On support vector decision trees for database marketing

A support vector decision tree method for customer targeting in the framework of large databases (database marketing) is introduced to provide a tool to identify the best customers based on historical data.

Abstract

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. This tool is then used to forecast the best potential customers among a pool of prospects. We begin by regressively 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 gain chart 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.