In business marketing, corporations and institutions are interested in executing a sequence of marketing actions to affect a group of customers. For example, a financial institution may derive marketing strategies for turning their reluctant customers into active ones and a telecommunications company may plan actions to stop their valuable customers from leaving. These marketing plans are aimed at converting groups of customers from an undesirable class to a desirable one. In this paper, we formulate this group marketing-plan generation problem as a planning problem. We design a novel search algorithm to find a cost-effective and highly probable plan for switching a group of customers from their initial states to some more desirable final states. We explore the tradeoff among time, space and quality of computation in this planning framework. We demonstrate the effectiveness of the methods through empirical results.