Losing customers, also referred to as churning, is something that any company wants to prevent. But not by predicting churn, assuming correlation is causation, or by acting on prescribed actions. Let me show how to combine techniques from uplift modelling, causal inference and reinforcement learning, into one contextual bandit system that balances exploitation & exploration and deals with biases.
This talk has been presented at PyData Eindhoven 2019 (Netherlands): https://pydata.org/eindhoven2019/schedule/presentation/16/preventing-churn-like-a-bandit/
The first section, why to predict Uplift to prevent churn, is explained further in the following blogpost: https://medium.com/bigdatarepublic/for-effective-treatment-of-churn-dont-predict-churn-58328967ec4f