Bayesian feature selection to estimate customer survival

We consider the problem of estimating the lifetime value of customers, when a large number of features are present in the data. In order to measure lifetime value we use survival analysis models to estimate customer tenure. In such a context, a number of classical modelling challenges arise. We will show how our proposed Bayesian methods perform, and compare it with classical churn models on a real case study. More specifically, based on data from a media service company, our aim will be to predict churn behaviour, in order to entertain appropriate retention actions.