In this introductory talk, Max Brun explains why data science is central to technology‑enabled marketplaces. He begins by contrasting ancient, non‑digital markets—where transactions left no record—with modern digital platforms that generate abundant “digital exhaust” for every buyer‑seller interaction. This data foundation makes marketplaces uniquely powerful. Brun then distinguishes between generic data‑science work that any digital business performs (e.g., marketing campaign analysis) and the special set of problems that arise only when a platform has two or more scaled sides. He highlights the core marketplace challenge: matching each side with the most relevant counterpart, a dual‑sided recommendation problem that differs from the classic Netflix‑style recommendation because it must operate simultaneously for buyers and sellers. Multi‑sided examples such as Instacart and DoorDash illustrate how the problem scales with additional participant groups. Throughout, he emphasizes that innovative marketplace data science revolves around solving these matching problems, which set marketplaces apart from traditional businesses like Johnson & Johnson.
Chapters
maybe help us kind of, you know, define, you know, what we mean when we mention data science and why it's important for marketplaces? Yeah. And I know a lot of people on the call may have either heard or like read, you know, some snippets of the podcast I did with Lenny, and I talked about this a little bit there. For the purposes of this call, I'll kind of be succinct in describing some of the points that I made there. The most important thing for a digital marketplace, like a technology-enabled marketplace, Is first of all, just that, you know, if you think about what markets looked like thousands of years ago, like Trajan's Market in Rome or the Agora in Athens, these were non-digital markets and any transaction that took place was lost to the ether. Like we never have a, you know, we don't have a record for posterity about like the buyer or the seller, you know, the terms of trade. how they found each other. And digital marketplaces upend all that. We, all of these digital footprints, the digital exhaust is like recorded for us. So that, first of all, in a nutshell, why data science? It's because data is, I think, the foundation of what makes these marketplaces technologically unique as opposed to something like Trajan's Market. And what does data science mean and when does it matter? I think one could arguably make the case that basically any aspect of a modern marketplace should be informed by what we do with this data ranging from marketing to product to ops. But I want to focus on a distinction between kind of 2 different types of things you could do. One type of thing you could do is something you would do even if you weren't running a marketplace. So let's take marketing as an example, right? Whether you're running Johnson Johnson or you're running Airbnb, you know, figuring out how to run your marketing campaigns is a thing that you have to spend time on. And it wouldn't surprise me at all if J&J and Airbnb cross-recruit, you know, people that are working in marketing at the other company. There's not really a huge difference there, like mechanically in terms of the things that data scientists that work on marketing do, right? You're trying to evaluate campaigns, you're trying to figure out multichannel marketing, you know, those kinds of things. What's different in a marketplace, I think, though, is the set of data scientific questions that are specifically related to having 2 scaled sides, say buyers and sellers, at least 2. Some markets have more than 2, like Instacart, DoorDash are multi-sided marketplaces. And as soon as you have multiple sides of the marketplace and you're thinking, what does data science mean there? It brings in a sort of new roster of problems for data science to grapple with, which basically involve helping each side sort through the mass on the other side to understand which are the best matches for me and why, and then to help enable those matches. And that's a very different type of problem than the kind of thing that usually arises. You can think of it as sort of the equivalent of the Netflix recommendation problem, but on both sides of the market simultaneously. And we'll talk a little bit more, I think, over the course of the chat about some of the ways in which this problem is, let's say, different than the Netflix problem. But I think fundamentally, like, one of the things that I try to draw people's attention to is what kinds of problems and data science problems in particular are different because you're running a marketplace. Which ones are just kind of what you would see no matter what kind of digital business you were running? And so I want to draw attention to the fact that, you know, finding and making good matches, I think, is the core of what innovative data science in a marketplace, you know, what makes that look different than maybe what you would see, let's say again, if you were working at like Johnson Johnson or something like that on the product side.
