All randomized data, but this is essentially kind of what we've landed on at our metric tree of late. And you'll kind of see kind of some examples of of how stakeholders will come in and and, you know, be able to decide for themselves or see for themselves what's going wrong, and then obviously also help us as data scientists dive into things. So as Emily mentioned, at the very well, actually, first things first, for, like, our account dashboards, we we like to keep things pretty clean. So we have, you know, pretty descriptive stuff going on. Just so if anyone's coming to this metric tree for the first time, they've never heard of a metric tree. They can actually, you know, quickly understand or read up about what it is and and how people use it and what exactly it's measuring. And in this instance, as Emily mentioned, our NorthStar metric is revenue. So what we have, for this metrics tree is comparing the last twenty eight days versus twenty eight days before that. And we split the metric tree almost into two sides. So we have a customer focused side and a transaction focused side. And this goes back to what I was saying about how when we first came together as a team to figure out, you know, how we're gonna build this thing, Each data scientist that kind of owns a certain pillar within the organization, there's a few of us on transaction side, a few of us on the customer side. We had to kind of go off onto our own sides, chat on what's important, and then be able to bring that together to see how that actually impacts revenue. So we essentially came up with almost a formula of sorts of of how revenue is driven at MoonPay. And what we came to is we obviously have revenue, which is essentially a combination of our monthly transaction you transacting users and our average revenue per user. And these metrics themselves were if we originally used to look at this and you'd thought, okay, MTUs are down month and month. So, obviously, this dummy data is down forty two percent. That would raise alarm bells. But where to go from there? So, historically, it was very complicated. I don't even know. I can't even remember how he did it. Maybe I blocked it on my memory. But, essentially, we would maybe, you know, say, okay. What are key regions? What's gone wrong in a key region? We'd look into that, see if anything's weird. It would take ages to kind of get to the bottom of things. Whereas now you can very quickly kind of just follow this path on on what's actually driving each step. So we break down MTUs further, so new and returning, and then we break our new and returning into their respective, basically conversion steps or, you know, are they were they dropping off? Are they not coming back? And what's the reason? So quickly, I'm just gonna run through things, not spend too much time, though. But for instance, the new MTUs, we look at, you know, who's getting converted and who's coming coming back, essentially. And then we break that down just based on conversion rates. On the returning MTU side, we've got our retained MTUs. So the ones that, you know, from the last month we're bringing back and our resurrected ones, which essentially are the ones that, you know, hadn't transacted for a certain period and are now back and further break that down. And what this enables us to do on each side is you could quickly just distinguish which is the driver of these two and quickly go to the essentially, all the way to the bottom. And what we found now in debugging things is or looking into, you know, what's gone wrong is it might not just be one of these. Obviously, it can be a combination of things, but at least it directs you where to look because there's a bunch of things happening with within each of these. We have different partners, different regions, different customer types, but it makes your life, significantly a lot easier to to quickly be able to say, okay. It's our new customers with a problem and their conversion's down, and then figure out what where in the world and what type of product what segment segment of our product is it down, for example.