3 ways to predict your customer is about to churn

This is the third blog post in a series covering churn and lifetime customer value (Introduction to Churn & Introduction to LTV). There are many ways to predict churn rate on the individual customer level. The full code is available in the Jupyter notebook. In this dataset, we have users of the KKBOX music streaming service along with their attributes, transaction histories and churn label (whether a customer will churn out in the next 30 days). Due to the nature of the business, customers can put subscriptions on pause or change subscription intervals, which makes this dataset both contractual and non-contractual simultaneously.