Optimization of control policies for corporate customer relationship management (CRM) systems can boost customer satisfaction, reduce attrition, and increase expected lifetime value of the customer base. However, evaluation of these policies is often complicated. Policies can be evaluated with real-life marketing interactions, but such evaluation can be prohibitively expensive and time consuming. Customer simulators learned from data are an inexpensive alternative suitable for rapid campaign tests. We summarize the literature on the evaluation of direct marketing policies through simulation and propose a decomposition of the problem into distinct tasks: (a) generation of the initial client database snapshot and (b) propagation of clients through time in response to company actions. We present open-source simulators trained and validated on two direct marketing data sets of varying size and complexity.