by Y Combinator12/1/2017
This episode is a follow-up to Anu’s Growth Guide.
Craig Cannon [00:00] – Hey, this is Craig Cannon, and you’re listening to Y Combinator’s podcast. Today’s episode is with Anu Hariharan and Gustaf Alströmer. Anu and Gustaf are both partners here at YC, and this episode is kind of a followup to Anu’s growth guide which came out in July. If you have more questions about growth, we’re going to do a round two, so you can just tweet those our way and we’ll add them to the list. Alright, here we go. Alright, Gustaf, probably the best place to start here is, let’s just differentiate growth from growth hacking and all these buzzwords that people talk about.
Gustaf Alströmer [00:34] – Sure. The growth hacking term, to me, is a marketing term. It’s kind of like the new social media manager.
Craig Cannon [00:42] – Okay.
Gustaf Alströmer [00:43] – If you want to make people believe you work on growth, you call yourself a growth hacker. And if you actually work in growth, you call yourself a product manager working on growth, or an engineer, or a designer. That’s the simplest definition. I think a more kind of deeper explanation might be that you can’t hack your way to true, sustainable growth. You have to invest long term. The term growth hacking just somehow gives the idea that there’s one small thing you can do, just look for that one thing, and then you’re done. And that’s just not how it works.
Craig Cannon [01:16] – Maybe the best entry point into this then is to talk about the growth post and talk about how someone actually might go about investing in growth. Anu, where would you start?
Anu Hariharan [01:28] – Before I start on the framework for growth, the reason we actually wrote the post was because a lot of growth state CEOs come to us and ask the number one question, which is, “What does a growth team do and why should we set up a growth team?” Because let’s be honest, it’s a relatively new concept, it didn’t exist a decade ago, and Facebook was the first to pioneer that. I think as Gustaf mentioned rightfully, growth pros don’t like the term hacking because it sort of implies a gut driven approach. That’s where a growth team comes into play. It is a very data and scientific approach to scaling growth. Right after you achieve that product market fit, which we call the zero to one phase, to accelerate growth, you have to use a data driven approach and that’s when a growth team comes into play. But before you set up a growth team, the number one step you need to check is whether you have strong retention. Because too often, most companies form a growth team and then wonder why they’re not growing fast. Well, you have leaky bucket of water. The most important step is retention. Even if you look at the post, the framing is, first, check whether you have good retention. You want to check whether your users that you acquired in the long term have stable retention, which is parallel to the x axis, and also it’s good retention. For example, if you’re a social network and you have less than 10% retention and it’s stable, like, it’s meaningless, because as a social network you need to have at least 50 to 60%, right?
Anu Hariharan [03:12] – It’s more important to also benchmark whether your stable retention is good versus benchmarks or better than benchmarks before you start focusing on growth.
Craig Cannon [03:23] – Gustaf, when you were at Airbnb, how did you decide on that number? What retention number were you shooting for?
Gustaf Alströmer [03:29] – Good question. You like to give the impression that you did everything right from the beginning. When I got to Airbnb, we were just kind of trying to figure things out, so I don’t think that that was the very first thing we looked at. We were just trying to figure out, where are the metrics? What are the things, what does the funnel look like? If you have great metrics and measuring use of funnels through a product, it’s pretty easy to figure out your retention rate. It’s pretty easy to figure out where people are dropping off and where the opportunity is. When I joined Airbnb, we were just kind of scrambling to get all the different places we have the metrics together and that’s kind of where we started, just making sure that we’re tracking our user base and our hosts and our guests in one single place and there was single source of truth we can all agree on. From that, it gets easier to start measuring some of these things. And a marketplace, I think today there’s a lot of knowledge around how to measure and evaluate the performance of the marketplace. I had not seen a ton of these things before when I was at Airbnb, almost over five years ago, that I didn’t know exactly what to look at. We just started looking at all the different metrics and trying to figure out where the opportunities were. Now, today, Airbnb is quite different than most other products in that travel is a very rare occurrence. You travel once, maybe twice a year, which means that looking at retention on the guest side,
Gustaf Alströmer [04:53] – you’ll have to wait a long time, or you’ll have to have a long couple of years of historical metrics that are actually in good shape. Very often, when you start something, you never have a year’s worth of metrics. That means, it’s hard to figure that out. For most companies, you just want to try to figure out repeat purchase rate, repeat booking, repeat use of some kind, and that repeat use has to meaningful. It can’t just be like, oh, I sent them a Twitch notification and they came back, and now I have repeat use. Just the act of coming back isn’t meaningful unless you do something that gives you value from the product.
Craig Cannon [05:28] – Anu, when you were putting data together for the post, what were those metrics that other companies used to discern this is a good use case rather than just checking the site.
Anu Hariharan [05:39] – The most important thing is a sign of the action of usage of your product. In the case of Airbnb, I mean, you guys attract bookings. That actually shows, you booked a room with Airbnb, not just visited the site. In the case of Uber, it was a trip completed, right? You booked the trip and you didn’t cancel it but you actually completed the trip. In the case of Stitch Fix, this is an interesting one because, you know, now, obviously, there’s a lot of talk about how Stitch Fix has done amazingly well, but for the first four years they actually only focused on retention. They didn’t focus on growth because they had organic growth, but they realized that they had a lot of opportunity to improve retention. Their sort of north star metric for the longest time was number of second fixes in the first four months. What that means is, how often did the customer order a second Stitch Fix after the first purchase within the first four months? Because data had shown that people who did that retained much better than others, so they focused on driving that. The growth team actually was really focused on driving the number of second fixes.
Craig Cannon [06:54] – Wow. What caused them to wait four years rather than just going for it from the beginning?
Anu Hariharan [07:00] – Because if you have figured out how your product has to evolve to improve retention, then you’re much better off scaling growth because now you know how to retain them better. With a product like Stitch Fix, which is more frequency than travel because it’s not once a year, but at the end of the day, it’s a battle, and it’s a lot about style and fit. For the longest time, people still liked walking into a store. One of the reasons why Amazon didn’t do extremely well for dresses, they do well for standard clothes. Stitch Fix had to get that element right. It was about hitting the right style and the right fit. If you just scaled pretty quickly, you would’ve grown quite fast but you wouldn’t have retained a lot of users and those are wasted marketing dollars because you have to go back and reacquire them. Really, that metric and that laser focus on retention is really what helped them scale. In the process, they were getting a lot of organic growth. In the last one and a half years, they’ve actually invested now on growth as well. But only now, the north star metric has probably moved a number of first fixes, but for the first four years, it was about repeat purchase.
Craig Cannon [08:15] – Okay, gotcha.
Gustaf Alströmer [08:16] – Certainly, entrepreneurs, the investment community have gotten more sophisticated over the last five, seven years around how to think about growth and retention. Probably, seven years ago, people were more obsessed with new user growth and people going top of the funnel, and they have learned that it’s not everything, and it’s actually not even the most important thing. I think we’ve learned that quite a bit. When I do YC companies, the first two questions I try to figure out is basically, what is the kind of ideal or expected behavior? How often do I use this product? Let’s say, it’s some kind of retail product. How often am I expecting to this? Or an e-commerce product, how often am I buying this thing? If it’s a subscription, well, it should be on the regular basis. If it’s not a subscription, I still want to figure out how often I do it. And then, once you have that number, and you can get this number by thinking out from what is the ideal use case? Or, you can look at the data and see, well, how does your most typical active users or retained users actually look like? Then from there, and this is exactly how we did it at Airbnb, we looked at the most kind of typical retained users, and what is the typical kind of booking window? How long does it take between each booking? From then, you can kind of come up with, this is the scale, and then after that, you figure out, okay, how many people fit the scale? How many people actually are able to get to do it the way that the ideal metrics suggest?
Gustaf Alströmer [09:41] – And then, you figure out your retention rate. That’s kind of how you start off, and you don’t get everything right away, but that’s kind of a good way to start. Many people don’t really know what the expected time between these events should be.
Craig Cannon [09:54] – Yeah, exactly.
Gustaf Alströmer [09:55] – But it’s very important to figure that early on.
Craig Cannon [09:57] – This is kind of an easy, possible question for you guys, but just tools in general, basic things, what should someone set up in the beginning to start tracking this stuff?
Gustaf Alströmer [10:10] – When you start, you’re probably saving your user data somewhere. You’re probably saving that, some kind of–
Craig Cannon [10:16] – Hopefully.
Anu Hariharan [10:16] – Hopefully.
Gustaf Alströmer [10:19] – Some kind of database that isn’t too complicated. Very often when startups go a little bit bigger, they tend to have their data in several different places. It’s important that you have one person responsible for the single source of truth data, the data that everyone can trust so that when you’re arguing about metrics, you’re not pulling from different sources, you’re pulling from the same source. That’s critical. Now, when you make, say, funnels or experiments, often you don’t have the data stored in the same place you store user data. You might have that in, like, let’s say, Mixpanel or maybe you have your own events database, but it’s very easy to start with something like Mixpanel. Those are typically the two places where you store your data but there are many different solutions. There’s Mixpanel, there’s Amplitude, a host of them–
Anu Hariharan [11:01] – OptimizeLeads.
Gustaf Alströmer [11:01] – OptimizeLeads, funded many of these companies, but there are lots of different ones you can try out. Segment is one way to try them out all at the same time. You can use Segment, you can test all of them at the same time. But that’s usually how people start, they start with something external. The external ones are pretty good right now, and when they go to scale, and many of them, they’ll end up building something of their own for different reasons–
Craig Cannon [11:25] – Yeah, let’s dig into that. At Airbnb, at what point did you decide to roll your own analytics around this stuff?
Gustaf Alströmer [11:31] – When you have one or two data engineers that you can spare for this product. Typically, 10, 20 engineers, then you have enough that you can say, “I’m going to build this myself.” With the case of Airbnb, we started saying, okay, we have our own event database built around clients to send data to that database from different platforms, iOS, Android, web. Then you have to kind of start learning how to make sure that data is always available. There’s a lot of work that goes into just making sure data is always available for all the dashboards and the things you need the data for, and make sure that it’s correct. You have to spend a lot of time on this data. It’s actually quite a lot more work than you think. You can’t do it part time. But there are some benefits to doing it yourself when you get to that stage.
Anu Hariharan [12:20] – How big was the growth team when you guys started building your own tool?
Gustaf Alströmer [12:24] – When I started at Airbnb, we were three people on the growth team. That was excluding performance marketing, there was probably another three, or four, five people. We didn’t actually build the first database, it was a separate team, but I think when I joined we were about 35 engineers. Maybe three to six months after I joined, we decided that this was the time to start investing in internal tools, and over the next year, we basically built everything that we needed internally. It wasn’t perfect, so over the next three years, we made it perfect, but within a year after we started, we had all the components that we needed. That was basically a segmentation tool that allowed us to show different parts of Airbnb to different users. It was an event tracking database where you can send all of the events, so we run queries and look at this usage from different clients. We can do that from our own data. Then, we built an experiment framework UI, so it would automatically show us the different, say we run an A/B task, it would show us the different metrics in the control group and the experimental group. It’ll automatically calculate statistical significance and power and all these different things that you need. We didn’t have it all done in the beginning. Some of the very common ways people start is they store the data somewhere and they have a data scientist in Excel pulling the data from the different groups and then kind of figure out if that’s something significant or not.
Craig Cannon [13:39] – This is an insane amount of work. What are the things you guys did that maybe were too much work too early? And maybe things you saw, Anu, putting together the post. If you’re an early stage company, and even if you’re a later stage company, you don’t need to do that right now. This is the effective stuff to focus on in the beginning.
Gustaf Alströmer [13:56] – One of the mistakes that people make early is that the role of using data to inform the rest of the company is kind of on the founders and the CEO.
Anu Hariharan [14:07] – Yes.
Gustaf Alströmer [14:08] – One of the mistakes I see is that, this might not answer the question, but one of the mistakes I see is that data becomes someone’s responsibility who is a data engineer, but no one else’s. Then, you’re not really succeeding, because the goal is to use this data to make decisions throughout the entire organization. Then, you have to make the data accessible and available, and if you start building all the technology first, it’s not going to be 100% accessible or available, ’cause you have to build dashboards and all the kind of visualization things, and that takes a lot of time, so the number one goal is not to build everything. The number one goal is to make it available, accessible for everyone so everyone can start making decisions, and Mixpanel and Amplitude are great to start with. You can give everyone a login, and everyone has access. You can build email reports, all kind of things like you can build on day one. Just building an email report from your own system, that takes a lot of work, so there are a lot of benefits to start with something external, and I would recommend most people that.
Anu Hariharan [15:02] – That’s probably the single biggest statement we heard as well when we were interviewing all the growth experts, you need a common source of truth which is why you use these tools, or you try to build something internal if you feel that tools are not helping you build a common source of truth. But the more important thing is engagement from the CEO and alignment on taking action based on data. This is a very difficult thing for a CEO to do as well, especially if you’re a product-driven CEO, because there’s always intuition and there’s always data. It’s not necessary that data always drives all decisions. Sometimes you want to run experiments or you want to build things which are not driven by data, but I think that the CEO that sort of knows or learns how to balance both, it actually gets the most out of it. To this day, I think from all the interviews, I’d say the CEO that really stood out in that decision making is actually Zuckerberg at Facebook. Pretty much every growth expert that I’d spoken to who either worked at Facebook, still works at Facebook, or now no longer but used to, said that Zuck was very clear when the data came, to pose a question, but at the same time, if he wanted a product decision to be pursued which the data didn’t support, he was clear about that as well, saying, “Look, my gut says this is the right way to try, let’s try it.” If you use scenario B, where you’re not using data but you’re using some product intuition that you have, the growth team actually works as defense.
Anu Hariharan [16:41] – Say you make a change, and the change probably is slightly more detrimental than you thought, the growth team can actually discover the impact in minutes, not hours, not days. A great growth team can alert pretty quickly and course correct if need be. If you remember the famous slogan that Facebook had, I think it’s like, you know, I forgot the exact–
Gustaf Alströmer [17:10] – Move fast and break things.
Anu Hariharan [17:12] – Move fast and break things, they’ve dropped the break things now, because they have one billion users. And this is exactly why. You can’t afford to break things when you have one billion users.
Craig Cannon [17:21] – Well, they break things for some fraction of those users.
Anu Hariharan [17:23] – A fraction of them, yes. I think that the single most important thing is, if you decide to form a growth team, the CEO has to be aligned. It’s not the growth team’s responsibility. They won’t be set up for success if the CEO is not aligned, and so it’s extremely important that the CEO endorses the goal of the entire growth team with the company and when data is surfaced, is asking objectively the right questions and helping different teams make decisions based on the data.
Craig Cannon [17:52] – Were there moments at Airbnb where you guys were like, we’re just going to go with our gut on this one?
Gustaf Alströmer [17:56] – Oh, many times, many times. Here’s how I think about it. This is a great kind of way to talk about experimentation. When you start with a product, most of the ideas you have, like, I know what I’m building, I know what I’m building, and you build it, you talk to users, you build more things. You have a good idea. At some point, your product is so complex, so many things in that product that you make a change, and a human cannot fully comprehend all the impacts that that change will have. Software and metrics will, because software keeps track of everything. Airbnb and Facebook are certainly at this stage where if you make a significant change or a small change on a product then it will have some impact down the line that we cannot comprehend, we do not foresee that happens. In order to solve that, you run experiments. Experiments, basically, I divide the user group of Airbnb into two groups, control and experiment. I launched a change in my experiment group, and I look at all the metrics that I care about and see how they change. Let’s say I changed the date picker on the Airbnb website because I have new idea how it make it better. Well, I really want to see if the search for dates is going up or down, and at that point, you don’t really know. We run this thing called Experimenter at Airbnb, I think Facebook does as well, where you literally take the entire team in a room, you show a bunch of experiments you’ve been working on. Before you tell the results, you ask everyone what they think. Turns out, the room is very often divided and they have different opinions,
Gustaf Alströmer [19:24] – but that’s because making product decisions are really hard. It’s really, really hard when you have such a scale and such a complex product to know what’s going to perform better or worse with certain metrics. That’s why you need experimentation. Growth teams tend to attract people who are very black and white in their mindset, ’cause they can use data to hit other people in the head and be like, “No, look at the data. It shows this, so we have to do that.” That’s not a good approach, because it’s much more complicated than that. That vision of the company might not end up exactly the place of the experiment. I take Booking dot com as an example of a product that is probably very successful, but not a product that I’m super proud of to be working for. Because if you experience it, it is incredibly in your face. There’s pop ups, there’s things that go all over. It’s great for conversion, but I wonder if it’s something that a product designer would feel proud of. Now, you can still do all those things right and still build a really awesome product. Facebook and Instagram are an example of something that is very, very optimized, very, very good, but it doesn’t feel like you’re … all the time. You can get to a point where you combine a strong product vision, you have some kind of design guidelines or product guidelines, and still use experimentation and validation to make your decisions. I 100% believe you can do both.
Anu Hariharan [20:46] – Yeah.
Craig Cannon [20:47] – Is there a good example you guys know of that, probably Airbnb, where you pushed something forward and said, “This is optimizing the funnel for us. This is better.” But, something about it is just a little too hacky or whatever term you might have used where you had to pull back.
Gustaf Alströmer [21:04] – No, I think that absolutely happens. What people that work on growth tend to do bad is explain the purpose of what they’re doing. A lot of people that work in growth, let’s say you work in SEO, the term SEO sounds bad. It sounds like I’m hacking Google. Let’s talk about, what is SEO? There are a lot of people on Google, like all of us, that go to Google to look for answers. If your product does not show up on those first three links on Google, you’re not going to be clicked on, and you’re not actually there. SEO is a way to get your product to be one of those three answers. You’re not hacking people’s mind, you’re not doing anything kind of abusive by doing SEO in any way. You’re literally trying to help people that are on Google trying to find answers to questions. If Airbnb offers the best product to people that are looking for vacation rentals in the world, we should be on the top three results on Google. I think that’s a great example. You just have to change the way you talk about it and people will appreciate it a lot more. Why it’s so important to make all this effort into being on the top of Google.
Anu Hariharan [22:10] – Another example I can give is actually, Facebook versus Instagram. Facebook was actually known for, probably, on the higher end of aggressive growth tactics in the early days because when they were building their social network, there was no other social network that had more than 50 million MAUs. People thought that they would cap at 100 million, right? They were paranoid about breaking that paradigm and sort of telling the world that they can be the first global network. And they did it, but in the early days, there were lots of email marketing campaigns. If you remember, if you didn’t log into Facebook, you would get an email saying, “Somebody uploaded a photo of you.” Now, what are the odds you wouldn’t log into Facebook to see what that photo is about? But Instagram, on the other hand, A, the team that founded Instagram is very different, the DNA of Instagram is very different, and so they didn’t feel as comfortable using all the tactics of Facebook. That is a big difference in the growth tactics that the Instagram growth team uses versus Facebook’s growth team, even within the same company. In fact, when you talk to the heads of growth in the respective groups, they would say, they set something called heuristics. There are X set of things that are okay that Facebook can try, but not all of those things are okay in Instagram. The rule of thumb they generally use within each team is don’t ship something you won’t be comfortable shipping to everybody. If Instagram’s whole DNA is, “I don’t want to send an email to anyone saying your photo is tagged,”
Anu Hariharan [23:48] – then that won’t be something that would be experimented on. It has to also go with the entire flow of the experience that you envision for the product, and as long as you set those heuristics for the team, people know how much to experiment in order to achieve growth.
Craig Cannon [24:07] – What would you tell a founder who is just going to get started and they want to set their own heuristics?
Anu Hariharan [24:13] – The main thing is, look, every founder knows what’s best for the product and the vision if they’re listening to their users. They should definitely set guidelines, but also be open to adjusting guidelines as they evolve. One of the simple reasons for that is, as you expand internationally, you may have to adapt your product in different ways locally that you may have thought you would never do, right? For example, even with Instagram, which is heavily among women as users. When they launched in the Middle East, they saw all men, and they realized, well maybe we should change our targeting mechanism. For some reason, in the Middle East, we’re attracting a lot more men. One of the important elements of the growth team is to have user groups that do user research. When the team was on the ground, they realized that all these profiles that had male photos were actually women. Because people in the Middle East are worried about harassment, so they didn’t want to post their pictures. Imagine, if you don’t really understand the product or the users, you may not be able to tailor it to local tastes, so you may have to make differences in how you onboard users so that it’s easier for people to adapt locally. Always start with a set of heuristics, but decide what you’re going to flex and not flex for expansion. You may have to adjust certain things, but there are certain things which you may never want to change.
Craig Cannon [25:42] – How do you start building out a team as your company is growing that follows these principles? Gustaf, you weren’t the founder of Airbnb, but you were very early. How do you think about scaling out a growth team?
Gustaf Alströmer [25:55] – The typical team within growth have eventually all those disciplines, engineering, product, design, data science, and user research. Sometimes, there’s a specific discipline like online marketing, performance marketing, SEO. There might be very specific skillsets that you add to that but that’s typically how each team looks like. In the very, very beginning, and you need engineering. Typically, the very first person, a product manager can’t get much done without engineering. Ideally, either one of those two people are technical enough to run their own data and be able to run their own queries and analyze data. After that, I would kind of grow out from there. Maybe a typical early growth team is two engineers and a product manager, where one of those, ideally the product manager, is very savvy in understanding and using data to make decisions. Then, from there, you kind of add on the different disciplines that you need. The term growth team, the reason most companies kind of start the growth team is because it’s very hard to tell a company that now you’re all in charge of growth. I’ve almost never seen that work. Now, you have a couple of different options. You either do that and that doesn’t work, or you don’t have anyone responsible for growth. That’s kind of like how growth teams start ’cause you make one individual, or one individual team, responsible for growth, and say, “This is your area of opportunity and this is your metric that you’re trying to optimize.”
Gustaf Alströmer [27:33] – That, eventually, can teach the rest of the company to kind of slowly break out into different funnels and have kind of a similar mindset, a similar way that they’re working towards solving those problems. That’s kind of often why growth teams are called growth teams when they start. Now you can start them within the product team, you can start them after the product team. My recommendation would be to start them within the product team as a specific part of the funnel. If you start them after the product team, you’re effectively saying that the product team might not be able to learn growth as good as the growth team and therefore you should be a separate organization. I’m not sure if I believe that’s a good idea. My recommendation would be, within the product team. And it needs to have, the CEO needs to basically be onboard with this idea that they have a growth team that is moving a little bit faster, is using data a little bit more than the rest of the team. And the typical people you get on that team are people that are ready to throw away a lot of stuff. You run a lot of experiments, they’re not going to work. And you want to throw them away when they don’t work. People that are willing to try a lot of things, that are moving pretty fast,
Gustaf Alströmer [28:40] – like you’re going to do a lot of small experiments, and it’s often the velocity of experimentation that’s more important than the quality of the idea early on. Because you don’t really know where to look, you just have to have a framework to look everywhere. And then someone who uses data to kind of validate which ideas are going to work well.
Anu Hariharan [28:58] – The single source of debate, at least I heard when interviewing all the growth experts, was actually whether they should be part of product or should be separate. Actually I would say, majority of them would agree with you that it’s better to be part of product, but if they had it their way, they’d want it to be independent and report directly to the CEO. The main reason for that, like a lot of the experts cited, was the tension between the head of product and the head of growth. Because growth is so data-driven, and if you have a product lead who is usually not as data-driven, because they come at it from the product design perspective, then there can be tension. Second reason for tension is, a startup is all about growth. And so it almost feels like, you know, the growth team is owning growth, so what are the other teams owning? And I think that tension is the second tension that teams have, and there’s tension in types of experiments or changes that a growth team does versus what a product team does. Which is why I would have thought that being part of the product team helps stem that, but I think it really comes down to the communication between the head of product and the head of growth. Because you could be part of the product team, but if you’re not aligned, and if the growth head is two levels away from the CEO, and that CEO endorsement doesn’t exist, it tends to fall apart. And I think the only company that actually has done this successfully, being independent, is Facebook again, right?
Anu Hariharan [30:27] – Facebook is the only one that has still maintained it as a separate growth team, and from what I understand there are very separate accountability metrics, which is why it works. I think it works, any structure can work, as long as there’s accountability for each. The growth team owns the MAU and DAU, in Facebook. The product team owns the engagement and retention. It’s damn clear how it’s split. And so, I think whether you’re part of product or whether you’re not, as long as the metrics are clearly defined, then you have better understanding and articulation of who is doing what. Uber actually had two separate teams; they had a product team and a growth team. And at scale just 1 1/2 years ago they merged. But even when they merged, they had different subgroups. Ed Baker who was the head of growth took over head of product and head of growth, but the teams underneath were Rider, Driver, Marketplace, and Platform and a few other teams. I was actually curious as to why there was Rider, Driver, and Marketplace, right? And the interesting thing is, Rider team focuses on rider growth; the Driver team focuses on driver growth; but someone has to make sure that the supply and demand are matched, that’s the Marketplace. They were actually measuring true proficiency matches, percentage of times and ETA that the Marketplace owned, because that was the check of liquidity, versus Rider and Driver and Growth teams. And that’s worked well for Uber. These things all allude to the fact that what is most important, whether you sit as part of product or not,
Anu Hariharan [32:01] – is what is that accountability metric for that group, and how does it all roll up into your north star metric, and is it clear? Because if there’s overlap, it breaks. Right, it’s just A plus B plus C equals D, but you need to be able to build your Driver team, and sort of give each team the metric.
Gustaf Alströmer [32:19] – What can be uncomfortable for a founder is that at some point you realize that in the early days you built a product, you talked to a lot of users, spent a lot of time in interacting with users, and some point you got to scale, and you can quantifiably make a lot of decisions by just testing things. And I can imagine that being very uncomfortable for a founder, that it’s just like a completely different way of building product at scale. And that’s often why introduction of growth thinking in a startup can be a little bit painful. Because it just changes the way you do things. You do things in a very different way in the very early on and then you do things quite differently at a later stage.
Craig Cannon [32:56] – Alright guys, this has been great. We’re probably going to have to do a round two on this one. But thanks for coming in.
Gustaf Alströmer [33:02] – Thank you so much.
Anu Hariharan [33:02] – Thank you. Thanks for having us.
Craig Cannon [33:05] – Alright, thanks for listening. As always, the video and transcript are at blog.ycombinator.com, and if you have a second please subscribe and review the show. Alright, see you next week.
Y Combinator created a new model for funding early stage startups. Twice a year we invest a small amount of money ($150k) in a large number of startups (recently 200). The startups move to Silicon