Q which is basically just virtual, you know, casino lines, right? Or nightclub lines. They're, they're focusing on things like the viral coefficient, right? Which like you have a number of invites that go out multiplied by the conversion rate, multiply by the time it takes to send out those invites. Um, at its most basic core kind of structure, is that same equation applicable to the stuff that you're doing?
A It is. So, you can think about how AlphaRank works as we take time series data in, and in that time series data, there needs to be a minimum of three things. You need a persistent unique ID, you need a product ID, and you need a timestamp. So essentially we're looking at, you know, who was it? Uh, what did they do? Did they buy a pair of shoes? Did they open up an app? Um, and then when did they do it? Um, and then you take all of that data for as much as you can, right? Like if you're thinking of retail, you know, two to The three years is best. If it's something like an app, like a dating app, like Tinder or Bumble, maybe just a year. Um, and you're looking at a pattern. So in retail, it'd be like who buys shoes after who, um, with the dating app, it would be who opens Bumble after who, right? So you open Bumble, then I open Bumble. And we live in the same city. And then you open Bumble, then I open Bumble. And then you open Bumble, then I open Bumble. You could say, Hey, man, you know, maybe this isn't random. Maybe these guys are looking at Bumble together. Um, so how that applies to B to B is it could be very similar. We actually haven't ran tests on it. I spoke at the customer success summit last week, and it was my first sort of experience sharing this, um, with people in the B to B space, but I completely, uh, have absolute a hundred percent faith that someone will figu…
AI assessment note: “It is. So, you can think about how AlphaRank works”