A simple advertising strategy that can be used to help increase sales of a product is to mail out special offers to selected poten­tial customers. Because there is a cost as­sociated with sending each offer, the optimal mailing strategy depends on both the ben­efit obtained from a purchase and how the offer affects the buying behavior of the cus­tomers. In this paper, we describe two meth­ods for partitioning the potential customers into groups, and show how to perform a sim­ple cost-benefit analysis to decide which, if any, of the groups should be targeted. In par­ticular, we consider two decision-tree learning algorithms. The first is an "off the shelf" al­gorithm used to model the probability that groups of customers will buy the product. The second is a new algorithm that is sim­ilar to the first, except that for each group, it explicitly models the probability of purchase under the two mailing scenarios: (1) the mail is sent to members of that group and (2) the mail is not sent to members of that group. Using data from a real-world advertising ex­periment, we compare the algorithms to each other and to a naive mail-to-all strategy.