I have a data set containing many client's id, and its behavior characteristics measured each month before churn or censored. Data looks like:
id || lifetime period || folow-up time before churn of censores || churn or censored || large list of behavior variables ||
Each id exist for many times in my dataset during its lifetime.
I want to build model that allow me to calculate churn probability for each client for each its future lifetime period and update this probability each month during clients lifetime. I started with Cox regression with time-varying covariates but further realized that it is inappropriate for predicting purposes. Now I started building Cox proportional hazard model.
It is appropriate choice for my purposes? Should I build new model for each lifetime period? Or I can include in train sample many observation for each ID but with different covariates and different folow-up time?