Author(s): Marie Humbert-Droz, PhD

Originally published on Towards AI.

Using survival analysis to predict when customers leave — and avoid a common data leakage trap

Most churn models answer the wrong question: They tell you who might leave but not when.

This article explores the application of survival analysis to churn prediction, focusing on how it provides more meaningful insights by addressing the timing of churn events instead of just the likelihood of customer departure. It discusses the importance of avoiding data leakage, particularly with features that inadvertently contain time information, and presents strategies for modeling churn effectively using customer data, including the significance of contract types, payment methods, and the impact of various service add-ons on customer retention. The results indicate that survival analysis can yield better actionable predictions in environments where timing is crucial, thus providing a valuable framework for customer retention strategies.

Read the full blog for free on Medium.

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Published via Towards AI