What you really want to be looking at is not "what is the behavior of our entire customer base," but really "what is the behavior of the new customers who we're bringing in".
Once you’re measuring churn in cohorts, you can answer questions like:
Are the economic behaviors of our new customers, in the sense of producing lifetime value for your company, taking place more and more impactfully?
Is the new revenue that we're bringing in on track to produce as good or better lifetime value than the past revenue that we were bringing in?
And ultimately, what are the drop-off trends for a particular time in the customer lifecycle? Let's say we look at the new customers from six months ago: If we look at how many of those customers renewed in month two, five months ago, there's gonna be a particular drop-off. We look at how many people renewed in month three, which was four months ago, there's gonna be another drop-off. We can observe the churn within the context of that particular cohort.
And generally speaking, especially when we're talking about doing Self-Serve, which is theoretically higher churn, but also at a higher volume, you're going to have a lot of data coming in that lets you know whether your month two churn rate, month three churn rate, et cetera, is improving or not from one cohort to the next rather than looking at: is your churn rate across your literal entire customer base, changing from one month or one quarter to the next.
And when you look at it that way, you get a much faster feedback loop around what kind of impact the different changes that you're putting in place might be having. Because if you wait around if you want to implement something to improve churn and you're looking at it in the aggregate and the churn rate across six years’ worth of customers, it's gonna take a really, really, really, like an impossibly long time for you to find out if your efforts have really moved the needle regarding churn at all because it's, it's a big summary, macro trend kind of a number.