Hybrid Models Using Unsupervised Clustering for Prediction of Customer Churn

In this paper, we use two-stage hybrid models consisting of unsupervised clustering techniques and decision trees with boosting on two different data sets and evaluate the models in terms of top decile lift. We examine two different approaches for hybridization of the models for utilizing the results of clustering based on various attributes related to service usage and revenue contribution of customers. The results indicate that the use of clustering led to improved top decile lift for the hybrid models compared to the benchmark case when no clustering is used. Index Terms – Churn, Clustering, Data mining, Decision trees, Lift, Prediction