Customer Churn Prediction, Segmentation and Fraud Detection in Telecommunication Industry

Abstract

Every telecommunication market player is launching innovative business models and offering better services which in turn have increased the cost of retaining customers. Realizing the importance of customer retention, service providers are now putting more efforts in prediction and prevention of customer churn. This work being divided into two phases, with phase one presenting some of the commonly used data-mining techniques for the identification of churners and customer segmentation based on various Key Performance Indicators (KPIs). While phase two phase discusses how social network analysis extracts relationships between different subscribers to improve the results produced by the traditional learning algorithms at individual subscriber level. Satisfactory results have been achieved from Base model which were significantly enhanced by applying the social network analysis technique.

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