Analysis of customer churn prediction in telecom industry using decision trees and logistic regression

Abstract

Customer churn prediction in Telecom industry is one of the most prominent research topics in recent years. It consists of detecting customers who are likely to cancel a subscription to a service. Recently, the mobile telecommunication market has changed from a rapidly growing market into a state of saturation and fierce competition. The focus of telecommunication companies has therefore shifted from building a large customer base into keeping customers in house. For that reason, it is valuable to know which customers are likely to switch to a competitor in the near future. The data extracted from telecom industry can help analyze the reasons of customer churn and use that information to retain the customers. We have proposed to build a model for churn prediction for telecommunication companies using data mining and machine learning techniques namely logistic regression and decision trees. A comparison is made based on efficiency of these algorithms on the available dataset.