Optimizing customer lifetime value (LTV) techniques are described. In one or more implementations, a simulator is configured to derive a prediction model based on data indicative of user interaction online with marketing offers. The prediction model may be produced by automatically classifying variables according to feature types and matching each feature type to a response function that defines how the variable responds to input actions. The classification of variables and/or corresponding response functions per the prediction model may consider dependencies between variables and dependencies between successive states. An evaluator may then be invoked to apply the prediction model to test a proposed marketing strategy offline. Application of the prediction model is designed to predict user response to simulated offers/actions and enable evaluation of marketing strategies with respect to one or more long-term objectives.