The Use of Hybrid Predictive C&RT-Logit Models in Analytical CRM

Abstract

Predictive models in analytical CRM (customer relationship management) are closely related to the customer’s life cycle. Prediction of binary dependent variable refers to the most common areas such as customer acquisition, customer development (cross-selling and up-selling), and customer retention (churn analysis). While building static predictive models one usually applies decision trees, logistic regression, support vector machines or ensemble methods such as different algorithms of boosted decision trees or random forest. Recently one can observe increasing use of hybrid models in the analytical CRM, i.e. those that combine several different analytical tools, e.g. cluster analysis with decision trees, genetic algorithms with neural networks, or decision trees with logistic regression. The purpose of this paper is to compare the results obtained by using hybrid predictive CART-logit models with single decision tree models and logistic regression models. All analyses have been conducted on the basis of data sets relating to analytical CRM.