Hybrid Models Using Unsupervised Clustering for Prediction of Customer Churn

Abstract

Churn management is one of the key issues handled by mobile telecommunication operators. Data mining techniques can help in the prediction of churn behavior of customers. Various supervised learning techniques have been used to study customer churn. However, research on the use of unsupervised learning techniques for prediction of churn is limited. In this article, 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 services 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 with the benchmark case when no clustering is used. It is also shown that using cluster labels as inputs to the decision trees is a preferred method of hybridization. Out of the five unsupervised clustering techniques used, none is found to dominate others. But interesting attributes and rules that can help marketing experts identify churners from the data are obtained from the best hybrid models.