A Decision Theoretic Approach to Targeted Advertising
This work addresses the challenge of cost-effective advertising for businesses, though it is incremental as it builds on existing decision-tree methods.
The paper tackles the problem of optimizing targeted advertising by developing a decision-theoretic approach to partition customers into groups and perform cost-benefit analysis, showing that a new algorithm modeling purchase probabilities under different mailing scenarios outperforms a naive mail-to-all strategy in a real-world experiment.
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.