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

Survival analysis is a statistical method for predicting the time until a specific event occurs, such as customer churn or equipment failure. It is particularly useful for handling "censored" data, where the event of interest has not yet happened for some subjects by the end of the study period. The methodology involves key concepts like the survival function, which calculates the probability of an event not occurring over time, and the hazard function, which measures the instantaneous risk of the event. The guide contrasts the non-parametric Kaplan-Meier model for simpler analyses with the more robust Cox Proportional Hazard model, which can incorporate multiple predictor variables, and demonstrates a Python implementation using the `lifelines` library to model customer churn.