I've only recently moved into customer analytics, and would love to get some advice around designing a reasonable approach to modelling my data. I want to be able to predict customer churn (that is, predict if individual customers are going to leave our service, a binary outcome) based on customer attributes (some are constant, some are due to recent account activities) but also choose the timing, campaign type and delivery method (e-letter, physical letter) of a retention missive from the company.
I feel like this needs to be broken into two models, the first being a survival model to predict when a customer would leave, and a multinomial logistic regression to identify the campaign and delivery type.
Does this sound reasonable? Any suggested reading would be appreciated.