Develops a multi-stage machine-learning framework for personalized targeting policy design and evaluation analyzing data from a field experiment by a leading SaaS firm where new users were randomly assigned to 7, 14, or 30 days of free trial. Uses an Inverse Propensity Score (IPS) reward estimator to evaluate the reward or gain from each targeting policy.