The Exchanges

Q You've been building this location data asset for well over a decade now. Can you give us a sense for the breadth and the depth of the location?

A Yeah, I mean, I think one of the great things about, um, you know, uh, the legacy that we get to build off of in the social app context is we were crowdsourcing effectively a map of the world. Um, and so we operate in a 190 countries globally, You know, two hundred million global POI, or places of interest, including a level of specificity of knowing the difference between Foursquare's headquarters and being in Fat Denny's cafeteria, and having that telemetry of what's in the world, um, and then building mechanisms that keep that fresh. The world's obviously super dynamic in terms of how places change over time, um, and so the starting foundation of what we do is really understanding Places in the world at scale on a global basis. And then it's attaching in real time how devices move in relation to those places. So what does that mean for all sorts of pattern, uh, development for understanding foot traffic? If you're a retailer, uh, you know, you can think about applications in places like logistics or real estate understanding neighborhood patterns or, or so forth. And so that's the, you know, sort of nature of creating this very dynamic asset, which is constantly changing. And building the mechanisms to be able to have what we think is unique other than basically Google in the world, which is human confirmation. We have elements of humans telling the machines that we're right…

AI assessment note: “we operate in a 190 countries globally, You know, two hundred million global POI”

Q Going back to that evolution of the platform, is the Foursquare graph, is that a part of the new generation of products? If so, what is it, and what does it do?

A If you're looking at data sets of, you know, hundreds of millions of places with You know, 70 rows of attributes with a bunch of movement data and a bunch of user data. The real question from a data science perspective is how do you understand those relationships in the world to answer really basic questions? Like what are the most important, you know, uh, intersections in New York City if I'm Diageo and I'm trying to make sure my alcohol is in every major, you know, uh, bar in New York that's most trafficked during happy hour and things like that. Those questions sound very benign, but when you actually think about the data science, trying to put together SQL queries in such a manner to understand those interrelationships is virtually impossible, and honestly was part of the reason that you don't see a lot of the value derived when you buy the data, because you have to spend so much time, energy, and effort just to try to make sense of it, and by the time you've done that, it's changed. Knowledge graphs are very interesting because you can understand the inference of how these things are related in a much more dynamic and real-time way. Which is basically the core of our, our value from a, a business perspective, and so we've redistributed our entire data architecture to be, ah, much more in the data primitive knowledge graph construct so that, um, you know, I, I wish we could…

AI assessment note: “we've redistributed our entire data architecture to be, ah, much more in the data primitive knowledge graph”

Q Do you want to double click quickly on that whole privacy aspect, which obviously is a key thing for personal location data? What are the new regulations and, um, what do you all do to preserve anonymity and safety of location data?

A Yeah, I think there's really been kind of, you know, two things I would sort of say, like, if you go back to the founding of the company, I give Dennis and team a lot of credit Uh, from the founding of understanding that you're kind of working with superpowers when you understand a person's location, right? And so from the beginning, the way that the systems at Foursquare had been built was really this thing we've all gotten used to now because Apple and Google have caught on, and at the operating system level, we have to opt in to location tracking, and it's, it's very, very clear to the user. You know, in the early days, that was not the case. Um, and so Foursquare from the very beginning Whether it was tech we built or when we would allow folks to use our SDKs and otherwise, we always had that location tracking opt-in, and that's been very key to, like, the, the business going forward. What's changed, Matt, is, um, ironically, with the, uh, Dobbs decision in the Supreme Court overruling Roe versus Wade, you kind of set off this, uh, interstate sort of blue state, red state dynamic, where blue states are very concerned that red states can subpoena data around women's health. Which is, like, a very interesting thing for a location enterprise software company to, like, realize, ok, this is, like, the thing that I would not have predicted, you know, three, four, or five years ag…

AI assessment note: “with the, uh, Dobbs decision in the Supreme Court overruling Roe versus Wade”

Q And if you were going to pick, uh, two or three out of the current array that are going to be part of that platform long-term, uh, whether that's, I don't know, the SDK or attribution, what, what would those be?

A Yeah, it's, you can kind of think of, so across our portfolio today, we do everything from, um, uh, giving mobile developers APIs to build personalization into their mobile apps. Uh, we use, Data analytics to do things like measuring ROI and attribution. In some sense, we'll do some version of almost everything that we do today. I think the nuance difference around how we are approaching problems going forward is more and more we're exposing the components that we've, ah, you know, built to house a product like use attribution, where we take data from the world, location data, we take impression data, We do a bunch of statistical analysis on Lyft relative to your exposure, and we give back to the customer a report that says, here's your metrics, right? A lot of our customers want to build some of those capabilities natively, and so, um, and many have tried, and most have failed, because it's a very, very hard technical problem. So when you think about how we evolve the product, you can either buy the product from us with reporting and UI and customer service and all that, Or there's a bunch of the component pieces that we will make available for developers to build their own versions of these things, where you can take any event catalog and say, I exposed a customer to X. Did that drive more people into my store? Did it lead to more incremental sales? And so, in some sense, mos…

AI assessment note: “In some sense, we'll do some version of almost everything that we do today.”