Literature Review of Data Mining Techniques in Customer Churn Prediction for Telecommunications Industry

Customer churn is one of the most critical issues faced by the
telecommunications industry. In the telecommunications industry, it is more expensive to acquire a new customer as compared to retaining the current one. Hence, customer churn prediction is currently the main mechanism employed by the industry in order to prevent customers from churning. The objective of churn prediction is to identify customers that are going to leave the telecommunications service provider in advance. Customer churn prediction would allow the telecommunications service provider to plan their customer retention strategy. The high volume of data generated by the industry, with the help of data mining techniques implementation, becomes the main asset for predicting customer churn. Due to this reason, recent literature of different data mining techniques and most popular data mining algorithms for customer churn prediction are reviewed in this paper. Additionally, recent literature on newly developed algorithms based on the popular algorithms are also reviewed.