Description
Today’s credit card issuers are increasingly offering a broad range of products and services with separate lines of business responsible for different product groups. Too often, the separate lines of business operate independently and information available to one line of business may not be used productively by others. In this study, we examine the potential of using information from customers of multiple products to identify customers most likely to respond to cross-sell product offers. Specifically, we examine the potential for offering home loans to a population of credit card holders by studying individuals who do hold both a credit card and a mortgage with the card issuer. Using real world data provided to the 2007 PAKDD data mining competition, we employ Friedman’s stochastic gradient boosting (MART™, TreeNet® ) for the rapid development of a high performance cross-sell predictive model.