Literature Review on Customer Churn Prediction in Telecom Industry

Shuwen Liu

doi:10.54254/2753-8818/2025.DL27120

Theoretical and Natural ScienceOpen access

Literature Review on Customer Churn Prediction in Telecom Industry

Research Article

Open Access

Literature Review on Customer Churn Prediction in Telecom Industry

Shuwen Liu ^1*

¹ The Ohio State University

^*Corresponding author: liu.12169@osu.edu

Published on 24 September 2025

TNS Vol.132

ISSN (Print): 2753-8826

ISSN (Online): 2753-8818

ISBN (Print): 978-1-80590-305-5

ISBN (Online): 978-1-80590-306-2

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Abstract

Customer churn is a critical issue in the telecommunications industry. With digital transformation and the growth of customer data, churn prediction models have become an indispensable tool for telecom operators. This paper comprehensively reviews recent research on customer churn prediction in the telecommunications industry. Common datasets are examined, including public benchmarks such as IBM Telco and Cell2Cell, as well as large-scale proprietary datasets. Four categories of approaches are discussed: traditional machine learning, ensemble and hybrid methods, deep learning, and explainable artificial intelligence (XAI). Ensemble models and deep learning hybrid methods maintain excellent performance, in some cases reporting accuracies exceeding 90%. However, deployments still face common challenges such as class imbalance, overfitting, and limited interpretability. Furthermore, research gaps are identified, including the need for multimodal data integration, real-time prediction, improved interpretability, and privacy-aware methods. By summarizing state-of-the-art techniques and remaining challenges, this review provides guidance for future research and practical implementation of customer churn prediction in the telecommunications sector.

Keywords:

Customer churn prediction, Telecommunications, Machine learning, Deep learning, XAI

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References

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