Improved churn prediction in telecommunication industry by analyzing a large network

Abstract

Customer retention in telecommunication companies is one of the most important issues in customer relationship management, and customer churn prediction is a major instrument in customer retention. Churn prediction aims at identifying potential churning customers. Traditional approaches for determining potential churning customers are based only on customer personal information without considering the relationship among customers. However, the subscribers of telecommunication companies are connected with other customers, and network properties among people may affect the churn. For this reason, we proposed a new procedure of the churn prediction by examining the communication patterns among subscribers and considering a propagation process in a network based on call detail records which transfers churning information from churners to non-churners. A fast and effective propagation process is possible through community detection and through setting the initial energy of churners (the amount of information transferred) differently in churn date or centrality. The proposed procedure was evaluated based on the performance of the prediction model trained with a social network feature and traditional personal features.