Enhanced Customer Churn Prediction using Social Network Analysis

There were 6.8 billion estimates for mobile subscriptions worldwide by end of 2013 [11]. As the mobile market gets saturated, it becomes harder for telecom providers to acquire new customers, and makes it essential for them to retain their own. Due to the high competition between different telecom providers and the ability of customers to move from one provider to another, all telecom service providers suffer from customer churn. As a result, churn prediction has become one of the main telecom challenges. The primary goal of churn prediction is to predict a list of potential churners, so that telecom providers can start targeting them by retention campaigns. This work describes work in progress in which we model churn as a dyadic social behavior, where customer churn propagates in the telecom network over strong social ties. We propose a novel method for measuring social tie strength between telecom customers. We then, incorporate strong social ties in an influence propagation model, and apply a machine-learning based prediction model that combines both churn social influence and other traditional churn factors. The goals of our proposed model is to enhance churn prediction by modeling churn as a dyadic phenomena, provide an enhanced evaluation for the social tie strength based on customers social interactions, and to study the effect of strong social ties on churn propagation over mobile telecom networks.