A Survival Analysis Guide with Python: Using Time-To-Event Models to Forecast Customer Lifetime

Survival analysis (time-to-event modeling) is introduced as a powerful alternative to standard regression for predicting when events like customer churn will occur. The guide covers core concepts including censored data, survival functions, and hazard functions, then walks through practical Python implementations using the lifelines library. Two models are demonstrated on a Telco churn dataset: Kaplan-Meier for simple group comparisons (with log-rank testing) and Cox Proportional Hazard for multi-covariate analysis. Results show that customer complaints increase churn risk by 5.36x, while higher charge amounts are protective, and individual survival probabilities can be predicted at specific time points.