Analysis and Prediction of Churn Customers for Telecommunication Industry

Abstract

The telecom industry is always booming with volumes of data due to its enormous client base. The business analysts and decision makers of these companies have started to highlight the fact that retaining the old customers is easier and much cost effective than attaining new ones. The customer relationship management (CRM) analyzers are focusing on the reasons as to why a certain customer moves out of a particular service. They are also trying to figure out the various behavioral patterns of the present churn customers from their data. The paper proposes a prediction model that employs various classification and clustering techniques to identify the churn customers well in advance and also figures out the reasons behind the churning. In the current scenario, various combinations of Machine Learning algorithms are being used, for example, Logistic Regression along with decision tree or SVM can be used. Many a times, single algorithms like Naïve Bayes or Neural networks can be used as well. In our proposed system, we use the Random Forest for better accuracy and precision percentage. A customer profiling is also performed using k-clustering method for tracking down the behavioral patterns of the customers. This helps the CRM to come up with innovative ideas in terms of retention policies and also helps in creating efficient marketing strategies for the company.