While this article is mostly focused on collecting/gathering/visualizing cohort metrics, rather than on analysis, I would like to plug my college professor Peter Fader’s research on empirical Bayes modeling and analyzing Customer Lifetime Value.

One thing I learned from his class is that survival rates will naturally trend upward over time, which marketers erroneously attribute to (1) improving the product, (2) better customer service, (3) network effects / lock-in, etc.

However, if you have a heterogeneous customer base with latently better and worse customers, inevitably your worse customers will churn before your better customers, showing that “decrease” in churn.

These models also let you do cool things like conditional expectation: “if a customer has survived 13 months, what’s the probability they churn in the 14th?”

Here’s a paper of his from 2004: https://repository.upenn.edu/cgi/viewcontent.cgi?article=141...