How we built our metric tree

Yeah. So, actually, we did it, in a sort of bottoms up approach. The reason why I did this, and this goes back again to the ownership piece I was just speaking about, where data, we really do need to stop deferring to others. We are the experts in a lot of ways. And so when we went to build this tree that we have, which, again, I think some of you have been asking, you know, can we see or a sample? We will be showing a a a sample of ours. We really kept it to the team to determine. So each data scientist was assigned essentially an area. Well, they were every data scientist on our team is already assigned an area. And so for their area, they were basically tasked with coming up with one to two, absolute max two, metrics that they thought should go on the tree. And then we all got together and took about, I wanna say, about six weeks from end to end to decide exactly how we were gonna break everything down. Our North Star is pretty easy because that does come from our company OKRs, so that that works really well for us. But, you know, that is revenue and profits, which I think for every business is the case except for nonprofits. So, you know, that is also why we decided to build a tree because there's always a question of, like, what is driving revenue? And a million pays, as we we all know, can be driving revenue. And that's exactly as we were talking about about the investigations and sort of the bugs and incidents that we were that we were encountering was that it was so hard to understand what exactly is the root cause of the movement. And sometimes, maybe we don't even care that much. Right? I mean, let's just be completely honest. Not everything reserves, needs to be looked at, and it creates a lot of, just, like, misspent resources when we don't have that level of clarity. So that is essentially how we approached our tree. Mhmm. We came together as a team and essentially established these look good, And then everyone has ownership again. Each data scientist has ownership over one at least one, sometimes a collection of the metrics on the tree. So what we do, and Matt can speak to this about how this works on my day to day, is that we do monitor what's going on, and it becomes a lot easier to do a couple things, which is one, troubleshoot. But two is also equally as important. And that when we go to build and, develop parts of our product, we're no longer sort of like, okay. Well, what is this supposed to do? We have a metric on the tree that we want to explicitly move, and we want to make sure that that is very clear. Because if you're talking about wanting to increase your user base, that is a very different conversation than needing to improve your transaction success as an example. Mhmm. And so that's also another way that we've used the tree in order to make sure and, actually, we that was one of the requirements of the tree, that we were able to use it for that purpose and and many use cases like that.