Beta-Geometric model for subscription churns over time.
Have customers that churn? You have come to the right place!
Let’s say that you have a service up for subscription. Customers love your product, they roll in to register and renew their subscription each month. But not all, some of them stop.
In this post, I describe the Beta-Geometric model for modelling such behavior. We assume that the customer churns at a constant rate p. Then the probability that the customer churns at exactly month k is 
The casual way is to infer p from a dataset with maximum likelihood, like this.
But we’re not so casual now, are we ? We also care about the uncertainty of our estimates. In this notebook, I demonstrate such a model on a synthetic dataset and prove that we can model such a business process and some more.
Not only can we find out the distribution of churn rates, we can also track it over time and make inference on the churn behavior of our customers.