Systematic Review of Customer Churn Prediction in the Telecom

The Telecommunications (telecom) Industry is saturated and marketing strategies are focusing on customer retention and churn prevention. Churning is when a customer stops using a company’s service thereby opting for the next available service provider. This churn threat has led to various Customer Churn Prediction (CCP) studies and many models have been developed to predict possible churn cases for timely retention strategies. This review looks at the existing models in literature, using 30 selected CCP studies particularly from 2014 to 2017. Data preparation methods and churn prediction challenges have also been explored. This study reveals that Support Vector Machines, Naïve Bayes, Decision Trees and Neural Networks are the mostly used CCP techniques. Feature selection is the mostly used data preparation method followed by Normalization and Noise removal. Undersampling is the mostly preferred technique for handling telecom data class imbalances. Imbalanced, large and high dimensional datasets are the key challenges in telecom churn prediction. The issue of large telecom datasets is handled by use of new big data technologies such as Hadoop, Hbase and NoSQL (Not only Structured Query Language) though their adoption is still at a low rate.