Tuesday, October 2, 2018

Using survival plot to analyze churn in Power BI



I did not guess I'd be working with Kaplan-Meier survival plots so soon.

Analyzing churn and trying to figure out what kind of users churn more likely is not so easy. In order to calculate churn, you need to have a good volume of users that you can follow month over month, having a number of active users per month and what share of them are left behind every month. But what if you want to select a different set of users, another segment? Producing an analytics cube with the necessary dimensions takes time. And if you end up with a segment that doesn't have high volumes every month, interpreting the results can be quite tricky.