Applying Bayesian Belief Network approach to customer churn analysis: A case study on the telecom industry of Turkey

In telecommunication industry, for many organizations, it is really important to take place in the market. As competition increases between companies, customer churn becomes a great issue to deal with by the telecommunication providers. For an effective churn management, companies try to retain their existing customers, instead of acquiring new ones. Previous researches focus on predicting the customers with a propensity to churn in telecommunication industry. In this study, a model is constructed by Bayesian Belief Network to identify the behaviors of customers with a propensity to churn. The data used are collected from one of the telecommunication providers in Turkey. First, as only discrete variables are used in Bayesian Belief Networks, CHAID (Chi-squared Automatic Interaction Detector) algorithm is applied to discretize continuous variables. Then, a causal map as a base of Bayesian Belief Network is brought out via the results of correlation analysis, multicollinearity test and experts' opinions. According to the results of Bayesian Belief Network, average minutes of calls, average billing amount, the frequency of calls to people from different providers and tariff type are the most important variables that explain customer churn. At the end of the study, three different scenarios that examine the characteristics of the churners are analyzed and promotions are suggested to reduce the churn rate. (C) 2010 Elsevier Ltd. All rights reserved.