Watch some of the key highlights from our recent MoonPay webinar as Emily Loh (Director of Data) and Matthew Reuvers (Senior Data Scientist) take a deep dive into the metric tree they use to identify and solve problems the business actually cares about.
We got to a stage where we, as data scientists, were never able to spend any time on on the real, like, juicier problems. So when the metric tree idea came around, it was kind of one of these things. Okay. Cool. Like, we can very easily show it to the rest of the organization. This is this is what's gone wrong where, and then we can either enable them to go dig into it further or very quickly allow us as data scientists to dig into it further. And, initially, we tried to do it in a looker, which was a mess. We really do need to stop deferring to others. We are the experts in a lot of ways. We really kept it to the team to determine. So each data scientist was assigned essentially an area. Well, ownership again, each data scientist has ownership over one at least one, sometimes a collection of the metrics on the tree. Took about, I wanna say, about six weeks from end to end. You took six weeks in total, but you got a long way in a week. What what now? I went after the metrics tree. Right? Oh, did you get buy in from exec leadership, for example? To be honest, no. I think the clarity needs to come from us. It was sort of like, that's cool, and then it was just kind of that. That was it. And we were the ones who needed to push for, what is the success metric here? What are we trying to move? What has gone wrong? What has you know, what do we need to work on? Where do we need to optimize? And all of those things. We were becoming the ones who were asking the business those questions as opposed to the opposite. Excuse me. Conversion rate has been dropping or what have you for the past four weeks. How do we fix this? What's going on? Everyone starts to look to the data team, which is exactly what happened to help them understand how they're going to maximize their impact on the business. 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, I'm involved in, like, the payment side. So this transaction success rate tile is is my baby, pretty much. And I I have my own metric tree for this, specifically for the transaction success rates. I don't it's it's available for everyone to see, but it's not something I broadly push on everyone because it as Emily said, then you might be overloading overloading, you know, stakeholders or relevant parties of so we essentially came up with almost a formula of sorts of of how revenue is driven at MoonPay. Whereas now you can very quickly kind of just follow this path on on what's actually driving each step. So you can you can keep going down the tree. It may not be practical to have it at all every metric of the business in one view, but when you get to a certain level, like, new customers, you can then go to Metrica, which is more about marketing and the the lead, the the acquisition funnel and break those metrics down even further. Like Within each kind of product in MoonPay, you can actually have its own, metric tree within that. Right? So this is, like, kind of the company wide one, which is awesome and enables us as the company to see what's going on. We actually created the upper level metrics tree by design, and we maximized it. The reason for this, and this is, again, you know, different depending on your organization, is that we know at MoonPay specifically, within our own organization, it can create too much noise.