@andrewchen

Subscribe · Featured · Recent · The Cold Start Problem 📘

Author Archive

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

without comments

Above: One of my favorite moments in 2018, with the a16z team and POTUS44. 

 

Dear readers,

Wow, so 2018 was a year with a lot of change – I started a new job, recommitted myself to writing (and tweeting), traveled a little too much, moved back down to Palo Alto (temporarily!), and much more. And in one of my favorite moments of the year, the office got swarmed by the Secret Service because Barack Obama came to visit – that was fun.

I’m also happy to redouble my efforts to writing and publish more, which I can do my new role as an investor at a16z. Previously, my pace was maybe once every other month – things were always too crazy at Uber, and it didn’t directly help my job there, so I couldn’t carve out time. These days, I consider writing as part of my work and dedicate time to it, blocked out on my calendar. As a result, I’ve been able to publish a few times a month lately – I want to continue pace into 2019!

In the spirit of trying something new, I decided to take all of my 2018 essays and turn it into an ebook PDF that you can read at your own leisure. It’s over 50 pages, includes all my essays, and alongside 200 slides in decks I published this year, you should have more than enough content to read through for a while. If you have feedback on this format, shoot me a tweet! And as always, you can get future updates by subscribing to the newsletter or follow me at @andrewchen.

Thank you for reading! And happy 2019.

Best,
Andrew

 

Download a PDF with 2018 essays

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

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

 

Links and notes

The red flags and magic numbers that investors look for in your startup’s metrics – 80 slide deck included! I put together a deck that summarizes the way that I think about evaluating the “quality” of growth for a new product. The deck unpacks a lot of different topics: How “growth accounting” metrics are great, but are lagging indicators. How to think about acquisition loops and engagement loops, and how to look for red flags like being overly dependent on a channel, or abusing notifications, to artificially boost metrics. The deck was designed for investors, but for every entrepreneur that wants to honestly evaluate where they are, it’s a good read too.

Consumer startups are awesome, and here’s what I’m looking for at a16z (70 slide deck). For the a16z annual summit, I put together a presentation introducing myself and what I’m excited about in the consumer investing world. It goes over historical precedents for give/get referrals, content marketing, and trying to bootstrap two-sided marketplaces. The deck also explains some of the big technologies and platforms coming down the pipe, and why I’m particularly excited about esports/gaming, offline experiences, and much more.

How to build a growth team – lessons from Uber, Hubspot, and others (50 slides). For a recent conference, I put together a series of lessons for companies that are looking to start growth teams. It starts simple, with the question of what growth teams are meant to solve, but also goes into organizational structure, ideal profiles/backgrounds for the team, how to ideate and prioritize projects, and more.

How startups die from their addiction to paid marketing. It’s so easy to get your product jumpstarted by buying ads to drive users, and hey, the LTVs and CAC ratios are working! But as I describe in this essay, it’s also easy to get addicted and ride the cost curves all the way up to the point where it makes no sense, and the degradation of these channels is a given.

What’s next for marketplace startups? Reinventing the $10 trillion service economy, that’s what. Co-authored with a16z partner Li Jin, we write about the next generation of marketplace startups. Whereas the previous generations have been about getting “stuff” to people, the next big opportunity will be to get services. The essay talks through why this has been so hard in the past, the benefits of having software intermediate the interactions, and the various ways that supply can get unlocked using technology platforms as the foundation.

Required reading for marketplace startups: The 20 best essays. This one’s not included in the PDF since it’s just a bunch of links, but wanted to include it here anyway. It’s a collection of links about marketplaces – from solving the cold start problem to metrics on marketplaces to specific case studies. It’s a must-read for anyone working in the space.

Why “Uber for X” startups failed: The supply side is king. One of my big lessons from Uber is that the supply side of the market is critical for any startup. I explain in this tweetstorm-turned-essay why the various “Uber for X” startups did a poor job satisfying that side of the market, even as the promise for us as consumers sounded great.

The Power User Curve: The best way to understand your most engaged users. At a16z, we often use frequency histograms – aka “Power User Curves” – to evaluate whether or not there’s a core community of users who are highly engaged. In this essay, co-authored by Li Jin from a16z, we break down what we look for, the variations on the curves you might see, and how this curve relates to the popular DAU/MAU we also ask for.

DAU/MAU is an important metric to measure engagement, but here’s where it fails. The DAU/MAU metric is an important measure of usage frequency, and was popularized by Facebook from the early days. This essay breaks down when its history, when it’s useful, and where it breaks down.

Conservation of Intent: The hidden reason why A/B tests aren’t as effective as they look. Everyone’s had the frustrating experience of running an A/B test, seeing a big lift, closing it out, and expecting the top level metrics to move a lot. But they don’t. This post explains why – “user intent” can be thought of as a fixed amount of energy as they approach the top of your funnels, and it’s hard to move it a lot.

The Startup Brand Fallacy: Why brand marketing is mostly useless for consumer startups. One of the opinions that always stirs up the hornet’s nest on Twitter is my opinion is that startups should do less brand marketing, PR, and other related activities and instead just focus on product/market fit and highly accountable performance metrics.

The Scooter Platform Play: Why scooter startups are important and strategic to the future of transportation. I’m a big fan of scooters, and here, I unpack why I’m excited about the entire category. Because scooters are used more frequently, and for shorter trips than rideshare, it creates a huge opportunity to be the “starting point” for transportation.

The IRL channel: Offline to online, Online to offline. As digital customer acquisition channels become saturated and easily copyable, one of the unique opportunities is the “IRL Channel” where people engage your product in their everyday, physical lives. Whether it’s a group of people walking around playing Pokemon Go, a microwave that has Alexa embedded, or scooters, this is one of the opportunities to combine our offline and online worlds.

I’m joining Andreessen Horowitz!. Here’s the initial announcement I made about joining a16z! Includes a few notes on how I know the folks at the firm, and what prompted my decision.

 

Podcasts
I didn’t include any of the podcast transcripts into the downloadable PDF, but wanted to include the essays here for completeness.

a16z Podcast: Why paid marketing sucks, Network effects, Viral Growth, and more. An interview with my a16z partner Jeff Jordan (who led our investments into Airbnb, Instacart, Pinterest, etc.) and we discuss some of the nuances of growing marketplaces, how to measure traction, and things to watch out for.

a16z Podcast: When Organic Growth Goes Enterprise. In this podcast, a16z partner Martin Casado and I talk about the intersection of enterprise sales and consumerized growth tactics. He’s on the enterprise team and I generally focus on consumer, but we look at a lot of companies together.

Product Hunt Podcast: Silicon Valley network effects, OKRs for your personal life, and more. My sister Ada (ex-Linkedin, SurveyMonkey) talk about life in the tech industry together, why we moved to the Bay Area, using OKRs to set goals, and a breadth of other topics.

Written by Andrew Chen

December 26th, 2018 at 9:00 am

Posted in Uncategorized

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

without comments

I recently did a podcast with Ryan Hoover, co-founder of Product Hunt and my sister Ada Chen Rekhi, previously SVP Marketing at Survey Monkey – here’s what we talk about:

  • The network effects that makes Silicon Valley what it is. The uniqueness of the Silicon Valley tech ecosystem, how network effects conspire to create a “rich get richer” situation for cities, and why new communication tools enabling distributed teams to work together across continents could mean that there will be no “next Silicon Valley.”
  • Big companies versus small ones. Ada shares her insights on the contrasting skill sets needed when working at a big company versus a small startup, after having herself gone from a small startup to a huge organization like LinkedIn back to a two-person startup with her husband.
  • Personal life OKRs. How to port the concept of OKRs — objectives and key results, a personnel management framework originated by legendary Intel CEO Andy Grove — to your personal life from your business (and why you would want to). We talk about you can use them to help manage your exercise, social life and relationship with your SO.

Of course, we also chat about some of our favorite products, including an app that lets you pop in to a luxury hotel for a few hours to shower or have a nap, a super cool way to greet visitors to your office, and a new app for emailing yourself.

Here it is below as an embed, but if you don’t see it inline, you can listen to the podcast via this link too. If you like the podcast, you can subscribe here. Thanks to Ryan for putting this together, including the transcript!

Some quotes from the episode

“When you’re executing at a small startup, or a small team, or just by yourself, it really comes down to ideating, picking and prioritizing, and then rolling up your sleeves and just getting things done as quickly as possible. It’s a night and day difference from a big company.” — Ada

“If you graph cities, there’s a power law: the biggest cities are really big and there’s this long tail of all these little tiny cities, and the reason for that is that there’s a network effect within cities. These ecosystems emerge because the designers are here, because the engineers are here, because the capital is here, because the marketing people are here, and on and on and on.” — Andrew

“When it comes to working at a large company, it’s much more cerebral and much more about the heart. You’re thinking about how to collaborate and communicate across a cross-functional team to get the initiative done: can you communicate what it’s about; can you motivate people to get it done; can you manage all the working pieces?” — Ada

“Either these network effects will continue to hold and the Bay Area will continue to be strong, or we make big structural shifts in how we organize teams and workforces and the network effects become less strong. But that doesn’t mean some other city becomes the next Silicon Valley, there won’t actually be a “next” Silicon Valley — it either continues or will just be distributed.” — Andrew

“The irony of it is that sometimes when you are working on projects with such large scale, because the skill set is so different, it actually feels like you’re not doing anything at all — you’re merely managing the appendages of the other groups and trying to make sure everyone is staying on track and executing.” — Ada

On joining a venture capital firm: “The idea that I would do the thing I want to do for fun as my full-time job feels like I’ve won an ice cream eating competition, and the prize is more ice cream.” — Andrew

Companies and Products Mentioned in This Episode

Transcript

Ryan: Hey everybody, this is Ryan Hoover with Product Hunt Radio and I’m here at Andreessen Horowitz down in Menlo Park with two people I’ve known for a little while now, two brothers and sisters, Andrew Chen and Ada Chen. This is the first brother and sister duo and hopefully the first of many. Thanks for having me over here. First off, Andrew, you joined Andreessen Horowitz, is it six months ago?

Andrew: Yeah, I think I’m on month five. I’m quickly reaching my half year mark, which has gone incredibly fast.

Ryan: Are you completely swamped with meetings and pitches or how has it changed since before Andreessen Horowitz?

Andrew: Yeah, so when I was at Uber I really loved meeting with startups and hearing about new ideas and staying in touch with the tech community, but I can only do it first thing in the morning and on weekends and it quickly filled up my schedule. So I would work at Uber and then I would do that [meet with founders] basically. The idea that I would do the thing that I wanted to do for fun, like as my full time job sort of feels like I’ve won an ice cream eating competition and the prize is more ice cream. I could do as much as I want, which is super awesome.

Ryan: Yeah. And so your, your background, just maybe for those that aren’t super familiar, you were at Uber right before this and then what’s your short version of your history?

Andrew: Yeah, yeah, totally. We were just talking about. So Ada and I, who’s my little sister, by the way, I want to clarify —

Ada: [laughter, eye-rolling and protestation]

Andrew: So we grew up in Seattle, and we both made our way to the Bay Area. Actually, the funny thing is my first job ever was actually in venture capital and was something I did right after college. Then after that I ended up working at a series of startups, I moved to the Bay Area 10 years ago to start my own company. I had actually met Marc and Ben [of Andreesen Horowitz] here and they actually led the seed round for a startup I was working on during the Facebook platform days when everyone was working on crazy viral apps.

Ryan: So that’s around when we met.

Andrew: Yeah, right. Yeah, that’s exactly, that’s right around when we met and they invested out of a Horowitz Andreessen Angel Fund, which was really funny because that would have been like H16N and so different. So, I met them and I worked on that for a while and ended up basically deciding that it’d be better to go to a larger organization, ended up at Uber running various growth teams there. So I spent three years there, like a really, really fun experience —

Ryan: Probably pretty wild too, right?

Andrew: — Yeah, the first 18 months was like really, really incredible startup like hockey stick growth, then the last 18 months were very eventful and everyone’s read about it in the news. So I don’t have to summarize that.

Ryan: Yeah, and Ada, you’ve had a pretty interesting journey at Microsoft, LinkedIn, Survey Monkey, and then a two-person startup with your husband.

Ada: Yeah. Yeah. Actually multiple two person startups as well as, I spent some time in the game space as well at Mochi Media. So, after I graduated from college, I was in Seattle at Microsoft for a year and Microsoft at the time I think was around 80,000-100,000 employees? Very, very structured. Worked in the ad center space and the online advertising space when search marketing was just becoming a thing and exactly 367 days or so later, moved out to the Bay Area in 2007 and so worked at a tiny little startup that had just raised Series A called Mochi Media, which was an online games ad network, and spent multiple years there after it was ultimately acquired by Shanda Games and then actually started my first company which was a contact management app called Connected. It was all about contact management without the work. We raised some funding for that, ultimately sold it to LinkedIn and I had my experience sort of joining LinkedIn as a just as a company that was really maturing at the time. They had just had their IPO. There are about 1700 employees and experienced hyper growth for the first time, focused on things like relaunching Connected as LinkedIn Contacts, growth, learning a lot about subscriptions and consumer SaaS and was recruited out of that to work at Survey Monkey, where I was SVP of Marketing and then recently left a couple of years back to start a new company that’s actually a husband and wife team with Sachin Rekhi and we started a company called Notejoy, which is a collaborative notes app for teams and so we’re really focused on, how do we actually create a fast and focused workspace for teams that gets them out of the noise of chat and email.

Ryan: Yeah, team collaboration and productivity is so important because if you can even improve collaboration and efficiency within a team by like even just 10 percent, it can have such a huge impact on both your productivity but also just like your joy.

Ada: That was actually part of the inspiration behind the name and it’s one of those things where even when you go to a small team like small tight-fitting teams or larger organizations, you see this friction today that still exists when it comes to communication and collaboration and just think about how many decrepit out-of-date Wikis you see and Google Docs that are sort of lost in the ether and then people joining and getting forwarded random emails from way back when because that’s the only place that knowledge lives, we were really thinking about how do we create something that tackles that and productivity has always been a huge space where I’ve been passionate about.

Ryan: This is a really broad question, but what’s it like working at such a big company like LinkedIn and Microsoft and others to now just you and your husband?

Ada: Yeah, I mean it’s hugely different and I think the biggest dimension where I would say working at a large company versus a small startup is different is that effective execution looks completely different. It’s a night and day difference. So when you’re executing at a small startup or a small team or even just by yourself, it really comes down to ideating, picking and prioritizing and then rolling up your sleeves and getting things done as quickly as possible from an execution pace. When it comes to working at a large company, it’s actually much more cerebral, right? And it’s much more in the heart. You’re actually thinking about how do you communicate and collaborate across the cross functional group of teams to get the initiative done. So can you communicate what it’s about? Can you motivate people to get it done? Can you manage all of the pieces?

Ada: And the irony of it is that sometimes when you’re working on projects with large scale, because the skillset is so different, it actually feels like you’re not doing anything at all yourself. You’re actually merely managing the appendages of all the other groups and trying to make sure that everyone’s staying on track and executing. And so as organizations scale, the execution work around how much collaboration it takes gets orders of magnitude greater in terms of how hard it is to get everyone aligned and marching in the same direction versus one person. And so, I really think that that’s one of the biggest differences, like you go to a startup to learn how to do things and maybe not very well and you go to a large company to see how things are done really well, but across a broad range of disciplines and functions and really see how the whole thing comes together as an engine sort of humming smoothly and operating.

Ryan: You mentioned communication is one skill or trait of people in larger companies. And Andrew, you used to blog, I mean you still do, but you used to blog a lot. That’s largely how I think you built a pretty massive following over the past decade or so. How did you even get into writing to begin with?

Andrew: So, first I love writing. That’s kind of the very first thing, and I was always one of these, teenagers where like, I kept a journal and I would like write in it and then delete it and then start a new one and literally I was the only audience. I just like enjoyed it myself. And so before starting my current professional blog, I think I had like three other blogs that I had started over the years. Just basically, just getting going and then deleting them and not really sticking with it over time.

Ryan: Why did you delete the previous blogs?

Andrew: Because you get bored with it, and you’re just kinda like, okay, I’m done, kind of thing. And then like I think on those it was literally, it’s like who’s reading it? It’s like Ada, like my parents, like —

Ada: — Fun fact about Andrew’s early blogs: he would actually forcibly subscribe us to the emails to make sure that we wouldn’t miss anything.

Ryan: That was before some of the ICANN email laws and certainly before GDPR.

Andrew: Yeah, right. Yeah, exactly. So I think, don’t tell a 20 year old who they can subscribe to a blog or not. So I really enjoyed that. And then when I moved to the Bay Area 10 years ago, what I basically decided to do was I was like, I’m gonna write down everything that I’m learning and I’m just gonna start, like going out and so the funny thing, I was learning so much in my first year that I was just writing a lot of, like pretty random snippets, some of it would be like a paragraph or two, and I would do it like, maybe twice a week or something like that. So like pretty often and that’s actually how I met Marc Andreessen originally. It turned out that he somehow randomly had stumbled on my blog via Hacker News and then through that, had ended up seeing some of my content and then he cold emailed me and that’s how I met him in 2007. So it was like a pretty random and amazing adventure but at the time, I was an entrepreneur in residence. I was a 24 year old entrepreneur in residence actually across the street from here, which is really funny. And one of the things that my colleagues would tell me is they would say like, why are you wasting your time blogging, you’re giving away all your best ideas? Like, what are you doing? Like, these are the secrets that you’re going to use to understand the thing. And at the time I was like, well, I’m never going to be a venture capitalist so like it doesn’t matter. And so as a result, I’m just going to give away all this stuff and then, and it’s obviously so ironic now that like, so much of the job is, is obviously, sharing your ideas and giving back to the community via Twitter and Medium and writing, writing essays and all that.

Ryan: Now that’s the norm.

Andrew: Yeah, right, exactly. Yeah. And in fact it was like, it would have been considered very contrarian I think to actually share a bunch. But anyway, so I’ve kept it up and I think, I’m, I’m well into the many, many hundreds of essays, over 10 years and I think at times I’ve taken like a hiatus, I think I took a two year hiatus in the middle. But like I think my goal now is really to publish like regularly, but to do it at the kind of like a high level of quality and to go deeper into ideas and to sort of break new concepts and new kinds of data to the community versus literally the, the early days it was like, it’d be like 500 words, like what did I learn today kind of thing.

Ryan: So I’m going to take a tie into that a little bit. You mentioned a term called, correct me if I’m wrong, but something along the lines of mullet startups, is that correct? Or do you remember that there’s a tweet in a conversation with you and some others around the distributed nature of companies?

Andrew: Oh yeah, okay, mullet, yes.

Ryan: Mullet startups is a catchy term because it’s a trend that we’ve identified. Product Hunt is a mullet startup I guess, we’re headquartered in San Francisco, but we have a distributed team.

Andrew: So The Economist’s cover for this week is Peak Valley, is, is it over in Silicon Valley?

Ryan: Right.

Andrew: So then I think there’s been, there’s been a lot of like really interesting dialogue around that. I think, and obviously a lot of it has to do with like housing and the Bay Area and there’s so much to unpack there, right? But I think that one of the reactions to it has been that we see many companies, with their leadership and their executives in the Bay Area, but when it comes to hiring engineers and designers and all sorts of other folks, then they’re much more likely to distribute the team, anywhere.

Ryan: Right.

Andrew: And so, yeah, to your point, this is sort of the mullet, because it’s sort of business in the front and party in the back kind of thing. AndI think it’s fascinating because it is actually just the reverse of one of the models that we’ve seen over the years where, for example, you’ll have a really strong technical team out of Paris or out of Israel or out of Singapore and they’ll get started, they’ll get funded and then they’ll realize, okay, hey, all of our customers are in the US, let’s move the CEO and the sales and marketing function to the Bay Area. And so you end up with the, the mullet, but just like kind of, but now you do it in reverse. Right. So I think that’s like a pretty interesting, reverse mullet, which is kind of an interesting trend these days.

Ryan: Yeah. So it’s just you two right now Ada at Notejoy, but if you were to, let’s say you needed to hire 10 people tomorrow, how would you approach it? Would you hire in the Bay Area or would you go remote?

Ada: Yeah, I mean that’s actually a fascinating question because it’s something that we’ve debated and thought about because things have changed so much. Not only from the costs, but then also, what is the ability for you to access and interact with people at scale, if they’re located in other places. We actually talked to this close friend of mine who’s a founder who, built his company and scaled it to revenue, pretty substantial revenue in the Bay Area. And he basically said to us, if I were to do it again, I believe that Silicon Valley is the worst place to self-fund a company or to start a company or even to have funding and try to build a team. And the biggest challenge that he was having was actually access to talent. I think it would really depend. I think on one hand I think we have really strong networks within the Bay Area and so it would be possible to kind of peel people off and that’s really how many startups start with their founding team. They pull people that they respect, that they work with, that have shared belief in to kind of create that initial nucleus of a team and that gets you to your first couple of headcount. So maybe we can get to 10 that way, but I do think that now when it’s coming to scale, like yeah, we would definitely be looking very closely at could we build a remote team and create a really distributed workforce for Notejoy.

Andrew: I think one of the distinctions is do you hire a lot of folks who are doing the kind of individual contributor work versus the managers because I do think that it ends up being really hard once you want to find the engineering director that’s managed 200 engineers to find that elsewhere, versus it being, kind of a main thing. So, so there’s a really interesting thing about cities, right? Which is like if you graph the population of cities and sort of like, stack rank them, you’ll see that there’s a power law in it. And like the biggest cities are really, really, really big and then there’s this like there’s this long tail of all these like little tiny cities. And the reason for that is that there’s really like a network effect within cities, right? Like, whether it’s show business in LA or it’s, finance in New York, like these ecosystems that emerge happen because, you end up with the designers who are here because the engineers are here because the marketing people are here because the capital is here because and on and on and on and all in one place. And so one of my colleagues here at Andreessen Horowitz, Darcy, had mentioned, he tweeted the idea that, one of two things will happen, right? Either these network effects continue to hold, meaning that then, actually the Bay Area will just continue to be what it is, right? Or, we actually make really interesting structural shifts in how we organize teams and workforces and all that stuff. In which case the network effects become less strong. But what that means is not that then all of a sudden, some other city like becomes a quote unquote the next Silicon Valley. It actually just means that everyone just lives where they want to live and eat and that’s that. And so, so if you believe that thesis, then you’d actually say there is no quote unquote next Silicon Valley. It either just continues or it’ll just be distributed. Right. I think that’s like a pretty interesting —

Ada: — I think you see that already emerging even within online communities. So when you think about where the discourse actually taking place, right, it’s taking place on Medium, it’s taking place on Twitter, it’s taking place on Product Hunt. We went through the experience of launching on Product Hunt and we were really amazed by how international the community was in contrast to the earlier startup Connected that we’d done several years before that. It may not be as important in the future for everyone to be physically co-located in the same space.

Ryan: Yeah. I’m super fascinated by this space and I’m actually committed to investing in a company that’s rethinking how people communicate with a distributed and remote team by video because we have a lot of different tools out there like Zoom and Google Hangouts and others and they are all kind of utilities in that they’re not much different from each other. It’s just like a big screen with your face on it and they’re rethinking, in a world where everyone is distributed or a group of people are distributed and another group of people are working from their home, how do you communicate more effectively? Yeah, I find it an interesting trend. I think one observation too is that the mullet strategy can work really well if your home base is where your customers are. So like you said Andrew earlier, like if you’re building an entertainment company, it’s probably good to have connections and live in LA so that you can be around those people and that can create a lot of serendipity in business partnerships and so on, but you don’t necessarily need your entire team there. You can also have them around the world. If you can build a culture that can facilitate working effectively remote. I’m pro-remote, if it makes sense for your company. Just saying, I’m slightly biased. It’s been five years now with Product Hunt running distributed.

Andrew: I think what’s hard is that basically there’s a whole class of interactions were being in person is actually better. And so if you’re meeting people for the first time in a partnership type scenario or a sales kind of scenario or in investing kind of scenario, like you do want to go old school, you do want to see the other person. and so I think in those cases, that’s where, that’s actually, I think where the network effects actually kick it right where then it’s like, okay, yeah, let’s get everyone clustered together, in those cases.

Ryan: SF in particular, is so dense. I mean, granted I’m driving down to Menlo Park, but it’s a small, short trip. Whereas LA and New York as well, it’s actually hard to have a lot of meetings within a five hour period because everyone is distributed across different locations. I’m curious to hear from an investing side, are you actively looking at sort of the future of work or distributed teams and looking to invest in companies building for that?

Andrew: Totally. I mean I think, I think there’s a couple different angles on the future of work that are, that are worth mentioning. So I think one is, I learned a ton of really, really interesting lessons at Uber, but I think one of the most important ones is that there are 80 million hourly workers in America. Right? And so these are folks that are often working multiple part time jobs, they don’t have steady sources of income, and what they’re often doing is they are driving Uber kind of between their other things, right. And so I think when you look at that, you’re like, wow, like the future of work has to encompass that industry, which is what are all the other kinds of interesting work that can actually happen? So like just to call out a couple really interesting ones: there’s a company called VIPKID which caters to — the consumer side is basically kids in China and then the supply side of the market is basically often like Midwestern like ex-teachers, stay at home moms, that kind of thing and they’re spending time on video together and they’re getting this whole experience around teaching and tutoring. And this is something that you can do from your home. Like super interesting. Right? There’s obviously lots of really interesting things happening in real estate. Our portfolio company, Airbnb obviously provides a lot of really important, supplemental income —

Ryan: If your HOA will actually allow it. I’m speaking from experience. They will not allow me, unfortunately to rent my place out. But it’s pretty typical, right?

Andrew: I mean, I think within all these different kinds of work, there’s obviously different rules that need to be be in place and that’s true for rideshare and that’s true for many other things as well. But I think that’s kind of one notion of a future of work that I think is important for us to consider even though it’s sort of outside the tech bubble a little bit, but it’s a really huge market. I think the flip side is, I’ve been an on again off again advisor to Dropbox for many years and I’ve known that team for a while and when you look at what these horizontal products are trying to do, it’s sort of like, we’re in a world where, if we can get all of these professional white collar workers — just make their jobs better, right? And just like make all these workflows, these really complicated workflows that you know for the most part are still being managed in spreadsheets and docs and chat and sort of like streamline all that. There’s tons and tons of opportunity across many different dimensions.

Ryan: So let’s talk about some apps or products you guys love. Ada, what’s on your home screen that people need to know about or is there a product you use maybe every day, every week that is bettering your life, changing your life?

Ada: Yeah, that’s a great question. So I am a huge fan of personal productivity and so, every year I make my New Year’s resolutions and one of my resolutions for example, was get to a point where I was working out three times a week and the challenge that I always had was the accountability, right? And tracking. And so this is probably not a particularly popular app, but one of my favorite apps for that is actually this iOS app called HabitShare. And it basically lets you share accountability, like share your to do list, like check I did it today and set a goal and make sure that you’re keeping track of how accountable you are against it. I’m a huge fan of that. And then I think Andrew actually introduced me to this, but I love this app called Captio as well and it’s a very quick way to email yourself and you wouldn’t think that it’s that many taps to email yourself to remember a quick idea. But after you experienced it, it’s pretty mind-blowing.

Andrew: Can we just go on a quick tangent about Ada’s goal setting strategy?

Ryan: Yes. This is one of the reasons why we had brothers and sisters on the show.

Andrew: Yeah. So one of the things that’s impressive, but also a little bit scary is the the level of — she actually uses OKRs, objectives and key results. There’s a whole book about it. In order to handle her goals, but this is the best part. Her husband also does the same, Sachin also does the same and they actually will score each other on the OKRs. Do you want to talk about this a little?

Ada: True story. So, both my husband and I love productivity. I mean, this is why we’ve been spending all of our time working on Notejoy, but I spent probably a decade of my life at this point thinking about productivity apps and so OKRs is actually something that we’ve adopted as a process from LinkedIn, which originally came from Google, which originally came from another company before that. It’s widely adopted.

Ryan: John Doerr actually wrote a book about it recently.

Ada: Yeah, that’s right. And so with objectives and key results, we actually found that it was a really good way of establishing goals that are both measurable as well as very distinct specific. And so we actually do annual OKRs is on a personal level, whether it’s around like personal infrastructure, like fitness or how to relate to your life —

Andrew: — You have like a KR that’s like hanging out with friends three times per month.

Ada: Yeah. So I actually had a reconnection OKR at one point where I basically made a list of people, 50 people that brought me joy that I was really engaged with, always wanted to get to know better, the bar was basically just interesting and that I hadn’t spoken to in four to six months and then the goal was basically to take a one month period and meet with half of them and it was actually one of the most energizing and transforming goals that I’d had because it was a great way to kind of have a focused effort at reconnecting with people and building relationships. And yeah, we score each other on it so we actually have business OKRs in terms of managing the business that we do on a quarterly basis and then I have annual OKRs around some of my goals such as like learning a new skill or whatever else. Thanks for bringing that up.

Andrew: I do not use OKRs to score anything in my life. Do you?

Ryan: Not really. I mean, that is extremely nerdy but also I’m kind of inspired because the beauty of OKRs is when you craft the right OKRs it’s binary, like you pass it or you didn’t and a lot of people they set goals like New Year’s resolutions and they’re like, I want to work out more and that’s their goal. And they end up not actually pursuing it oftentimes in part because it’s not specific. It’s like, well does that mean you need to work out three times a week, minimum, for the rest of the year, and what are your goals and what are your outcomes and expectations out of that?

Ada: Right. Yes. I actually tapped into this HabitShare app in addition to that and specifically with the fitness goal, it was actually, Q1 was like, okay, get to once a week, twice a week, Q3, is at three times a week. And so that’s actually how I’ve been tracking and achieving it.

Ryan: Love it.

Andrew: Okay. So one of my favorite things on Twitter is I, I tried to do this at least once a year where I will just screenshot my, my homescreen and then I’ll just ask everybody else to just do the same and then like reply and it’s really cool. First of all it’s a very personal thing what your home screen is and so I always have to look at it and be like, is there anything like weird on here I don’t want, a stealth beta company —

Ryan: — Right, right, exactly. Yeah, stuff like that.

Andrew: Exactly. And then, and then similarly like looking at other people’s homescreens are really interesting. Like occasionally you’ll see people where they’ll, they’ll like sort their homescreen by like color and that’s how they organize everything and I’m just like, that’s insane. Anyway, so I was going to mention some of the apps that are like on my home screen these days. So I think one, as Ada mentioned, there’s an app called Captio which is great but this morning actually you had tweeted something that the Fin team had come out with a new app called Nota Bene, which is sort of like Captio on steroids. So I actually just installed, I put it on my home screen, I’m actually really excited to try it.

Ryan: Yeah. What, what does it allow you to do for those that aren’t familiar with Kaptio?

Andrew: Right. So, basically both of these apps they allow you to, you basically open up the app, there’s a blank text screen, you type in whatever you want and then you just hit send and then it emails you. So that’s Captio. And then what Nota Bene does is it has a couple more aliases. It has things like, this is something that like I actually really want and need which is sending to my work email versus my home email. And then I might do like work email plus like admin, is a thing. And so I think that’s Fin’s hook to try to get you into the workflow, that way. But yeah. So, I think that that one I use all the time. I was mentioning that, for my first year on the job at Andreessen Horowitz, I moved down to downtown Palo Alto but I’m spending two days a week in the city and so one of the things that I’m finding is that, I’m trying different kinds of like, solutions for like, oh, if I want a place to hang out and do email, like what should I use? And so one of the things that I’ve been trying out over over the last couple months now has been Breather, which lets you rent , basically like a conference room that’s been built out and kind of doing meetings there. Another one is Spacious, which just got launched actually I think in the last couple of weeks. It’s a really cool concept. So what they do is they basically, you have these really high end restaurants, right? And like they have a very nice interior and all that stuff, but they’re basically closed the entire day all the way until like 5:00 PM. And so the idea is between nine to five or eight to five or whatever the hours are, can they actually just literally put like one person there and just have like coffee and water and then you use the interior of this beautiful restaurant. So one of the places in SF is the Press Club, right? Which is this great —

Ryan: — Yeah, great spot.

Andrew: It’s tons of space and so, you can basically just hang out in the press club during the day and it’s basically completely empty and it’s like —

Ryan: — How much does it cost typically?

Andrew: It’s like a membership basis. I think it’s like 90 bucks a month or something like that. And they have spots in Cole Valley and Hayes Valley and the Castro and a bunch of other places. Another one that’s kind of like this, it’s pretty interesting I think when I first heard this idea I like laughed because I thought it was so funny, but, but now I actually have like used it like in a real way which is a company called Recharge and that it lets you rent hotels by the minute. And so you’re kind of like, what is the use case for that? The actual use case is, you need a place to make a phone call. Right. And so in the same way that like Breather or Spacious, it’s like, it’s sort of like, oh well you have all this built-in inventory and like maybe you hold out a room —

Ryan: — Or maybe just a shower. Some people are traveling, flying. I just need in between meetings to have a shower.

Andrew: Right, right. And then you can pay like one fourth the price of a hotel, and like that actually is like kind of useful. So anyway, those are fun. I think I’m now up to, I’m trying one of these like kind of almost on a weekly basis which has been pretty cool.

Andrew: We’re going to talk about Reddit a little bit? Yeah, because I’m like a daily active user. I don’t check it all the time, like I’d probably check it not as often as Twitter, but it’s like, it’s like my default late night read, when I want to just like chill out,

Ryan: You can just turn your brain off. It’s different than Twitter, it’s different from any other community. I actually got a book, a pre-release book by Christine. She’s been writing about this for years now, about the history of Reddit. It’s about 400-500 pages long. You would enjoy it.

Andrew: Well, I just bought a Alexis’s book that he had written a couple years ago, so yeah. I’ve been into Reddit for like several years now, but it’s funny, one of my good friends, Noah Kagan was literally like, you need to go to your favorite subreddits and sign up and follow and actually set up your Reddit and then it’s amazing.

Ryan: Kind of like Twitter.

Andrew: Exactly. Right. Right. And I think I didn’t get it because I would go to the homepage and I would kind of be like, well this is kinda fun, is this like another cat memes website or whatever? I think the one that I want to recommend that folks start out with is actually if you just go to the /bestof subreddit. So it’s like reddit.com/r/bestof. Then it basically just links to some of the best comments on Reddit over the last 24 hours and then you can actually sort it by the last month or something like that. So I think anyway, that that’s a good one. But yeah, I follow a ton of different subreddits at this point. The other one I really like is r/firstworldanarchists and that’s basically, when you have like a sign that’s like don’t step here and then someone takes a photo, like they’re stepping in the grass or whatever, that’s like my kind of like rule breaking. Anyway. So yeah, so there’s that. And then the one, one, one last thing I’ll mention is Bose Quiet Comfort — the wireless noise canceling things as I’ve been commuting from SF and Palo Alto are like amazing. And so they actually have an app that lets you like adjust, how much noise cancellation you want so I use it all the time.

Ryan: These are the ones that just go in your ear, right? Kinda like Airpods?

Andrew: Right. The battery actually hangs on your neck and then they go into your ear. So they’re not over-ear, they’re the ones that go in. But I actually, I have both and yeah, I prefer this one the most and I like bring it with me everywhere at this point.

Ryan: Do you live on, on Reddit at all Ada?

Ada: I’m probably a weekly active user. I check it in and I just like to see r/bestof and see what people are talking about. But you’re right. I mean it’s such a, it’s such a passive way to kind of see a lot of interesting content stream by.

Ryan: What’s also nice about Reddit in a world where content is delivered by algorithms and people you follow and things like that, like on Twitter and Facebook and so on. It’s kind of refreshing to go to a place like Reddit where you can get out of your bubble and you can explore the weirdest stuff if you really want to. It’s not socially curated, it’s not personally curated necessarily to like everything that Reddit knows about you, but it’s really a community of people geeking out about this stuff. One of my favorite subreddits, I don’t visit Reddit all that often, I try to actually avoid it because it’s kind of a rabbit hole, but one subreddit I love is called r/internetisbeautiful and we’ll find there’s actually a lot of really weird projects and websites and little hacks that people are building and it’s almost always delightful. You go there and find something weird, just some crazy website that someone created and the name, r/internetisbeautiful is just such a wonderful feeling. It’s really, really well crafted subreddit. Cool. Thanks for having me over here. By the way, is there anything else you’d like to to plug — anything in the portfolio maybe Andrew? Or anything at Notejoy, Ada?

Andrew: We were talking about how when when you were just signing in at Andreessen Horowitz, I was like, oh, you should install the Envoy app. Yeah. Because it makes it so much easier. You literally get a photo of your face on it and you just tap on it and then like, and you go in and it works over Bluetooth. So anyway, so I always like to plug Envoy, it’s one of our portfolio companies.

Ryan: I mentioned this earlier, but we didn’t get into it. Envoy is such an interesting company. I’m fascinated by social graphs and when you look at the uniqueness of a social graph like Twitter and Facebook and LinkedIn and, and I think some of the most interesting ones that are less talked about is one, like Slack is very interesting, like the people that are in your Slack team or the people that you actually work with, no one else has access to that sort of graph right now. And Envoy is also really fascinating. It’s like a graph of the people and business partners and people that you’re meeting at your company. No one else has that.

Andrew: Well, I mean I think it wouldn’t surprise you to know that the CEO, Larry, was actually really early employee at Twitter. Right. And so a lot of the sort of thinking around both the product experience and just like how nice it is. I think we’ve gotten to, to this whole trend now where your office and your office experience is this extension of your brand. And so now people like really care about it. So they don’t want like this kind of kludgy pen and paper thing. And that product experience is so important. But to your point on the graph is, totally agree — I think that’s also one of the reasons why it’s like, Envoy’s pretty special in sort of the pantheon of these like B2B companies in that it actually grows virally. Like the way that the company grows is that people experience it and they’re like wow, this is really nice. And then as soon as they go back to their office they’re like we should have one. And there’s really not that many products that grow that way. Dropbox grows that way, Slack grows that way. It’s like a viral B2B thing. And so I think, in, in the same way it’s like that, that graph means that not only is it spreading virally and enables that spread, but then the other part I think is wow, okay, cool, you get a list of all the people that are visiting and who they’re visiting and then that can then feed into like all your other like, offline data.

Ryan: Or CRM.

Andrew: Yeah, so you take your offline data and turn it into online stuff and it’s another touchpoint which is super important.

Ryan: You don’t have Envoy at the headquarters, Ada?

Ada: No.

Andrew: I think she has a doorbell.

Ryan: Old school doorbell. So yeah, what’s down the pipe?

Ada: What’s new on the pipe is that we’ve actually been doing a ton of mobile enhancements and so we’re actually bringing Android very soon in terms of bringing it out as an app. It’s interesting now because the bar for consumer and business apps is so much higher than it used to be, right? Like it’s actually really important to be fully multi-platform, so we’ve always had Mac and PC and then the browser and we’ve had iOS but it’s really exciting to actually bring that to Android because that’s been a big factor for a lot of teams that are trying to adopt Notejoy as an overall group and so yeah, just cranking and hard at work.

Ryan: Cool. Awesome. Well thanks for coming on. This will be the first of hopefully many brother-sister Product Hunt Radio shows.

Andrew: Awesome. Thanks for having us.

Ada: Thanks.

Written by Andrew Chen

December 13th, 2018 at 10:00 am

Posted in Uncategorized

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

without comments


Above: New technology has always captivated consumers!

Dear readers,

I’m often asked- so what kind of startups are you investing in at Andreessen Horowitz? And since I’m focused mostly on consumer companies – is there anything exciting happening? After all, if we’re “between” platforms, and there isn’t something as big as the iPhone coming up, is there anything interesting left?

I’m really bullish about what’s around the corner – and I want to unpack what I’m looking for, how I’ve drawn insights from history, and what’s around the corner. In the 70 slide deck below, I cover a couple key concepts:

  • Accelerating technology adoption. Why the telephone took 50+ years to adopt, but the mobile phone was <10 years
  • Three historical examples and their modern antecedents
    • Content marketing. The origin of the Michelin Guide and why content marketing still works
    • Viral growth. How chain letters were invented and rethinking its effectiveness in the framework of viral growth
    • Marketplaces. How to bootstrap marketplace businesses and the cold-start problem, and what the story of toothpaste can tell us about that
  • The most exciting new technologies coming around the corner, and how to evaluate them for producing new startups
    • Video. Why video is big, and will get even bigger
    • Offline. How the offline-to-online channel has been used by scooters and rideshare, to great effect
  • My investing thesis. The intersection of growth hacking, new tech, and pre-existing consumer motivations
  • Closing. Technology changes, but people stay the same

I presented all of this at the Andreessen Horowitz Summit in 2018, which gathers our portfolio companies, partners, LPs, and close friends. It’s great to be able to publish it here as well. Hope you enjoy it.

Another note is that this is closely related to, and complimentary, to this deck: The red flags and magic numbers that investors look for in your startup’s metrics. If the below deck is the macro view of how I’m looking at markets, industries, and technologies, then the metrics deck gives my POV on how to diligence each company.

Finally, before I jump in, it’s true that I talk about what sectors I’m into as well – and here are few areas I’m digging into:

  • Unbundling my Uber expertise
    • Marketplaces (particularly the $10T service economy – more on that here)
    • Transportation and travel
    • The future of work (Bottoms up SaaS, full-stack autonomy, etc)
  • Next generation entertainment and networks of people+content
    • esports, gaming, virtual worlds
    • Reinventing traditional media (Podcasting, eBooks, etc)
    • Content creator / influencer economy
  • … plus, anything else that looks like a network with network effects

Obviously if you are working on anything in this area, and have some traction in the US, would love to talk more. Get an intro through your investors and come find me! Happy to chat.

Thanks again!

Andrew
San Francisco, CA

Download the deck

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

The Deck

Today, we’re going to talk about what’s exciting and new in consumer startups, and what I’m investing in at Andreessen Horowitz. But to hit this topic, I want to start by zooming out. Here’s a graph of many of the new consumer technologies that have been introduced in the US over the past 100 years. The X-axis is years, and the Y-axis is the % of US households that tech reached.

Each line represents a different technology – you can see the car, the radio, air conditioning, the microwave, and so on. Lots of important consumer tech that was new at some point. But you also see something pretty interesting – some of the most important tech took decades to adopt.

Let’s take a look at the telephone, in particular.

Above: Now, remember that the motivation of communicating with your friends and family has been around since the dawn of time. But when you look at the phone, it took 5 decades to break into the majority of US households. Wow! And of course at the time, there were other technologies competing for engagement – there was the telegraph, postal mail, etc. In fact, early on the phone was marketed as the “speaking telegraph.” Nevertheless, for something we now take for granted, several decades is a long time.

Why is that?

Above: Here’s why. These were the kinds of instructions that had to be packaged alongside the Bell Telephone System – how to hold the phone, which side went to your ear and which side was next to your mouth. And if someone called, you were supposed to say hello!

While the human motivation was there to speak to friends and family, we had to build the behavior from scratch.

Above: Let’s contrast this to the cell phone, which took a much shorter time to conquer the market. And in fact, if you were to think about the next few years after it hit mass penetration in the US households, we also know it hit several billion active handsets worldwide. Some developing countries are truly mobile-first – they have mobile phones before they have computers, land lines, or reliable access to water.

Above: Each new technology is able to build on each other. You can use radio ads to market the TV. You can use the TV to market mobile phone services. And so we see an accelerating adoption rate of new technology introduction.

What a time to be alive! It’s only going to go faster.

Above: And yet, even with the backdrop of all of these new technologies, we are still fundamentally the same people from many eras ago. We haven’t physically changed much.

Above: We are the same humans who painted the walls of caves, because we love art, and love creativity.

Above: We are the same people who built massive theaters, because we love to be entertained.

We took selfies as soon as the technology allowed.

 

… and it turns out, we have always loved scooters. In fact, the US Postal Service tried these gas-powered units out to deliver mail a century ago.

Above: In other words, while technology changes rapidly, people stay the same. And that’s the opportunity.

When we spot new startups who can take advantage of a moment in time, at the intersection of new technology, pre-existing human motivation, and can find a clever growth trick to get going – that’s exciting. That gets my attention.

Let’s look at a couple historical examples where these kinds of intersections have happened, and also some modern echoes of their impact.

First example, we’ll go back in time.

Above: It’s 1900, and there’s a new technology – cars. But there’s only 3,000 automobiles in France, because they’re hand-made, they break down all the time, and it’s not actually clear why they are superior to horses.

Above: They look like this.

In the introduction of these new technology, there forms an ecosystem of new companies that stand to benefit from more cars on the road. There’s gas stations, there’s maintenance businesses, and there’s also tires.

Above: One of these companies you’re familiar with – they’re called the Michelin Tire Company. And certainly you recognize the Michelin Man on the left over here.

Now they have a tough problem to get their business to grow. Remember, there’s only 3,000 cars. Selling tires is hard because what you actually need is to get car owners to drive more, and to create more car owners as well. That’s tricky! It’s a very indirect problem that requires a clever solution.

What was Michelin’s solution? We’re all familiar with the answer: They created the Michelin Guide.

Above: This small red book is one of the first Michelin Guides, given out with the subtitle, “Free for Drivers.”

This is a really clever effort for Michelin, because by packaging all the destination restaurants across France, and eventually Europe and the world, they gave people a reason to drive. And for existing automobile-owners, a reason to visit more towns and drive longer. And in fact, the Michelin Guide is so successful that many of us today don’t have much need for their tires, but certainly rely on their recommended restaurants.

This is a great example of a “hack” that gets their core business growing. And today, we’d call it Content Marketing, and it still works.

Let’s us a contemporary example that builds on their content marketing push.

Google wants us all to be engaged in their mobile apps, search functions, and other properties – but they want to be relevant in our lives in other ways too, for example in our culture and media. One way Google does this is that they have a great app, called the “Google Arts and Culture” app, which demonstrates the world’s great works of art. They have virtual tours of museums, 360 degree photos, videos, and more.

But the best feature they built is the “take a selfie and see what kind of famous artwork you resemble” feature. As we saw earlier, we’ve always been obsessed with selfies. So this was successful. Very, very successful.

Above: We saw famous people like Kumail from HBO’s Silicon Valley take selfies and publish them – this is a pretty good one! And not only did celebrities  share their photos, many everyday consumers did too. A lot of them.

This was so viral, in fact, that eventually this app was downloaded millions of times.

In early 2018, it became the most downloaded app at that time. More than YouTube. More than Facebook. Wow! That’s fantastic.

But what does this have to do with Google? This is such an indirect way for Google to tell their message, and to engage us in their products. But it’s a much fancier form of content marketing that lives in a mobile app. It worked for Michelin a hundred years ago, and it works for Google today too.

The second example I’ll talk about is more of a consumer user-generated content play. It starts in 1775.

Above: In 1775, the US Postal Service was founded!

You may know that this guy – Benjamin Franklin – started it.

One way to think of the service, in contemporary jargon: The postal service was a new user-to-user communications platform that allows millions of consumers to communicate with each other for the first time. Before social media, and before email, the postal service let people do what we now take for granted.

There are, of course, a lot of reasons to use the postal service – there’s personal correspondence, bills, advertising, and many other uses. But one of the major uses of mail came unexpectedly, and introduced millions of people to new ways to use mail – the chain letter! It turns out sometimes, as a platform, you’re super lucky, and your customers find new ways to engage and grow your service for you.

In the photo above, you can see one of the world’s first chain letters. When enterprising individuals started to experiment with postal mail, they figured out they could get a ton of engagement when they worded the letters a certain way, and promised certain things.

The variant above behaved like the following: When you received one, it asked you to remove the top name, add your name to the bottom of the list, and mail a new dime to everyone on the list. And then to share the chain letter with 5 of your friends within 3 days. Specific, clear call-to-actions. If you followed the instructions in the letter, you would receive 15,625 letters with $1,500+ in dimes. In today’s dollars, this is about $33,000. What a great outcome! For folks who’d never seen this kind of letter, and who saw their friends slowly getting rich – one dime at a time – this was enticing.

These chain letters worked. In fact, they worked really well – too well. Within the first few months, this chain letter reached tens of millions of copies. It eventually became so successful that the US Postal Service had to shut it all down.

And thus, to this day, chain letters are illegal to send on the US Postal Service!

The chain letter was a clever creation, of course, but today we’d just call this viral user acquisition. Getting people to tell their friends and family to spread the word is something that’s always worked – and it works today as well! The modern version is far more sophisticated.

Above: Companies like Airbnb and Uber have referral programs, where you can send credits to your friends that can be redeemed on their trips. And you get credits in your account when they accept it too – it’s a reciprocal give/get program. Of course, we’ve improved the whole thing based on the latest tech. It integrates into Facebook Messenger and your email addressbook. It has tracking codes so you can see how well it circulates, and you A/B test the whole thing to make sure it’s highly optimized to be viral and spread.

Yet in the end, the mechanics are the same – you can get people to tell their friends and family, if you make it enticing for them, and also for yourself.

The last historical example I’ll use is a story about the “cold start” problem – but we’ll use grocery stories and toothpaste as our example.

In the early 1900s, it was the dawn of consumer packaged goods companies, who were still figuring out their distribution models. Amazingly, many of the household goods that we’re now familiar with hadn’t been invented yet. People still weren’t really bathing on a regular basis. It was an earlier, simpler time for CPG companies.

The amazing thing about the story of toothpaste – the above is a box by Pepsodent – is that toothpaste had to be invented. Even more amazing, people needed to be taught how to use toothpaste, and why.

You could advertise to spread the word with consumers, of course, but there was a second problem: How do you get the toothpaste in the hands of consumers?

Above: Across the US, there were tons of “mom and pop” grocery stores like these. They needed to carry the toothpaste so that consumers could come in and buy them. The problem is, they don’t want to stock the toothpaste (which they would need to buy) if consumers weren’t asking for it. And of course, consumers wouldn’t ask for the product – at least you couldn’t count on it – unless it was in stock.

This is a classic chicken and egg problem. So how do you solve this?

Above: The answer was simple: Advertising, and lots of it, and coupons too. First, it was important for the CPG companies to convince consumers that they had a yucky film on their teeth that could only be solved with toothpaste. And then they offered them coupons to come and try it.

Before running a big campaign like this, they could go to the grocery stores and say, “We’re about to create a ton of consumer demand! Folks are going to come in and ask for toothpaste, so now’s the time to stock it.” This solves the chicken and egg.

Solving the chicken and egg problem was hard then, and it’s hard now. And yet it’s something that every marketplace company has to do.

Above: If this example sounds familiar, it’s because it was used recently by our portfolio company Instacart too. Today, Instacart has deep relationships with the nation’s top grocers. But when they first got started, they just built a great app, got consumers buying things, and started dispatching shoppers to pick up their orders. As more demand was built, eventually Instacart could approach the grocery chains and set up a formal partnership to make the experience even better.

A hundred years ago, CPGs used advertising and coupons to drive demand to solve their chicken and egg. Today, startups use awesome mobile apps to create demand and to solve the same problem. It still works.

Above: As you might imagine, you could go for hours on these kinds of historical examples. There are a ton of them.

The important, core concept here is simple:

  • When there are new technologies and platforms hitting scale…
  • … and products tap in pre-existing consumer motivations
  • … and there are “growth hacks” that create slingshot opportunities to quickly and scalable grow

At the intersection of these three factors, amazing things can happen.

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

So it’s my goal to spot new products that look like this, and to evaluate them. (In a separate deck, I talk more about the extensive techniques from the metrics and growth function that can be used to evaluate startups).

Of course, the first of the triumvirate is critical – and that is new technologies and platforms. And there are a ton of exciting ones right around the corner. But let’s first cover many of the new platforms that have hit major scale.

We have IoT devices, particularly voice assistants that live inside Google Homes and Amazon Echoes.

We have over a hundred million units of smart TV devices that combine media and computing. That’s exciting.

We have platforms like YouTube with over a billion active users. Wow!

And wearables with hundreds of millions of units that sometimes run apps themselves, or help augment experiences on your phone.

 

Not only there many platforms at scale, but it’s exciting to see a couple emerging categories as well.

There are Nintendo Switches, which have sold tens of millions of units. They focus on games, of course, but you can run cloud-connected games like Fortnite. And perhaps people will creative about what kinds of other apps work too.

All modern appliances are adding internet-connectivity. Fridges are an obvious one, but we all have seen Amazon add Alexa to microwaves too. What’s next after that??

There continue to be companies working on smart glasses. Above is from North, who are making Augmented Reality inside a pair of glasses that almost look identical to the ones you already have on your face. I think this will be a really compelling category in the next decade.

And finally, as autonomous cars come out, we’ll have to rethink the entire driving experience to mostly be a riding experience. I expect a lot more video, gaming, and interactive media in the car. This is an emerging area too over the next decade.

So there are a ton of new technologies right around the corner. We just need one or two to break out, in addition to the surefire opportunities around marketplaces, B2B, mobile, and other existing categories.

The question is: Which platforms am I most excited about? What are examples of growth tactics that are working now that are super clever? In the intersection of the three things I mentioned earlier, what would I zoom in on?

Let’s talk through a couple.

The first category of products I’d call “Video Native” products.

Above: The new technology at scale is video. We already talked about how big it is – but let’s give a really concrete example.

You all remember Gangnam Style, our favorite Korean pop song from 2013. And we’ve all heard Despacito (even if you don’t know you have). Here’s the link if you need a refresher.

Both videos are very popular, and have been viewed billions of times.

It took Gangnam Style nearly 5 years to be viewed three billion times. It’s an amazing feat, but even more amazing is that it took Despacito just a year!

Today, as of this writing, Depacito has been viewed 5.7 billion times. Wow.

 

Video is huge, but not just for music videos. It can be used by many other forms of entertainment and media to boost their growth as well.

My hypothesis: One of the big opportunities right now is that any product that automatically generates video when users engage will create more video sharing activity, thus more viral acquisition and engagement.

No wonder eSports are such a big deal right now. And it’s one of the reasons I’ve been spending time in this space.

When you look at a game like League of Legends, created by Riot Games, you see some amazing stats.

The 2017 League of Legends championship was viewed by over 100 million live viewers. Compare that to Wimbeldon, which had a mere 9.4 million viewers. That’s over 10X. And yet we think of video games as a vertical niche – it’s certainly not. It’s mainstream, and it’s big.

One startup I’m excited about is Sandbox VR and the category of location-based virtual reality (LBVR). I think this is the format that is most likely to break virtual reality into the mainstream – not in-home. Sandbox asks for people to bring their friends, as a group, to a retail location to use what I think is the best VR experience on the planet. You wear haptic suits, there’s a motion capture system, props, and special effects. It’s next level.

It’s an incredible experience – you can see the trailer here and try it in San Mateo here.

With your friends, you fight pirates and zombies. And pirate zombies. They currently have two games, with more coming.

The whole experience is cool, but part of the reason I’m excited about the company is that they have an awesome growth tactic that connects directly to video.

Above: Every time you go with friends, it’s an event – you take a ton of pictures and video. In fact, Sandbox helps you generate a mixed reality video with that’s shareable. You publish it on Facebook and other social media, and it looks like so much fun that friends want to try it too. All of this generates viral growth! It’s a fantastic growth tactic.

It’s no wonder that one of the company’s slogans is – “Fun to play, but fun to watch too.”

The second example I want to use is “Offline to Online.” We all know about going online to offline, which has been enabled by companies like Amazon, eBay, and more. You can think of the first generation of marketplaces and internet products as filling this niche. However, this is the other way around.

Above: The fundamental technology shift that’s allowing this is everything to do with maps, GPS, and AR – all in your pocket, on your mobile phone. This enables both new product experiences but also new growth tactics too.

 

The growth hack I have in mind is that you can now have highly visible offline experiences that then drive people towards using their app. As online channels become saturated – Facebook and Google ads are expensive, there are literally millions of apps in the app store – it turns out the real world gets pretty attractive.

Let’s look at some examples:

First, there’s Pokemon Go, by Niantic. You see yourself on a map, with Pokemon all around you. Collect them all! It’s fun, but it also means that people are watching others play. Sometimes this is a small reminder, if you see a small group gathered trying to collect a rare Pokemon.

But sometimes it gets big – really big.

Here’s a photo of tends of thousands of people who showed up for a Pokemon event. This is just one example, but Niantic does events all over the world, all the time.

Of course, rideshare looks like this too. Who can forget the pink mustaches from across the city that remind us to try and use Lyft?

Transportation is an intrinsically viral product – they are social activities. You bring your friends and loved ones in the car with you, to share the costs. Even the fully utilitarian version – going from point A to point B – can be social, since there’s often a person on the other side. These mean that the rideshare companies all benefit from significant viral and organic traffic to their products.

Scooters are another great example. Our portfolio company Lime has their scooters deployed across a city, and each scooter is literally a mini-billboard to try it out. And the first time you’ve seen someone ride a scooter, they probably had a big smile on their face! It looks fun. And because of that, they benefit from the offline to online effect.

Above: So these are two quick examples – I’m keeping my eye on more, but again, it’s all about the intersection of new tech, existing consumer behavior, and an insight about growth. If you can get these three together, it’s super interesting.

There are a ton of new plaforms hitting scale. I’m also interested in GSuite, which is hitting critical mass across SMBs and enterprises. I mentioned Alexa. You can see products like Twitch and Tik Tok growing quickly – with the former adding extensions and the ability for apps to integrate. And Minecraft and Roblox are fascinating virtual worlds that bundle social networks and content together in one place – also fascinating to track.

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

As these platforms emerge, there will be new startups can be built adjacent or on top of them.

I’m very excited about what’s going on in consumer – and am excited to see what people build.

Again, here’s my investing framework – 1, 2, and 3. It’s important to see the intersection.

The important idea here is simple:

Technology changes, but people stay the same. If we can spot the new, breakthrough products that can grow at the intersection of this technological change, and peoples’ behaviors, then we’ll build the next generation of startups. (And yes, we have really always loved selfies – it’s not a new thing).

Written by Andrew Chen

December 10th, 2018 at 8:00 am

Posted in Uncategorized

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

without comments

[Dear readers, this essay is on the future of marketplaces. Is there still room for marketplace startups to innovate? We answer, emphatically, yes! Am excited to share a vision on the past and future of the service economy, in a collaboration by my a16z colleague Li Jin. From “Unbundling Craiglist” to “Uber for X” – we lay it all out in a single framework. Hope you enjoy our thinking! -A]


Above: 4 eras of marketplaces focused on the service economy – and what’s next

Goods versus Services – why a breakthrough is coming
Marketplace startups have done incredibly well over the first few decades of the internet, reinventing the way we shop for goods, but have been less successful services. In this essay, we argue that a breakthrough is on its way: While the first phase of the internet has been about creating marketplaces for goods, the next phase will be about reinventing the service economy. Startups will build on the lessons and tactics to crack the toughest service industries – including regulated markets that have withstood digital transformation for decades. In doing this, the lives of 125 million Americans who work in the services-providing industries will join the digital transformation of the economy.

In the past twenty years, we’ve transformed the way people buy goods online, and in the process created Amazon, eBay, JD.com, Alibaba, and other e-commerce giants, accounting for trillions of dollars in market capitalization. The next era will do the same to the $9.7 trillion US consumer service economy, through discontinuous innovations in AI and automation, new marketplace paradigms, and overcoming regulatory capture.

The service economy lags behind: while services make up 69% of national consumer spending, the Bureau of Economic Analysis estimated that just 7% of services were primarily digital, meaning they utilized internet to conduct transactions.

We propose that a new age of service marketplaces will emerge, driven by unlocking more complex services, including services that are regulated. In this essay, we’ll talk about:

  • Why services are still primarily offline
  • The history of service marketplace paradigms
    • The Listings Era
    • The Unbundled Craigslist Era
    • The “Uber for X” Era
    • The Managed Marketplace Era
  • The future of service marketplaces
    • Regulated services
    • Five strategies for unlocking supply in regulated markets
  • Future opportunities

Let’s start by looking at where the service economy is right now and why it’s resisted a full scale transformation by software.

Get essays about marketplaces and more by subscribing to my newsletter.

Software eating the service economy, but it’s been slow
We’ve all had the experience of asking friends for recommendations for a great service provider, whether it be a great childcare provider, doctor, or hair stylist. Why is that? Why aren’t we discovering and consuming these services in the same digital way we’ve come to expect for goods?

Despite the rise of services in the overall economy, there are a few reasons why services have lagged behind goods in terms of coming online:

  • Services are complex and diverse, making it challenging to capture relevant information in an online marketplace
  • Success and quality in services is subjective
  • Fragmentation – small service providers lack the tools or time to come online
  • Real-world interaction is at the heart of services delivery, which makes it hard to disaggregate parts of a purchase that might be done online

Let’s unpack each reason below:

First, on the complexity and diversity of services, services are performed by providers who vary widely, unlike goods which are manufactured to a certain spec. Even the names of services can vary: what one home cleaning service calls a “deep clean” can be different from another provider’s definition. This lack of standardization makes it difficult for a service marketplace to capture and organize the relevant information.

Second, services are often complex interactions without a clear yardstick of success or quality. The customer experience of a service is often subjective, making traditional marketplace features like reviews, recommendations, and personalization more difficult to implement. Sometimes just getting the job completed (as in rideshare) is sufficient to earn a 5-star review, whereas other higher-stakes services, like childcare, have complex customer value functions, including safety, friendliness, communicativeness, rapport with child, and other subjective measures of success.

Third, small service providers often lack the tools or time to come online. In many service industries, providers are small business owners with low margins; contrast this with goods manufacturing where there are economies of scale in production, and thus consolidation into large consumer products companies. As a result of industry fragmentation, service providers often don’t have time or budget to devote to key business functions, such as responding to customer requests, promoting and marketing themselves, maintaining a website, and other core functions. While major e-commerce platforms have taken on the role of distribution, merchandising, and fulfilling orders for goods, there are few platforms that service providers can plug into to manage their businesses and reach customers.

Fourth, real-world interaction is central to services, which can pull other steps of the services funnel into the offline world as well. Many services are produced and consumed simultaneously in real-world interactions, whereas goods entail independent stages of production, distribution, and consumption. The various stages of the goods value chain can be easily unbundled, with e-commerce marketplaces comprising the discovery, transaction, and fulfillment steps. Conversely, since the production and consumption of services usually occur simultaneously offline, the discovery, distribution, and transaction pieces are also often integrated into the offline experience. For instance, since getting a haircut entails going to a salon and having interactions with the providers there, the stages of the value chain that precede and follow that interaction (discovery, booking, and payment) also often get incorporated into the in-person experience.

All of these factors make it very hard for services to come online as comprehensively and widely as commerce – but there’s hope. We’ve seen multiple eras of bringing the service economy online, and we’re on the verge of a breakthrough!

The 4 eras of Service Marketplaces, and what’s next 
There have been 4 major generations of service marketplaces, but coverage of services and providers remains spotty, and many don’t provide end-to-end, seamless consumer experiences. Let’s zoom out and talk through each historical marketplace paradigm, and what we’ve learned so far.

Above, you can see that there have roughly been four major eras of marketplace innovation when it comes to the service economy.

1. The Listings Era (1990s)
The first iteration of bringing services online involved unmanaged horizontal marketplaces, essentially listing platforms that helped demand search for supply and vice versa. These marketplaces were the digital version of the Yellow Pages, enabling visibility into which service providers existed, but placing the onus on the user to assess providers, contact them, arrange times to meet, and transact. The dynamic here is “caveat emptor”–users assume the responsibility of vetting their counterparties and establishing trust, and there’s little in the way of platform standards, protections, or guarantees.

Craigslist’s Services category is the archetypal unmanaged service marketplace. It includes a jumble of house remodeling, painting, carpet cleaners, wedding photographers, and other services. But limited tech functionality means that it feels disorganized and hard to navigate, and there’s no way to transact or contact the provider without moving off the platform.

We’ve all had the experience of a listings-oriented product, like Craigslist. You find something you want, but everything else – trust/reviews/payments/etc – that’s all up to you!

2. The Unbundled Craigslist Era (2000s)
Companies iterated on the horizontal marketplace model by focusing on a specific sub-vertical, enabling them to offer features tailored to a specific industry. We’ve all seen the diagram of various companies picking off Craigslist verticals – it looks something like this:

As a reaction to the “Wild West” nature of Craigslist, to improve the customer experience, each startup would create value-add via software. For instance, Care.com carves off the Childcare section of Craigslist, and provides tech value-add in the form of filters, structured information, and other features to improve the customer experience of finding a local caregiver. It’s a huge leap in terms of user experience over Craigslist’s Childcare section.

Angie’s List, a home services site founded in 2005, carves off Craigslist’s household services category. The platform has features including reviews, profiles, certified providers, and an online quote submission process. But the marketplace doesn’t encompass the entire end-to-end experience: users turn to Angie’s List for discovery, but still need to message or call providers and coordinate offline.

Unmanaged vertical marketplaces like Angie’s List go a step beyond Craigslist and take on some value-add services like certifying providers when they meet certain standards, but customers still need to select and contact the service provider, place their trust in the provider rather than the platform, and transact offline.

Like previous listing sites, these platforms in this era try to use the ‘wisdom of the crowds’ to promote trust. These platforms have a network effect in that more reviews means more users and more reviews. But user reviews have their limitations, as every user has a unique value function that they’re judging a service against. Without standardized moderation or curation, and without machine learning to automate this process, customers have the onus of sifting through countless reviews and selecting among thousands of providers.

3. The “Uber for X” Era (2009-)
In the early 2010s, a wave of on-demand marketplaces for simple services arose, including transportation, food delivery, and valet parking. These marketplaces were enabled by widespread mobile adoption, making it possible to book a service or accept a job with the tap of a button.

Companies like Handy, Lugg, Lyft, Rinse, Uber and many others made it efficient to connect to service providers in real-time. They created a full-stack experience around a particular service, optimizing for liquidity in one category. For these transactions, quality and success were more or less binary–either the service was fulfilled or it wasn’t–making them conducive to an on-demand model.

These platforms took on various functions to establish an end-to-end, seamless user experience: automatically matching supply and demand, setting prices, handling transactions, and establishing trust through guarantees and protections. They also often commoditized the underlying service provider (for instance, widespread variance on the driver side of rideshare marketplaces is distilled into Uber X, Uber Pool, Uber Black, Uber XL, etc.).

Unlike the previous generations of marketplaces, in which the provider ultimately owns the end customer relationship, these on-demand marketplaces became thought of as the service provider, e.g. “I ordered food from DoorDash” or “Let’s Uber there,” rather than the underlying person or business that actually rendered the service.

Over time, many startups in this category failed, and the ones that survived did so by focusing on and nailing a frequent use case, offering compelling value propositions to demand and supply (potentially removing the on-demand component, which wasn’t valuable for some services), and putting in place incentives and structures to promote liquidity, trust, safety, and reliability.

4. The Managed Marketplace Era (Mid-2010s)
In the last few years, we’ve seen a rise in the number of full-stack or managed marketplaces, or marketplaces that take on additional operational value-add in terms of intermediating the service delivery. While “Uber for X” models were well-suited to simple services, managed marketplaces evolved to better tackle services that were more complex, higher priced, and that required greater trust.

Managed marketplaces take on additional work of actually influencing or managing the service experience, and in doing so, create a step-function improvement in the customer experience. Rather than just enabling customers to discover and build trust with the end provider, these marketplaces take on the work of actually creating trust.

In the a16z portfolio, Honor is building a managed marketplace for in-home care, and interviews and screens every care professional before they are onboarded and provides new customers with a Care Advisor to design a personalized care plan. Opendoor is a managed marketplace that creates a radically different experience for buying and selling a home. When a customer wants to sell their home, Opendoor actually buys the home, performs maintenance, markets the home, and finds the next buyer. Contrast this with the traditional experience of selling a home, where there is the hassle of repairs, listing, showings, and potentially months of uncertainty.

Managed marketplaces like Honor and Opendoor take on steps of the value chain that platforms traditionally left to customers or providers, such as vetting supply. Customers place their trust in the platform, rather than the counterparty of the transaction. To compensate for heavier operational costs, it’s common for managed marketplaces to actually dictate pricing for services and charge a higher take rate than less-managed marketplace models.

Managed marketplaces are a tactic to solve a broader problem around accessing high-quality supply, especially for services that require greater trust and/or entail high transaction value. If we zoom out further, there’s many more categories of services that can benefit from managed models and other tactics to unlock supply.

What’s next: The future of Service Marketplaces (2018-?)
We think the next era of service marketplaces have potential to unlock a huge swath of the 125 million service jobs in the US. These marketplaces will tackle the opportunities that have eluded previous eras of service marketplaces, and will bring the most difficult services categories online–in particular, services that are regulated. Regulated services–in which suppliers are licensed by a government agency or certified by a professional or industry organization–include engineering, accounting, teaching, law, and other professions that impact many people’s lives directly to a large degree. In 2015, 26% of employed people had a certification or license.

Regulation of services was critical pre-internet, since it served to signify a certain level of skill or knowledge required to perform a job. But digital platforms mitigate the need for licensing by exposing relevant information about providers and by establishing trust through reviews, managed models, guarantees, platform requirements, and other mechanisms. For instance, most of us were taught since childhood never to get into cars with strangers; with Lyft and Uber, consumers are comfortable doing exactly that, millions of times per day, as a direct result of the trust those platforms have built.

Licensing of service professions create an important standard, but also severely constrains supply. The time and money associated with getting licensed or certified can lock out otherwise qualified suppliers (for instance, some states require a license to braid hair or to be a florist), and often translates into higher fees, long waitlists, and difficulty accessing the service. The criteria involved in getting licensed also do not always map to what consumers actually value, and can hinder the discovery and access of otherwise suitable supply.

Above: Bureau of Labor Statistics. (11/9/18)

Get essays about marketplaces and more by subscribing to my newsletter.

Five strategies to unlock regulated industries
We’re starting to see a number of startups tackling regulated services industries. As with each wave of previous service marketplaces, these new approaches bring more value-add to unlock the market, with variations in models that are well-suited to different categories.

The major approaches in unlocking supply in these regulated industries include:

  1. Making discovery of licensed providers easier
  2. Hiring and managing existing providers to maintain quality
  3. Expanding or augmenting the licensed supply pool
  4. Utilizing unlicensed supply
  5. Automation and AI

1) Making discovery of licensed providers easier
Some startups are tackling verticals that lack good discovery of licensed providers. Examples include Houzz, which enables users to search for and contact licensed home improvement professionals, and StyleSeat, which helps users find and book beauty appointments with licensed cosmetologists.

2) Hiring and managing existing providers to maintain quality
Companies can raise the quality of service by hiring and managing providers themselves, and by managing the end-to-end customer experience. Examples are Honor and Trusted, managed marketplaces for elder care and childcare, respectively, which employ caregivers as W-2 employees and provide them with training and tools. In the real estate world, Redfin agents are employees whose compensation is tied to customer satisfaction, unlike most real estate agents who are independent contractors working on commission.

3) Expanding or augmenting the licensed supply pool
Expanding the licensed supply pool can take the form of leveraging geographic arbitrage to access supply that’s not located near demand. Decorist, Havenly, Laurel & Wolf, and other online interior design companies enable interior designers around the world to provide design services to consumers without physically visiting their homes (yes, in many parts of the US interior design requires a license!). With improvements in real-time video, richer telepresence technologies, and better visualization technologies, more synchronous services are also shifting from being delivered in-person to online. Outschool and Lambda School are examples of de-localizing instruction, enabling teachers and students to participate remotely while preserving real-time interaction.

Another approach is to help suppliers navigate the certification process. A16z portfolio company Wonderschool makes it easier for individuals to get licensed and operate in-home daycares.

Lastly, there’s the approach of augmenting certified providers so they can serve more customers. Fuzzy, an in-home veterinary service, uses AI and vet technicians to augment the productivity of licensed veterinarians; and a16z portfolio company Atrium builds automation and workflow management to provide efficiency gains in the legal industry.

4) Utilizing unlicensed supply
Some companies utilize unlicensed supply–notably Lyft, Uber, and other peer-to-peer rideshare networks. Another example is Basis, a managed marketplace for guided conversations with trained but unlicensed specialists to help people with anxiety, depression and other mild to moderate mental health issues.

In the pet space, Good Dog is a marketplace that brings together responsible pet breeders and consumers looking for a dog. Going beyond existing breeder licensing, which the company felt didn’t map to what consumers valued, Good Dog established its own higher set of standards and screening process in conjunction with veterinary and academic experts.

5) Automation and AI
Other startups automate away the need for a licensed service provider altogether. These include MDAcne, which uses computer vision to diagnose and treat acne; and Ike Robotics and other autonomous trucking startups which remove the need for a licensed truck driver.

Opportunities for companies addressing regulated services
The last twenty years saw the explosion of a number of services coming online, from transportation to food delivery to home services, as well as an evolution of marketplace models from listings to full-stack, managed marketplaces. The next twenty years will be about the harder opportunities that software hasn’t yet infiltrated–those filled with technological, operational, and regulatory hurdles–where there is room to have massive impact on the quality and convenience of consumers’ everyday lives.

The services sector represents two-thirds of US consumer spending and employs 80% of the workforce. The companies that reinvent various service categories can improve both consumers’ and professionals’ lives–by creating more jobs and income, providing more flexible work arrangements, and improving consumer access and lowering cost.

The companies mentioned in this essay just scratch the surface of regulated industries. You can imagine a marketplace for every service that is regulated, with unique features and attributes designed to optimize for the customer and provider needs for that industry. (A full list of regulated professions in the US can be found here.) We fully expect more Airbnb- and rideshare-sized outcomes in the service economy.

If you’re a founder who is looking to take on the challenge of tackling more complex services and bringing them online, we’d love to hear from you.

Thank you for reading!

Written by Andrew Chen

November 26th, 2018 at 6:45 am

Posted in Uncategorized

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

without comments

Dear readers,

Building a new growth team is hard. You have to figure out the macro organizational issues – how it fits in with marketing, product, and other functions – as well as the micro, like how to measure the success of these teams. It’s a tricky topic and something that a lot of teams are thinking about right now.

A few months ago, I spoke on lessons learned from various organizational structures for the growth teams at Uber, organized as 5 broad topics:

  1. Why create a growth team?
  2. What’s the difference between a “growth hacker” and a growth team?
  3. What’s the difference between growth and marketing/product/whatever?
  4. Where should growth teams focus?
  5. I’m starting or joining a growth team! What should I expect?

To answer these questions, Brian Balfour and I worked on a deck, based on materials from Reforge. (Check them out for more practical reference materials on this topic)

The deck is presented below! Hope you enjoy the materials, and feel free to reach out or follow me for realtime updates at @andrewchen on Twitter.

Thanks,
Andrew

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

The Deck

Above: Today, I’m going to present a few key topics that you need to figure out as you build a growth team for your company. First, why you might want to create one in the first place. Then, the differences in skillsets for both individual practitioners versus the org – and versus existing functions like Product and Marketing. And finally, where teams should focus and how to make an impact in the early days.

The ideas within these topics are drawn from several places – interviews and discussion with the folks who lead growth teams at places like Slack, Dropbox, Hubspot, Pinterest, and others, but also my own personal experiences at Uber.

Above: Many of you may remember when Uber looked like this. It was all up and to the right.

The growth team was originally created in 2013, founded by Ed Baker. It experimented with a ton of different organizational configurations – I joined a few years after it was created and spent about 3 years there, and spent most of my time on driving growth on the rider side of the platform.

Above: While I was at Uber, a lot of amazing projects were run out of the growth team. My colleagues in China Growth made incredible progress – shoutout to Ben Chiang, Han Qin, Michelle Chen, Jia Zou, Vinay Ramani, and many others – in addition to much of the progress being made across the US and the rest of the world.

At its peak , the growth team included China Growth and had over 500+ people. It was an amazing, dynamic time for the company. I learned a ton and am excited to share some of the ideas today.

Uber has changed a lot over the years. We certainly changed logos many times. But I think there are some really critical things that we can pass along to others in the ecosystem.

Let’s start with the basics…

Above: First, why create a growth team in the first place? We know that a lot of companies have folks with formal growth teams, and informal ones with growth PMs/marketers/etc running around.

Above: When you just look at the cross-section of companies in the industry, many of the newest and best B2B and consumer companies have all built growth teams.

We’ve also heard many Boards ask their CEOs to invest in growth teams. Why did this even emerge in the first place?

Above: The easiest way to talk about The Product Death Cycle.

Unfortunately, this is how products are often shipped and released. You have someone with a vision, who builds some features and does a launch. They might get an initial spike of traction, but when growth flattens, it’s not clear where to take things. They talk to some customers, ask what they want, and try again. They add a few more features, re-launch, and so the cycle goes on.

Do that too many times, and all of a sudden, you’re dead.

Why?

Above: If you build it, they may not come, it turns out. Better products, and more features, do not necessarily equal growth.

Many of the key levers for driving more user acquisition, retention, engagement, can sometimes sit outside the toolkit for most great product leaders. There’s a long laundry list of skills that are critical, but not often considered core to the product: adtech integrations, signup funnel A/B testing, optimizing notification delivery, testing price points, testing cohort curves, etc. Yes, occasionally there are people who know all of it – but they are rare!

Furthermore, no one individual can drive this. Instead, you need to bake this into your organizational goals and DNA. You need to collect these efforts within the larger framework of the company.

Above: Thus, we seek to build a framework for growth that’s a discipline and organizational structure within its own right.

We’ve come to see that “design thinking” and “agile engineering” are their own systems of organizational structures, workflows, philosophies, and skillsets. They are key to how we work within a company.

In the same way, we can build growth teams as a system too.


Above: Product Growth is the discipline of applying the scientific method to business KPIs.

It provides an underlying system for increasing metrics whether it’s revenue, acquisition, retention, engagement, or another key business metric.

Above: And just as you’d expect with the scientific method, the steps are build on understanding the data, creating hypotheses that identify why certain processes are happening, prioritizing those ideas, running the experiments, and then repeating the cycle.

That way, if you think your active user count is low, you can analyze the data to understand that you need more top of funnel user acquisition, then hypothesize that a combo of paid advertising and referrals can help, and then execute against that.

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

That is much, much better and more targeted than just building more features that your users ask for, and expecting growth to magically increase as a result. (Maybe you should build those features anyway, but don’t do it for growth!)

Above: Second topic. Let’s talk about the difference between the “Growth Hacker” – a term that Sean Ellis invented and I helped popularize – and a “Growth Team.” This is an important one.

Above: In the early stages of the growth skillset, there were no teams. There were a number of individuals and startup founders who were putting the necessary ideas, workflows, and tactics together. Some of these folks would refer to themselves as “growth hackers” in a tongue-in-cheek way.

As the skillset grew, it was clear that to do anything impactful, especially within the context of a larger/complex product, you needed to organize entire groups of people.

Above: Thus the growth team emerged, with the philosophy that you don’t want a lone genius with all the levers, and a team of helpers. Instead, you need to create an organization with a broad set of skills.

Growth is a team sport, and to run the scientific method on your KPIs, you need a lot of people who can help you.

Above: For most of the missions for a growth team, you need many different functional roles to help – from Product, Marketing, Engineering, Data, Ops, Finance, etc., etc. You combine all of these folks into individual teams and organize them together into a growth org.

Above: What are people doing within all of these roles?

  • Growth PM: A product manager that’s responsible for the experiment roadmap
  • Growth engineer: An engineer who’s focused on technical decisions and implementing experiments
  • Growth marketer: A versatile marketer with an expertise in a given channel – from paid marketing to SEO to email to others
  • Growth data: An analyst focused on creating insights – both macro on the user lifecycle, and micro, on specific experiments
  • Growth design: A designer leading the UX, but with an emphasis on speed

You might also loop in other function – for example at Uber, a lot of decisions around incentive spend had to include folks from Finance or Pricing. And you’d always have to include Ops to think through how it affected things on the ground.

 

Above: Depending on the problem you’re trying to solve, you might have a different makeup on the team. For the new user experience – which might include increasing signup conversion, and maybe even integration into ads – you’d probably emphasize engineers. You’d want an Android and iOS engineers. Plus even performance marketing folks, some data analysts to look at the metrics, etc.

 

Above: If you were working on SEO, on the other hand, then maybe you wouldn’t need designers. This might be more about optimizing page structure, where the content goes, etc. In this case, you might emphasize SEO marketing, data, and a full-stack engineer for web.

Ultimately, the goal is to define the problem based on your insights and hypotheses, and staff the team to solve that particular problem. The individual teams might emphasize different skills, and the macro organizational structure of where the growth team fits has the some complexities depending on the missions of other teams.

 

Above: One common structure is to treat the Growth Team as a set of pods, each one matrixed to their respective functions. So you might have a Growth PM that reports into Product, plus the others, and all together they are the growth team. Many product teams look like this, and this is set up to match.

Alternatively, at Facebook and in an early incarnation of the Uber growth team, you have things set up more like a business unit. You have functions reporting into a GM, and the pods underneath. This has the advantages of creating a lot of independence within the team, with the complication that you split the various orgs – this can cause complexity and sometimes conflict as well.

 

Above: You can obviously pick and choose and have hybrid models as well.

 

Above: Too many startups are beginning with “I need a growth team!” and accepting a random org configuration, without thinking it through from the fundamentals. Ultimately, You have to start with the problems you are trying to solve. Begin with the KPIs, the insights you’ve generated, and then move onto execution. You staff the problem area and the type of execution you want. The organizational structure follows from there.

 

Above: This is a question I often get. Isn’t growth and marketing just the same thing? Isn’t growth and product just the same thing? Can’t everyone just be responsible for growth?

In this section, I’ll walk through some of the practical differences.

First, when it comes to Marketing and Growth, there are a lot of specialties that you want to solve:

  • Brand marketing
  • PR
  • Events
  • Content marketing
  • Email
  • SEO
  • Paid marketing
  • Viral/referral features
  • New user experience
  • User-to-user notifications
  • etc

You could house all of these in a bunch of different configurations, but roughly speaking, you often have three categories of functions:

  • Brand
  • Growth marketing
  • Growth product

It’s usually obvious that Brand ends up in Marketing. And similarly, things like NUX and product-generated notifications end up in a Growth team. But some of the middle levers, like SEO/Paid marketing/Email/etc, could potentially sit in either. I’ve seen both. Facebook has much of performance marketing sitting inside the Growth team. Uber started that way, but ended up having it all go into Marketing. There’s a lot of different possible configurations.

Above: If core product teams have engineers, designers, and PMs, and so do growth teams, what’s the difference? It’s all dependent on what they do. Product teams focus on creating core value. Enhancing product/market fit over time. This means obsessing over every little interaction in the core engagement loop – it’s a game of inches, and those inches count.

On the other hand, growth teams should focus on getting the core value out there to the world – getting as many folks as possible to experience that value.

There’s a middle ground on making users experience core value as frequently as possible – you could imagine putting that in either team, but if the solutions tend to be very iteratively/quantitatively-driven, then maybe put it in the Growth team.

Above: You also have to decide the ownership model. There are two extremes: Growth-as-a-Service and Autonomous. And everything in between.

In “Growth as a Service” – the team doesn’t technically own any feature or codebase. They jump into the highest value part of the product, do their analysis and optimizations, ship a bunch of improvements, and move on. It’s important for the team to be gentle, as they are the guests, but it’s also important that they stay lightweight. If the growth team ends up owning every piece of code they touch, then they would eventually get stuck in maintenance mode for everything.

On the other hand, a full ownership model means that the growth team could own the new user funnel, notifications, ad tech, the A/B testing platform, payment flows, and many other critical areas where numbers trump intuition. This can work, but then the team needs to be staffed properly.

Above: There are ultimately lots of pros and cons to each model. Uber went through the entire spectrum, but over time, came to own more and more pieces of the product. But you’ll have to decide based on your own constraints, org, and product requirements.

 

Once the growth team’s been set up, where should they focus? As discussed previously, their mission and toolkit ought to be distinct from those used by the marketing or product team. Especially in the early days of the team, there should be low-hanging fruit that can be picked off easily.

Although it’s easy to jump right into user acquisition, or looking at churn, let’s zoom out and look at the system. Let’s start with a prioritization framework.

 

Above: Ultimately there are three key things you’re trying to trade off – and one is particularly tricky:

  • Effort. How much design/eng/marketing resources does it take to execute?
  • Success. How likely will it be to be successful?
  • Upside. This is the tricky one – but if it works, how much will it affect overall growth?

Every growth experiment is ultimately a prioritization based on the ranking of these three axes, and over time, your growth team will be smarter about how to pick. But I wanted to provide some notes on where a growth team is likely to go wrong in their prioritization.

The most common anti-pattern on picking growth projects is where a +50% increase on a feature touching 0.01% of users is celebrated, but a +5% increase that touches 50% of your active users feels smaller. Of course when you do the math, the latter is much more important as you ultimately want these bottoms up experiments to hit your top-down KPIs.

Another common anti-pattern is to focus on large effort, large upside projects over low effort, low/medium upside projects. Almost everyone overestimates their chances of success, so it’s better to go for more execution throughout over big bets… until you run out of easy ideas or you have enough resourcing to build a portfolio of small and big projects.

Some notes on each factor:

  • In general, Effort is the easiest to understand. If you define a project, your team will be able to execute against it like anything else. The usual advice I give here is to bias towards low effort projects early on in a growth team
  • Success rate can be controversial, because the things that work in growth are not necessarily things that users will self-report – and thus, people on teams will usually say, “I would never want this. I would never do this.” And yes, you implement the best practices and things work. The classic example here is the desire to add comprehensive content on landing pages, with links to a million other places. It’s a well understood design pattern to provide just as much information as is needed to get the signup – nothing more.
  • Upside, of the three, is the trickiest thing to understand though. It’s also the lever with the most power, as it provides strong guidance on where in the product the growth team should be focusing.

Let’s do a deep dive on Upside.

Above: Upside is ultimately measured in absolute terms – how many additional subscribers did you gain, the number of signups generated, etc. You calculate it using two components – Reach and Impact. Reach is the measurement of how many end users are touched by the change of a feature. Impact is how much the metric moves as a result of the change.

Between these two factors, Impact tends to be the most random. Sometimes a change you’ll make moves things by +5% and sometimes it can move things +50%. In the main, you’ll get something in between for the vast majority of your projects. For some projects, impact can be huge if it’s a product experience that can happen multiple times – for example, a new highly-relevant notification that’s sent in the core engagement loop of a product. Or something that significantly amplifies a viral loop, causing the flywheel to spin faster and faster. (But that’s out of the norm- but also tells you that you might want to focus on these outsized impact cases)

Reach, on the other hand, is an amazing lever that is often misunderstood. This is often the sweet spot for understanding the best kinds of projects.

 

Above: In the main, most product teams focus on making their core product experience better, which benefits their core users. This has a lot of benefits – after all, they are the most engaged, the most valuable from a monetization perspective, and in a multi-sided platform, they produce the photos/content/sales/etc that sustain the rest of the ecosystem.

On the other hand, core users are often only a small % of your total active users.

Above: Depending on how you define core users, they are usually only 5-25% of your active user base. If you are looking at the segment of your userbase that actually produces content, rather than just consuming it, you’ll see it’s usually a small %. Or the ratio of your hardcore users who are generating a ton of content, versus purely passive consumers. It’s always a small amount.

As a result, if you have projects that can target your active users, but not your core ones, then you might have 4-20X more reach! Wow.

But that’s not all, there are more concentric circles.

Above: On average, only 10-50% of your registered users might be active in any given month. 50% is world-class good – like Facebook and their ilk. Usually most products are closer to 10-20% because the vast majority of products have a ton of one-hit wonders: People who try the product once, but then forget to ever come back.

Projects at this level ought to focus on activation. If you can understand what gets a user to become active, then you can introduce that during the onboarding flow, thus converting them to active or core users.

The other set of activities here – for products with large, established audiences – is the flow from being inactive/churned to coming back into the product. Are they getting relevant emails to get reactivated? If they’ve forgotten their password, are you optimizing that flow as critically as if it were a signup flow?

Above: Of course, for many products – and this is more of a web thing – there are people who look at a product but never sign up. Most landing pages might only have 10-50% conversion rate to signup! Furthermore, a lot of products have “side doors” – like Dropbox shared file links, or YouTube video pages – that get the majority of the traffic. Those become critical places to optimize.

Above: Of course, even bigger than all the people who have interacted with your product at all – even in a logged out state – there’s everyone in your primary acquisition channel (whether that’s on Facebook or Google or something else) that have never heard of you before. This is true top of funnel acquisition.

And of course, there are all the channels you haven’t even experimented with. That’s why adding a new channel – like trying a referral system when it doesn’t yet exist – can be such a big needle mover.

Above: All of this is to say that if you are looking for the biggest lever on growth upside, it’s probably in addressing  Reach. And think of the concentric circles when you are finding that your growth team’s projects collide with the core product team – move to further and further concentric circles, whether that’s targeting new users, churned users, and everyone out on the edge who hasn’t yet bought into your product.

The other fascinating exercise is to look through your existing features and roadmap and circle everything that touches non-users (or inactives) as opposed to active/core users. You’ll be surprised that there are generally very few.

The above was shared from Airbnb’s growth team who did exactly this exercise – only 6 items out of 33 were for non-users. (Shoutout to Gustaf, who ran their guest growth!) A growth team can rapidly expand this list and give some love to everyone out on the edges of the audience.

 

Above: Final topic. Let’s say that you as an individual are thinking about joining (or starting) a growth team at a company. What should you expect, and how might you evaluate the opportunity?

Above: There are a lot of organizational and cultural aspects that can get in the way of setting up a successful growth team. First, there’s leadership DNA – is there an understanding of what the growth team does? In particular within the Product and Marketing peers? Or is this something that’s being forced top-down by the CEO or board without leadership buy-in? It can get painful if people don’t fundamentally get the mission of the growth team.

Company culture is an important aspect too. If the culture is like Uber 1.0’s, where experimentation is encouraged – as long as it’s scoped to a city or two at a time, or as a 1% test – then that’s great. “Move fast and break things.” On the other hand, if the company is extremely design and brand conscious, it can be harder. Famously, Apple and Snap are two companies that rarely ran A/B tests until the recent era. In evaluating a company for experimentation, it’s good to understand how open folks are to a big change to the homepage, for instance, even if it’s 1%. Or in the new user flow.

The ownership model, as previously discussed, can either on a spectrum: SWAT team model, with little/no code ownership, or strong ownership of areas like NUX/notifications/adtech/etc. Both can work, just make sure you know what you’re getting into and that the staffing reflects it.

IMHO, the best case scenario, in my opinion, is to have a team that is:

  • Bought into having a growth team, and knows how it compliments the existing functions
  • Supports experimentation, even extreme as long as its tested with a small group
  • Dedicated staffing that’s already in place, with a bias towards strong ownership on for everything outside of the active user base

The worst version, of course, is where people don’t really get why you have the growth team, there’s a ton of risk aversion on rapid experimentation, and no staff… just an expectation to run around and convince other teams to build your amazing ideas. That’s a recipe for failure.

Above: There are common lines of disagreement to implementing a growth team. Sometimes the incentives of a company are set up to reward large, complex projects (with codenames and executive oversight) rather than many lightweight changes that move the business along. This can get baked into everything from how projects are reviewed to perf review, to everything else.

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

Similarly, before starting a growth team, almost certainly there were also folks looking after growthy parts of the product. By moving those responsibilities away, or starting to encroach on “engagement” which overlaps with the core product team, there can be anti-bodies that make growth projects much, much slower.

Above: The foundation of the organization has to be ready to accept a growth team, and that starts with a fundamental understanding that the environment has changed:

  • Growing tech products has changed, and the playbook has changed in the last decade
  • Explicit headcount/roadmap has to be dedicated towards making growth happen – “build it and they will come” doesn’t work
  • Creating a pipeline of growth experiments will need a different process. The scientific method as applied to KPIs. Not just a subset of marketing and product projects
  • And finally, the team structure and skillsets to make this successful are different

As you might imagine, creating this foundation of mutual understanding is a big effort by itself. And y0u’ll need the help of your startup’s CEO, or your business unit’s GM, and the layer above them too. And all your peers.

Above: There are tactics to overcome the inevitable organizational friction you’ll hit. Here are a few of them.

OK, that’s all folks! Thanks for reading this far, and hope you enjoyed this deck

 

Written by Andrew Chen

November 13th, 2018 at 7:30 am

Posted in Uncategorized

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

without comments

Growing startups and evaluating startups share common skills
Earlier this year, I joined Andreessen Horowitz as a General Partner, where I focus on a broad spectrum of consumer startups: marketplaces, entertainment/media, and social platforms. This was a big moment for me, and the result of a long relationship that began a decade ago, when Horowitz Andreessen Angel Fund funded a (now defunct) startup I had co-founded. One of the reasons I’ve been excited about being a professional investor is the ability to apply my skills as an operator. The same skills needed to grow new products can be used both to evaluate new startups to invest in, and once we’ve invested; to help them grow.

The reason for this is that the steps for starting and scaling a new startup share many of the same skills as investing in a new startup: 1) First, we seek to understand the existing state of customer growth – including growth loops, the quality of acquisition, engagement, churn, and monetization. 2) Then, to identify potential upside based learnings from within the company as well as across benchmarks from across industry. 3) And finally, to prioritize and make decisions that impact the future. Of course, as an investor you can’t run A/B tests or analyze results directly, but you can form hypotheses, ideate, and apply the same type of thinking.

As part of my interview process at a16z, I eventually put together an 80 slide deck on how to use growth ideas to evaluate startups. In the spirit that this perspective can help others in the ecosystem, and to share my thinking, I’m excited to publish the deck below.

Disclaimer: This was just one presentation in a 10 year relationship
But before I fully share, I have a disclaimer. This is one presentation I made within a series of dozens of meetings and interactions I had with the Andreessen Horowitz team. It was just one ingredient. I’ve been asked by friends and folks on the best path into venture capital. From my experience, it’s a long, windy experience – others have written about their processes as well.

My journey took a while too:

  • 10 years in the Bay Area (and blogging, building my network, etc)
  • Dozens of angel investments and advisory roles in SaaS, marketplaces, etc
  • Once kicked off, 6 months of interviews (dinners, sitting in pitches, analyzing startups)
  • 100+ hours of interviewing and prep

This deck was just one step, but one that I’m proud of, and want to show y’all.

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

The Deck

Above: I presented this deck as part of my interview to join Andreessen Horowitz to help demonstrate my expertise and “superpower” and how it might be used in an investing context.

As a result, it’s split into three sections:

  • About me and my superpower
  • How to apply user growth ideas in an investing context
  • My continuing leadership in the field

Let’s get started!

Above: When I first arrived in the Bay area, if you had searched for “growth hacking” – you would have gotten zero results. It wasn’t a thing. Some early companies like Linkedin and Facebook had started the notion of “growth teams” but this wasn’t a widely understood set of ideas in the industry.

While there were people thinking about user acquisition and ad tech, and some early consumer teams (like Eric Ries’s IMVU) thinking about cohort curves to mention retention, it hadn’t been centralized into a team that could execute against it.

I started my blog originally to write down everything I was learning. My previous background up to that point was in user acquisition and ad tech, and I was making the pivot to consumer products. There was a lot to learn.

As I learned from the best in the industry – in particular from the PayPal mafia who had employed a metrics-driven viral approach to build some of their most iconic companies – I started to write about what we’d now call growth.

If you look at Google Trends, you’ll see that “growth hacking” all of a sudden became a term people in the industry were interested, and were searching for, in 2012.

There’s a reason for that. I’d like to take some credit :)

I was lucky with the right timing, the right content, and with inspiration from my friend Sean Ellis to be able to popularize the terminology and ideas around “growth hacking” in an essay I wrote in 2012.

And these days, it’s spread and become its own ecosystem.

Teams focusing on user growth have spun up across some of the best companies in the ecosystem!

(As of early 2018, when I had presented this, these were some of the companies that had growth titles or formal growth teams)

Of course “growth hacking” has changed a lot – it’s no longer about hacks as much as a much bigger umbrella as it’s become a more professionalized, formal function within a team.

One evolution is the number of books and conferences now dedicated to growth.

The other evolution in the ecosystem is that people are thinking about different things – about how to build growth teams, not just hacks. Thinking about new user experience, engagement metrics, and other important concepts.

I continue to contribute to this ecosystem by writing, being involved in social media, and press.

As part of that, as folks search for important concepts like “product market fit” and “user growth” – my essays are often on the front page. These are evergreen concepts and were relevant 5 years ago, relevant today, and will be important in the next phase of tech as well.

Beyond writing, I’ve also extended my efforts to bring together the high-end professional network of people working on startup growth. This hits a different part of my network as it’s a deeper relationship, and Bay Area focused, as opposed to my essays and social media which are global.

To accomplish this, I’ve been working with Brian Balfour (ex-VP growth from Hubspot) to start up Reforge which has educated 1000s of employees from top tech companies.

The flagship program on growth is 8 weeks and pulls together some of the foundational concepts.

The speakers include executives who run growth or related functions from across the industry. (Thank you to all the wonderful people who are involved with Reforge! Y’all are awesome and I’m happy to count you as my friends)

In the past few years, over 1500+ folks have attended the program from almost every company in the Bay Area and many F500 enterprises as well. This includes CEOs/founders, VPs, PMs, marketing folks, data science, engineers, and so on.

In the coming years, I want to stay as active as possible – to stay ahead of the curve by spending time with the smartest people from across industry, to bring communities together, and to continue to publish ideas. Establishing myself in the industry has taken a decade in the Bay Area and I intend to spend the next few decades at the same pace!

Next, let’s change gears. After all this talk about startup growth, how might you use this to evaluate new products in an investment context?

In this next section, I’ll present some of the central ideas in user growth and how you might use that to evaluate the quality of a startup’s growth as opposed to getting stuck on vanity metrics.

 

Above: To start, oftentimes you’ll find a new startup that presents their growth curve, which might look something like this – up and to the right! This is great. Time to invest, right?

The problem is, you don’t know where it’s going to go.

In the long run, over the course of an investment, you’ll find that this curve might go in a direction you may not want it to go – perhaps it’ll plateau. Perhaps it’ll even collapse. Or you may find that it’s going to continue going up, and even hockey-sticking.

How do you predict the future? Is it working and will it sustain? Will it even accelerate?

There’s a couple common frameworks to try to understand this, and one is the Growth Accounting Framework.

The Growth Accounting Framework looks something like this – within each time period (say a week, or a month) – you’ll add some users, reactivate some folks who had previously churned, and some go inactive. You add this up and it’s the “Net MAU” for a product – the difference between each time period.

If your positive terms (New+Reactivated) are smaller than your negative terms (the number who become Inactive) then you stop growing, and the whole thing goes negative.

Let’s look at each term in isolation.

The New+Reactivated term tends to look linear or be an S-curve. The reason is that it’s really really hard to scale acquisition – only a few, like viral loops, paid marketing, and SEO can bring you to millions or tens of millions of users. And as the acquisition channel gets bigger, it tends to get less effective. Ads become more expensive to buy, viral loops end up saturating your target market, etc. This term dominates.

Reactivation tends to be hard to control. If someone quits your product, emailing them a bunch of times probably won’t help. (But if you have a network, something like photo-tagging or @mentions might!). But most products don’t have a network, and as a result, the acquisition term tends to be much bigger than the reactivation one.

Above: The Inactive curve is also an S-curve, but it lags acquisition. It’s simple to understand why, which is that until you have a base of active users, you can’t really churn. You can’t churn anyone when you have zero users. So it goes up over time. So usually your acquisition curve pushes you up, and then churn starts.

At the moment that your New+Reactivated is equal to your Inactive users, each time period, then you hit peak MAUs. This is the thing to watch for, because then it’s all flat or down from there.

I use MAUs in this example but you could also use active subscribers, or users who have bought something in the past 30 days, or some other definition. The underlying physics are the same.

If you’re following all of this, it’s already a pretty profound insight. We’ve moved from looking at a single curve that might have been growing and decomposed it into its underlying terms, and shown how a curve that’s been going up and to the right for a while might go flat the next month. And why. That’s important.

But there’s a problem.

The problem is that the Growth Accounting Framework provides for lagging metrics. It’s hard to predict the future. It’s the equivalent at looking at company’s current year P&L and its constituent parts – it’s useful, but not enough. It’s hard to be predictive. It’s also hard to be actionable for product teams.

That’s why for the growth and product teams I’ve advised over the years, this isn’t something you can look at every day or every week. It’s not helpful.

Instead, you need leading indicators and a more predictive conceptual model.

Above: To do this, I advocate that we look at two key loops:

  • Acquisition loops, which power the positive term for New
  • Engagement loops, which power the negative terms on Reactivation and Inactive

Understanding these underlying loops is the key to the whole problem of predicting where a graph is going to go.

In understanding these loops, I don’t mean to simply chart them out in a spreadsheet. I mean to think about the quality of the loops – how defensible and proprietary are they? How scalable and repeatable? Is there upside in optimizing them or adding to them further?

In other words, we want to understand the quality of the user growth. If we understand that, we can forecast into the future as opposed to looking backwards.

To start, let’s look at the Acquisition Loop.

Above: There’s 4 sections of content we’ll go through- first, to understand the examples, then what metrics to examine. Then to look at how to best improve the loops. And finally, we’ll try to apply the framework.

Let’s start with examples.

Above: The key thing to ask for the Acquisition Loop is to understand how a cohort of new users leads to another set of new users. If you can get that going, then by a conceptual proof by induction, you’ll be able to show how it scales.

Importantly, these loops are flows within the product that are created on top of pre-existing, large platforms. Sometimes the loops are built because they are bought – via Ads. Sometimes they are built via API integrations, to allow for easier/faster sharing. And sometimes it’s via a partnership.

Let me talk through some examples.

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

Paid marketing is also an obvious loop. Spend money, sell products, take the money and buy more ads. Keep going.

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

The important point here is that loops aren’t just conceptual, but you can actually measure their efficiency as well. If you can get 1000 users to invite and sign up +600 of their friends, then you have a ratio of 0.6. But that’s just in the first cycle of the loop. But then those 600 new users generate 0.6*600=360 new users, who then generate 216, and so on, until the entire cohort is +1500 signups total from a base of 1000. Wow! That’s meaningful because then for every user you get through other means, you’re amplifying their effect.

This can be particularly important when you have a large paid marketing budget, because it can drive down your cost of acquisition by blending in a scalable form of organic. It can be a huge advantage.

Above: What about PR, conferences, in-house content marketing, etc.? Aren’t they important? Yes, they can be- but they don’t scale. For example, conferences happen irregularly, have poor ROI/attribution tracking, and every dollar made from a conference can’t quickly be reinvested. Contrast that to paid marketing, which can be highly accountable, become very optimized, and can scale to $1B+ spend/year.

So when it comes to PR, conferences, partnerships, etc. – they’re useful, but they are more like one-off opportunities, and certainly not where the bulk of your customer acquisition takes place. Instead, you use them to drive traffic into your loop, which then gets amplified.

As a result of this model of linear channels versus loops, when you are meeting a company for the first time, you have a framework to understand if their growth will scale over time or not. If it’s a one-time launch, like they just got announced as part of the latest YC batch, well that’s not a loop.

If they have been quiet on PR, conferences, etc., but users are telling each other as part of the native functionality of the product – okay then you have my attention!

 

Once you understand the loop, you have you understand if there’s upside. Is it possible to improve the loop? Maybe it sucks now, but maybe it can be fixed? Or even better, maybe there’s a product growing like gangbusters but you could accelerate even further.

To understand this, you have to move out of spreadsheet world and get into product experiences.

The first move is to decompose the simplified loops we were looking and actually get into the details.

Above: Instead of just 4 steps, as shown before, now we go even more tactical. Of course new users will have to land on the app store page, then sign up. They have to mobile verify. They have to go to a certain screen on the product, then add something to their cart – hypothetically. And so on. Each step is friction. Each step drives down performance.

We ought to be able to look at every single one of these steps and improve them further.

Let’s dive into one example, which is the app store screen.

On the app store screen – and this is a real example – there’s reviews. There’s a star rating. The bounce rate on the app store screen can often be very high, sometimes 50-80%.

In 2016, the star rating on Uber’s rider app was low. 1.7 stars, in fact. Ouch.

There were a lot of reasons for this, but on fundamental issue was that only unhappy riders were rating the app. It’s a common best practice to ask a broad spectrum of users to rate your app, and the Uber app wasn’t doing that. This was controversial because there was some desire to “cherry pick” only happy riders, for fear that the rating might stay low.

Nevertheless, the best practice was implemented and shipped.

Here’s what it looked like- after a trip, regardless of what the rider rated their trip experience, it would ask the rider to rate the app. And very quickly, the 10s of millions of users who had happy, successful trips weighed in. Quickly things moved from 1.7 stars to over 4.7 stars, where it still sits today.

A change like this is worth on the order of millions of incremental downloads for Uber. It’s a small change, but had a lot of upside. (Congrats to the Rider Growth team for shipping this! Miss you guys!)

Let’s look at another example- having all of your users verify their phone numbers. You’ve done this a million times.

It turns out, having people verify their numbers is a high friction step and oftentimes, there’s a 10-40% dropoff rate just on this screen. It might be because your phone number was entered incorrectly. Maybe you’re international – an important use case for travel-oriented apps like Uber. There’s a whole series of updates you can make to improve this step – from partnering with carriers, allowing a voice call to verify, and so on.

One more example on creating upside – which is on the back part of the paid marketing loop, when a new user clicks on an ad and lands into the product. The landing page they see is important.

And it’s so important, years later, they all look the same.

There’s a reason why so many landing pages are just signup forms. Not a ton of information about the product, not a lot of frills- just an ask to sign up. The reason for this is that after years of testing, this is what performs best when you are invited by a friend.

So if I see a startup that doesn’t directly ask for the signup, I assume there’s upside that can be gained.

These landing pages – often the first experience of a new user – are super important because the bounce rates are often over 80%. Wow. That’s almost everyone! So there’s a playbook of common changes you can make – from removing friction, pre-filling fields, adding video, optimizing for everything being above the fold, etc.

OK, we’re done with the examples. Now once you understand the upside, let’s say you want to dig into the data. What KPIs do you look at, and what are you looking for?

Above: The first thing to ask for is the product’s Acquisition Mix. This is a look at signups broken down by channels/loops and by time period (ideally weeks). I’m looking for signals that the dominant channel(s) are proprietary and repeatable. Ideally they are loops. I want low platform risk, where there isn’t a dependency on a larger company that might change their mind. (I.e., Instagram, Google SEO, etc.). A good mix might be 33/33/33 where you have a third organic, plus two loops, like viral and SEO.

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

The red flags I look for are around new channels appearing, but which aren’t sustainable. Especially ad spend that comes and goes, indicating maybe everything’s been juiced for before the fundraise. I don’t love to see spikes for that reason.

But a signup isn’t always a signup – thus it’s important to understand the quality of a signup.

A startup shouldn’t care much about signups, they should care about how well they translate into paying customers, or active users, or whatever an “activated user” looks like. It turns out that one of the biggest determinants of “quality” of new users is the source of the user. As a result, you want to understand both how signups are being generated by various channels, via the Acquisition Mix report above, but also a sense of the quality by understanding the activation rate by channel.

The red flags here are a bunch of new users from a new channel that’s actually low quality. Or a doubling down on a new low-quality channel just to pump up the signup numbers. After all, a spike of new users count into whatever month’s MAU metric that they joined under, and it’s an easy way to juice their short-term MAU. Watch for that.

The other aspect to analyze is the concentration of new users from different sources. Perhaps a particular channel/loop dominates but seems brittle or is expensive. If all the users have come from beta users list or Product Hunt, that won’t scale over time.

On the other hand, if marketing spend and product efforts are going towards high-quality channels, that’s fantastic.

Above: As noted before, loops are usually build on top of another platform. Sometimes that’s Google SEO, email systems, Instagram, or more.

If the startup’s new product adds value to the underlying platform, and isn’t too horizontal, it might be stable. There might be a strategy to become a destination product in itself. That’d be great. But that’s often not the case.

The red flags here are focused on the integrations between the growing product and its platform- if it’s built on iOS and one of the core integrations is push notifications (like the recent live quiz apps), then look at the clickthrough rate trend for the notifications. If it’s decreasing over time, then you know it’s not working. Or on a per user basis, perhaps the average user is tapping through on the first push but isn’t engaging much with the fifth. Or perhaps the underlying platform is shrinking. If you built a product that depended on AOL Instant Messenger to thrive, that’s not a smart bet.

It’s important to understand the underlying platform of any acquisition loop because things can collapse quickly.

One cautionary tale is what happened with Branchout, which was trying to build a Linkedin on top of Facebook Platform. You can see how fast it grew – to 14 million Daily Active Users, and how it was 1/10 the size just 4 months later. You don’t want to invest at its peak.

Once you understand the acquisition loop concept, can forecast the upside, and have metrics to look at to evaluate quality- then it’s time to go back to our original challenge: The up-and-to-the-right graph.

OK so does this go up, or not?

The key here is to ignore the graph, and instead use all the tools we discussed to create a baseline forecast on the engagement and user growth. Do the signups stay linear? Grow as a percentage over time? Or go flat?

Above: Using our understanding of the potential product improvements, we ought to be able to create a bottoms up roadmap of all the improvements. We can use our expertise to understand when changes might be a +5% and when they might be a +20%. Combine all of it together, and you get a picture of the upside.

Once you have all of this together, then you ought to be able to create a series of scenarios on where your growth curves are going to go. Perhaps you can assume the product and marketing teams execute aggressively, and capture all the upside you saw. Or perhaps you can assume there’s no engineering help, and it’s just a matter of adding a few new advertising channels. All of these scenarios can be combined to create a new curve. This is your forecast. It’s a prediction of the future.

If you did all of this, you’d still have a major problem. Your prediction would suck, because you only looked at one half of the problem. The other side is Engagement, and all the loops there.

There’s an Engagement Loop, similar to what we looked at with the Acquisition Loop. Let’s take a look there.

Above: We’ll go through the same format. First examples, then how to improve, then how to measure, and then let’s bring it together and apply it.

Above: The key question with engagement is similar to the one we asked on acquisition. If you have a network-based product, like Dropbox or Slack, then you need active users to engage each other. If it’s purely a utility, then you want engagement in one time period to help set up engagement in a future time period.

Let’s run through some examples.

In an engagement loop that’s based on social feedback, you get a game of ping pong. One user messages/follows/mentions another, and they draw them back. And then that user might do the same, and draw in a different user. And this repeats. This is why achieving network density and easy content creation is so important- you need ways to bring people back into the network.

 

On the other hand, there are engagement loops that are more like planting seeds. If you sign up for Zillow and put in your home address, and favorite a couple new real estate listings, then Zillow will start re-engaging you with personalized emails. Sometimes it’ll be when your house goes up in value, other times it’ll be when new listings show up in your neighborhoods. Credit Karma is the same, where a single setup session leads to important notifications about credit score changes over time.

These are just two engagement loops, and there are many more.

Another fun one is rideshare, where seeing physical on-the-street reminders of the product might prompt you to use it too. Mapping works in a similar way, often starting with a real-life trigger of “I’m lost!”

Just like the acquisition loop, there are linear channels to re-engage users. These are useful, of course, but again, they don’t scale. It’s better when users re-engage each other or when users re-engage themselves.

This is part of why marketing-driven one-off email campaigns are often ineffective. They don’t scale, aren’t interesting to users, and with enough volume, can cause folks to churn. Not good.

It’s much better to see a natural engagement loop that leverages push notifications and email in a way that’s user-initiated.

 

In the same way we analyzed acquisition loops to understand upside, we can do so for engagement loops.

The first step is to break down the loop into much smaller, more granular steps.

Above: Here, we’ve taken a Social Feedback loop that starts with a user creating content and publishing, to their friends viewing, adding comments, and then the notification back to the original user.

Now let’s zoom in on a particular step.

Above: The social feedback loop fundamentally is built on the content creation step. If it’s not easy, then it won’t work. So it has to be an activity that a lot of users want to do. That’s why taking a photo, typing in a text, or hitting a heart are all so effective. They’re dead simple actions.

 

Above: Pinterest has many examples where they’ve optimized content creation – or more specifically, more pinning/repinning per new signed up user. One method is to use the term “Save” as opposed to the more wonky term “Pin it.” Another is to up-sell the mobile app where it’s easy to interact. Education during onboarding helps too. All of these changes doubled the activation rate for new users, causing them to repin more, kicking off engagement loops for themselves and other users.

Once you create content, then you need to circulate it within your network.

One key aspect of every network is the density of connections. It’s important to build the number of connections up, but they have to be relevant. And there’s diminishing returns too.

A decade+ into the social platform paradigm, there’s now a playbook for how to do this. Let’s cover some of these ideas.

 

Above: An important way to build a social graph is to bootstrap on an existing network. For consumer products, that might be your phone’s addressbook or Facebook. Within the enterprise, it might be your colleagues’ emails in ActiveDirectory or GSuite or your work email. There’s tactics like asking people to “Find Friends” and to build “People You May Know” features to increase density.

The red flags here are folks who claim to have explosive viral growth just based on inviting. It won’t last, and they’ll be low quality signups. Similarly, if the core activity is all inviting and friending and there’s no main activity, that’s not good either. Better to let those ones go.

 

As a final examination of looking for upside in user engagement, it’s important think about an otherwise innocuous step- your users clicking on a notification, trying to get back into your product, but perhaps they’ve logged out.

How bad can it be to get logged out?

Turns out, being logged out and failing your password attempts can become a huge drag for established products with large audiences. It’s common for 50-75% of signed up users to actually be inactive – that is, the majority of your users will have tried the product but never get hooked.

The problem is when those inactive users come back, perhaps because of a notification or some other reason, and try to log back in. They often are locked, can’t remember their password, and become permanently inactive. Not great. The solution is manifold – first to treat this flow seriously, with KPIs and optimizations. There’s tactical things, like integrating into iCloud keychain, logging in with other apps if you have a multi-app strategy, and so on.

A company like Uber might literally see tens of millions of failed sign in attempts. Amazing. And perhaps a good percentage of those riders are trying to log back in, standing at an airport wanting to take a trip, and eventually, in frustration, they walk across the street and grab a cab. It’s worth fixing.

 

Now that we have the conceptual idea of an engagement loop set, and understand potential upsides, let’s dig into the metrics. What should we look for?

Above: The first, as everyone knows, is to look at everything in cohorts. We want to understand conceptually why the user cohorts are being brought back – is there value being created at each visit that makes the product more sticky over time? Are they building a network? We want to understand the classic D1/D7/D30 metrics – for which there are many comps – and also look at the month to month numbers.

There are a couple key things to watch for: The cohort curves need to flatten. Ideally >20%, so that each signup activates into a sticky, active user over time. If only 5% of users stick, then you’d have to sign up 2B users to get 100M MAUs. Not tenable.

You can project out the total size of the company with this, by combining TAM with the cohort % you have left after a year (D365 or D730) and then the ARPU. This needs to be big enough to have venture scale.

 

Above: One of the key tools for the engagement loop is the use of notifications – whether that’s email, push notifications, or some other on-platform channel. They are easy to be abused.

To detect artificial engagement that’s being manufactured, not organically created by users, you can look at a breakdown of every notification that a product sends out. And the volume and CTRs over time. You should do a quick spam check on Reddit, Twitter, Google, and other places.

Ultimately, the right attitude towards notifications is that they accelerate engagement that’s already there – you can’t make it out of thin air. Some products naturally generate a lot of notifications, and others don’t. Some are higher CTR than others.

Above: This is one push notification chart I’ve used in the past. Ecommerce companies often use push to advertise sales- no wonder the CTRs are low. But if you are looking at ridesharing, you’ll probably interact with the push because you want to make sure your car is here!

Another set of metrics we want to understand on user engagement is frequency of use. Almost every product I’ve seen has a “ladder of engagement” where you come for one use case, but ultimately become stickier and higher frequency by adding use cases.

For Uber, riders would often do their first trip because of travel use cases, like getting to the airport – this is a 2 trips/year activity. Then they’d layer on “going out” – like dinners on the weekend, which might be 1 trip/week. And eventually a number of other use cases until they got to commuting, which could be 2 trips/day.

What I want to understand with a Frequency diagram is to segment high- and low- frequency segments, and start digging into their usage of the product. If you can upsell new use cases, then there’s a ton of upside.

Now that we have all the tools, we can build the forecast.

The prior forecast on the acquisition loops can plug into this, because each cohort starts with the number of new users who have been acquired. We can then use the cohort retention curves to build curves that translate to monthly actives or customers.

We can forecast MAUs once we have both the acquisition and engagement curves. Project that out a few quarters, and you can get a fine-grained understanding of where MAUs will be in 2 years.

Engagement metrics are very hard to move compared to Acquisition. As a result, it’s better to assume the curves are what they are. But if you must add a bullish forecast, the right way to go is to focus on new user activation. And up-selling users from one frequency segment into the other. That’s the quantitative way to do it.

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

And so there we have it!

We have the engagement loop, and the acquisition. We have forecasts for each. We have upside scenarios.

So what can we do with this?

This whole discussion started with the Growth Accounting Framework. If we have a deep understanding of both acquisition and engagement, then we have the inputs.

With the inputs, we can build scenarios that model the outputs.

We can get a granular sense of the risks involved. Ultimately this is about a forecast that’s about the quality of acquisition, and the quality of engagement, not a single number in 2 years.

Startups aren’t spreadsheets.

With all of this, we can answer the questions that matter. If a startup walks in the door, and shows a graph, we can have a real discussion of what might happen next.

OK, and that was it. (I chopped off a couple slides off the end since it’s more self-promotion – you got the meat of it!)

The epilogue
One month after I presented this deck, I got the offer to join a16z! So it worked. 10 years in the bay area, dozens of angel investments, 6 months of interviewing, culminating in my new role.

For all of you read this far – thank you! Hope you enjoyed this deck and essay. If you have feedback, shoot me a tweet: @andrewchen.

Thank you
Also, special shoutout to Brian Balfour, Shaun Clowes, Casey Winters, Bubba Murarka, and Aatif Awan who helped me at various points in iterating the content here. Couldn’t have done it without you guys! Appreciate your help on this.

Written by Andrew Chen

November 1st, 2018 at 9:00 am

Posted in Uncategorized

a16z Podcast: When Organic Growth Goes Enterprise

without comments

The consumerization and developerization of B2B
Dropbox is the fastest SaaS company to $1B in revenue run rate with 600+ million users. This is just an example showing that companies are adopting software in a completely different way in recent years – we have individual users/developers picking out products that they want to use, and then it eventually spreads inside the organization.

This is the engine that powers Dropbox, Slack, Asana, and many other new companies. It brings together all the growth levers: Viral growth, performance advertising, consumer growth techniques – but also inbound marketing, enterprise sales, etc., etc.. It’s a great trend that brings together folks with consumery backgrounds (like myself!) and my colleague Martin Casado (prev Nicira, acquired by VMWare).

There’s a spectrum that goes from Atlassian (all self-serve, no enterprise sales team) all the way to a traditional enterprise company like Oracle. Startups have to choose where they want to play, and what organization they want to build. A lot of interesting nuances here.

a16z Podcast
Today, I want to share a new podcast on When Organic Growth Goes Enterprise – this is a podcast that includes Martin and myself, with DocSend CEO and co-founder Russ Heddleston, in conversation with Hanne Tidnam.

(I’ve previously been interviewed on the Andreessen Horowitz podcast – you can subscribe here. My previous one was a two-part series on the basics of thinking about growth, from acquisition to engagement.)

Topics
Questions we talk about:

  • What exactly does more bottoms up growth for enterprise look like?
  • How does organic growth map into the direct sales model we traditionally see in enterprise?
  • How does it affect company building overall?
  • What changes in how we evaluate growth
  • How can those two different models work best together?

Transcript

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Hanne: What kind of whiskey?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Russ: It’s account-based sales.

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

Andrew: Totally.

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

Andrew: It’s definitely yes, right?

Martin: Yeah, the answer is yes.

Andrew: Because definitely yes.

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

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

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

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

Russ: No. Not at all.

Martin: The common refrain.

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

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

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

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

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

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

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

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

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

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

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

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

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

Martin: What kind of whiskey?

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

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

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

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

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

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

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

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

Hanne: So how do you think about that balance?

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

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

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

Hanne: And why do you think that was happening?

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

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

Hanne: “Oh, it must be good.”

Andrew: There’s some price signaling as well.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Hanne: Really? That’s so surprising.

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

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

Hanne: They’re sort of mind shifts.

Martin: They’re all mind shifts.

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

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

Andrew: Right, exactly.

Hanne: That’s interesting.

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

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

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

Hanne: Add on another layer, right?

Russ: Right.

Hanne: As Jeff would say.

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

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

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

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

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

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

Hanne: Illicit Slacking?

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

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

Hanne: Oh my gosh.

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

Hanne: Doughnuts.

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

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

Group: Thank you.

Written by Andrew Chen

September 24th, 2018 at 9:51 am

Posted in Uncategorized

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

without comments

Dear readers,
It was my pleasure to be on my first ever Andreessen Horowitz podcast! if you haven’t checked it out, you can subscribe here. I’ve linked to the Soundcloud and included a transcript below.

In the podcast, we cover a broad overview of growth/marketing topics, including:

  • The natural “gravity” that slows down high-growth businesses
  • What’s really happening beneath the surface of exponential growth curves
  • Organic, paid marketing, and LTV/CAC
  • Why blended CAC numbers are misleading
  • Why offline products are so compelling for acquiring customers
  • Cohort analysis and looking for “smile curves”
  • The Power User Curve aka L28
  • Why onboarding is so important for retention/churn
  • Phases of growth- why early companies focus on acquisition, but big companies focus on churn
  • High frequency versus episodic usage products
  • Why adding lots of spammy email notifications decreases your DAU/MAU
  • Network effects and why different products’ network effects are different from each other
  • Why Google measures many short sessions, versus other products focus on long sessions

Hope you enjoy it!

And thank you to my colleagues Sonal and Jeff for making this happen :)

Andrew
Palo Alto, CA

Part 1: User Acquisition

Hi everyone welcome to the a16z Podcast, I’m Sonal. Today’s episode is all about growth, one of the most top of mind questions for entrepreneurs — of all kinds of startups, and especially for consumer ones.

So joining to have this conversation, we have a16z general partners Andrew Chen and Jeff Jordan. And we cover everything from the basics of growth and defining key metrics to know, to the nuances of paid vs organic marketing and the role of network effects and more.

Part one of this conversation focuses specifically on the aspect of user acquisition for growth, and then we cut off and go into the aspects of growth for user engagement and retention, in the next episode. But first, we begin by going beyond the concept of growth hacks — and beginning with the fundamental premise that businesses do not grow themselves…

💌 Are you up to date?

Get new updates, usually once a week – featuring long-form essays with what’s going on in tech.

Sonal: So the topic we wanted to talk about today is growth, which is a big topic. What would you say are the biggest myths and misconceptions that entrepreneurs have about growth?

Andrew: You know, not only is there the misconception that it happens magically, then the next layer I think is that it’s really just like, oh, a series of, you know, tips and tricks and growth hacks that kind of keep things going as opposed to like a really rigorous understanding of, you know, how to think about growth not just, as kind of the top line thing but actually that there’s acquisition, that there’s engagement, that there’s retention, and each one of those pieces is very different than the other and you have to like tackle them systematically.

Jeff: It is a scientific discipline, done right, because it requires you to understand your business and business dynamics at this incredibly micro level.  

Sonal: I love that you said that because one of the complaints I’ve heard about “growth hacking” is that it’s just marketing by a different name, and what I’m really hearing you guys say is that there’s a systemic point of view, there’s rigor to it, there’s stages, there’s a program you build out.

Jeff: If you’re fortunate enough to achieve product-market fit and your business starts to take off, typically, you know, when in the wonderful situation do you get this hyper growth where you’ll grow year over year, you know, it’s triple digits. It’s just exploding. And then gradually the law of the large numbers starts to kick in and maybe the 100% growth becomes 50% growth the next year, and then the law of large numbers continue to kick in and there’s 25% and then it’s 12.5% and so growth tends to decay over time even in the best businesses. And so the–  

Sonal: — Didn’t you use to call it like “gravity”?  

Jeff: I called it gravity, you just would…it comes down to earth. And then the job of the entrepreneur is to be looking years down the road and say, “Okay, at some point growth in business A is going to stop and so I want to keep it going as long as I can and there’s a whole bunch of tactics to do that,” but then the other tactic, the other strategies, okay, I need new layers on the cake of growth.

At eBay the original business was an auction business in the U.S. and so, you know, some of the things we layered on early days we layered on fixed price in the U.S. — it’s not revolutionary but it really did grow then we went international. And then we layered in payment integration and each time we did that the total growth of the company would actually accelerate which is very hard to do at scale.  

Sonal: That’s the whole point… like there’s intentionality to it. It’s not an accident that you guys introduce new businesses, new layers on the cake.

Jeff: Businesses don’t grow themselves, the entrepreneur has to grow them. And, you know, occasionally, you stumble into a business that seems to almost grow itself but they’re just aren’t many of those in the world and that growth almost never persists for long periods of time unless the entrepreneur can figure out how to continue its growth.  

Sonal: Right. I remember a post you wrote actually a few years ago on “The ‘Oh, Shit Moment!’ When Growth Stops” because people are a little blindsided by it.  

Jeff: And that’s the flip side of it. You know, early on you get this great growth, you had to keep it going. When it stops your strategic options had been constrained dramatically.

Andrew: A lot of times when you’re looking at what seemingly is an exponential growth curve. In fact, it’s really something like, oh, you’re opening in a bunch of new markets, right, so there’s sort of a linear line there, but then you’re also introducing products at the same time and you’re also reducing friction and, you know, sign-ups or retention or whatever, and so, the whole combination of those things is really kind of like a whole series of accelerating pieces that looks like it’s, you know, this amazing viral growth curve. But it’s actually like so much work underneath.  <Sonal: Right.> You know, that makes that happen.

Sonal: I’ve also heard you [Andrew] talk about, being able to distinguish what is specifically driving that growth, so you don’t have this like sort of exponential-looking curve without knowing what that lever that you’re pulling to make that happen or knowing what’s happening even if it’s kind of happening naturally or organically. Can we break down some of the key metrics that are often used in these discussions including just what the definitions are and maybe just talk through how to think about them?  

Andrew: Right. Yeah, so when you look at a large aggregate number like, you know, total monthly active users, right, or you’re looking at like —   

Sonal: — “MAUs”  

Andrew: –Yeah, MAUs, right. Or you’re looking at, you know, the GMV like all the…adding up all the transactions in your marketplace–

Sonal: — So, “gross merchandise value”.

Andrew: Yup. And so, you know, when you look at something like that and if it’s going up or down, you don’t have the levers at that level to really understand like what’s really going on. You want to go a couple levels even deeper: How many new customers are you adding? As you’re growing more and more new customers, a bunch of things happen. If you’re using paid advertisement channels, things tend to get more expensive over time because — you know, your initially super, super excited core demographic of customers — like they’re gonna convert the best and as you start reaching into different geographies, different kinds of demos, all of a sudden they’re not gonna convert as well, right?

Sonal: Just to pause in that for a quick moment, you’re basically arguing that growth itself halts growth in that context.

Andrew: Right. Yeah. So the law of large numbers means that you know there’s only a fixed number of humans on the planet, there’s only a fixed number of people that are in your core demographic, right? Once you surpass a certain point, it’s not like it’s it falls off a cliff, it’s just more gradual that you know that the customer behavior really changes.  

Sonal: How do you determine what’s what when you don’t have product-market fit? Sometimes aren’t these metrics ways to figure that out or is this all when you have product-market fit… like is there a pre- and a post- difference between these?

Andrew: Very concretely, you want to understand how much of the acquisition is coming from purely organic (people discovering it, people talking to each other), as opposed to, oftentimes you’ll run into the companies that have over 50% of their acquisition coming from paid marketing and that tells you something that you’re, you know, needing to spend that much money to get people in the door.  

Sonal: Yeah. So CAC, “customer acquisition cost”, that’s what you’re talking about when you talk about acquisition.

Jeff: CAC is what it cost to acquire a user, “blended CAC” is what it costs to acquire a user on a paid basis plus then also what free users you acquire. So if you’re acquiring half your users through paid marketing you’re paying a $100 to acquire a user but half of your users are coming at zero, paid CAC is 100, blended CAC is 50.

I think blended time is a really dangerous number. Most of the best businesses in the internet age of technology haven’t spent a ton on paid acquisition. And so the truly magical businesses, you know, a lot of them aren’t buying tons of users… Amazon’s key marketing right now is free shipping. And then, yeah, the economics of paid acquisition tend to degrade overtime.  

Sonal: As it grows.

Jeff: As it grows and you just try to scale it and, you know, largely you’re cherrypicking the best users and then you’re trying to also scale the number you get to grow. I need twice as many new users this year as last year and you typically pay more so that magical LTV to CAC ratio which early on says, “Oh, we are three to one, you know, in two years it’ll probably be one and a half to one if you’re lucky,” or something like that. So we typically do try to look for these other sources of acquisition be it viral, be it, you know, some other form of non paid <crosstalk>

Sonal: I want to quickly define LTV — it’s “lifetime value” of the customer, but what does that mean?  

Jeff: When you’re showing an LTV to CAC ratio you have no idea of what you’re seeing essentially given all the potential variations of the numbers. So we will almost always go for clarity. LTV, lifetime value, should be the profits, the contribution from that user after all direct costs.

Sonal: How do we define the LTV to CAC ratio? What do the two of them in conjunction mean?  

Jeff: Well, let’s break them down. LTV is lifetime value. What you’re describing there is the incremental profit contribution for a user over the projected life of that user. So not revenue per CAC is that you know typically there’s cost associated to user. What’s the incremental contribution that the user brought from that <crosstalk> <Sonal: And that you mean the user brought to your company’s value.> To the company, yeah.

Sonal: So it’s a value of your customer to the bottom line?  

Jeff: It’s the value of each customer to the bottom line, and then you compare that to the CAC or “cost of acquired customer” to understand the leverage you have between what I need to spend to acquire a customer and how much they’re worth. If your CAC is higher than your LTV you’re sunk. Because it’s costing you more to acquire a user…

Sonal: Than the value you get out of it. Now I get it.

Jeff: …then you’re going to get out of that user.

Sonal: Yeah.  

Jeff: If it’s the opposite, at least you’re in the game. You know, I get more profit out of the user than I get the cost to acquire that user. And then there’s this dynamics on how does it scale over time, CAC tends to go up, LTV tends to go down. Because you’re, on the CAC side, you’re acquiring the less interested users over time. So they cost more to acquire and they’re worth less, and so that the LTV to CAC ratio, in our experience, almost always degrades as over time with scale.

And so, you know, when you’re in that conversation, you’re in a very specific conversation of, “Okay, how much room do you have?” “How is it gonna scale?” “You know, what’s gonna impact your CAC like a competitive thing?” So there has to be a lot, it had to be like 10 to kind of get you over that concern that oh, my goodness, those two were so close, that you have no margin for error.

Sonal: Right. This also goes back to the big picture, the layers on the cake, because if you have other layers you don’t have to only worry about one layer CAC to LTV ratio.  

Jeff: It really does affect the calculation. If it’s, I’m in a new business, and I have a whole different CAC versus, you know, LTV ratio then that’s a different conversation as well.

Sonal: And the big picture there, is that if you don’t know the difference of what’s doing what when you may get very mistaken signals, mixed signals about your business, and so you guys don’t want blended CAC because you want to know what’s driving the growth.

Andrew: I think what blended CAC gives you is it gives you a sense for at this particular moment in time, you know, what’s happening. The challenge is that when it comes to paid marketing, in particular, it’s easy to just add way more budget and a scale that than it is to scale organic or to scale SEO. So your CAC is giving you a snapshot, but then as you’re trying to scale the business you’re trying to increase everything by 100% over the next, you’re trying to double everything then all of a sudden, you know, your blended CAC starts to approach whatever your dominant channel actually looks like.  

And so if you’re spending a bunch of money then it’ll just approach whatever is your paid marketing, you know, CAC. What entrepreneurs should think about is what is the unique organic new thing that’s gonna get it in front of people, without spending a bunch of money, right?

Jeff: A lot of the best businesses have this very interesting, I’ll call it a growth hack. I mean OpenTable, when I was managing it, did not pay any money at all to acquire consumers. Like how can you do that? You know, it had millions of consumers. The restaurants would mark it OpenTable on our behalf.  

Sonal: Right.

Jeff: They go to The Slanted Door website like when they were an OpenTable customer and you’d see, you’re looking…you go there to try to get the phone number to make a reservation and they’d say, “Oh, make an online reservation.” And we then got paid to acquire that user in its core form. But that hack was a wonderful thing. It scaled with the business and got us tons of free users.


Sonal: To be fair, and this is another definition we should tease apart really quickly before we move on to more metrics, that also had a quality of network effects which we’ve talked a lot about in terms of these things growing more valuable to more people that use it… is that growth? What’s the difference there?  

Jeff: Well, the business grew into the network effect. The key tactic to build the network effect was that free acquisition of consumers that the more restaurants we had, the more attractive it was to consumers the more consumers who came, the more attractive it was to restaurants. So there is a wicked network effect.

Sonal: Like a flywheel effect, right.

Jeff: If you’re not spending anything on paid acquisition of consumers, how do you start it? And the placements that OpenTable got in the restaurant book both physically in the restaurant but particularly in the restaurant’s website was the key engine that got the network effect started. You had to manually sell some restaurants come for the tools, stay for the network, but then once the consumers got enough of a selection and started to use it, it was game over.  

Sonal: Right, that was one way of going around the bootstrapping or the chicken-egg problem and seeding a network.

Andrew: Network effects have…there’s a lot of really positive things about them and one of the big pieces is that virality is a form of like something that you get with the network. You know, the larger your network is, the more surface area, the more opportunities you have in order to encounter it, right. And so, you know, in the case of Uber (where I was recently), by seeing all the cars with the Uber logo like those are all opportunities to be like, “Oh, what is this app? I should try it out.” And so it’s mutually reinforcing: then you get more riders and then you get more drivers that are into it and so, I think all of that kind of plays together.

Jeff: I’ll bring two examples up, the pink Lyft mustache when I first got to San Francisco.  

Sonal: I remember that.

Jeff: You can see it once in the car and you’d go, “Oh, that’s pretty weird.” You see it twice in the car and you say, “Something is going on here that I don’t know about, and I have to understand what it is.” Lime is the same kind of thing.

Sonal: Right.

Jeff: They’re bright green and they glow essentially. So when someone sees one in the wild, someone bolts by them in a glowing green electric scooter and you’re just like, “Okay…what is that?” And Lime hasn’t spent a penny on consumer acquisition. <Sonal: Right.> Because their model is such that physical cue in the real world leads to it.  

Andrew: The other one I’ll throw in as well is within workplace enterprise products there’s a lot of kind of bottoms-up virality that comes out of people, you know, kind of sharing and collaborating.  

Sonal: Like with Slack.

Andrew: Yeah, like for example Slack is a great, it’s an example of this. And so, these are all kind of really unique ways that you can, you know, get acquisition for free. And so then your CAC is, you know, “zero” as a result.

Sonal: Yeah.

You guys have talked a lot, about organic. It makes it sound to me as a layperson that you don’t want paid marketing! Like what’s your views on this — is it a bad thing, is it a good thing; I don’t mean to moralize it but — help me unpack more where it’s helpful and where it’s not. Are they any rules-of-thumb to use there?

Jeff: I mean a lot of great businesses that have leveraged paid marketing. The OTA sites (online travel agencies – Priceline and Expedia) just spends, you know, they spend a GDP of many large countries in their acquisition; and then it’s often a tactic in some good business. But if it’s your primary engine, a couple of things happen: One is it tends…the acquisition economics tend to degrade over time for the reason we’re saying…  <Sonal: Right this is…> And it leaves you wide open to competition.

Sonal: It gets commoditized basically.

Jeff: If you need to buy users, I mean if you’re selling, you know, the new breed of mattress and you need to buy users and early on, you’re the only person competing for that word, flas-hforward a year or two, they’re like six new age mattress manufacturers with virtually identical products competing for the same consumer. The economics are not going to persist over time. And so, you know, one of the key questions in businesses driven by heavy user acquisition is how does the play end? You know, it usually looks pretty good at the beginning of the play but in the middle it’s starts getting a little complex and there’s tragedies at the ends.  

Sonal: There’s literally an arc.

Andrew: And I think, you know, if it is something that you’re using in conjunction with a bunch of other channels and you’re kind of accelerating things, that can be great. For example when Facebook in the past broke into new markets they started with paid marketing to get it going. And so in a case like that really paid marketing is a tactic to kind of get a network affect jumpstarted right? <Sonal: Gotcha.> And then you can kind of like pull off from that if you’d like. <Sonal: Right.>

Andrew: But if you’re super, super dependent on it and you don’t have a plan for a world that you know all the channels atregonna degrade [in] then you’re gonna be in a tough spot in a couple of years.  

Sonal: Totally. Do you have sort of a heuristic for when to stop the paid? Is there like a tipping point, you know, THIS is when you move?

Andrew: I think in terms of how much paid should you do as part of your portfolio, I think that’s the right way to think of it is it’s one out of a bunch of different channels, right? And so I would argue the following: First is you really have to measure the CAC and the LTV and be super disciplined about not spending ahead of where you want it to be and not to do it on some, you know, blended number that doesn’t make any sense. <Sonal: Right.> And then I think the other part is you really want it to be kind of a small enough minority of your channels. Such that if you were to get to a point where it turns out to be capped that you’re okay, that you can live with that.  

Sonal: Your business will survive and you continue to grow and be healthy.  

Andrew: Right, exactly, and you can still get the growth rates you want and you can still, you have such strong product-market fit that you’re able to maintain that.

Jeff: Take a couple of sector examples. You know, ecommerce, a lot of companies struggle with, “Okay, how do I get organic ecommerce traffic?” So most ecommerce companies rely heavily on paid user acquisition, you know, typically one of the interesting things is they degrade over time and they’re all competing for the same user. It’s hard for ecommerce companies in most segments to be profitable and you’d look at the same kinds of dynamic and restaurants delivery. You know, if you can’t differentiate yourself and you’re highly reliant on paid marketing, the movie typically doesn’t end really great, and so, we look for segments where there’s a balance or they come up with that really unique growth hack and they’re not then reliant on paid channels.

And then by the way, paid channels can degrade too. I mean, I’d made a couple of investment mistakes where the paid acquisitions looked really good and then actually what they were doing is they’re arbitraging something like Facebook’s early mobile attempts where the people who participated with Facebook mobile ads early got real deals. They were nowhere near kind of the price they should have been trending at, so you’re like, “Look at these user analytics. They’re awesome!” And then Facebook, you know, kind of got the equilibrium when supply and demand met and the cost went up multiples, and those businesses that looked so good early just got incredibly stressed because they had no alternative to that inflation.  

Sonal: That’s the case of platform risk where you’re dependent on the channel of on Facebook mobile or whatever the specific channel was there. But Andrew, you were also earlier talking about just a cap on how much is possible, and you both referenced the fact that things can become very competitive, that your competitors can also buy the same channels and then it gets very crowded or very expensive. So there’s multiple layers of the risk of the paid is what I’m hearing, but you have to be aware of that.

 

Andrew: Yeah. So I think on the acquisition side today, there’s a couple of really interesting opportunities that might be, you know, temporal, right, and like it may go away, right? <Sonal: Like, anything that crosstalk> For example, I think that if you have a product that is very highly visual, and I think this is, you know, one of the reasons why eSports has gotten so huge is because you have a product that naturally generates a ton of video in an age which all the platforms are trying to rush to video.  

Sonal: That’s fascinating.  

Andrew: Right? And so, you know, maybe this will be less of an opportunity coming up but like, you know, that’s a thing.  

Sonal: Why would you say that’s temporal because it seems like…  

Andrew: Because the competition will…

Sonal: …Do the same thing?

Andrew: …Yeah, will do the same thing, right. I think we’re now gonna move to a thing where all of these different kind of software experiences all are incredibly sharable. Like there’s no point these days in building a new game that doesn’t have like built-in recording and publishing the Twitch stream. And built-in tournament systems and all the community features and all that stuff that you need and, you know, I think it used to be that you would think of a game is just the actual IP but in fact, it’s sort of these layers and layers and like social interaction and content around it. And I think that’s about true as well as, all of these different brick-and-mortar experiences that are making themselves highly Instagrammable, they are adding the areas where you actually stand there and pose…  

Sonal: Oh, my god, my favorite story about this is the restaurant trend of making square plates and layouts so it really fit beautifully with Instagram. That’s like one of my favorite cases; that’s one of my favorite things in the world is when the physical world adapts to the digital!

Andrew: And then you can go the other way too which is, physical products like scooters that remind you to engage digitally. The other, fun example I always like is everyone’s had the experience now where they’re just like in the room talking and then their Amazon Echo just turns on and it’s trying to go and I’m like, you know, they have no incentive to fix that. <Sonal: Yeah.> Because it reminds you that it’s there and reminds you to talk to it.

I think the big takeaway here is that you have to really be creative and really be on the edge of what everyone’s doing, right? And so if it turns out that everyone’s really into video and they’re really into Instagram right now, you have to think about like how does my product actually fit into that trend? <Sonal: Yeah.> And if you can find it, then you can get an amazing killer way to get jumpstarted and if the trend lasts then great, accelerate it with paid marketing, accelerate it with PR, do all that stuff to kind of keep it going.

I also want to make the distinction that we’re mostly talking about growth and acquisition.

Sonal: Yesss!

Andrew: And that is what startups mostly care about in the early days, because you don’t really have any active users, right? But the other part of this is that you see all the users would show up and how active they are starts to change over time… <overlap/crosstalk>

Sonal: <overlap/crosstalk>…The engagement. Well, thank you guys for joining the a16z Podcast.

Part 2: Engagement and Retention

Hi everyone welcome to the a16z Podcast, I’m Sonal. Today’s episode continues our series on growth — the first part covered the basics of user acquisition — and so this part covers, more specifically, engagement and retention. Including, as always, key metrics and how to think about them.

Joining us to have this conversation, we again have general partners Andrew Chen and Jeff Jordan. And we cover everything from how do network effects come in to is there really a magic number or aha moment for a product? To who are the power users and what is the power user curve for measuring them. But first, we begin with what happens after the initial acquisition phase, as different kinds of users join a product or platform over time — what does that mean for engagement; and how do you analyze them, using cohort analyses?

Andrew: One of the things that you see is that people end up using these products very differently. Because the kinds of users that you’re getting are changing over time. When you look at something like rideshare, you know, all the early cohorts are basically people in urban areas. And in these days all of rideshare is more like suburban or rural folks because you’ve saturated all of the center. And so what you tend to see is as you acquire your folks, your core demographic out that actually ends up showing up in the engagement.

And so, you know, going back to a natural “gravity” to the whole thing [discussed in episode one], this gravity also hits the engagement side of things as well — and then ultimately the LTV because your users were typically getting less valuable. I may take years to see this kind of play out but that’s kind of the natural law of things.

Jeff: There is a progression in these and particularly the ones that are really successful. Early on it’s all about getting users. <Sonal: Right.>  
And it’s just like users, users, users. If you’re widely successful at doing that you run out of users (or you start running low on users) and you have to go to engagement. So Pinterest has a very high-quality problem right now. Most women in America, have downloaded the Pinterest app.

Sonal: Oh yah, I’ve had it for years.

Jeff: Some growth can come through, okay, there’s some number of women who never heard of Pinterest somewhere in the country. But much more so they need to engage and re-engage the existing audience. I mean, we love engagement from an investor standpoint because it’s just, you know, that [crosstalk]  

Sonal: [crosstalk] It shows stickiness.  

Jeff: You can often hack your way into new users. It’s really hard to hack your way into true engagement. <Sonal: Keeping them.> Someone is spending 20 minutes a day on your site. Offerup, Pinterest the major investment thesis was, “Oh, my God!” look at that engagement … And, you know, if they can scale the userbase it’s a beautiful thing.

Sonal: Right. What we mean by engagement is actually interacting with them and seeing their activity. Because to Andrew’s three points of acquisition, engagement, retention, the third piece is keeping them.

Andrew: The way that we’ll often analyze this is looking at cohort analysis.

Sonal: Yesss.

Andrew: Where we’ll look at kind of each batch of users that’s joining in each week and really start to dissect like well, how active are they really and to compare all these cohorts over time. You’re basically putting the users that come in from a particular timeframe, let’s say it’s a week, and you’re putting them into a bucket, right? And what you’re doing is you want to compare all of these different buckets against each other.

And so what you typically do is you look at a bucket of a cohort of users and you say, “Okay, well, you know, once they’ve signed up the week after, how active are they?” And what about the week after that and the week after that and you kind of like can build out a curve. And it just turns out that these curves once you’ve looked at enough of them surprisingly, human nature, they all look kind of the same. They kind of all kind of curve down and for the good ones they start to flatten out and plateau and then, for the really good ones they’ll actually swing back up and people will come back to the surface. What you want to do is you want to compare the various cohorts against each other in time to see if you can spot any trends on how the usage patterns are, increasing or decreasing. When you add a new layer to a layer cake, you might unlock a bunch of new behavior. You might unlock a bunch of new frequency that didn’t exist before. Or alternatively, over long thresholds of time, people tend to become less active as you move out of your cohort segment.  

Sonal: The cohort graduates.

Andrew: Whether or not a specific cohort of users flattens out is really important, right? Because, you know, if you’re in a world where they kind of slowly degrads and then all of a sudden it’ll actually go to zero, that means that you’re always kind of filling up the bucket — You have a leaky bucket, you’re constantly filling it up.  

Sonal: You’re always filling it up. Right.

Andrew: Right, and what happens is that gets progressively harder because, if you want to keep your overall growth rate, because that means you have to double, triple, quadruple your acquisition in order to counteract for that.

One growth accounting equation that’s often thrown around is that you know your incremental — your net — MAUs, right? So your net monthly active users equals all the new people that you’re acquiring, minus all the people that are churned, and then plus all the people that you’re resurrecting…  

Sonal: …Re-engaging.

Andrew: Re-engaging, exactly, that are coming back after they’ve churned. And so what happens is for a new startup you are completely focused on new users because you don’t really have that many users to churn, and over time as you get bigger and bigger and bigger what you find is that your churn rate starts to — it’s a percentage of your actives.

And so the evolution of most of these companies as they’re getting bigger tends to start with acquisition, then focus much more on churn and retention, and then ultimately also to layer in resurrection as well.  

Jeff: And the cohort curves have a couple of other features that I love. Usually in marketplace businesses, the best models are built off of the cohort curves.  

Sonal: Interesting!  

Jeff: Because you have to understand that degradation and where it goes. Using cohorts really give you a sense of their network effects, and network effect is the business gets more valuable to more users that use it; if it gets more valuable, your newer cohorts should behave better than your early cohorts.

Sonal: Why is that?

Jeff: Because the service is more valuable given how they are.

Sonal: Interesting. So that’s kind of a tip–

Jeff: So in OpenTable if there’s ten times more restaurants you’re going to get a whole lot more reservations per diner because you were serving more of their needs. The OpenTable cores would climb up and get more attractive over time versus, you know, we talk about typically they tend to degrade over time. If you’ve reversed the polarity and they’re growing over time it means you’ve made the business more valuable. And then you start projecting forward. Okay [crosstalk]

Sonal: What a better way to know the business is actually more valuable than thinking it’s valuable and believing your own myth.  

Jeff: In a network effects businesses we always ask, show us the cohorts. Everyone is [inaudible] on network effect, I’m a network effect But, you know, when you say, “Show me the data, cohort curves, or [crosstalk].” They don’t like it.  

Sonal: It’s like show me the money, it’s now show us the cohorts, I get it.

Jeff: They don’t lie.

Andrew: The other really interesting part is segmenting it.

Sonal: I was about to actually ask you what are “the buckets” of cohorts? Are they all demographic data?

Andrew: For a bunch of hyper-local type businesses, the reason why segmenting it based on market geography, why that’s so valuable is because then you can compare markets against each other. You can say, “Well, you know, this market which is like, has much more density in terms of the numbers of scooters behaves like this.” And you can start to draw conclusions, sort of a natural A/B test in order to do that.

And I think the similar kind of analysis you can do for B2B companies is for products that have different sized teams using it. If you have a really large team that they are all using a product, well, are they all using the product more as a result? And let’s compare that to something that maybe only has a couple. … And so this way you can start to kind of disassemble the structure of these networks and do they actually lead to higher engagement.

Jeff: Slack would be a perfect example of that, you know, just if you have five people in the organization using Slack you get one use curve. If you have the organization it’s the operating system for the organization; you have a very different curve.

Sonal: Though it’s not just an accident, you have to sort of architect it, not just expect, like, serendipity to fall into place.

Andrew: So after you get the new users, the way that you have to think about it is around quality, right? You have to make sure that the new users turn into engaged users. One of the things people often talk about is just sort of this idea of like an “a-ha” moment or a magic moment where the user really understands the true value of the product. But often that involves a bunch of setup. So, for example, you know, for all the different social products (whether that’s Twitter or Facebook or Pinterest, etc.), you have to make sure that when you first bring a new user in, they have to follow all the right people. They have to get, you know…

Sonal: It’s like the onboarding experience.

Andrew: …which, by the way, isn’t just signing up but it’s actually doing all the things to get to this a-ha where you’re like, “Oh.”

Sonal: “I get this product.”

Andrew: I get this product. It’s for me, And once you get that, then they’re kind of, you know, then you have the opportunity to keep them in this engaged state over time.

Sonal: Is that really such a thing that there is, like, an a-ha moment? Or is it sort of like a cumulative… a lot of the later users on Facebook came because everyone else was already there. Is this only tied to new users?

Andrew: In the case of Facebook actually, the fact that everyone was already there makes the a-ha moment that much more powerful, right? Because all your friends and family, they’re already there; your feed’s already full of content. And the first time that you see photos that maybe, you know, someone that you went to high school with, right? That is like whoa.

Sonal: That’s actually what happened to me. I was so excited when I saw an old friend, right?

Andrew: Right. Yeah, exactly. And so what that means is, you get the product and then afterwards, when you actually are getting these push notifications or emails that are like, “Hey, it’s someone’s birthday,” or whatever, you’ve internalized what that product is. And, you know, this moment is different for all sorts of different companies.

Jeff: I’ve always heard this referred to as the magic number. When you show up and it’s a blank slate, it’s like, “What is this about?” But they would drive you maniacally to follow people because when you got to their magic number where they had statistically correlated the number of followers with long-term engagement and retention — they would kill you to get you there, doing what felt like unnatural acts of, like, you log on, follow, and you say no, and they say yes — but when they got you there, it kicked in, and the service then quote/unquote worked for you.

A lot of the entrepreneurs I work with are trying to figure out what is my magic moment that then creates the awareness of the value prop. So take the example of Pinterest. Pinterest when it goes into a new market, first of all, they figured out they need a lot of local content to make it compelling to local users. The U.S. corpus of images doesn’t necessarily…is helpful in international markets but isn’t sufficient. And so they needed to supplement…

Sonal: …You’re right. If I’m Indian, I want, like, saris. I don’t only want, like, skirts, which I may not be able to wear in certain regions.

Jeff: Yeah. Exactly. I haven’t worn a sari in North America in a long time ;) <team laughs> But then once you have the content set, then you have to get compelling information to that individual in front of them, which, you don’t know the individual when they walk in the door, the faster they do that, the more quickly, the better the business performs; engagement goes up; retention goes up; and it works. So different entrepreneurs had to figure out what is that…what experience do they want to deliver where people get it? And then how do you engineer your way into delivering it?

Sonal: Okay. So we’ve come up through acquisition and you’ve gotten new users. They get the product. You even hopefully have a way to measure that and see and track it over time. Do you want then go into trying to get different users? Do you take your existing users? One of the things that we covered very early on is that with SaaS, you always wanna try to take existing users and upsell them because it’s way more expensive to acquire a new customer in that context. (I mean, of course, you wanna grow your customers.) How does this play out in this context? What happens next?

Jeff: In a lot of companies, it’s a progression. So almost all the early activity in a company is, “Okay, how do I get the users?” As you get users, you get more and more leverage from efforts at activation and retention and engagement. So, I mean, use Pinterest as an example: again, a very high percentage of women in America have downloaded Pinterest. Then the leverage quickly goes into, “Okay, how do I keep them engaged? Reactivate the ones who disappear?” And their acquisition efforts in the U.S. get de-emphasized and all the leverage is there except as they’re going international, they’re still in that acquisition part of the curve. And so I think the leverage changes over time based on the situation of the company. Facebook hasn’t had any users in the U.S. in forever because they have them all.  

Sonal: This kind of goes back to this portfolio approach to thinking about your users.

Andrew: Once you have an active base of users and customers, what starts to get really interesting is to really analyze what are the things that actually set that group up to be successful really long-term sticky users versus what are the behaviors and profiles where users aren’t successful, right? You actually throw your data science team on it to figure out all the different weights for both behavioral as well as the demographic and sort of profile-based stuff on there. And so one of the first things that you figure out is that each one of these products actually has this ladder of engagement where oftentimes new users show up to do something that’s, valuable but potentially infrequent. And you need to actually level them up to something that happens all the time.

For example, when you first install Dropbox, the easiest thing that you can do is you can use it to just sync your home and your work computers, right? And that’s great but really the way to get those users to become really valuable is for them to share folders at work with their colleagues. Because once they have that and people are dragging files in, and they’re really starting to collaborate on things, that’s like the next level of value that you can actually have on a daily basis versus this thing that kind of is in the background that’s just syncing your storage.

Sonal: So what are some of the things that people can then do to move those users up that “ladder of engagement”?

Andrew: Step one is really segmenting your users into this kind of engagement map, oftentimes you’ll see this visualized as a kind of state machine where you have folks that are new, you have folks that are casual, and you want to track how much they’re moving up or down in each one of these steps.

And then once you have that, then the question is, okay, well, great, how do you actually get them to move from one place to the other? First there’s like content and education; they need to know in context that they can actually do something. So for example, if you can get your users to set their home and work for a transportation product then you can maybe figure out, okay, should I prompt them in the morning to try a ride based on what the ETAs are?

Sonal: Like in the app, there would be some kind of notification.

Andrew: Like lifecycle messaging kind of factors in there. The second is of course if your product has some kind of monetary component, then you can use incentives like $10 bucks off your next subscription if you do this behavior that we know for sure gets you to the next step. And then the third thing is really just like refining the product for that particular use case, maybe there are certain kinds of products that are transacted all the time and so you maybe want to waive fees or you give some credits or you do something in order to get people to get addicted to that as a thing.

Jeff: The really interesting thing is the frequency with which something is consumed. I mean, eBay had enormous levels of engagement early on for an ecommerce app in particular. People would spend hours just browsing because early on it was about collectibles and it was about people’s passion. So if someone’s passionate about Depression-era glass, they will spend hours if you give them that depth of content to say, “Oh, my God. I just found the perfect item.”

OpenTable and Airbnb are both typically much more episodic. Most people don’t dine at fine dining restaurants with high frequency; our median user dined twice a year on OpenTable. And so that has completely different marketing implications and user implications. Measurement is probably even more important in the engagement/ retention thing because we got our data scientist to understand the different consumer journeys through our product, and then we tried to develop tactics to accelerate the journeys we wanted and limit the journeys we didn’t want. But in order to develop your strategy, you really need to understand how your users are behaving at a really refined level.  

Sonal: So what are some of the engagement metrics?

Andrew: One really important area is frequency, like, just how often are you using the product regardless of the intensity and the length of the sessions and all that other stuff. Literally just frequency of sessions. We might often ask for a daily active user divided by monthly active user ratio, and that gives you a sense for how many days is a user active?

Jeff: DAU to MAU.

Sonal: You recently put a post out on the DAU/MAU metric.  

Andrew: Right.

Sonal: And when it works and when it doesn’t. There’s a lot of nuances around when to apply it and when not to.

Andrew: DAU/MAU was very much popularized by the fact that Facebook used it, including in their public financial statements, and it really makes sense for them because they’re an advertising business and it matters a lot that people use them a lot all the time.

Sonal: It’s like counting impressions and being able to sell that to advertisers.

Andrew: Exactly, their products have historically been 60% plus daily actives over monthly actives. And that’s very high. You’re using it more than half the days in a month. On the flip side, what I was talking about in my essay about this is that DAU/MAU can tell you if something’s really high frequency and if it’s working, but a lot of times products are actually lower DAU/MAU for a very good reason because there’s sort of just a natural cadence, you know, to the product. Like, you’re not gonna get somebody who is using a travel product to use it more than a couple times per year. And yet there are many valuable travel companies, obviously.  

Sonal: So you’re saying don’t live and die by that alone.

Andrew: Exactly.

Sonal: Because it really depends on product you have, the type of nature of use it has, etc.

Andrew: You just want to make sure that the metric reflects whatever strategy that you’re putting in place. If you think that your product is a daily use product and you’re gonna monetize using a little bit of money that you’re making over a long period of time but your DAU/MAU is low, is like sub 15%, then it’s probably not gonna work.

And then a metric called L28, which is something else that was pioneered certainly early at Facebook: It’s a histogram and what you want to do is —

Sonal: — A histogram is a frequency diagram.

Andrew: Right. A frequency diagram that basically says, okay, show a bar showing how many users have visited once in that month, then twice in the month, and then three times in the month, and then four times in the month. And you kind of build that all the way out to 28 days.  

Sonal: Because there’s 28 days in the month on average.  

Andrew: And the 28 days is to remove seasonality and then a related one obviously is like L7, right? So just like last seven days. And so what you want to see…

Sonal: So would this be WAUs (“wows”)? Weekly active users? Is that really a thing, by the way? Or am I just making that up?

Andrew: Right. WAUs, DAUs over WAUs.

Jeff: You just coined it.  

Sonal: I know. Great. I coined retainment. Why not?

Andrew: Right. And so the idea with L28 or an L7 is the idea that you should actually start to see when you graph this out that there’s a group of people who just use it 28 days out of 28 days, right? And that there’s a big group of people who use it 27 days out of 28 days, and that there’s a big cluster. And so that’s how you know that you actually have a hardcore segment. And that’s really important because in all these products you typically have a core part of the network that’s driving the rest of it, whether that’s power sellers or power buyers or, in a social network the creators vs. the consumers.

Jeff: I actually have heard this referred to as a smile because the one use is always pretty big. A lot of people show up once, “I don’t understand what this is,” and disappear… And then they typically slide down, more people use it…fewer people use it two days than one, three days than two. Done right, it starts to increase at the end. So you basically get a smile. [inaudible] And I mean, that’s really powerful. Facebook had a smile. WhatsApp had a smile. Instagram had a smile. If you take a step back, it’s a precondition for investing in a venture business essentially that there’s growth. If it’s end market [inaudible] you want to see growth, but growth by itself is not sufficient. Investors love engagement. So Pinterest, the key driver of Pinterest, it was growing but the users were using it maniacally.  

Sonal: Oh, my God. I think I spent an entire Thanksgiving using Pinterest.

Jeff: It was the engagement that blew my mind much more than the growth. OfferUp has engagement that’s similar to social sites like Instagram and Snap. I mean, a ecommerce site, you know, mobile classifieds, people just sit there and troll looking for bargains, looking for interesting things.

Sonal: It’s a little addictive to see what’s nearby that you can buy. Why not? Yeah.

Jeff: So DAU to MAU, smile, all these metrics are so core to us because a big engaged audience is so rare and, as a result, it’s almost always incredibly valuable.

Andrew: And the engagement ends up being very related to acquisition because when you look at all the different acquisition loops — whether it’s paid marketing or a viral loop or whatever — all of those things are actually powered by engagement ultimately. Like, you need people to get excited about a product in order to share content off of that platform to other platforms in order to get a viral loop going. And so one of the things I was gonna also add on DAU/MAU and L28 is that they’re actually really hard to game, right? Which is fascinating.  

Sonal: Yeah, why is that?

Jeff: [inaudible] growth can be very easy to game.

Andrew: Right, exactly.  

Sonal: Why is that? What’s the difference?

Andrew: The typical approach is to say, “Well, you know, I’m gonna add in email notifications. I’m gonna do more push notifications. I’m gonna do more of this and that.” And then automatically, you know, these metrics ought to go up, right? The challenging thing is actually usually sending out more notifications will actually cause more of your casual users to show up because your hardcore users were already kind of showing up already. And what that does is that’ll increase your monthly actives number but actually not increase your daily actives as much. So I’ve actually seen cases where sending out more email decreases your DAU/MAU as opposed to increasing it.

Sonal: That’s really interesting. When you think about this portfolio of metrics, it really tells you a story about people are kind of coming but not really staying–

Andrew: If you get an email or a push notification every day, eventually you turn them off, and then you just stop. So then you get counted as a MAU for that period of time and then you lose them as a DAU. Acquisition is super easy to game because you can just spend money.

Jeff: Or you’ve got a distribution hack that works. Early on in the Facebook platform, companies literally got to a million users and it felt like minutes. Just because there were so many people on Facebook and the ones who were early just got exploding user bases. There were a number of [inaudible] whose mean number of visits was one. They never came back. So you get to see these incredibly seductive growth curves but our job is essentially to be cynical and just say, okay, we need to go be it below that because there are a lot of talented growth hackers who can drive growth. I looked at a number of businesses that had tens of millions of users and no one ever came back. [inaudible]  

Sonal: This is why engagement is so, so key.

So we’ve talked especially about the fact that growth and network effects are not the exact same thing. Because network effects by definition are that a network becomes more valuable the more users that use it. What happens on the engagement side with network effects? What are the things we should be thinking about in that context?

Jeff: Typically network effects, if they’re real, manifest in data. Things like the cohort curves improve over time. Usually there’s a decay. With network effects, there often is a reversal where they’re growing because it’s more valuable. Another smile, essentially. My diligence at OpenTable was I looked at San Francisco, which was their first market, and sales rep productivity grew over time, restaurant churn decreased over time, the number of diners per restaurant increased over time, the percentage that went that booked through OpenTable versus the restaurant’s own website moved towards OpenTable dramatically. Every metric improved. And so, you know, that’s where it both drives good engagement, but also it just improves the investment thesis.

Sonal: The value overall, right?

Andrew: One of the interesting points about network effects is that we often talk about it as if it’s a binary thing.

Sonal: Right. Or homogenous, like all network effects are equal when they’re not.

Andrew: Exactly right. When you look at the data, what you really figure out is that network effect is actually like a curve, and it’s not like a binary yes/no kind of thing. So for example, [turns to Jeff] I would guess that if you plotted the number, if you took a bunch of cities, every city was a data point, and you graphed on one side the number of restaurants in the city versus the conversion rate for that city, you would quickly find that when cities have more restaurants, the conversion rate is higher, right? I’m just guessing.

Jeff: It’s actually almost perfect but with one refinement. The number of restaurants you have as a percent of that market’s restaurant universe; because having 100 restaurants in Des Moines is different than having 100 restaurants in Manhattan.

Andrew: Makes total sense. So not only that, what you then quickly figure out is that there’s some kind of a diminishing effect to these things often in many cases. So for example, in rideshare, if you are gonna get a car called 15 minutes versus 10 minutes, that’s very meaningful. But if it’s five minutes versus two minutes, your conversion rate doesn’t actually go up.

If you can express your network effect in this kind of a manner, then what you can start to show is, okay, yeah, we have a couple new investment markets that maybe don’t have as many restaurants or don’t have as many cars but if we put money into them and invest in them and build the right products, etc. then you can grow.

You can do this kind of same analysis whether you’re talking about YouTube channels and the number of subscribers you might have, having more videos is better; I’m sure you can show that. If you go into the workplace, and you start thinking about collaboration tools, then what you ought to see is that as more people use your chat platform or your collaborative document editing platform, then the engagement on that is gonna be higher. You should be able to show that in the data by comparing a whole bunch of different teams.

Sonal: Okay… So we’ve talked about engagement and also how it applies to network effects. Are engagement and retention the same thing? I mean, in all honesty, they sound like they would be the same thing.

Jeff: There’s overlap, but they’re different.  

Andrew: Yeah, there’s overlap, right. Just to give a couple exampleS: So weather is low frequency but high retention because you’re actually gonna need to know what the weather is… <Oh right!>

Sonal: Only once a day, unless you live in San Francisco and you gotta check it, like, 20 times a day with all the microclimates.

Andrew: Right, exactly.  

Jeff: Or if you live down here, you have to check it twice a year.

Sonal: That’s true, it’s actually the same year-round.

Andrew: That’s actually what it showed, was actually more that generally people didn’t really check it that often. However, you are highly likely to have it installed and running after 90 days because it’s a reference thing. You might need it.

Sonal: It’s so important, yeah.

Andrew: Like a calculator. Versus if you look at something like games or ebooks or those kinds of products, like Really high engagement because you’re like, “All right. I’m gonna get to…I’m gonna finish this like trashy science-fiction novel that I’ve been reading. I’m just gonna get to it.” But then as soon as you’re done, you’re like, “Okay, there’s no reason why I would go back and read it again.”

Sonal: So the real difference is that engagement obviously varies depending on the product, the type of thing it is, whether it’s weather or ebook, and retention is are you still using it after X amount of time.

Jeff: And different companies have different cadences. If the average user is twice a year, retention is did they book annually. Other businesses are, did they come daily? The model behind retention is completely different and the model behind engagement is completely different.

Andrew: The chart that I’d love to really see is one that was like a bunch of different categories that’s, you know, retention versus frequency versus monetization. I think you got to be, like, really good at least on one of those axes.

Sonal: So we’ve done sort of this taxonomy of metrics. We’ve talked about the acquisition metrics. We’ve talked about some engagement metrics, primarily frequency.

Jeff: On engagement, it’s also time. Not just how frequent someone is, but just how much time did they spend.

Sonal: Right. Time spent on site, on the… piece, writing comments, not just pageviews.

Jeff: Because, I mean, the number of businesses that have great engagement is not high. Because there are only so many minutes in the day. And so, you’re just looking for where, okay, they’re just passing time and enjoying, and they both have obvious monetization associated with that behavior.

Sonal: This is why Netflix is so freaking genius because when they literally invented the format of binge-watching, which you could not do — I love it because it’s a very internet native concept — I mean, they’ve literally fucked up everyone else’s engagement numbers.

Andrew: I think that’s one of the narratives on why building consumer products is much harder these days. Cuz–

Sonal: –And, do you think it’s true or not?

Andrew: Well, because it used to be. It used to be that you were…what kind of time were you competing for in the first couple years of the smartphone. [inaudible] you were competing against literally I’m gonna stare at the back of this person’s head, or I can like use some cool app that I downloaded, right? Versus these days you actually have to take minutes away from other products.

Sonal: Yes.

Jeff: And it’s typically other [?] because the top apps are almost all done by Facebook, Amazon, Google. And you know, breaking through jusT — Marc calls it the first page, the people who are on the first screen — are just such the incumbents. And sure, most people have Facebook on the screen and YouTube on the screen and Amazon on the screen.  

Sonal: It’s hard to take that down, right?  

Jeff: You have that competition. It is a big overhang right now in consumer investing because you have to displace someone’s minutes.

Sonal: Yeah. I would add one more layer to that, at least on the content side, which is I think a lot of people make a lot of category errors because they think they’re competing with like-minded players and, in fact, when it comes to things like content and attention, you’re competing with just about anything that grabs your attention. It’s not just other media outlets. It’s…

Andrew: …Tinder.

Sonal: It’s a dating app. It’s something else.

Jeff: I’m riding in the train for an hour, I could, you know, see what my friends are doing on Facebook, watch videos on YouTube.

Sonal: It actually changes with time blocks. Xerox PARC did a really interesting study on “micro-waiting moments” and they’re literally the little snatches of time, like two seconds here and there, that you might be waiting in line or doing something, so you can do a lot of snack-sized things in that period, which is also another interesting thing to think about for how people engage with various things.

Jeff: So it’s actually funny because there’s some businesses that have good engagement where it’s one session that goes on for a while, YouTube or Netflix or something like that. There are others that are multiple small sections that in aggregate…

Sonal: …Like a podcast which might not finish in one sitting.

Jeff: …Because it’s the micro-opportunities…

Andrew: …And Google is the best example of this, right? In fact if you spend a lot of time on Google.com, you know, refining your searches and clicking around, that means actually the service is doing poorly.

Jeff: They’ve failed. Their goal is to get you to their advertisers as fast as they can.

Andrew: That’s a frequency play and a monetization play ultimately as opposed to an engagement one.  

Sonal: Yes, that’s fascinating.

Andrew: And some products are more on the engagement side.

Sonal: So sometimes you have to optimize it based on how you’re monetizing. What are some of the metrics for retention? I mean, is it just should-I-stay-or-should-I-go? Is that the retention metric?

Andrew: I think the big thing is the concept of churn. Is a tricky one in some cases like subscription Hulu, Netflix, and then also in the SaaS world. Whether or not you’re still continuing to pay or not. And that’s really obvious.

The thing that’s tricky for a lot of these consumer products especially episodic ones — and, it’s actually less whether they’ve quote-unquote churned or not — it’s actually just whether or not they’re active or inactive, and whether or not that’s happening at a rate that you in your business strategy have decided is acceptable or not. If every Halloween, you know how there’s those costume stores that open all over the place. If every Halloween, you go back and you buy a costume, but you’re inactive the rest of the time, have you churned or not? It’s not clear and I would argue you’ve not churned because you’re doing exactly what they want, which is to buy a costume every Halloween.

Sonal: It seems like it smakes assessing the retention of a consumer business very difficult.

Jeff: You adjust the time period that you’re relevant on. If the average diner dines twice a year…

Sonal: …Then that’s your time frame.

Jeff: You can [inaudible] apply that metric. Travel’s a similar thing. Airbnb is for the average user relatively infrequent. You have to tailor your look to what are they trying to do, so if you’re trying to stake up with your friends and you’re doing it twice a year, yeah, that doesn’t work. So Facebook has got a whole different setup.

Andrew: One of the things that companies can often do is to measure upstream signal. So for example, Zillow, you’re probably not gonna buy a house very often. Maybe a couple times in your life. However, what’s really interesting is they can say, “Well, you know, maybe folks aren’t buying houses but at least are we top of mind? Are they checking the houses that are going on sale in their neighborhood? Are they opening up the emails? Are they doing searches?” Right?  

Sonal: Interesting. Why do you call that “upstream”?

Andrew: In the funnel. You’re kind of going up in the funnel and you’re tracking those metrics.

Sonal: I get it now!

Andrew: As opposed to, you know, purchases. So even for OpenTable, it’s like, okay, great. Well, maybe if you’re not actually completing the reservations, are you at least checking the app for availability?

Jeff: Or what’s new restaurants where I want to dine? There’s some level of content consumption.

Sonal: So throughout this entire episode, there seems to be this interesting “dance” between architecting and discovering. Like, you might know some things upfront because you’re trying to be intentional and build these things, and then there are things that you discover along the way as your product and your views and your data evolves. How do you advise people to sort of navigate that dance?

Jeff: You iterate. You develop hypotheses. You put it out there and you test the hypothesis. I think my product’s gonna behave this way. And then, did it?

Probably the most important thing is for me, marketing can be art, marketing could be science; in the consumer internet, it’s more science. Some companies can effectively do TV campaigns, large media budgets, things like that. For me, the better companies typically just rip apart their metrics, understand the dynamics of their business, and then figure out ways to improve the business through that knowledge. And that knowledge can feed back into new product executions or new marketing strategies or new something. It’s constant iteration but it’s informed by the data at a level that on the best companies is really, really deep.

Andrew: Ultimately, you have a set of strategies that you’re trying to pursue and you pick the metrics to validate that you’re on the right track, right? And a lot of what we’ve talked about today has really been the idea that actually there’s a lot of “nature versus nurture” kind of parts to this. Your product could just be low cadence but high monetization, and so you shouldn’t look at, you know, DAU/MAU. And so you have to find really the right set of metrics that show that you’re providing value to your customers first and foremost and then really build your team and your product roadmap and everything in order to reinforce that.

Find the loops and the networks that exist within your product because those are the things that are gonna keeps your engagement improving over time even in the face of competition.

Jeff: Growth is good. Growth and engagement is really really, really good. Sonal: That’s fabulous. Well, thank you, guys, for joining the a16z Podcast.  

 

 

Written by Andrew Chen

September 4th, 2018 at 10:10 am

Posted in Uncategorized

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

without comments

Remember all the “Uber for x” startups?
A few years ago a ton of “Uber for x” startups got funded, but very few of them – maybe none? – worked out. It sounds good but ultimately most failed on the supply side. Let’s explore why.

Rideshare has better economics, at the same acquisition cost
Rideshare is special. Acquiring a broad base of labor for driving is expensive, often $300+. But then they can get requests all day. You can work 20 hours and even 50 hours a week if you want. You continually need the driver app to find new customers

Where a lot of “Uber for x” companies fall down – valet parking, car washing, massages, etc – is that demand is often infrequent and there’s spikes at a few points in the day. What’s your supply side supposed to do the rest of the time?

In other words, “Uber for x” cos often have the same cost of acquisition and cost of labor as rideshare, but can’t fill their time with work as smoothly / profitably

Marketplace outcomes are sensitive to unit economics
Rideshare networks are fickle and require a long period of being unit economic negative before they can break even, with enough scale/density. But a lot of “Uber for x” cos can never dig out of that hole, and stay unprofitable forever

This is one of the reasons why I’m bearish on food delivery as a stand-alone business in the long run. Uber can tap into their supply side and augment with food delivery earnings. Pure food companies have to get the same drivers but can’t pay as well

💌 Are you up to date?

Get new updates, usually once a week – featuring long-form essays with what’s going on in tech.

The key is to go for a different pool of workers
So what kind of “Uber for x” ideas can work? Ultimately the ones that go for a completely different pool of labor. Folks who prefer to work from home. People who don’t live near a city with rideshare. People who don’t own cars. Etc.

If you can find a different pool of labor, they still have the same motivations around flexible schedules and easy earning potential. You can use the same techniques as Uber – simple UX, transparent pricing, etc – and apply them to these marketplace opportunities

In that way, the lessons from “Uber for x” are a subset of best practices you can learn from marketplaces. You need a strong strategy to get the supply/demand flywheel going. A big market with a defensible moat. Fragmentation that can be solved w transparency and aggregation

Don’t emulate – approach from first principles, starting from the workers’ POV
IMHO “Uber for x” cos failed to become a thing because they sought to emulate ridesharing when they should have just approached their particular market from first principles. There’s still a ton of marketplace opportunities out there and am excited to see what people do!

Because all these marketplaces tend towards supply constrained, you should evaluate each opportunity/company from the POV of the supply side. Does it work for them? Can they do it 40 hours/week and stay sticky? When can you pull away subsidies? These are the key questions

The key lesson!
Supply side is 👑.

If you’re interested in more reading about Uber and marketplaces, I collected my favorite 20 links here

First published on Twitter here!

Written by Andrew Chen

August 27th, 2018 at 10:24 am

Posted in Uncategorized

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

without comments

[Today we have an essay on one of the common frameworks we use to analyze investments at Andreessen Horowitz: The Power User Curve. I worked closely with Li Jin, a partner on the investing team, to collect our ideas into this essay which she wrote. You can follow @ljin18 on Twitter for more thoughts. -Andrew]

The importance of power users
Power users drive some of the most successful companies — people who love their product, are highly engaged, and contribute a ton of value to the network. In ecommerce marketplaces it’s power sellers, in ridesharing platforms it’s power riders, and in social networks it’s influencers.

All companies want more power users, but you need to measure them before you can find (and retain) them. While DAU/MAU — dividing daily active users (DAUs) by monthly active users (MAUs or monthly actives) — is a common metric for measuring engagement, it has its shortcomings.

Since companies need a richer and more nuanced way to understand user engagement, we’re going to introduce what we’ll call the “Power User Curve” — also commonly called the activity histogram or the “L30” (coined by the Facebook growth team). It’s a histogram of users’ engagement by the total number of days they were active in a month, from 1 day out of the month to all 30 (or 28, or 31) days. While typically reflecting top-level activity like app opens or logins, it can be customized for whatever action you decide is important to measure for your product.

The Power User Curve has a number of advantages over DAU/MAU:

  • It shows if you have a hardcore, engaged segment that’s coming back every day.
  • It shows the variability among your users: some are slightly engaged, whereas others are power users. Contrast this with DAU/MAU: it’s a single number and so blurs this variance.
  • When mapped to cohorts, Power User Curves let you see if your engagement is getting better over time — which in turn helps assess product launches and performance of other feature changes.
  • Power User Curves can be shown for different user actions, not just app opens. This matters if the core activity that matters for your product is deeper in the funnel.

In other words, while the DAU/MAU gives you a single number, the Power User Curve gives entrepreneurs several avenues of analysis to assess their product’s engagement to the most addicted users — in a single snapshot, over time, and also in relation to monetization. This is useful. So how does it work?

The Power User Curve will “smile” when things are good
The shape of the Power User Curve can be left-leaning or smile-like, all of which means different things. Here’s a smile:

The Power User Curve above is for a social product, and shows the characteristic smile shape that indicates there’s a group of highly engaged users using the app daily or nearly daily. Social products with frequent user engagement like this lend themselves well to monetization via ads—there’s enough users returning frequently that the impressions can support an ad business. Remember that Facebook would have a very right-leaning smile, with 60%+ of its MAUs coming back daily.

💌 Are you up to date?

Get new updates, usually once a week – featuring long-form essays with what’s going on in tech.

What matters is that, over time, the platform is able to retain and grow its power users: successive Power User Curves should ideally show users shifting over more to the right side of the smile. As the density of the network grow, and with stronger network effects, it’s expected that there’s more reason for users to return on a daily basis.

The Power User Curve can show when strong monetization is needed
Let’s look a different example, which doesn’t smile:

This Power User Curve of a professional networking product looks quite different than that of a social product. It’s left-weighted with a mode of just 1 day of activity per month, and decays rapidly after those few days. There’s no power users. But this light engagement can be okay — not every company needs to have a smile-shaped Power User Curve, just as not every product category necessarily lends itself to an ultra-high DAU/MAU.

When there’s low engagement, what matters is that the company has a way to extract enough value from users when they are engaged. Think about an investing product like Wealthfront or networks like LinkedIn — few users are likely to actively check it on a daily basis, but that’s ok, since they have business models that aren’t tied to daily usage.

CEOs of such companies should therefore,think about: Is there a way to create revenue streams where the business can still monetize effectively despite users’ infrequent engagement? Or, who are the users using this product more frequently, and how can I get more of them? Is there something about the product — e.g. onboarding, the core experience, etc. — where a significant chunk of the user base isn’t experiencing the ‘aha moment’ that makes them “get” the product, and therefore not getting value from it right now (and if so how to get there)?

Some products should be analyzed in a 7 day timeframe – like SaaS/productivity – and others on 30 days
Another flavor of the Power User Curve is a histogram of users’ engagement for a 7-day period, also commonly called L7. The 7 day Power User Curve shows weekly actives, not monthly actives. Plotting this version can make sense if your product naturally follows a weekly cycle, for instance, if it’s a productivity/work-related product that users engage with Monday through Friday. B2B SaaS products will often find it useful to show this version, as they want to drive usage during the work week.

Note that using DAU/MAU wouldn’t be the appropriate metric for this product as it’s not designed to be a daily use product. You can also see there’s actually a smile curve through 5 days, but fewer users are using it 6-7 days, which makes sense for the power users of a workweek product like this.

CEOs of such product companies should therefore want to understand: Who are the users engaging just 1 or 2 days each week? Are there certain teams or functions within an organization that are getting more value, and how can I build out features to capture the teams with less engagement? Or, if the product is really driving a lot of value for specific departments — how can I understand their needs better and make sure we continue building in a direction that supports their daily workflow (and that we can upsell new features)?

The trend of over time can show if the product is getting more engaging over time
Plotting the Power User Curve for different WAU or MAU cohorts can also be very insightful. Over time, you can see if more of your user base are becoming power users, by seeing the shift towards higher-frequency engagement.

Here’s an example:

The Power User Curve for MAU cohorts from August through November shows a positive shift in user engagement, where a larger segment of the population is becoming active on a daily basis, and there’s more of a smile curve.

You can see when the line starts to inflect in order to see when a critical product release or marketing effort might have started to bend the curve.  This might be a place to double down, to increase engagement. For a network effects product, you might expect to see newer cohorts gradually improve as you achieve network density/liquidity.

On an ongoing basis, you can measure the success of product changes or new releases by looking at different cohorts’ Power User Curves. If a product unblocks a bunch of features for power users, you might see a gradual increase in power users.

The Power User Curve can be based on core activity, not just app opens or logins
The frequency histogram can be keyed on actions beyond the visit — did someone show up or not — you can also go with deeper user actions. For instance, you may want to plot the core activity that maps closely to how your business is monetized… or that better represents whether users are getting value from your product. This is important because it forces you to think about what really matters to measure.

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

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

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

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

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

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

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

Written by Andrew Chen

August 6th, 2018 at 9:45 am

Posted in Uncategorized

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

without comments

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

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

Are you up to date?

Get new updates, usually once a week – featuring long-form essays with what’s going on in tech.

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

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

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

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

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

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

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

In this chart, a couple categories jump out:

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

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

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

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

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

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

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

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

Written by Andrew Chen

July 23rd, 2018 at 10:00 am

Posted in Uncategorized

Required reading for marketplace startups: The 20 best essays

without comments

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

💌 Are you up to date?
Get new updates, usually once a week – featuring long-form essays with what’s going on in tech.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Written by Andrew Chen

July 10th, 2018 at 10:00 am

Posted in Uncategorized

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

without comments

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

Wrong :(

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Written by Andrew Chen

July 2nd, 2018 at 9:45 am

Posted in Uncategorized

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

without comments

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[Originally tweetstormed at @andrewchen]

Written by Andrew Chen

June 21st, 2018 at 10:00 am

Posted in Uncategorized

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

without comments

(📷 lime)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

🛴+📱=🤖🚗🚁.

(Originally tweetstormed at @andrewchen)

Written by Andrew Chen

June 18th, 2018 at 10:00 am

Posted in Uncategorized

The IRL channel: Offline to online, Online to offline

without comments

(📷 dmagazine)

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

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

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

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

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

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

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

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

(📷 Boba Guys fb page)

(📷 laweekly)

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

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

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

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

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

Written by Andrew Chen

June 12th, 2018 at 11:57 am

Posted in Uncategorized

How startups die from their addiction to paid marketing

without comments

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Dropbox has done well since then, of course!

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

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

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

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

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

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

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

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

 

Written by Andrew Chen

June 4th, 2018 at 9:45 am

Posted in Uncategorized

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

without comments

[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!

without comments

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.

without comments

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

without comments

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

without comments

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

without comments

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

without comments

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

without comments

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

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

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

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

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

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

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

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

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

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

Source: Nielsen, Dec 2016

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

… and many more examples too.

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

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

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

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

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

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

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

More discussion on this in a future writeup!

Written by Andrew Chen

June 26th, 2017 at 9:30 am

Posted in Uncategorized

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

without comments

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?

without comments

[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

without comments

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

That was a long time ago :)

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

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

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

Meet 5 new people per day, every day.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Written by Andrew Chen

January 3rd, 2017 at 10:00 am

Posted in Uncategorized

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

without comments

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)

without comments

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

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

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

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

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

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

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

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

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

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

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

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

“Does it look like it?”

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

growthhacking_trends

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

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

Where it came from

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Start with the product

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

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

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

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

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

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

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

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

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

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

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

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

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

Tactics decay

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

2016-03-16 01-44-46.062211-10x-ctt

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

without comments

2015-10-08 16.16.282015-10-08 16.16.20
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

without comments

[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

18853007256_1c7ee52ee5_z

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

without comments

BpfMqKzCUAAL7Xw

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

without comments

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

without comments

IMG_0114

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

without comments

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

without comments

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

without comments

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.

💌 Are you up to date?

Get new updates, usually once a week – featuring long-form essays with what’s going on in tech.

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

without comments

MbqWtR5lZQTvdBGP9Z-VTcl_lIiLs5EkFH9aTzxkiXE
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.

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

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

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

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

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

Here’s the video:

 

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

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

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

AcGIg0iRI07nt5o2iVFddV__znTmK890tPs2baJMMO4

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

without comments

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 Armée 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