Marketplace lessons from Uber, Airbnb, Bumble, and more | Ramesh Johari (Stanford professor, startup advisor)

Marketplaces are a little bit like a game of whacK-A-Mole. Like one example that I came across with one of the companies I worked with that I love is our new, you know, supply side was having a pretty bad experience. So what we decided to do is build some custom bespoke features that were really going to direct them to more experienced folks on the other side of the market. Good. And then yeah, lo and behold, you know, pretty soon those metrics start to look better. But then we're looking at, we're like, wait a second, now, you know, the existing folks on the other side are having a worse experience. So you kind of whiplash around, you're like, oh, wait a second, we better do something about that. So we take them, we try to match them up with the more experienced folks. And now suddenly a month after that, you're like, you know, wait a second and your metrics just keep moving around. And that's because the whacK-A-Mole game here is ultimately a lot of marketplace management is moving attention and inventory around. Many of the changes that are most consequential create winners and losers. And rolling with those changes is about recognizing whether the winners you've created are more important to to your business than the losers you've created in the process.

Today, my guest is Ramesh Johari. Ramesh is a professor at Stanford University, where he does research on and teaches data science methods and practices with a specific focus on the design and operation of online marketplaces. He's advised and worked with some of the biggest marketplaces in the world, including Airbnb, Uber, Stripe, Bumble, stitch fix, upwork, and many others. And in our conversation, we get super nerdy on how to build a thriving marketplace, including where to focus your resources to fuel the marketplace flywheel of growth, why data and data science is so central to building a successful marketplace, how to design a better review system, why as a founder, you shouldn't think of yourself as a marketplace founder, but instead simply as a founder. Also, how AI is going to impact data science and marketplaces and experimentation. And so much more. If you're building a marketplace business, or thinking about building a marketplace, or just curious, this episode is for you. With that, I bring you Ramesh Johari, after a short word from our sponsors. This episode is brought to you by Sanity. Your website is the heart of your growth engine. For that engine to drive big results, you need to be able to move super fast, ship new content, experiment, learn, and iterate. But most content management systems just aren't built for this. Your content teams wrestle with rigid interfaces as they build new pages. You spend endless time copying and pasting across pages and recreating content for other channels and applications. And their ideas for new experiments are squashed when developers can't build them within the constraints of outdated tech. Forward-thinking companies like Figma, Amplitude, Loom, Riot Games, Linear, and more use Sanity to build content growth engines that scale, drive innovation, and accelerate customer acquisition. With Sanity, your team can dream bigger and move faster. As the most powerful headless CMS on the market, you can tailor editorial workflows to match your business. Reuse content seamlessly across any page or channel and bring your ideas to market without developer friction. Sanity makes life better for your whole team. It's fast for developers to build with, intuitive for content managers, and it integrates seamlessly with the rest of your tech stack. Get started with Sanity's generous free plan, and as a Lenny's podcast listener, you can get a boosted plan with double the monthly usage. Head over to sanity.io/lenny to get started for free. That's sanity.io/lenny. This episode is brought to you by Hex. If you're a data person, you probably have to jump between different tools to run queries, build visualizations, write Python, and send around a lot of screenshots and CSV files. Hex brings everything together. Its powerful notebook UI lets you analyze data in SQL, Python, or no code in any combination and work together with live multiplayer and version control. And now Hex's AI tools can generate queries and code, create visualizations, and even kickstart a whole analysis for you, all from natural language prompts. It's like having an analytics copilot built right into where you're already doing your work. Then when you're ready to share, you can use Hex's drag and drop app builder to configure beautiful reports or dashboards that anyone can use. Join the hundreds of data teams like Notion, AllTrails, Loom, Mixpanel, and Algolia using Hex every day to make make their work more impactful. Sign up today at hex.tech/lenny to get a 60-day free trial of the Hex team plan. That's hex.tech/lenny. Vramesh, thank you so much for being here. Welcome to the podcast.

Thanks so much for having me, Lenny. It's great to be on.

It's great to have you on. A big thank you to Riley Newman for connecting us. Riley was the first data scientist at Airbnb and head of data science at Airbnb. and that role is actually a really good microcosm of what we're gonna be focusing on in our chat today. We're gonna get super nerdy about marketplaces and experimentation and data. I know that's your jam. Are you ready to dive in?

I really am, yeah. And I actually wanna thank Riley too. I got to know Riley when I was at Odessk, first as a research scientist, and then I directed their data science team. This is like way back in 2012. And, you know, I was looking around for people who are experts on how we think about data in marketplaces. And Riley Newman came up. And so I invited him to come talk to us at Odessa, and we've stayed in touch since then. You know, those were early days of where this industry was, and I've had a kind of lengthy career now thinking about those kinds of problems. So I'm pretty excited to talk about it with you.

Let's start broad and set a little foundation. You have a really interesting way to describe what a marketplace business even is. So Ramesh, what is a marketplace business and also why is data so important and such an integral part of building a successful marketplace business? You know, it's interesting when people sit.

Down and think about, say, Airbnb. What does Airbnb sell? Average person is like, oh, that's pretty obvious. Airbnb sells rooms. I go there to book a room I want to stay at, right? Other people say, oh, what does Uber sell? Uber sells me rides. I'd use Uber and I need to get a ride from somewhere to somewhere else. And in some sense, you're not wrong. I mean, you go there, that's a platform to get these things, but that's not what the platform is selling. That's a really important distinction. There are people on the platform that are selling that to you. The hosts on Airbnb are selling you listings. The drivers on Uber are selling you rides. But Uber and Airbnb are selling you something, the taking away of something, which is a weird thing to think about. What they're taking away is the friction of finding a place to stay. They're taking away the friction of finding a driver. In economics, we call those things transaction costs. When you take Econ 1, you learn about markets and how supply meets demand and we get prices out of that. But you don't learn until Econ 201 is that markets don't always work. And one of the reasons markets don't always work is because we have what are called market failures due to the presence of these kinds of friction. So what's a market failure? It's that Lenny wants to get from Palo Alto to Burlingame, and he can't do it. Why can't he do it? Because he doesn't have anyone to drive him. Well, why doesn't he just call someone to drive him? Well, who's Lenny supposed to call? Who are those people? Are they out there? Are they willing to drive him right now, right at like, you know, 10:00 a.m. on a Friday? Are they willing to take him somewhere? When I want to stay somewhere when I'm traveling, a friction is who's willing to give me their room? I mean, in principle, there's people who are willing to let me stay in their living room, but I don't know who they are. So those are frictions and what the marketplaces are selling you is taking the friction away. That's what you're paying them for. And it's an important observation because what that means is the marketplace's customers aren't just the people buying the rides or buying the listings. Actually, the hosts are Airbnb's customers and the drivers are also Uber's customers, right? So both sides of the marketplace are the customers of the platform. Both sides depend on the platform. to help the platform take that friction away. Because just like you want a place to stay or you want to ride, the driver is at Uber because he wants to earn money by taking people places. And the host is on Airbnb because they want to earn money by selling their listing. I think this concept that we're making money by taking transaction costs away is such a fundamental idea that's misunderstood around marketplaces, that when you're an entrepreneur starting a marketplace or thinking about your business model, I think you can be wildly off if you forget that that's the thing that's fundamentally your value proposition. And then you asked about the role of data and more broadly data science in marketplaces. So it's an interesting thing, right? The example I always love to give are the ancient agoras in Greece or Trajan's market in Rome. When you look at pictures of these things, what really stands out to me is the rock. I mean, these things are made of stone. It's not like you were going to move a booth from one place to another place without moving a lot of rock from one place to another place. So you flash forward to 2023, and here we are with technology undergirding pretty much every kind of commerce now. And it means we can architect and re-architect the marketplace kind of on the fly, and we really are doing it all the time. And these frictions that are getting taken away, they're getting taken away because of data and data science. So I really want to like highlight kind of three pieces of this for people, which I kind of, I want you to think of them as a cycle, but to start with, let's just lay them out one at a time.

Okay.

One of them is finding people to match with. So that's the problem of, I want to stay somewhere. Who is out there who's willing to let me stay with them on a given, you know, timeframe. And then if I'm a host, I have a listing. Who is out there, who's willing to stay at my place when I have it available? so that's finding matches. Then there's making the match. And so here, you know, going back to my time at oDesk, you know, a big problem that we dealt with there was if I've got multiple applicants to my job, who should I hire? Who should I interview? Right. It's a common problem we face in the real world, but now it's all remote. I don't meet these people in person. All I've got is this application they submitted to me. I need help triaging that. Okay, so that's helping make a match out of possible partners you can match with. And then finally, we make matches. Well, what do the matches tell us? Right? I mean, if you stay somewhere in Airbnb, you learn something about the host. You learn something about the listing. The host learns about you, too. And, you know, that's all information that the marketplace should feedback in. So this is where we get to rating systems and feedback systems, even passive data collection. Right? Did you leave your booking before you were supposed to leave? Well, maybe that's a sign that something can quite work out the way you want it to work out. So that's passive data collection. Did you leave five stars? That's active data collection. Get all this back in. And what does that do? Well, that lets us do a better job finding potential matches and make potential matches in the future. Every single thing I just said, finding potential matches, making matches, and then learning about those matches, and then, you know, cycling back again, that is the data science of marketplaces. And I feel like every marketplace that you could think of, in any vertical has those three problems to deal with and relies on algorithms and data science to help them solve it. And in turn, that is, I think, really the underpinning of taking those frictions away.

Many founders try to start a marketplace business, think about marketplace opportunities where they don't exist. And there's often these recurring failures of types of marketplaces that just don't work in an area. I was just writing a couple ideas down while you were chatting. Like cleaners, getting cleaners as a marketplace doesn't seem to work ever. Car wash, there's a classic failure too. Like getting tasks done for you on demand as a marketplace seems to not often work. So this might be too big of a question, but I'm just curious if anything comes up of when someone is starting a marketplace or thinking about starting a marketplace business, what do you find are the most common flaws in like, this is probably not going to work as a marketplace.

That is such a fantastic question. And I want to preface what I say with a couple of comments. So one of them is that, you know, I've worked with a lot of different marketplace companies, but anything I say is pertaining, you know, to something more sensitive. I may not name the company just over the course of the podcast. But the other more important thing I want to say is that, you know, I'm a professor at Stanford and there's a reason I'm not like a successful, scared, scaled entrepreneur of marketplaces. And that's because I probably haven't unlocked the key to exactly the question you asked, but nevertheless, I have some thoughts on it. The most important one is this. What I found talking to people who want to start what they think is a marketplace is that they think too much about a marketplace before they're a marketplace. That, in my view, is the biggest failure mode. You know, you mentioned specific things, cleaners. You know, I wonder about that, right? Is it about something about the cleaning industry? It possibly is. I don't claim to be an expert on the microeconomics of the cleaning industry. But often it's not that, it's that I thought I was building a marketplace from the beginning. And that's not the way the world works. So I'm gonna give you one vignette of this that I really like, and that's UrbanSitter. So an UrbanSitter first, UrbanSitter is a babysitting marketplace. Okay, we can talk about kind of their whole life story, but I think what's most interesting is really the early days. And in the early days, what I found interesting, the way I found out about them actually, is that we were stuck looking for some help, and I found out about this new platform. Where the clever thing was, when you used to hire a babysitter, this is like pre-venmo days, you needed cash on hand. Because when the babysitter's done at the end of the day, they're usually high school students or something, they want to get paid. They're not gonna take your IOU that you'll send them some check in the mail the next day. And unfortunately, you often don't have cash. They don't take credit card. They're high school students. That was an incredible friction to address, which is literally just, we accept credit card payments for babysitting. That's it, right? Now, from there, what happened is they took advantage of Facebook networks between parents and babysitters to build trusted introductions. So, like, let's say my sitter wasn't available, I get to know sitters in the Facebook network of that sitter, right? And once they overcame that first thing to get some liquidity onto their platform, they could move towards asking, how do I solve for these frictions that I talked about earlier? How do I solve for helping people find potential matches? How do I solve for people making those matches? You can't do that when you don't have liquidity on your platform. It's silly to tell someone, Hey, I'm really going to help you find all those drivers out there, even though I only have three drivers on my platform. That's not a friction you're solving for. So in their example, as they evolved, they actually shifted their monetization model away from billing specifically for this friction of allowing you to pay with credit cards instead to now billing for how you were interviewing and contacting sitters. They had kind of a two-part plan for that, you know, one with like a pay as you go menu, one with a more of like a subscription option. But the key thing was either way, what you were paying for now was finding potential babysitters, not paying them with a credit card. That wasn't the key thing anymore, right? So what's the moral there? The moral is a marketplace business never starts as a marketplace business. Because what we think of as a marketplace business is something which at scale is removing the friction of the two sides finding each other. But when you start, you don't have that scale. So when you start, you had better be thinking, what's my value proposition in a world in which I don't have that scale liquidity on both sides? And, you know, that's bespoke. It means different things. And in the case of Odessk, where I started that initial thing, was that remote work is a weird thing because basically you've somehow got to know that this person who you're not next to is doing what you're asking them to do. And so the initial value proposition of oDesk was to provide tools for workers to verify they were working the hours and doing the things that they said they were doing, you know, screenshots and various kinds of tracking. And then in return for that, to be able to provide guarantees on both sides, right? So now the workers could say, hey, I worked what I said, so I should get paid. And the employers could say, oh, I actually see that you worked what you said, and so I feel comfortable that I got what I paid for. That was the initial value proposition, is resolving a trust issue at a remote scale. At that point, liquidity isn't the game. It's asking what's a problem that people in this space are facing that I can deal with when I'm not a scaled marketplace. Again, with the cleaning industry, I can Comment on that from personal experience, but otherwise, I think that's the way I would think about it. It's almost never about building a marketplace when you're building a marketplace.

That's very similar to the advice I always give marketplace founders is like 90% of your problems are going to be non marketplace specific problems. They're going to be the same problems any startup is going to have. Like, how do I grow? It's going to be like the same things you need to do.

So, you know, one thing you said was that's what you tell marketplace founders. I mean, something I've actually pressed hard on in, in my own way of thinking about this, is that maybe we shouldn't talk about the concept of a marketplace founder. Really, there's founders. And I think every entrepreneur, I mean, one way to think about it, right? It's very hard to think about a human business endeavor that has not been disrupted by the potential for transactions to take place online. And if that's the case, it means literally any founder is a marketplace founder. It'll be a choice they make after they grow. as to whether they want to build a platform. I mean, to take a very, you know, a very hot recent example, no one in their right mind would have thought of OpenAI as a marketplace. Right? But OpenAI is a marketplace now. They may not want to call themselves a marketplace, but they have plugins. The plugins are flooding that platform. I don't know, you know, if people have played with it. I mean, it's not, it's not an easy thing to find the plugin you need for what you want to do. And that really is a two-sided thing now. There's the plug-in creators and there's the users and they may believe it, they may not believe it, but they are a marketplace. So I think a different way to think about it is every founder is a marketplace founder. It's going to be a choice they want to make for themselves of whether they want to become that platform. That's, I think, one. And two is because that's the case, I think one of the other challenges I find founders struggle with is you don't want to overcommit your future. And what I mean by that is that you're building up trust and you're building up a sense of what kind of business you are in your early days. If you believe that this kind of platform future awaits you or market, you know, market platform future awaits you, there may be choices you're making early on that are tying your hands later. A great example of this is, you know, when Oda started, it was because the tools they were providing were for ongoing monitoring of work. It's a very natural thing to say, we'll just take a constant fraction of the dollars that cross the platform. That all works well and good until after you become mature, some of these relationships between worker and employer last a long time. And most of the value was generated now, not so much because they're able to track each other because the trust is now there, but because they found each other, because they're able to build that relationship through Odessc. And that meant that the longer that goes on, the less value the platform is. adding into that relationship, right? And, but you're still pulling 10% of all the dollars. So what does that lead to? A word that, you know, most marketplace CEOs know well is disintermediation, which is where you were intermediating between the two parties and now they disintermediation means, you know, that essentially they're like, Hey, we don't need you anymore, right? Favorite example is we had some stuff delivered from Ikea by a Thumbtack worker once and, you know, my wife is like, oh, you know, thanks a lot. You're so reliable. He's like, Hey, great. Here's my business card. ever need me again, just call the number on the back, right? And that was it. Like Thumbtack got there one lead gen, and then, you know, we didn't need the platform anymore. And I think this issue for Odusk meant that after they merged with Elance and became Upwork, they had to think a little bit about, okay, what's the monetization strategy we want to use? How do we address this issue that longer term relationships may disintermediate? Does that mean you need a pricing plan that actually takes that into account, right? So early commitments in this case to like a particular pricing scheme, particular monetization can really tie your hands as you then realize later you actually are a platform.

I really like this message. It makes me think about Substack actually, which started as a, just a platform for newsletter writers. And then they're like, how do we make this more valuable? Cause they take a cut of everyone's revenue. And they've actually invested heavily on helping drive demand to writers, for example, me. And at this point, over 80% of my subscribers come from Substack's network. And so they've built this marketplace element exactly as you're describing, where they just found, here's a pain point. Writers need more subscribers.

How do we help them drive subscribers? So they figured out all these ways to create demand. That's like a really positive story, right? Where they managed to actually expand the frontier of their business by enabling that network. you know, for every one of those, there's unfortunately, you know, a lot of negative stories. I mean, one that I think is very painful is how eBay sort of had a lot of challenges with its seller community as it introduced, you know, more and more fine grained kind of sources of fees. And I think a lot of that, I mean, there's many, many treatises at this point written on eBay and their history and how they got to the point that they're at. But I think one kind of simple thing I do want people to think about there is, that the sellers on eBay who had matured with the platform, who had grown with it, had come to develop certain expectations about what their lives on that platform would look like. And it's understandable because a lot of these businesses, they had built their livelihood on that platform. That was their entire business. So when you now reach in and you say, I'm going to completely change the rules of the game in which your business model operates, from the perspective of those sellers, that's a breaking of a social contract that's been developed over a very long time. And so, you know, I love the Substack example because that's like, hey, let me amplify our social contract, right? But I think for every one of those, there's an eBay warning sign that you can also, you can trap yourself a little bit.

Just to close the loop on this really, I think important point, a lot of people listening to this are probably I am a marketplace founder. I'm building a marketplace.

Are going to hear this and be.

Like, oh shit, maybe I need to rethink how I think about what I'm doing. What would be your piece of advice to people like that? Is it focus on the friction point and it may be a marketplace solution? It may be a managed marketplace. It may be you own the supply. Is that the advice or how would you, what would your advice be to someone that's like, I'm building a marketplace.

How should they reframe their thinking? Let's go back to kind of thinking about this concept of a marketplace as reducing friction.

Right?

So the litmus test I like to give to someone who claims to me that they're building a marketplace business or their marketplace founder is, you know, do you have what I would call scaled liquidity on both sides of your platform? What does scaled liquidity mean? What it means in lay terms, and that's, by the way, I am a data scientist and I love to think about these quantitatively, but fundamentally, like if it doesn't pass the smell test, then you don't have to keep going with the data science. The smell test is scaled liquidity asks. do I have a lot of buyers and a lot of sellers on my platform? Or do I only have one of these two, or do I have neither? Okay. If you don't have both, you could call yourself whatever you want to call yourself. But at this moment in time, you're not a marketplace. All right. If you have one, congratulations. You've won the game on one side of the market. And now you can. You can. If you want, you have a choice point. You can lean into growth on the side that you're doing well with, right? you got a ton of users, ton of buyers, great, lean into it, get more buyers. That's one option. There's no shame in not being a marketplace. Scaling a business is scaling a business. If that's the way to do it, do it. If you decide you want to be a marketplace, then at that moment when you've got a lot of buyers but not a lot of sellers, or a lot of sellers but not a lot of buyers, the choice you're facing is how do I take advantage of having that one side scale to attract the other side? We can talk more about that, but there's a lot of ways to kind of hack that, to think about how, so, you know, to take Uber as an example, right? They would walk into new city and one thing that, you know, Uber was, was kind of commonly known for doing, this was back in the days when really Uber Black was the only service is they just hand out coupons for free rides at kind of, you know, events, parties, things like that to take people home. And that was a way of saying, Hey, we're subsidizing the drivers in the city. That's our scaled side. Now we're going to use that subsidized driver base to attract riders. Okay? So that's like how do you get that flywheel going? And again, you know, many people have written about how to take liquidity, scale liquidity on one side and use it to attract the other side. If you don't have either side, don't worry about it. Don't worry about being a marketplace. Worry about scaling one side. And in that world, it's opened up, it opens your visibility up completely into the advice of many, many startup advisors, right? People who have advice not so much about scaling a marketplace, but about scaling a startup. And I really, I want to say you got to let the ego go at that point, right? Like it's fine to articulate to people that your vision of the future is to be a platform or marketplace. As I said, virtually every business is going to have that option at some point in the modern tech-enabled economy anyway. So you're not saying something people don't already know when you tell an advisor or an investor, that. But I do think you need to be humble enough at the starting point to recognize that there's no sense in talking about a marketplace if you don't have scaling on either side yet.

And then it becomes a question of a business model unit economics of, can I build, say, a DoorDash not as a marketplace? Can I just hire a bunch of people delivering? Is this even possible in a different route?

Yeah, that's a great point. One of the things I think that's useful for people to think about here that you're raising, at some level it's kind of tied up, I think, with that question of whether I should have employees or kind of contractor freelance work on one side of the marketplace. And that's actually a pretty old question in economics. The way we talk about it often is a distinction between a market or a firm. And one of the interesting puzzles in economics, Ronald Coase is a famous economist who thought about this, is, well, if markets are so efficient, why do we need firms? Because if markets are efficient at matching labor up with things that need to get done, why would you ever need a firm? And that's one of the earliest recognitions that transaction costs are a real thing. And that's one of the things that firms are solving for. And I love what you're saying because what it's recognizing is, hey, for your frictions, The best resolution to that might not be to have a marketplace. It might actually be to have very tightly controlled labor. A good example of this actually, you know, Stitch Fix, I think one of the things that's cool about Stitch Fix is the experience that people had early on with stylists at Stitch Fix.

Am I a happy customer, by the way? I think I've fixed stuff on.

Yeah, I think one of the great things about that experience is it felt magical to have someone who kind of got to know you, right? But that depends on a relationship that doesn't feel like a freelance relationship every single time you're going back. Another example that I would pull out is pretty much any healthcare platform. So, for example, for physical therapy, it'd be weird if every time you went to a physical therapy platform, you just got randomly matched to whoever happened to be available then. So I think there's some curation that needs to happen of that relationship. Does that mean full employee? Maybe not, but it does mean you have to think a little bit about exactly as you brought up, you know, what's the nature of curation of your labor pool?

Awesome. Okay, so let's come back to a point you made early on around the importance of data and the power of data in actually making your marketplace a lot more efficient and work more effectively. So say that you have a data scientist or a data analyst or someone that is helping you optimize your marketplace? Where do you often find the biggest leverage and opportunity for a data person to help you make your marketplace more effective?

This is an incredible question, right? Because I think I could answer it, you know, a number of different ways. Like one question I think there that's kind of basic is just, what should this person be doing? And I'm not, I'm gonna actually kind of evade that question a little bit. I'm gonna give some examples of what they could do, but I feel like that's one where context matters a lot. So as an example, you know, at ride sharing or grocery delivery marketplaces, pricing means actually what do you pay for that ride or what do you pay for that delivery, right? So that's actually the price that's set at the moment you actually place there. Just to be clear, by the way, if you order from DoorDash, I don't mean the price of the restaurant. I mean, what do you pay to DoorDash, right? What's that fee? Is there a surcharge because it's, you know, surge or whatever, right? So, Okay, so that's a thing, right? But that's not really a thing in a marketplace where the platform's not setting the prices. So at Airbnb, really hosts are the ones who are charge of setting prices for their listings. So one answer to your question is, if I'm in a place like, you know, Uber, Lyft, DoorDash, I want to have good data scientists thinking about pricing, because that seems like something which should be heavily dependent on the instantaneous state of supply and demand in my marketplace, right? So that's one type of answer is, well, do I need people, data scientists working on pricing? Do I need data scientists working on search? Why search? Because maybe in my marketplace, finding the needle in the haystack is really the biggest, highest friction problem. So maybe I need a lot more data scientists to say about search. That's what I'm going to evade, okay? I'm going to focus more on something completely different, which is just a more philosophical point about what a data scientist does. So in a lot of companies today, especially, a main thing that you ask data scientists to do is build what's called a machine learning model. Machine learning model even already can mean a lot of things to a lot of different people. I'm going to focus on something very concrete. You're asking them to predict something. When I started at Odessa, this is in 2012, one of the funny things about me is I started at Odessa because I had an academic career up to that point, about 10 years, Just, you know, building mathematical models of things. I was not really very much of a data scientist up to that point. What I expected would happen is I go to industry and I'd be told, Hey, like, look how important data is, you know, and definitely my eyes were opened. And one of the first things I was asked to think about is, well, okay, someone comes to Odessa, posts a job, workers apply to that job. Predict which of these workers is most likely to be hired on that job. that was the narrow question. Okay. And so why is that a good question? Because we have a whole awesome set of tools now to solve that kind of a problem. Exactly. How do we do it? Take a lot of past data of past jobs, past applicants, past hires that were made. And then we ask, you know, these crazy big black box algorithms, all right, do the best job you can predicting who's going to get hired on this job with these applicants. And we use that data to test how well these algorithms are doing. That's like machine learning in 30 seconds. basically. Okay? So, you know, we're working on this problem, right? And then I kind of poked my head up a little bit. I go, why are we working on, what's, what is this gonna do? Well, it turns out the reason these kinds of things are important is they get used to make decisions, right? So what kind of decision do you make with that? Well, one thing you do is you say, oh, well, if I could predict who's most likely to be hired, then I should just rank people based on that. And that would be a good matching algorithm, right? That'd be a good way to sort and triage people. applicants for employers when they're screening, trying to figure out who to interview, who to hire. Great. Sounds pretty natural, right? And then you think about it a little bit. And this to me is really, it's such a passion project to get people to understand that this is why the humans in the loop that help us in businesses and making sense of data are so critical is the following problem. if you think about it a little bit, you realize, you know, what that algorithm is doing, it's really just picking up on patterns in past data, right? So yeah, that's great. This person is likely to be hired. But what we really want is something different. We're trying to add value by ranking people. So, you know, to give another example that's similar to this, when you're a marketing manager and you've got, you know, a crack data science team that's built a long-term value, lifetime value model for you, you're not going to get in trouble with anyone if you send your highest value promotions to the highest LTV customers, right? Who's going to, who's going to blame you for that? Cause you're like, oh yeah, you know, this person is worth a lot. And I sent them this promotion, you know, say that in your monthly report, nobody's going to give you a hard time. But the problem with that way of thinking is actually predicting what their lifetime value is, isn't really the question. The question is how much more are they going to spend on my platform? because I sent them that promotion. That's a very different thing. It's a differential rather than an absolute. I'm not interested in their absolute LTV. I'm absolutely interested in the difference in their LTV because I sent them this promotion. And when you look at it that way, what you realize can happen is picking up on patterns because of good predictions, right? Finding the people that have high LTV because you predicted that is very different than making good decisions, which is about saying, the difference in their LTV is going to be higher because I sent them this promotion. I love this example because I taught a class here at Stanford. It was like an executive education class. We had, you know, all the executives from a company in the room. And one of the people in the room was the chief marketing officer. And I just asked this question like, Hey, okay, let's see you got this great LTV model. Who would you send the promotions to? It's like definitely the highest LTV people. And there's a CMO in the room. And so, you know, it's like, it's a, it's a, it's a little bit of a delicate situation, like pushing back a little bit, right? I do want to be clear, there's reputational reasons you might do that anyway. I mean, I'm not trying to get away from that, but just to make the narrow point that predicting is about picking up patterns, but making decisions is about thinking about these differences. Now, why is that important? Because we learn in high school, correlation is not causation. That's a phrase everybody has heard all over the place. What does it have to do with this? Well, when we teach people to build machine learning models, we're asking them to make predictions, we're asking them to find correlations. Prediction is inherently about correlation. But when we ask people to make decisions, we're asking them to think about causation. If I make this decision, then will I actually increase the net value of my business? Will I have, by sending the promotion, increase the likelihood that this person is going to spend more on my platform? And so the first and most important thing that I feel very strongly about and what would I get a data scientist to do is no matter who they are, Right? Even if it was that person in the weeds thinking about building this, you know, prediction model for hiring, get them to be thinking in the back of their mind always that their goal is to help the business make decisions and that the distinction between causation and correlation matters a lot. You know, we could talk a lot more about how does that play out in terms of their day-to-day work, but at least at a starting point, you have to recognize that the first step is always recognition, that prediction isn't the same thing as making decisions.

So the takeaway here is help as a data team and as a data scientist on the team has helped the business make predictions. Are there a couple more examples you could share? Just like, what is an example of a decision that you think they often should be making and using data to help them with?

Maybe the right way, the right frame of reference for this and the word that an academic would use is causal inference, right? So what we're changing from is machine learning to causal inference. So let's think that through in a couple of different use cases that are related to that, you know, marketplace data science flywheel I talked about earlier, you know, finding matches, making matches, and then learning about matches. So finding matches, like you said, a core part of that is search and recommendation. And each of those relies on rankings. So, you know, I want to be able to rank order, let's say I go do a search on Airbnb, I want to rank order the different listings in the marketplace, right? At some level, it's true that what I'm trying to do there is I'm trying to just predict what are you going to like the most, right? But I think there's an important piece of that also, which is that I want to think a little bit about the distinction between two different ranking algorithms. That's the real decision that's being made. And when I think about the distinction between two different ranking algorithms, I don't want to be only comparing them in terms of how well they recreate the choices people made in the past. The way I'm really going to evaluate those is in my market, Does one of those lead to better matches or more matches than the other one? Right? So Airbnb as a business, like what are the most obvious core metrics? It's bookings and revenue. So you're going to want to ask a very basic question. If I use the ranking algorithm Lenny just developed last night versus the ranking algorithm Ramesh developed last week, does Lenny's ranking algorithm lead to more bookings than Ramesh's ranking algorithm? And it's so important to put it that way starkly because that's so different a question than, Does Lenny's ranking algorithm do a better job of predicting over the last two years what bookings people made than Ramesh's ranking algorithm? Okay. So that's, I think at that level, then we talked a little bit about ranking at the point of making a match. And I think that's where this kind of hiring issue popped up, right? Because in the end, while we might have these predictive algorithms to rank who you're going to hire, that's not the important question. Interestingly, the important question is actually to evaluate the quality of the match that's made. And we would do that through the next step of that flywheel. We'd ask ourselves, what ratings did they give back to that freelancer? Do they hire that freelancer again? So you're comparing two different algorithms, not through their ability to recreate the past, but their ability to make matches in the future that can be objectively evaluated to say, hey, I increased the value of the business. I actually made better matches this way. And then rating systems, I think we could talk quite a bit about kind of a similar A similar phenomenon there too.

This episode is brought to you by Eppo. Eppo is a next generation A/B testing and feature management platform built by alums of Airbnb and Snowflake for modern growth teams. Companies like Twitch, Miro, ClickUp, and DraftKings rely on Eppo to power their experiments. Experimentation is increasingly essential for driving growth and for understanding the performance of new features. And Eppo helps you increase experimentation velocity while unlocking rigorous, deep analysis in a way that no other commercial tool does. When I was at Airbnb, one of the things that I loved most was our experimentation platform, where I could set up experiments easily, troubleshoot issues, and analyze performance all on my own. Eppo does all that and more, with advanced statistical methods that can help you shave weeks off experiment time, an accessible UI for diving deeper into performance, and out-of-the-box box reporting that helps you avoid annoying, prolonged analytic cycles. Eppo also makes it easy for you to share experiment insights with your team, sparking new ideas for the A/B testing flywheel. Eppo powers experimentation across every use case, including product, growth, machine learning, monetization, and email marketing. Check out Eppo at geteppo.com/lenny and 10x your experiment velocity. That's geteppo.com .com/lenny. Yeah, I would actually love to talk about rating systems, but there's kind of an implication in everything you're describing of running an experiment versus looking at what would have happened in the previous world. You made a change, run experiments, see if it actually makes an impact on bookings and revenue. And that leads me to a question I wanted to ask, which is with experiments there's kind of this classic challenge and always elephant in the room of, if you just run a bunch of experiments, you're kind of gonna Micro optimize leads to these local minima, local maxima, and you may miss big opportunities and big unlocks if you're just extremely experiment driven. You spent a lot of time thinking about experimentation. What have you learned or what advice do you have for people to either be less worried about micro optimizing and missing something big or just finding a balance with running experiments, but also creating opportunity to find a huge new opportunity?

Yeah, first of all, I'm really glad you broached the E word. I was dancing around it and I'm really glad that we talked about experiments. Because yeah, that, you know, one of the big lessons of this like recent conversation we've been having is just how could you possibly know that difference without doing something like experimenting? Okay, so yeah, I am a big believer in experiments. I mean, I'll just lay those cards on the table. I love working with businesses that think experiments are important to helping make good decisions. Now, all that said, I am also someone who feels pretty strongly about this exact issue that you're raising, which is you can't experiment your way out of everything. And one way, you know, that one frame I like to give people is that although you might say you're an experiment-driven business, I, you know, some businesses will proclaim we literally test everything. But that kind of leaves aside a little bit, is there's a lot of degrees of freedom in what it means to test everything. because ultimately what's getting built and tested are choices that are made through the organizational structure of the data scientists, the PMs, the engineers, everybody's on the, you know, before we're running experiments, we're actually thinking about even what's worth experimenting, right? Like what designs are we coming up with? So that's one. And the other big one is how long do we run these experiments? Okay, that's a big choice. And what I genuinely believe And I think there's a paper we can link to later that I'll point your readers to as well, not my paper from some folks at Microsoft. What I generally believe is that we're risk averse on both these two dimensions. That what people decide to test in a world that has promoted experimentation for everything tends to be more incremental by design. Okay? And we'll come back to by actually answer the because in a second. So that's one. And two is, people tend to run experiments for a long time and probably longer than they should. Okay, now what do I mean by these two things? So what's interesting to me about this dynamic is experiments don't live in a vacuum. Companies have incentives. And in companies that really go all in on experimentation, one of the things that gets wrapped up in that is the incentives around experiments. Because if you go all in on experiments, a common thing you'll see is, data scientists get judged based on how many wins they had that quarter. And right, how do you get more wins? Well, it's easier to get wins when you're being incremental. And because it's important to have wins, you have to run them long enough to demonstrate that they're really wins, right? You're less willing to cut something off in exchange for trying something riskier. So the big lesson is Microsoft paper, it's called AB testing with what's called fat tails, which in lay terms just means you're running a business where there's potentially big opportunities out there. If you look at kind of the effects of the experiments that you run, but there's a couple of lessons there about both trying a lot more stuff that's not all risk averse and not necessarily running everything for so long. So really getting velocity up. So you can see that there's a big incentive problem there, right? Because the culture that says it's okay to fail big actually requires changing the terminology of wins. This is one of the things I hate most in AB testing, I have to say. I get where it comes from. Experimentation was never historically in science about winners and losers. It'd be weird if Ronald Fisher, who's kind of the father of experimentation with his agriculture experiments, talked about winners. I don't think that's necessarily how he talked about things. Experimentation is always very hypothesis driven. It's about what are you learning? And that's really an important distinction because what it means is if I go with something big, risky, and it quote unquote fails, meaning that doesn't win. Nevertheless, if I was being rigorous about what hypotheses that's testing about my business, I'm potentially learning a lot. So a great example of this kind of thing is that there's an important feature of marketplaces is badging. So sometimes it's really important to have badges on your kind of top rated profiles or whatever when people are searching. And without going too far into the details, a common kind of finding with badges is that badges you think are going to be great actually turn out to be terrible. And one reason they're terrible is they focus too much attention on the badged folks and pull too much attention away from the unbaged folks, right? And if we judge that only in terms of winners and losers, you throw the baby out with the bathwater. You're like, oh, well, that badging idea was terrible. So you'll ditch that with no badges. But that's not what it's telling you. It's teaching you something. about how inventory is being reallocated, how attention's being redirected through the badges. And you really want to think not in terms of winning and losing, but learning. So learning is a win. And I feel that that's a cultural thing fundamentally. It's very hard to somehow attach dollars and cents at the top to data scientists running experiments that fail but learn. And ultimately, I think getting into that space where you experiment more, meaning you don't run all your experiments for quite as long, and you accept the willingness to try experiments that are into the tails where you might fail bigger is a cultural thing. It's about saying that we're allowing that to be part of our social contract with our data scientists or actually our employee contract with our data scientists, that not everything is just about how many launches you had and how many wins there were. It's okay to say that's how I want to use experimentation, but if you're going to use it that way, Then I would say, don't be, we experiment everything business. Because then I think you need some other way to deal with these big changes that teach the whole company a lot, but maybe can't fall into the incentives you've created for your data scientists.

This badging example is, I don't know if you're referring to the Airbnb example, but actually led the launch of Superhost at Airbnb, which is like the ultimate badge on Airbnb. And there was a lot of concern from the data team that it would destroy the marketplace because they've built, as you described, this very well-crafted ranking algorithm with just like a prediction of exactly as you described, which listings that guest is most likely to book and be successful booking. And then we're about to throw a badge on random listings in the results. And so this one data scientist in our team is like, no, we can't do this. This is insane. We're going to destroy it all. and we still went ahead with it. We ran an experiment showing the badge to some people and some not actually, it was no impact at all. Which is like Super Roast itself had no impact at all on the business as far as we could tell initially, which is also bittersweet because it felt like why did we even work on this thing? There's like a slight benefit where hosts felt better. They felt more satisfied with being a host, but I went exactly through what you described, so that's pretty funny.

Without necessarily like going into the weeds on like the data science of Superhost, I think there's a lot wrapped up in what you said. I guess another thing I'll say is that I'm a big believer that you don't throw your understanding of the business out the window when you process experiment results. And it's partly, I guess what I mean by this is data science is really about accumulation of evidence. It's never about one finding in isolation. And so another kind of trap, I think, is to sometimes say, well, I hit stat sig on my AB test, green light, it's all go. And I think you had Ronnie Kohavi on your show and he made a similar point that there are different levels of evidence. And it's just having an outlier AB test that goes against everything you believe about your business doesn't mean that you somehow have controverted all your knowledge. I think that's one side of it. The other thing is you can't always measure everything that's important that's needed to really develop like a full sense. So with Superhost, right? One of the things that's hard to measure is the long-term impact of Superhost. Because in the short run, Superhost causes a rebalancing of inventory. There's going to be winners and losers. Part of Superhost is actually about retaining hosts that get the badge over a longer period of time. Recognizing that hypothesis actually says something about maybe how long the experiment needs to be run or what kinds of data analyses need to be done. And in the end, if you can't do that, you can't run it long enough, or you can't do that data analysis due to sparsity of data or lack of data to address the question, it matters what you bring to the table, right? What are your beliefs about that? So what I like to tell people to do there is I like to push people to be what's called quantified rather than data driven. which is, okay, fine, some things we can't measure, right? But, you know, maybe you've got a leadership team with different beliefs about what they think the retention value of Superhost is going to be. And they might be all over the place. You can process your experiment results in the context of these competing beliefs. It's almost like a prediction market kind of a thing. And start asking, well, okay, like if this is what we believe about our business, this is what the data is telling us out of the experiment. Let's put those two together and ask, is this, is this enough for us to make the bet that we're still gonna go with it, even though maybe that short term test you ran was flat?

That's actually exactly how I think of Superhost looking back. It was a great idea. I'm really happy. I can't even imagine Airbnb without that. Even though there's no evidence, at least initially, that it made any impact, I'm guessing they looked at it again and maybe there's something that came out of it. But even if it had no impact, It just feels like it made the marketplace better. And that was a big learning for me. It doesn't need to always drive a metric that you can measure. There's just like, this is the way it should work.

So one of the reasons the thing you said happens is because marketplaces are a little bit like a game of WhacK-A-Mole. Okay. And what I mean by that is so narrowly in the context of Superhost, because you're redirecting attention to some hosts at the expense of others, it's not even obvious if bookings can really go up. Okay, maybe you get lucky and maybe you get a bunch more bookings. One reason you probably wouldn't expect that in the first place is there's only a limited number of super hosts. How many more bookings are they going to be absorbing because of all this extra attention? And you're taking attention away from other people. Without doing any data analysis, my prior would have been that bookings should probably go down, right? And like one example that I came across with one of the companies I worked with that I love is, you know, we were working together over a period of time and And in, in a month we looked at some of the data and it suggested that our new, you know, supply side was having a pretty bad experience. So, oh, we gotta do something about this. So what we decided to do is build some custom bespoke features that we're really gonna direct them to more experienced folks on the other side of the market. Good. And then yeah, lo and behold, you know, pretty soon those metrics start to look better, but then we're looking at, we're like, wait a second, now, you know, the existing folks on the other side are having a worse experience. So you kind of whiplash around, you're like, oh, wait a second, we better do something about that. So we take them, we try to match them up with the more experienced folks. And now suddenly, a month after that, you're like, you know, wait a second. And your metrics just keep moving around. And that's because the whacK-A-Mole game here is ultimately a lot of marketplace management is moving attention and inventory around. Sometimes you get lucky and you really expand the pie for everybody. But I think, Servais Salen, who was a CFO at Upwork, that I got to know there and then went to Thumbtack later. He had this line when he came to visit our class that I love, which is, you have to recognize when you run marketplaces that many of the changes that are most consequential create winners and losers. And rolling with those changes is about recognizing whether the winners you've created are more important to your business view than the losers you've created in the process. And it's a hard reality because nobody likes to articulate the idea that a feature change is hurting some of the people in your marketplace. But because of this fundamental constraint baked into how marketplaces work, many of the things that we would choose to do and the reallocation they create can't necessarily create observed high expanding wins in the short run. You're often making bets that that's where you're headed partly through the reallocation that you're doing right now. And so I think that's what's interesting about Superhost to me is that partly points to thinking about what's the objective you would have defined, the metric you would have defined in the short run that captures this idea of a trade-off.

That's a great way to think about it. I wanted to come back to this idea you're sharing of maybe you should run experiments more quickly, not wait for statsig, have a culture of learning versus impact. In practice, it's very difficult because People are measured by impact. There's performance reviews, there's promotions, there's how much impact did this team drive? We're going to look at their experiment results. You've worked with a lot of marketplace companies, a lot of different companies. Is there anything you've seen about just like how to something you could do to help the company shift and actually work this way while also recognizing success and who's doing great, who's not, which team's driving impact, who's not?

Interestingly, it's actually sort of an active area of research for me now. What I mean by active area of research is I care a lot about the incentives. that we create for data science through how we set up reward mechanisms. So there's a couple things I think that could be helpful that are maybe, you know, there may be a little bit less about, like, maybe I'm not going to directly answer the question you asked because I think that's a hard one, right? I think I recognize that measurement on impact is critical. Well, let me answer that actually from the most obvious way first. I think there's a cultural issue here. that's really critical. One of the things I often find is that my PhD students, our PhD students here often go off and get great data scientist jobs. And in one sense, they're doing amazing stuff. They apply really technically sophisticated methods. But when I look at the problems they're working on, they're often more at the margins of the business than they should be. And it's a cultural thing. It's basically because if you're measured narrowly on impact and that's all anyone sees around you, then It's very hard to engage with the creative aspect of business change and the strategic aspects of business change. So the cultural aspect there is, I think it's partly incumbent on the leaders to expect something more of their data scientists. And what I mean by expect more is that you expect them to do more than deliver narrowly defined statistically rigorous results to you in their reports. You're actually expecting them to talk also about what they're learning about the business in the process. So where that's headed is this concept of being hypothesis driven, which is like the technical phrase. What does that mean again in a more lay sense? What it means is tests aren't going to be defined only in terms of winners and losers, but each test should also say something about what will we learn about a business flow, a funnel, preferences of the guests, preferences of the hosts, right? what will we learn about their demand elasticity if we're changing prices around, these kinds of things? So it's possible to articulate in an experiment doc, a launch doc, what are the hypotheses that are being tested? So that's one thing I would say. It's just culturally setting the norms that learning is part of the discourse. And it's expected actually, I think, is important. But the other thing I would say that's maybe a little bit more about programmatically, what could a team a data science platform team do. You know, a funny thing about experiments is that we throw past learning away effectively. And this is just an artifact of how we analyze experiments, that the methods used, the statistical methods used typically, you know, p-values, confidence intervals, these fall in a branch of statistics known as frequentist statistics. And the idea behind frequentist statistics, you know, without being overly technical, is just I let the data speak for itself. There's no beliefs brought to the table. about where that data came from. But if you think about this in like a company and AB testing a company, it's like a weird thing, right? Because I might have run a thousand AB tests in the past on this exact same, you know, button or call to action or color. And now I'm gonna completely ignore that and focus only on this. So there's, there's ways to take the past into account to build what's called a prior belief before I run an experiment and now take the data from the experiment, connect it with the prior to come up with a conclusion of like, okay, in light of the past, plus this experiment, what's it telling me about the future? And that falls broadly under the category of what's called Bayesian A/B testing. So that's one of the things I think can help culturally, weirdly. It's like a super technical thing, but I think it can help culturally because what it's doing is it's now rewarding people for contributing information to that prior. And I think it then becomes possible to say, oh, your experiment that failed actually moved our prior. And that's an important thing because by doing so, you're now altering kind of how we're gonna think about this flow or this pricing plan in all future experiments, right? So there's like an information positive externality, positive network effect that's generated for the rest of your business. If I can somehow encode what you learned into those, the analysis of future experiments. So this is one thing I, there's a strong connection between the culture and incentives of AB testing. and the ability to actually incorporate past learning into these prior beliefs.

I love that you're doing research in this area. We should bring you back when you've completed it and have the ultimate answer for everyone to change how they operate.

Yeah. One of the great things about professors is we never complete anything and never have ultimate answers. So, oh boy. Yeah.

I'll do my best, though. This touches on a really interesting concept that you shared with me around how learning, just learning isn't free. People think that they could just learn a bunch of stuff and there's not a cost to it. I'd love for you to just chat a bit about what that means.

Let me start with an anecdote that I just absolutely love this anecdote. I use it every year in class. So yeah, I was talking to a real estate platform and they had a marketing data science manager who's basically responsible as many marketing managers are for allocation of ad spend across different channels. And what they discovered had happened at the end of the year is on one hand, the team had done great, but the manager had held out some subset of arriving visitors, not shown them any of the innovations they were making.

Like a holdout group.

Yeah, exactly. What's called a holdout group in experimentation. And the weird thing about this holdout is it wasn't authorized. That's not the way things are supposed to work. They've got their ad spend, allocate out your ad spend, great. So at the end of the year, they looked at the whole out, they're like, wow, that cost us a couple million dollars, something in that range. And it's not a trivial amount of money. What's the deal? What were you thinking, basically? And of course the answer was, well, I get that I cost you that much. But number one, now you know what my team's worth. And number two, you would never have had that answer unless I'd done that on my own. Why is that so powerful? I think what I find so interesting about experiments is that when you don't know something, it seems not even a question that you would allocate some of your samples to all options, right? Treatment and control. I have two different ways of doing something. I don't know which one's better, so of course I'll give some samples to each of them. After the fact, you're like, oh, treatment was better. What the heck were we thinking? Why'd we give all those samples to control, right? That doesn't make any sense now. There's this great Seinfeld clip where, you know, they get the bill at the, he mentions getting a bill at the end of a large, luxurious meal, and people stare at the bill. They're like, We're not hungry now. Why'd we order all this food? Right? So it's the same thing. Like, I mean, you know, treatment's better now. Why'd you waste all those samples on control? And I think that is such a powerful observation that you have to put yourself in the frame of reference of when you didn't have the answer. And at that moment, What you're essentially saying to yourself is that it's worth paying to learn the answer. I think it sounds obvious the way we're saying it now, or this anecdote of the marketing manager and the holdout sounds obvious, right? What's culturally not baked in, I think, is that idea. And the reason I say it's not culturally baked in, by the way, is because of the language of winners and losers. Because if we use that language, what we're implicitly saying is that we wasted time. when we ran an AB test on loser. If I reward you for shipping winners, then what I'm really telling you is all the time that you spent testing out failures was wasted time. And I think, you know, of course, yeah, like you, you don't want to keep data scientists around who regularly are just generating failures. That's not my point. But my point is there's a disconnect there. On one hand, we can all look at the story of this marketing manager and chuckle at it. Right. And yet Every day we're instantiating language and processes that are reinforcing that same theme, which is essentially trying to say to you, if you're wasting samples on things that don't ultimately end up being a winner, then that is the act of doing so is a failure. So I really feel, you know, that that, that idea that you have to pay to learn is, again, it's a cultural thing, but it's also an education issue for, you know, businesses are populated by people of all stripes. Not everybody comes from data science or experimentation background. And this idea that learning is costly is not natural actually. It's not natural as a matter of human nature. It's certainly not natural as a matter of running a business.

I love that example of the real estate platform where it's very viscerally clearly cost. There's like they lost because they didn't roll out experiments to this group for a long time.

Yeah.

It's a good example of this idea in action. You mentioned star ratings. I know you spent a lot of time on designing rating systems. Sorry, I didn't mean to imply star ratings. That's just one implementation. Rating systems in general. So maybe just to keep it focused, say a marketplace founder is trying to decide and design how they do ratings and reviews and things like that. What's a couple pieces of advice you'd give them for how to do this correctly? And is there a model Like a model marketplace, you'd point them to like, oh, these guys really do it really well. And I know it's like super specific based on the marketplace, but is there.

One just like, oh, they really nailed it? Oh man, that's a tough one. I think I'll answer the second part first. I don't feel like anyone's really nailed this. Oh. Um, you know, I think there's a lot of innovation that's happened, but I think fundamentally we're still playing with the same kind of tools that we had when, you know, eBay and Amazon, like first started thinking about how to do rating systems ages ago. And part of the reason we haven't nailed it is because there's a lot of dynamics in play that lead to what's called rating inflation, where if you look at ratings over time in a marketplace, one of my colleagues, John Horton, who was a professor at MIT and has worked very closely with Upwork, we worked together when I was at ODesk. He was the staff economist there. He's written a couple of really nice papers with this empirical phenomenon that over time you see the median rating inflating, let's say, on marketplaces like Like Odesk, like Uber, like any of these, right? And there's a lot of reasons for this, but one of them is just that there's a reciprocity issue, right? Which is it's effectively, you know, from your perspective, it's kind of costless. If someone says to you, Hey, like, please leave me a nice rating, right? And, and you know, if you're seeing them or you're interacting with them, you know, most people don't wanna be mean. So that happens. No, but there's another aspect of it, which is norming. As the ratings in the marketplace go up, they get normed, right? So that now you're in a condition, you're like, oh, a four star rating, I'm really screwing this person over. Whereas maybe when the marketplace started, you had think that. So definitely one thing that we worked on in our research was to think about renorming the meaning of some of these labels. And renorming could mean something like rather than, you know, the star ratings just being, you know, poor to excellent, the top rating has actually exceeded expectations, right? You could go one step further and you could say, how did this compare to this experience you had in the past that you rated really highly? and Airbnb had something like this in place where they would actually ask you to compare or ask you questions about expectations. I find that that's really valuable because it's easier for people to say that was good but didn't exceed my expectations. That was good, but definitely not better than this amazing stay I had like two months ago than it is to say, well, I'm going to ding this person and give them four stars. So that's one issue. And I think another thing I want to point out for any marketplace founder is that something you want to be really careful about is the concept of averaging and what are the implications of averaging. And that's because a default for many marketplaces is to just average the ratings that people get. It feels very natural, right? Like Lenny's got five ratings, let me average them. And that actually has some pretty important distributional consequences for the marketplace, distributional in the sense of who wins, who loses. And that's because if you're averaging and you're really established on a platform, think of a restaurant on Yelp with 10, 000 reviews, it's irrelevant what the next review is. Doesn't matter. Nothing's moving at that point. If you're new and you break into that market and your first review is negative, you might be completely screwed. In fact, there are some early work on eBay that showed that your first, if your first rating's negative, that could actually in immediately cause like an eight percent hit. on your immediate expected revenue. You say nothing of long-term consequences. Subsequent work has found that that's a significant indicator of potential exit from the platform just because now it's very hard to find work. And some platforms do things like maybe they won't show your ratings until you've accumulated a few. But in the end, this kind of distributional fairness aspect of averaging is pretty significant. And one of the recent papers that we've written is trying to get platforms to think a little bit about that. There's ways to address that interestingly through the same concept of a prior. And the prior basically says, Hey, if someone comes into the marketplace and instead of averaging them, I average them together with a prior belief, then maybe what that prior belief does, it says, yeah, you got one negative rating, but maybe you got a little bit unlucky. And maybe my prior belief is something which actually pulls your rating up a little bit and allows me to still have you alongside others in the marketplace that give you a chance at getting work. getting rides, et cetera. So I believe pretty strongly in this kind of like distributional fairness element of designing rating systems. I think it's been understudied. And, you know, I'll say in general, actually, I think rating systems are understudied, which to me is astonishing because the biggest change from those Agoras and Trajan's market elements of, you know, those kinds of markets, to me the biggest change is that we get to see what happened with our matches. So, as a data scientist working on marketplaces, I feel like it's incredible that more of us don't spend our time thinking about what we're learning from the matches and what these rating systems are telling us and what the impact of that is on who wins and who loses in these markets. Kind of thinking about the social implications of these things. So that's something I'm pretty passionate about.

I also led the review system flows out for a while at Airbnb. One of the things I'm most proud of is launching what we call double blind reviews, where you don't see the other person's review until you leave your review. And the intention was to create more honesty and more accurate reviews. It turned out their biggest impact was review rate went up because people get this email, Ramesh left you a review. If you want to see it, you should leave a review. And that really increased review rate, which gave us more data.

And it was a really fun experiment to work on. There's a great concept in the literature on rating systems called the sound of silence, which is this idea that there's a lot of information in ratings that are not left. So Steve Teus, who's a professor at Berkeley, he had a really nice paper with some folks at eBay talking about what they called effective percent positive, where rather than normalizing just by the ratings, they normalized by including ratings that that weren't left. And what you found was this was much more predictive of kind of downstream, downstream kind of performance of a, of a seller. So there's a lot of information in that, in that, like, lack of a response, you know? So it's cool that you're able to.

Get more of that out. So much easier just to not leave a review than leave a bad review.

Right?

Like, the downside to you is just much better. Oh, man. Marketplaces are so fascinating. I could see why a founder would want to be a marketplace founder, because it's just like such an interesting. space and hearing your feedback of like, no, you're not a marketplace founder. Let's think about the problem you're solving. And it might be a marketplace, might change people's minds. Also, I feel like there's like a podcast episode in every topic we touched on. I know we just kind of scratched the surface a lot of things. I know you got to run. Before we get to our lightning round, is there anything else you wanted to highlight, touch on, leave people with that.

Are maybe working on marketplaces, thinking about a marketplace? I think one of the high level points I would make and like you said, there's an entire podcast in this topic, is that I think people want to imagine LLMs and AI-driven data science automating out large parts of what it means to do data science in industry. And I think that's probably the wrong perspective. In some mundane sense, that's true. It's easier for me to code than it used to be before. Easier for me to develop visualizations than it used to be. I can make dashboards faster. programmatically, I think it's true in some basic sense. But what I believe pretty strongly, and I teach data science here, and my students are asked to use LLMs and generative AI on a weekly basis on all their assignments. So I've got an up close and personal beat on this. But I believe very strongly actually is what AI has done for us is it's massively expanded the frontier of things we could think about our problems. hypotheses we could have, maybe things we could test. It's just an astronomical explosion of explanations and ideas and principle. And I really think, actually what that does is puts more pressure on the human, not less. I think it becomes more important for humans to be in the loop in interacting with these tools to drive the funneling down process of identifying what matters at all levels that ranges from you're carrying out a data scientific analysis. And now, because you've got these tools, you can hypothesize 10 explanations, maybe 100 explanations. Which of those are you going to focus attention on? What are you going to tell other people to focus their attention on? To you're running experiments, you used to have 10 creatives you're testing for a marketing campaign. Now you got a thousand creatives you're testing for that marketing campaign. Maybe that completely changes the game of what it means to run an experiment. You know, what are you actually looking for now? How do you evaluate that you found something that was good enough? and, and I think these questions are not getting enough attention. I think people are looking for the automated tool that, that really cuts the human out. But what I've seen so far, and again, you know, who knows? By 2024, I might have a totally different answer for you. I don't think so. But at the moment, what I see is that humans have actually become far more important to the productive data science loop, not far less.

Such an important point. I feel like we need to add AI corner to this podcast where we always think about how does AI impact What we're talking about on this.

Yeah, I can see that. I totally see that.

Okay, we might start doing that. Ramesh, with that, we've reached our very exciting lightning round. I've got six questions for you. Let's try to knock through them so.

You can go teach your class.

Are you ready? I am ready. All right.

What are two or three books you've recommended most to other people? When it comes to books, I have one I love that I start with always, which is How to Lie with Statistics. It's a tiny book by Darrell Huff from 1954, which is just For anyone that likes data at any level, it's such a fun read. It's a great book. The second thing I recommend to people, and actually this is true even for people who are not expert, is David Friedman was a statistician at Berkeley who passed away in the early 2000s. His writing was fantastic in getting us to think hard about process. But what, why, you know, he was especially fond of what he called shoe leather statistics, where you really got your, you know, you rolled your sleeves up, you got on the ground, boots on the ground, really getting in there, really.

Trying to understand your data.

His writing is fantastic. His explanations are fantastic. He has a few different books at different levels. I think people love reading. Most importantly, what I like about it is he puts such emphasis on driving evidence and understanding of your processes that generate data. I find often data scientists don't even look at examples. So I don't ask it meant, are you looking at actual jobs and what's actually going on in your product before you're trying to do data science on it? So I think that's like a Friedman insight, Friedman mantra, and so his writing is great. The last one I was going to mention has nothing to do with data science or anything. It's called 4,000 Weeks by Oliver Burkman. I'm not a huge self-help type person, But I really like this book a lot. It's a little bit, I think it's a little bit stoic in its approach, like stoic philosophy. But the basic point is you're only on earth for somewhere in the neighborhood of 4,000 weeks. And my wife and I have this term we called infinite queue, which is like no matter what you think you get done on a given day, more stuff's going to just keep coming in. And he basically says that recognizing that is liberating, because once you recognize it doesn't matter what you do, you're always going to have too much to do. There's no point in stressing out about having too much to do. And just that small shift in mindset then puts a lot more attention on the usual thing people worry about, which is where do I want to prioritize my time? So he has a great way of writing about it, some concrete rules of thumb to help manage that way of thinking. And yeah, I think it's a great book.

What is a favorite recent movie or TV show?

I am a climber, and one movie that I really liked was the Alpinist. I know a lot of people have seen Free Solo, but for anyone that kind of likes that genre, I would recommend they watch the Alpinist. I think climbing is an interesting sport because it has a very much like a psychological aspect of it. And I think that movie is pretty good at this sort of meta level where you kind of reflect a little bit on what does it mean to make a movie about people who are obviously putting themselves into such risky situations. So I really enjoyed that. On TV, we've been watching Only Murders in the Building, but I'm enough episodes behind right now that I probably won't say anything more because I'm trying to avoid any spoilers and I'm sure there's people out there trying to do the same.

So great show though on Hulu. What's a favorite interview question that you.

Like to ask candidates that you're hiring? I interview people probably that are a little bit different than most of your podcast listeners. But that said, there's one question I like to ask a lot. And that's if you imagine, so, you know, often in our interviews in academia, whether it's grad students or faculty, we'll ask people about their plans. And what I like to ask people is, okay, now imagine everything works out. All the challenges you're facing work out, all your plans work out, everything hits the top end of your vision for what this could be. What do you imagine is the impact of having done that? Like who's being impacted by that? why is that a big deal that that happened? And I find that's a really valuable question to ask because first of all, many people haven't thought about that. We're so short term folks that we don't even think like, boy, if everything worked out, what would be the big deal because of what I did? Startup founders tend to be better at this than most people, obviously. But another reason I like it is because you'll find in that conversation that their vision expands a little bit of additional spheres that are touched or impacted by what they're thinking about doing. So on both sides, it's kind of a revealing question, I think. So I find that important for my line of work, but my hunch is that might be useful for some of your listeners too.

Yeah, such a unique perspective on interviewing versus most of the guests that I interview in the tech company.

Yeah, normally there's like a coding question, right? I should say I would never ask a coding question like post November 2022 after after, after we got like AI to help us code, I just, I think it's a superpower.

AI corner. What is a favorite product you've recently discovered that you really like? I'm, I also really like cycling and.

I'm not ashamed to admit that I think that e-bikes are like the greatest thing for cycling. Admittedly, I'm like, you know, late 40s, so maybe I'm the right target demographic too, but yeah, I love my e-road bike. It, it's like great cuz it's, It's not like one of those with a throttle. You have to work, but it kicks in just when you're on like your, you know, sixth hill and you don't wanna go up the last hill anymore on the way home. So, so yeah, that's amazing. I think that just like transformative for people that like cycling, but have busy lives. And I think another one that my son who's 10 roped me into actually is we were like in Santa Cruz browsing at a, at a kitchenware shop of all places. And he saw an outdoor pizza oven like a tiny portable one. And he insisted, he just did research for like two weeks and insisted we get one. So we got one over the summer and after we got it, he refused to eat pizza out anymore as a 10 year old. So, so, you know, that's like a, maybe that's the best, best thing I could say about like the quality of pizza you can get from a home.

Outdoor portable pizza oven. Oh my God, I'm hungry. I am gonna go, I have to get some pizza now. What is a favorite life motto that you like to repeat to yourself, share.

With folks, find useful?

In your day to day?

A lot of my work involves talking to students of all stripes. And I guess these students go on to be data scientists, go on to be, you know, founders, and a lot of them go in the tech industry. So maybe in that sense, the advice is relevant. My main thing I tell people is slow down. 'Cause I think what I've found has been happening is just we're so convinced that speed is the way you're gonna find the right answer that I just don't think we slow down to develop meaningful mental models of the things we're doing. That's certainly true in the research projects I work on. It's consistently true. And, you know, when I talk to people in business and I ask them about their mental, by mental model, I just mean, you know, if you're running a marketplace, right? What, what is your model of what people care about? What, what makes people stay versus leave? You know, what makes matches work versus not work? all those things shape a roadmap in your mind. And I think a lot of road mapping, a lot of execution, paper writing and academia has all just become far more fast paced at the expense of deeper thinking about these structural features of the thing you're building. And so, yeah, with my students, but also I think with people I interact with in industry, I think slowing down is actually more of a virtue than it's given credit for.

Very similar to a motto that a recent guest shared, which I think was go slow to go fast or stay smooth to go fast.

Yeah, I like that. Maybe I'll pilfer that when I go to my grandparents. Yeah.

Final question. You're a professor at Stanford University, which sounds incredibly cool. What's something about being a professor at Stanford in particular or in general that would surprise people? Either good or bad. or bad?

Yeah, I mean, we've had a rough ride, as everybody probably knows. Stanford's been in the news for a lot of not so great reasons, I think, over the last five years especially. So, I don't know if this is the right kind of surprise, but I think one thing that I find really energizing at Stanford is people have never asked me for credentialing here. And what I mean by that is that, you know, I came from kind of a bunch of other good schools. And, you know, obviously I've spent time in industry with a lot of great companies and a kind of cultural dynamic that can often develop is, well, before I'm going to talk to you, I want to know something about why you're worth talking to. Like, give me your credentials, right? Like, oh, you know, where are you a grad student or like, where are you a professor? Like, tell me about yourself. first. One of the things that I found very surprising when I came here is just how that never happened at any level. Grad students tell me this all the time. Go talk to someone across campus and just launch right into a conversation about how your ex meets my why and we have something we can do together. You know, as a faculty member, it happens all the time that I just had a conversation a couple days ago with someone about like effectively a marketplace of experiment designs for nanofabrication here, right? Which is like totally out of left field for things I do and yet seamless, right? Our conversation was about the substance rather than the credentialing. I really think part of the reason for that is that Stanford is sort of unique in that it doesn't have a weakness across the board. We have strong professional schools, law, business, medicine, strong engineering school, strong humanities and social sciences, and then that and the weather is what I usually tell people honestly, which matters a lot. People are willing to walk anywhere. I think those things combine to create a culture and an environment where you don't credential everybody. And I think that means a lot, you know? And I think that's something that I haven't found elsewhere. And, you know, that if people want to know something about what Stanford's like on the inside, I think that's one aspect of it that probably isn't discussed very much. And that I, you know, I think that's part of what makes it really fun to be here.

It's also an incredibly dreamy campus that is in Very joyful to walk around. That helps, I'm sure. Ramesh, I feel like we got people's brains tingling. I think we've created new marketplace founders and also convinced people maybe they aren't marketplace founders, so maybe we netted out zero new marketplace founders. Two final questions. Where can folks find you online if.

They want to reach out? And how can listeners be useful to you? I think the easiest way, you know, if someone's interested more on the industrial side is probably LinkedIn. You know, you send me a message or connect there. I'm also, because I'm an academic, I have my own Stanford webpage and it's pretty easy to figure out how to find me there as well. And how can listeners help me? I mean, I kind of feel the most important thing that someone listening to this could do is take forward some of the messages that came out in terms of what it means to be data literate. I think there's a lot you can do to educate yourself there. Maybe one final thought I'll share is that, In the same way that AI generates a lot of ideas, AI also generates a lot of pros. And in data science, that can actually be deadly because you're getting more explanations that sometimes maybe are extraneous, you know? And so taking that as a little vignette, I think that what the world needs is data literacy on the part of people interacting with these tools and with each other. So, so that's the thing I care most about. The things I teach, the things I do research on, they're all connected to that theme. And so yeah, that's where I'm pretty excited. I do work with companies regularly and so if there's interesting opportunities that kind of fall in the sphere of stuff we've discussed on the podcast, I'm always happy to listen.

Awesome. I think we've made a dent in helping people become a little more data literate. Ramesh, thank you so much for being here.

All right, thank you so much, Lenny.

Bye everyone. Thank you so much for listening. If you found this valuable, You can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review, as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lenniespodcast.com See you in the next episode.