On support vector decision trees for database marketing

We introduce a support vector decision tree method for customer targeting in the framework of large databases (database marketing). The goal is to provide a tool to identify the best customers based on historical data. Then this tool is used to forecast the best potential customers among a pool of prospects. We begin by recursively constructing a decision tree. Each decision consists of a linear combination of independent attributes. A linear program motivated by the support vector machine method from Vapnik’s Statistical Learning Theory is used to construct each decision. This linear program automatically selects the relevant subset of attributes for each decision. Each customer is scored based on the decision tree. A gainschart table is used to verify the goodness of fit of the targeting, to determine the likely prospects and the expected utility or profit. Successful results are given for three industrial problems. The method consistently produced trees with a very small number of decision nodes. Each decision consisted of a relatively small number of attributes, which evinces the method’s power of dimensionality reduction. The largest training dataset tested contained 15,700 points with 866 attributes. The commercial optimization package used, CPLEX, is capable of solving even larger problems.