-page print article in the New York Times Sunday Styles section. It was literally the best non-TV press we could have gotten. It drove more traffic than we’d ever had by about 10x. It was an awesome achievement at that stage. Four years later, while an article like that would have been great, it would have driven a nearly indiscernible increase in traffic. It would be a cool, small, irrelevant bump. For early stage startups we were probably in the top 2 percentile for press converge. And this was key for branding and so on. But it was categorically NOT a business-creating source of traffic. This is very hard to understand for new entrepreneurs. They imbue press — like most things — with a magical aura of inexplicable growth creating powers.

BizDev: The problem here is distorted ideas about how much traffic other entities can drive. For instance, with HowAboutWe we had the idea that we would feature venues as great date spots and that, in return, they would drive their lists to us. But small venues don’t really have meaningful lists. We didn’t understand this at all — we believed in a magical conception of biz dev. Ultimately we found a biz dev strategy that has worked to a much more significant extent (see nymag.howaboutwe.com for an example of how we worked with much larger traffic sources to drive growth); but it is fairly rare to find such a tactic. Many — if not most — early BizDev ideas are rooted in delusion about the traffic-driving potential of proposed partners.

Content: Content is wonderful for branding. And if you have a product with high lifetime values, it can easily pay for itself. But it does not provide a business-supporting customer acquisition channel unless content is your product. HowAboutWe has a highly successful blog strategy rooted in thedatereport.com and nerve.com. But as a pure traffic-driver into our dating product, it was never, well, magical. Let’s say (none of these are real numbers) 100,000 people visited our articles each day. The conversion to the dating site is basically a glorified advertising system — so let’s say 1% of visitors click-thru. That’s 1,000 visitors. If we get a 20% conversion rates off those visitors, that’s 200 sign ups. If we have a 10% conversion to paid, that’s 20 paid users per day. Let’s say paid users are worth $100 to us. That’s ~$2,000 per day. That’s a bit over half a million bucks per year. Not bad; but it’s not a significant business. Content is cool, but not magic.

SEO: Yeah right.

Paid Acquisition / Direct Marketing: For dating, this is by far the most interesting category. It is the ONLY strategy that has ever worked to build a truly mainstream dating brand over time, with the sole exceptions of OKCupid (whose primary strategy was being free and which took nearly a decade to attain true scale) and possibly Tinder (tbd). Very few consumer web companies talk in their very early stages about buying traffic as a core part of their customer acquisition strategy (though this is changing). This relative absence is indicative — more than anything else — of the belief in magic. Advertising is the only reliable, scalable, predictable way of acquiring users for mainstream dating sites.

For those who do include direct acquisition in their strategy, there is usually a massive underestimation of the amount of work required for hardcore funnel and LTV optimization. Building a truly effective CRM alone is years of work, and this is just one piece of the optimization required to even begin to compete for positive ROIs with the major dating advertisers in the world (match.com, for example, spends hundred(s) of millions of dollars each year on ads; you can be sure their conversion funnel is fairly well-optimized).

So, either the absence of a paid acquisition strategy or the presence of one that underestimates what optimizing a conversion funnel really takes both echo the magical beliefs that pervade most early distribution plans.

~~

Delusion about customer acquisition is incredibly understandable, particularly for first time entrepreneurs. It’s painful to truly understand how hard attracting users is, and pain is hard to face.

Enter: PainMath, my antidote to blind magical thinking.

PainMath: An exercise in anti-delusion

a. Imagine you are building a new product. Magic aside, describe very clearly and mathematically a scenario in which there is genuine, business-validating, detectable desire for this product? Specifically, how many people will have to enter the top of your funnel daily for you to get to an annual revenue run rate of $10mm or a user base of 10mm? (This number/metric will be different depending on your business — but pick something that would be a significant achievement, that would leave you firmly outside of very early stage company building.)

b. Figure out from where you think these will people come. Include in your description conversion rates at every stage of your funnel plus virality coefficients.

c. Now, cut to a bare minimum all unexplained “organic” traffic (this includes press, unexplained word-of-mouth, any nondescript biz dev strategy, and content), cut your projected conversion rates by 50% at every stage of your funnel, and do the math again.

d. If you have included virality in your traffic sources, really do the math about what the virality coefficient will have to be to achieve what you are predicting. If it’s over .3%, you are likely deceiving yourself. Do your math again.

e. Then remember that almost every single startup, of which almost all have failed, had a reasonably smart but prideful person at the helm thinking their idea would work — a reasonably smart but prideful person like you and me — and do your math again.

f. Then, as you set out (because you almost invariably will, even if this math revealed a desert of implausibility), continue to do this math. Aggressively. And measure your results against this. Again and again.

The result of this PainMath is going to be rough for almost every new entrepreneur, particularly in the consumer web space.

No wonder we resort to the wand.

Making Magic

Here’s the rub: to win at the game of company-making, you have to believe in magic.

Why? Because by far the most important and powerful customer acquisition tactic is building a product that people love. And this — this is actually a matter of magic. It is something magical about what you make that will create true love and deep, sustainable distribution. The other tactics are important — and sometimes become the key to growth — but in almost no consumer product cases can they be relied upon.

To be clear: blind faith will destroy you almost every time. The key is to KNOW what magic you are believing in and to seek to move from magic to realism as quickly as possible. If you can’t bridge this gap, then you need to pivot. Magical thinking too-long harbored is failure in the works. But without magical thinking — in almost every case — you won’t be able to get started.

Instagram is a great example of this. I don’t know what their early thoughts about distribution were, but I can tell you right now that there is no customer acquisition plan they could have made that, when faced with the PainMath crucible, wouldn’t have yielded a quick sprint for the woods. What made them explode was the magic of the product. Instagram made everyday people into artists. And basically everyone in the early 3rd millennium crafts epoch of which we’re all part wants to be an artist or craftsperson. Instagram gave people a magical experience, transforming them, and this generated tremendous — magical—growth.

At HowAboutWe we created a new way to date based on the incredibly obvious idea that online dating interactions should be based on getting offline. We made dating about actually going on dates, in the real world. This was the newest dating idea since eharmony’s no-search matching algorithm. And there was a magic in it, in scrolling through a stream of people saying the romantic things they wanted to do. This created high conversion rates, which let us advertise increasingly profitably. This small bit of magic (and I have no presumptions here) allowed us to close biz dev deals with real distribution potential. It allowed content, press, and word-of-mouth to be above average contributors to growth.

Tinder has achieved this to a far more dramatic extent: swipe right
mutual match
magic! Safe, hot, addictive magic. This has been the absolute key to their growth.

Likewise with every other great consumer product for which the PainMath equation yielded hopelessness: magic has, ironically, been the solution.

It’s a lovely catch 22. It’s magical thinking that causes nearly every consumer web startup to fail. And yet it’s magic that’s at the root of customer love — and thus at the root of truly successful customer acquisition strategies.

You can’t believe in magic. But occasionally, you can make it.

Written by Aaron Schildkrout

October 13th, 2014 at 10:46 am

Posted in Uncategorized

Mobile retention benchmarks for 2014 vs 2013 show a 50% drop in D1 retention (Guest post)

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[Andrew: There’s very little data out there on mobile, and so in the last few weeks, I’ve had guest posts on the % of users who opt-in to push notifications, as well as clickthrough rates of push. Today, I have some new metrics around retention, including for the first time, a systematic study of D1/D7/D30 retention for mobile apps. This is released by Mack Flavelle of Tapstream, who you can follow on Twitter]

Mack Flavelle, Tapstream:

As most app developers know, retaining new users is hard and some would say it gets harder every year. At Tapstream we wanted to test that notion so we looked at anonymized aggregates of attribution data collected by our platform as our customers acquire and engage their mobile users. The steep decline of engagement rates year over year was a surprise even to us.

We compared data from May 2013 to data from May 2014. To give you some context, in May 2013 an average app developer would retain about a quarter of their users a day after acquisition.

This is what keeps marketers up at nights: it means that three quarters of their acquired users didn’t stick around even for one day.

But when compared to May 2014, 25.5% retention on day one suddenly started looking very good:

Tapstream-User-Retention-Report-graph-01-large

On average only 14% of users stuck around a day after downloading an app. That is less than one in seven users. Those are abysmal rates by any measure.

This is exactly why we at Tapstream created Onboarding Links: to engage new users the moment they run the app for the first time. Reducing the app abandonment rates is becoming a crucial part of user acquisition.

Beyond day one

Another dimension of this data is looking further down the funnel to see how user retention fared at day 7 and day 30 after acquisition.

Again, the results are not encouraging:

Tapstream-User-Retention-Report-graph-02-large

Day 7 retention went from a respectable 23% to a measly 10% in May 2014, while Day 30 retention plummeted from 14% to 2.3% – a full 84% decrease.

To put this into perspective, the average next-day retention rate in May 2014 is almost the same (14.06%) as Day 30 retention rate a year ago (14.30%).

The story behind the numbers

What’s causing this dynamic to play out in the mobile app ecosystem is up for debate, but here are the most obvious culprits for why user retention in mobile has dropped 50% in the last year:

Incredibly low barrier to entry so no sunk cost loyalty

It takes about 10 seconds, a smattering of taps and usually zero dollars before a consumer is the proud new owner of your app. But easy come, easy go – there’s usually so little investment in your app there’s no pain of switching. As table stakes for app design get raised (remember when every app used default nave elements and controller views?) the next shiny thing to come along and grab consumer attention really is very shiny.

Incredibly low barrier to entry so no filter on user quality

When you have an expensive product and somebody buys it you can generally assume they did some research beforehand and had an idea of what they were getting into. They essentially self-filter to be loyal users by the time they engage with the product. With apps any such assumptions are out the window – there is nearly zero intent signalling by the consumer, even after they have “purchased” your app.

Disconnect between time of download and first open

There’s often a lack of context when a new user opens an app. This isn’t Christmas morning, unwrapping the box hoping for an NES. Between the moment somebody absent-mindedly downloads your app and opens it for the first time they’ve probably checked Facebook six times, Twitter three, bid on two obscure statues on Ebay and attended a funeral. The likelihood of them remembering the emotional switch that got them to download is reasonably slim.

Forklifting apps and other bad initial experiences

One of the tricks du jour, championed by the Japanese card games and the genre they inspired, is forklifting content into the app on first open. Sure the app itself is tiny, you don’t even need WiFi to get it, but then there’s a six-minute load time the first time you open it. This and other terrible first-run experiences can lead to serious drop off.

None of those explain the drastic decrease in retention over the last year. That may be a byproduct of the explosion in available apps. In fact most people would assume so, considering the huge number of apps available today.

The problem with that theory is that in May of 2013 nobody said

“I really wish there were more than half a million apps available, because I would definitely download and use more”

Arguably the variety of supply already far outstripped the demand.

So the relevant info becomes how many apps are being downloaded per device in May 2013 versus a year later. Are people downloading more apps and that’s why they’re not sticking around, or are they downloading better apps which is why they’re not sticking around?

Lucky for us one of the smartest minds in mobile started looking at that last year.

“Finally, these numbers are accelerating. Apple did 5bn downloads in the three months from December 2012 to March 2013, and then another 5bn from March to 15 May. The lack of precision means we can’t say this was double the rate, but the trend is clear, and it looks the same at Android
”

– Benedict Evans, from his blog.

Assuming Ben is right (and he often is) we can infer a gentle increase in the number of apps downloaded per phone, coupled with a higher quality of apps across the board has meant that the competition in the app store has become more fierce not only in terms of number of animals in the jungle but also the ferocity of those animals. Both of these factors end up driving down user retention in mobile apps versus a year ago.

AppleAppStoreStatistics

source: http://en.wikipedia.org/wiki/App_Store_(iOS)

Though this chart ends in 2012 and is iOS-specific we know that the trend continues but only steeper across both ecosystems as seen here and here.

About data used to generate this report:

Data was collected by Tapstream for months of May 2013 and May 2014 from over 100M devices. It is anonymized and it includes both iOS and Android apps, spanning many verticals from gaming to travel. The data excludes apps with DAUs of over 1M.

Written by Mack Flavelle

September 24th, 2014 at 11:03 am

Posted in Uncategorized

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

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[Andrew: A few weeks ago, the folks at Kahuna released some great data showing that up to 60% of users opt-out of push notifications. Now they’re releasing some new data on the click-through rates of push notifications, showing the differences between app categories and breaking down the reasons why some apps are so much stronger than others. They were gracious enough to share this data for the first time, as a guest post  on here – you can get in touch with the author, Alli Brian or Adam Marchick, Kahuna’s CEO.]

Push notification click-through rates via Alli Brian @ Kahuna

The best apps know how to use push notifications to their advantage. They’ve figure out how to make their service part of their users’ daily routine, and they leverage push as a vehicle to do this.

Recent data from Kahuna reveals that push engagement rates vary widely across industries – utility and financial services apps seeing the highest performance, and retail and social experiencing the worst. Here’s a comprehensive look at the state of push engagement rates, as well as a roadmap for getting back on track if your app is trailing behind.

Here’s the data:

push engagement graph

You can see that push engagement rates for utility and financial services notifications (40%) are nearly four times higher than for e-commerce & retail notifications (12%). While all industry notifications benefit from significantly higher engagement rate than they see from traditional email engagement, the wide discrepancy can result in significant revenue loss for underperforming apps.

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Apps That Are Winning
High push notification engagement rates are most often seen by apps with high frequency users. These apps use notifications to nudge their users do something that has become part of their regular routines. Utility, Financial Services and Ride Sharing apps lead the way – think about how normal it is to make these apps part of your daily or weekly routine. Looking for directions, watching your budget and taking a taxi are very regular activities, and using push to incentivize these actions is extremely effective. Here are a few examples:

1 - waze push

Utility apps have an obvious job to do – know your users daily routines and help them out when they appear to be getting into trouble. That’s what this push notification from Waze did – it was sent to Waze users  folks who regularly take the 280 freeway from San Francisco to Silicon Valley. And Waze users appreciated the heads up. Other high performing notifications from utility apps like job search or apartment rental services include new job listings or apartment availability alerts.

User engagement with these notifications (when appropriately timed) can be off the charts – as high as 80%.

2-levelmoney notification

 

Financial services apps also experience strong push engagement. By nature of the industry, money management is an important part of our daily lives. Hold the purse strings (or send a notification about them) and your users will be quick to respond. Take a look at this notification from Level Money. Their push engagement is off the chart.

But What If You’re Having A Harder Time Of It?
If you are in an industry that suffers from low push engagement rates, how do you overcome this? Retail, social and media apps typically have a more difficult time creating push notifications in a way that provides real user value. The good news: research shows that you can influence push engagement rates by using strategies that motivate users to integrate your app into their regular routines. Here are the top three techniques that will improve your notification response rates.

1. Find your Cadence:
Notification tolerance varies across app industries and individual users, so make the most of your notifications. Not all apps should be sending push notifications once a day, and engaged users have a vastly different tolerance for notifications than do new users or dormant users. Rather, it’s about sending the right message to the right person at the right time. In many cases, the elegance is in knowing when not to send a message. Check out the example below.

crunchyroll

 

Netflix does a great job of personalizing their notifications to the individual receiving them. Every user receives a unique message about the specific show they have been watching.  Rather than sending every user a notification every time a new episode of any show is released, consider one perfectly personalized notification. Crunchyroll could take a page out of their book.

Note: Sophisticated automation that limits and prioritizes the number of pushes each user is eligible to receive is the best way to achieve the appropriate cadence, given the numerous corner cases.

2. Make it personal
Don’t assume every user wants to hear about the same thing. Sending a notification that is valuable to the user isn’t just about a 10% coupon – it’s about presenting a relevant offer. The most compelling offer is one that contains information that the user deems important. Check out reactions to the notifications below.

 

 

fantasy football

 

These notifications both came from sports apps but elicited very different user responses.  The FIFA notification about the world cup was perceived as spam – simply because the app users to which it was sent was uninterested in the particular game mentioned in the message. In contract, the notification from SportsCenter that referenced the user’s specific fantasy football league was perceived as delightful content.

Worst of all is the mis-personalized push notification. We can’t emphasize enough how critical it is to gather accurate, person-level data to inform your notification. Use a unique identifier so even anonymous users will get accurately personalized notifications. Check out what happens if you send notifications to “devices”, not people.

groupon

 

As you can see above, device-based tracking does more harm than good. For example, if your wife borrows your phone and does a bit of browsing, all of a sudden you’ll be receiving notifications about irrelevant flash sales.

3. Timing is everything
Great timing should consider both user behavior and urgency. Notifications that include urgent information need to be sent at a time that is relevant to the context of the message, such as the notifications below.

refresh

united

As you can see, the notifications sent by Refresh and United Airlines both reference urgent and important information, and are tailored to the specific person receiving the message. As such, the response to the notifications are very positive.

For notifications that are not critically urgent, the goal is to minimize disruption and maximize delight. The horror stories about waking up to a mis-timed push notification abound, and users are not forgiving (see below).

9 - espn8- guardian

 

Considering every user keeps a different schedule, the only solution is to send push notifications at the time when each user is most likely to engage with your app. Kahuna data reveals that customizing delivery time based on user preference results in an average conversion uplift of 384%.

Great push is all about inspiring delight – facilitating a relevant and valuable app experience for your users and securing a prized place in their daily routines. Whether you’re in a high-performing industry like Utility or Financial Services or a low-performing one like Retail or Social, there is always room for improvement. Focus on understanding what your users value about your service and tailor your messages to their unique needs and interests. You’ll see push engagement skyrocket, and your users transform into rabid advocates.

Written by Alli Brian

September 16th, 2014 at 10:00 am

Posted in Uncategorized