A Decision Theoretic Approach to Targeted Advertising
  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.
