A Review on Machine Learning-Based Customer Churn Prediction in the Telecom Industry

Abstract

In today's business landscape, companies are facing significant difficulties in achieving positive client interactions and maintaining customers' satisfaction. As a result, businesses are striving to focus on every aspect related to customers and their behaviors to compete in the industry. This has become increasingly important for building customers' loyalties, given the many opportunities available to customize services or products for each customer. To prevent customers from becoming dissatisfied and leaving, businesses are deploying various techniques to efficiently predict customers' behaviors and identify those who may churn or stop using the company's services or products. The rise of machine learning applications has significantly contributed to addressing the challenge of predicting customers' churn rates. Researchers worldwide are now moving towards applying machine learning techniques in this area. This paper aims to present a review of various studies conducted from 2019 to 2022 that utilized machine-learning techniques to predict customers' churn in the telecom industry. The paper summarizes the different machine learning algorithms that are used for customers' churn prediction, with a particular focus on the telecom industry, as well as their accuracy, to provide insights into the effectiveness of these techniques in addressing customers' churn in the telecom industry.

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