1;3409;0c A Decision Theoretic Approach to Targeted Advertising

A Decision Theoretic Approach to Targeted Advertising

UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, 2000
Pages: 82-88



A simple advertising strategy that can be used to help increase sales of a product is to mail out special o ers to selected potential customers. Because there is a cost associated with sending each o er, the optimal mailing strategy depends on both the bene t obtained from a purchase and how the o er a ects the buying behavior of the customers. In this paper, we describe two methods for partitioning the potential customers into groups, and show howto perform a simple cost-bene t analysis to decide which, if any, of the groups should be targeted. In particular, weconsidertwodecision-tree learning algorithms. The rst is an o 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 rst, except that for each group, it explicitly models the probability of purchase under the two mailing scenarios: (1) the mail is sent tomembers 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. 1