Overview
Survival analysis is the right tool for any question about time-to-event: time to churn, time to purchase, time to failure, time to conversion. The reason it is necessary — rather than a simple average — is censoring: many observations haven't experienced the event yet when the analysis is run. A customer who has been active for 18 months without churning is not a missing value. They are a censored observation that contributes information: we know they survived at least 18 months.
Ignoring censoring and computing the average time-to-event on only the customers who have churned produces a biased estimate — it systematically underestimates survival time because it excludes the customers who are surviving the longest. Survival analysis handles censoring correctly.
The Survival Analysis & Time-to-Event Modeling Prompt generates a complete survival analysis framework: censoring identification, Kaplan-Meier survival curve estimation, log-rank test for group comparison, Cox proportional hazards regression, and a hazard ratio interpretation that translates statistical output into business-meaningful risk statements.
What you get: - Censoring identification and handling - Kaplan-Meier survival curve estimation with confidence bands - Log-rank test for group comparison - Cox proportional hazards regression with assumption checks - Hazard ratio interpretation in business terms
Built for: data scientists and analysts studying customer churn, product adoption, equipment failure, clinical outcomes, and any time-to-event business question.