When would you use survival analysis instead of a binary churn classifier, and how would you set it up?

What the interviewer wants to hear

• Survival analysis models time until churn, not just whether it happens
• Handles right-censored customers who are still active
• Kaplan-Meier for descriptive curves, Cox or survival forests for covariates
• Check proportional hazards and time-varying covariates
• Evaluate with concordance index and calibration over time

Model answer

A binary classifier answers "will this customer churn in the next 30 days", which needs a fixed window and throws away information about when churn happens. Survival analysis models time to churn and uses customers who are still active as right-censored observations, rather than dropping them or treating them as non-churners. I would start with Kaplan-Meier curves by plan or acquisition channel to describe retention. For prediction I would use a Cox proportional hazards model for interpretable hazard ratios, or a survival forest or gradient boosted survival model if effects are non-linear. I would check the proportional hazards assumption, because effects like a discount often fade over time, and handle time-varying covariates such as recent usage carefully to avoid using future data. Evaluation uses the concordance index for ranking and calibration of predicted survival at key horizons like 90 days.

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