Churn Analysis

In this series of articles, we will explore churn analysis step by step — starting with the different data sources involved, then understanding how to calculate key churn metrics and apply them in various scenarios, followed by customer segmentation and churn forecasting using simulated data.

Image generated using an LLM, based on a reference illustration from the book Fighting Churn with Data.

Introduction

For any product with repeat customers or users, growth is not just about getting new customers. When customers leave, churn reduces growth and can even cause the business to decline. Churn should be treated as a practical business problem where data can provide support, but only when the analysis gives useful insights that teams can apply in real situations.

The article also explains a simple mental model for the overall process. First, measure churn. Then convert raw customer behaviour data into meaningful metrics. After that, analyse which behaviours are connected to churn, group customers into segments, and finally run actions or interventions to improve customer retention.

What is churn?

Churn is when a customer stops using a service or cancels their subscription. The term comes from subscription based products and refers to users leaving or stopping their use of a service.

The opposite of churn is retention, which means keeping customers active and continuing to use…