Development of a Customer Churn Prediction Model Using Machine Learning Techniques in the Telecommunications Industry
Authors
Ebonyi State University, Abakaliki (Nigeria)
Ebonyi State University, Abakaliki (Nigeria)
Ebonyi State University, Abakaliki (Nigeria)
Alex Ekwueme Federal University, Ndufu Alike, Ebonyi State, Abakaliki (Nigeria)
Article Information
DOI: 10.51244/IJRSI.2026.1306000049
Subject Category: Data Science, Information Science
Volume/Issue: 13/6 | Page No: 759-772
Publication Timeline
Submitted: 2026-05-22
Accepted: 2026-05-27
Published: 2026-06-20
Abstract
Customer churn remains a major challenge in the telecommunications industry due to increasing market competition and customer mobility. This study developed and evaluated machine learning models for predicting customer churn using the Telco Customer Churn dataset containing 7,043 customer records. The study applied data preprocessing techniques including missing value handling, categorical encoding, and feature scaling before implementing Logistic Regression and Random Forest classification models. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, and ROC-AUC metrics. Experimental results showed that the Random Forest classifier achieved superior predictive performance, with an accuracy of 80%, a recall of 57%, an F1-score of 0.62, and an ROC-AUC of 0.85. Feature importance analysis revealed that contract type, tenure, monthly charges, and total charges were the most significant predictors of customer churn. The findings demonstrate the effectiveness of machine learning techniques in supporting proactive customer retention strategies and data-driven decision-making in the telecommunications sector.
Keywords
Customer Churn, Machine Learning, Logistic Regression, Random Forest
Downloads
References
1. Ahmed, A., Aljahdali, H. M., & Awan, I. (2019). Customer churn prediction in the telecommunication sector using deep learning—International Journal of Computer Applications, 975(8887). [Google Scholar] [Crossref]
2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. [Google Scholar] [Crossref]
3. Amin, A., Anwar, S., Adnan, A., Nawaz, M., Howard, N., Qadir, J., & Hawalah, A. Y. (2016). Customer churn prediction in the telecom industry using data certainty. Telecommunication Systems, 61(3), 627–645. [Google Scholar] [Crossref]
4. Asif, M., Khan, S., & Shafi, J. (2025). Economic realities of customer retention versus acquisition in modern saturated telecommunication markets. Journal of Business Analytics, 8(1), 45–59. [Google Scholar] [Crossref]
5. Balaji, G., Gowtham, N. A. T., Tarun, S., Prajapati, G., & Manikandan, N. (2024). Customer churn prediction using machine learning algorithms. Proceedings of the 2024 International Conference on Emerging Research in Computational Science (ICERCS), 1–6. https://doi.org/10.1109/icercs63125.2024.10895079 Cited by: 6 [Google Scholar] [Crossref]
6. Bhattacharjee, B. (2026). Neural network approach enhancing churn prediction with categorical encoding and standard scaling. Data Insights and Business Analytics, 14(2), 112–126. [Google Scholar] [Crossref]
7. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. [Google Scholar] [Crossref]
8. Bhushan, S. B. (2025). Enhancing customer churn prediction in the telecom sector using advanced machine learning techniques and explainable AI (Master's thesis). National College of Ireland, Dublin, Ireland. Cited by: 1 [Google Scholar] [Crossref]
9. El Attar, A. (2026). Explainable AI-driven customer churn prediction: A multi-model ensemble approach with SHAP-based feature analysis. Frontiers in Artificial Intelligence, 9, Article 1748799. https://doi.org/10.3389/frai.2026.1748799 Cited by: 2 [Google Scholar] [Crossref]
10. Hadden, J., Tiwari, A., Roy, R., & Ruta, D. (2006). Computer-assisted customer churn management: State-of-the-art and future trends. Computers & Operations Research, 33(10), 2902–2917. [Google Scholar] [Crossref]
11. Idris, A., Khan, A., & Lee, Y. S. (2012). Intelligent churn prediction in telecom: Employing mRMR feature selection and RotBoost-based ensemble classification. Applied Intelligence, 39(3), 659–672. [Google Scholar] [Crossref]
12. Imani, M. (2024). Customer churn prediction: A systematic review of recent advances, trends, and challenges in machine learning and deep learning. Machine Learning and Knowledge Extraction, 6(4), 542–571. Cited by: 59 [Google Scholar] [Crossref]
13. Jibril, B., Adebiyi, F., & Usman, M. (2026). Customer churn prediction in the Nigerian telecommunications ecosystem: A hybrid GA-K-means-ANN computational framework. FUDMA Journal of Sciences (FJS), 10(1), 347–359. [Google Scholar] [Crossref]
14. Neslin, S. A., Gupta, S., Kamakura, W., Lu, J., & Mason, C. H. (2006). Defection detection: Measuring and understanding the predictive accuracy of customer churn models. Journal of Marketing Research, 43(2), 204–211. [Google Scholar] [Crossref]
15. Nguyen, B., Simkin, L., & Canhoto, A. I. (2021). Big data analytics in customer churn prediction: A hybrid approach. Journal of Strategic Marketing, 29(4), 319–335. [Google Scholar] [Crossref]
16. Payne, A., & Frow, P. (2005). A strategic framework for customer relationship management. Journal of Marketing, 69(4), 167–176. [Google Scholar] [Crossref]
17. Provost, F., & Fawcett, T. (2013). Data science for business. Sebastopol, CA: O'Reilly Media. [Google Scholar] [Crossref]
18. Saleh, S., & Saha, S. (2023). Customer retention and churn prediction in the telecommunication industry: A case study on a Danish university. SN Applied Sciences, 5(6), Article 163. https://doi.org/10.1007/s42452-023-05389-6 Cited by: 92 [Google Scholar] [Crossref]
19. Siddiqui, S., Sattar, M. A., & Jamil, S. (2020). Predictive analysis of customer churn for the telecom industry using supervised machine learning. International Journal of Computer Applications, 176(26), 1–7. [Google Scholar] [Crossref]
20. Verbeke, W., Martens, D., Mues, C., & Baesens, B. (2012). Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Systems with Applications, 38(3), 2354–2364. [Google Scholar] [Crossref]
21. Rosso, S., Bahilo, E., Velasco, M., & Angulo, C. (2021). Condition Assessment of Industrial Gas Turbine Compressor Using a Drift Soft Sensor Based on an Autoencoder. Sensors, 21(8), 2708. [Google Scholar] [Crossref]