Customer Churn in Mobile Markets A Comparison of Techniques

TL;DR

This paper empirically compares decision tree and logistic regression techniques for customer churn prediction in mobile markets, demonstrating the superiority of decision trees and highlighting the need for more advanced methods.

Contribution

It provides an empirical comparison of churn prediction methods, confirming the effectiveness of decision trees over logistic regression in this context.

Findings

Decision trees outperform logistic regression in churn prediction.

The study emphasizes the need for more advanced churn modeling techniques.

Empirical validation of methods in real market data.

Abstract

The high increase in the number of companies competing in mature markets makes customer retention an important factor for any company to survive. Thus, many methodologies (e.g., data mining and statistics) have been proposed to analyse and study customer retention. The validity of such methods is not yet proved though. This paper tries to fill this gap by empirically comparing two techniques: Customer churn-decision tree and logistic regression models. The paper proves the superiority of decision tree technique and stresses the needs for more advanced methods to churn modelling.