Analysis of Customer Churn in Mobile Industry using Data Mining

Mayuri Divekar · Sonal Dashpute · Prajakta Kamble

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

Customer churn prediction in Mobile industry is a latest research topic in recent years. A huge amount of data is generated in Mobile industry every minute. Data mining techniques have also developed in various ways. Customer churn is considered as one of the major issues in Mobile industry. The research signifies that it is more expensive to gain a new customer than to retain an existing one. The knowledge extracted from Mobile industry helps to understand the reasons of customer churn and Telecom providers use the data to retain existing customers. This paper surveys the commonly used data mining techniques to identify customer churn patterns. Classification and association models are the two most commonly used models for data mining in Customer Relationship Management. This study focuses on data mining techniques for reducing customer churn and also likely to reduce error ratio. The recent literature in the area of predictive data mining techniques in customer churn behavior is reviewed and a discussion on the future research directions is offered.

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2008Customer churn prediction using improved balanced random forests 2003Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers 2013A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services 2013Predicting Customer Churn in Mobile Telephony Industry Using Probabilistic Classifiers in Data Mining 2009Hybrid Models Using Unsupervised Clustering for Prediction of Customer Churn 2011Churn Prediction in Telecommunication Using Data Mining Technology 2013CHURN PREDICTION IN THE TELECOMMUNICATIONS SECTOR USING SUPPORT VECTOR MACHINES 2010Modeling Data Mining Applications for Prediction of Prepaid Churn in Telecommunication Services

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