Jeffrey Wang: recommendation

Transcript context

…So we've thought about this a little bit and I think the, right, these are sequence, token prediction models. And so at the very least, I would hope for a much better, we have a predictions feature right now, which says, hey, given what a user has done over the last 90 days, do we think they're gonna belong to this cohort in the future or not? So that cohort might be people who churn, people who purchase, people who upsell, whatever the customer wants. We think it would be much better at tasks like that, right, because if it just has a very good understanding of behavioral patterns and what's gonna come next, it would be able to do that. That's exciting, but not that exciting. If I'm trying to think about like the analogies to what we see in LLMs, it's like, okay, yeah, what is the behavioral equivalent of like learning physics concepts, right? It's like, oh, I don't actually know, but it might be this understanding of patterns of sessions and how that like, for example, categorizing users in a unsupervised way seems like a very simple output for a model that understands user behavior, right? Here's all the users and if you wanna discriminate them by their ability to achieve some outcome in the future, like here's the best way to separate that group and here's why, right? Be able to explain at that level and that would be super powerful for customers, right? A lot of times what our customers do is, hey, these people came back the next day and these people didn't, why? What was different about them? And so we have a bunch of heuristics to do that, but at the end, there's something like, causal impact is like one of the holy grails of product analytics. It's like, what was the causation behind some observed difference in behavior? And I think, yeah, a large behavioral model will be much better at assessing that and be able to give you potentially interpretable ways of answering that question that are like really hard to do, really hard, really computationally intensive, really like noisy, distilling causation correlation is obviously super hard. Those are some of the examples. The other one that I am, I don't know if I'm optimistic about it, but we really interesting is, one of the things that amplitude requires today is manual instrumentation, right? You have to decide, hey, this clicking of a button, this viewing of page, these are important things. I'm naming them in this way. There's a lot of popular tools out there that kind of just record user sessions or like track DOM events automatically. There's a lot of problems with those tools because the data is incredibly noisy. It's just so noisy, right? A lot of times you just can't actually interpret it. And so it's like, oh, it's great because I don't need to do any work. But like, well, you also don't get anything out of it. just so noisy, right? A lot of times you just can't actually interpret it. And so it's like, oh, it's great because I don't need to do any work. But like, well, you also don't get anything out of it. It's possible that a behavioral model would be able to actually understand what's going on there by understanding your user behavior in a correctly modeled and correctly labeled sense, and then figuring out. I don't know if that's possible. I think that would make everyone's lives a lot easier if you could somehow ask behavioral questions of data without having to instrument. All of our customers would love that, but also all of them are instrumenting because they know that's definitely not possible today. That's the goal, that's the goal. Thanks for listening.…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.