In this episode of The Data Science Podcast, Lucas and Luna explore survival analysis—a statistical method originally developed for medical research—and how data scientists are applying it to predict customer churn and retention. They walk through a concrete example: a subscription service using Kaplan-Meier curves and Cox proportional hazards models to understand when and why customers cancel. Lucas explains the concept of the 'hazard function' and how it differs from traditional churn models, while Luna challenges him on interpretability. The hosts also touch on real-world applications in SaaS, insurance, and telecom, and discuss the difference between censored and uncensored data. By the end, you'll understand why survival analysis offers a more nuanced view of retention than simple binary classification.

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