Customer churn prediction is a critical challenge for companies in the telecommunications industry. The competition is intense, and acquiring new customers can be costly. Predicting when existing customers might leave (churn) helps companies take proactive measures to retain them, reducing churn rates and costs. By analyzing historical customer data, including demographics, service usage, billing, and customer service interactions, this study aims to identify customers at risk of leaving their current service provider. The goal is to develop a robust predictive model that can help telecom companies take proactive steps in customer retention. To identify the most efficient model, the study assesses the performance of several algorithms using criteria such as accuracy, precision, recall, and others. Through thoughtful feature selection, preprocessing, and model evaluation, our research showcases how machine learning can be a vital tool for identifying and reducing churn, leading to cost savings and improved customer satisfaction. As the telecom sector becomes more competitive, the ability to anticipate customer behavior and preempt churn will play a critical role in sustaining profitability. By applying advanced predictive models, telecom providers can not only retain valuable customers but also make data-driven decisions that drive business growth. The findings of this research contribute to the growing body of knowledge on machine learning applications in the telecom industry.

Keywords: Customer churn prediction, telecommunication industry, machine learning algorithms, predictive modeling.

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1. Khamlichi FI, Zaim D, Khalifa K. A new model based on global hybridization of machine learning techniques for “customer churn prediction. In: 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS). IEEE. 2019 Oct 28. pp. 1–4.
2. Kaur I, Kaur J. Customer churn analysis and prediction in banking industry using machine learning. In: 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE. 2020 Nov 6. pp. 434–437.
3. Li S, Xia G, Zhang X. Customer churn combination prediction model based on convolutional neural network and gradient boosting decision tree. In: Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence. 2022 Dec 23. pp. 1–6.
4. Tsai TY, Lin CT, Prasad M. An intelligent customer churn prediction and response framework. In: 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE. 2019 Nov 14. pp. 928–935.
5. Malyar M, Robotyshyn MM, Sharkadi M. Churn prediction estimation based on machine learning methods. In: 2020 IEEE 2nd International Conference on System Analysis & Intelligent Computing (SAIC). IEEE. 2020 Oct 5. pp. 1–4.
6. Tang Q, Xia G, Zhang X, Long F. A customer churn prediction model based on XGBoost and MLP. In: 2020 International Conference on Computer Engineering and Application (ICCEA). IEEE. 2020 Mar 18. pp. 608–612.
7. Venkatesh S, Jeyakarthic M. An optimal genetic algorithm with support vector machine for cloud-based customer churn prediction. In: 2020 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE. 2020 Jul 3. pp. 1–6.
8. Sarnen F, Suyanto S, Rismala R. Lifelong learning for dynamic churn prediction. In: 2020 International Conference on Data Science and Its Applications (ICoDSA). IEEE. 2020 Aug 5. pp. 1–5.
9. Wu S, Yau WC, Ong TS, Chong SC. Integrated churn prediction and customer segmentation framework for telco business. IEEE Access. 2021;9:62118–36.
10. De S, Prabu P. A representation-based query strategy to derive qualitative features for improved churn prediction. IEEE Access. 2023;11:1213–23.
11. Al-Zuabi IM, Jafar A, Aljoumaa K. Predicting customer’s gender and age depending on mobile phone data. J Big Data. 2019;6(1):18.

Customer churn prediction is a critical challenge for companies in the telecommunications industry. The competition is intense, and acquiring new customers can be costly. Predicting when existing customers might leave (churn) helps companies take proactive measures to retain them, reducing churn rates and costs. By analyzing historical customer data, including demographics, service usage, billing, and customer service interactions, this study aims to identify customers at risk of leaving their current service provider. The goal is to develop a robust predictive model that can help telecom companies take proactive steps in customer retention. To identify the most efficient model, the study assesses the performance of several algorithms using criteria such as accuracy, precision, recall, and others. Through thoughtful feature selection, preprocessing, and model evaluation, our research showcases how machine learning can be a vital tool for identifying and reducing churn, leading to cost savings and improved customer satisfaction. As the telecom sector becomes more competitive, the ability to anticipate customer behavior and preempt churn will play a critical role in sustaining profitability. By applying advanced predictive models, telecom providers can not only retain valuable customers but also make data-driven decisions that drive business growth. The findings of this research contribute to the growing body of knowledge on machine learning applications in the telecom industry.

Keywords: Customer churn prediction, telecommunication industry, machine learning algorithms, predictive modeling.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Khamlichi FI, Zaim D, Khalifa K. A new model based on global hybridization of machine learning techniques for “customer churn prediction. In: 2019 Third International Conference on Intelligent Computing in Data Sciences (ICDS). IEEE. 2019 Oct 28. pp. 1–4.
2. Kaur I, Kaur J. Customer churn analysis and prediction in banking industry using machine learning. In: 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE. 2020 Nov 6. pp. 434–437.
3. Li S, Xia G, Zhang X. Customer churn combination prediction model based on convolutional neural network and gradient boosting decision tree. In: Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence. 2022 Dec 23. pp. 1–6.
4. Tsai TY, Lin CT, Prasad M. An intelligent customer churn prediction and response framework. In: 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE. 2019 Nov 14. pp. 928–935.
5. Malyar M, Robotyshyn MM, Sharkadi M. Churn prediction estimation based on machine learning methods. In: 2020 IEEE 2nd International Conference on System Analysis & Intelligent Computing (SAIC). IEEE. 2020 Oct 5. pp. 1–4.
6. Tang Q, Xia G, Zhang X, Long F. A customer churn prediction model based on XGBoost and MLP. In: 2020 International Conference on Computer Engineering and Application (ICCEA). IEEE. 2020 Mar 18. pp. 608–612.
7. Venkatesh S, Jeyakarthic M. An optimal genetic algorithm with support vector machine for cloud-based customer churn prediction. In: 2020 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE. 2020 Jul 3. pp. 1–6.
8. Sarnen F, Suyanto S, Rismala R. Lifelong learning for dynamic churn prediction. In: 2020 International Conference on Data Science and Its Applications (ICoDSA). IEEE. 2020 Aug 5. pp. 1–5.
9. Wu S, Yau WC, Ong TS, Chong SC. Integrated churn prediction and customer segmentation framework for telco business. IEEE Access. 2021;9:62118–36.
10. De S, Prabu P. A representation-based query strategy to derive qualitative features for improved churn prediction. IEEE Access. 2023;11:1213–23.
11. Al-Zuabi IM, Jafar A, Aljoumaa K. Predicting customer’s gender and age depending on mobile phone data. J Big Data. 2019;6(1):18.