Causal Machine Learning for Customer Retention

This article is the second in a series on uplift modeling and causal machine learning. The idea is to dive deep into these methodologies both from a business and a technical perspective.

1. We’ll start by clearly defining our use case. What is churn? Who do we target? What actions will we set up to try and retain our clients with?

2. Then, we’ll look into getting the right data for the job. What data do we need to implement uplift modeling and how to get it?

3. After that, we’ll look into the actual modeling, focusing on understanding the various models behind uplift modeling.

4. Then, we’ll apply our newly acquired knowledge to a first case with a single retention action: an email campaign.

5. Finally, we’ll deep dive into a more complicated implementation with many treatments, approaching user-level personalisation