One thing people often forget about retention is that the data are censored. When customers stop being active, you know it, but you don't know when the customer

One thing people often forget about retention is that the data are censored. When customers stop being active, you know it, but you don't know when the customers that are active today will stop being active. If you don't account for that ignorance, you get data that are biased one way or the other. Time-to-event ("survival") analysis has tools to deal with this, which are eerily underdiscussed. Here's one example: https://two-wrongs.com/survival-analysis-for-customer-retention https://two-wrongs.com/survival-analysis-for-customer-retent... Another common problem, especially when analysing what effect interventions have on retention, is ignoring selection bias. Most interventions are of the form where they will be preferentially offered to or accepted by users who are either more or less satisfied than the average. This will in turn make it seem like retention is positively or negatively affected by the intervention, when really it's just selecting for user groups that were different to start out with.