Analyzing Customer Churn in Telecommunications: Insights from Data Patterns and Trends

Md Shahib Khan; Sandeep Kumar Sahu

doi:10.69968/ijisem.2026v5i173-80

Authors

Md Shahib Khan Research Scholar, SAM global university
Sandeep Kumar Sahu Assistant Professor, SAM global university

DOI:

https://doi.org/10.69968/ijisem.2026v5i173-80

Keywords:

Customer churn, telecom analytics, descriptive analysis, usage patterns, tenure behavior, customer retention

Abstract

Customer churn has become a major issue for the telecom industry as the cost of getting new customers is much higher than that of keeping the existing ones. The patterns of churn are not straightforward and are affected by demographic, behavioral, and regional factors, which in turn interact in subtle ways. A thorough and descriptive analysis is carried out in this research using a real-world telecom dataset to identify the impact that a person’s age, gender, state or region, tenure, calls made, SMS usage and data consumption have on a person's decision to churn. The dataset goes through cleaning, processing and enrichment with engineered features in the form of tenure and age groups which allows for a deeper analytical insight. This study that focuses on three core research questions uncovers the following very clear churn patterns: young and mid-age groups have a higher propensity to churn, low-usage customers quit faster, and some states have consistently high churn rates due to regional competition. The results emphasize the significance of having the basic descriptive analytics before moving on to predictive modeling and also point out the demographic, behavioral, geographic dimensions which are vital for telecom retention strategies.

References

[1] M. Bagri, et al., "Churn analysis in telecommunication industry," 2018 International Conference on Automation and Computational Engineering (ICACE), IEEE, 2018.

[2] M. Al-Mashraie, S. H. Chung, and H. W. Jeon, "Customer switching behavior analysis in the telecommunication industry via push-pull-mooring framework: A machine learning approach," Computers & Industrial Engineering, vol. 144, 2020, p. 106476.

[3] Dahiya, Kiran, and Surbhi Bhatia. "Customer churn analysis in telecom industry." 2015 4th International conference on reliability, infocom technologies and optimization (ICRITO)(trends and future directions). IEEE, 2015.

[4] Kayaalp, Fatih. "Review of Customer Churn Analysis Studies in Telecommunications Industry." Karaelmas Science & Engineering Journal/Karaelmas Fen ve Mühendislik Dergisi 7.2 (2017).

[5] Ahn, Jae-Hyeon, Sang-Pil Han, and Yung-Seop Lee. "Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry." Telecommunications policy 30.10-11 (2006): 552-568.

[6] Gürsoy, Umman Şimşek. "Customer churn analysis in telecommunication sector." İstanbul Üniversitesi İşletme Fakültesi Dergisi 39.1 (2010): 35-49.

[7] Mbarek, Rahmoun, and Ya Baeshen. "Telecommunications customer churn and loyalty intention." (2019).

[8] Pandey, Yash, Roshan Jha, and U. Umamaheswari. "Customer churn analysis in telecom organization." Journal of Positive School Psychology (2022): 5475-5488.

[9] Ribeiro, Hugo, et al. "Determinants of churn in telecommunication services: a systematic literature review." Management Review Quarterly 74.3 (2024): 1327-1364.

[10] Dash, Manoj Kumar, and Susmita Dash. "A systematic review of literatures on customer Churn Analysis in the Telecommunication Industry." Orissa Commerce Association (2019): 24.

[11] Zdziebko, Tomasz, et al. "Optimizing customer retention in the telecom industry: A fuzzy-based churn modeling with usage data." Electronics 13.3 (2024): 469.

[12] Masarifoglu, Melik, and Ali Hakan Buyuklu. "Applying survival analysis to telecom churn data." American Journal of Theoretical and Applied Statistics 8.6 (2019): 261-275.

[13] Edwine, Nabahirwa, et al. "Detecting the risk of customer churn in telecom sector: a comparative study." Mathematical Problems in Engineering 2022.1 (2022): 8534739.

[14] Keramati, Abbas, and Seyed MS Ardabili. "Churn analysis for an Iranian mobile operator." Telecommunications Policy 35.4 (2011): 344-356.

[15] Bilişik, Özge Nalan, and Damla Tuğba Sarp. "Analysis of customer churn in telecommunication industry with machine learning methods." Düzce Üniversitesi Bilim ve Teknoloji Dergisi 11.4 (2023): 2185-2208.

[16] Pejić Bach, Mirjana, Jasmina Pivar, and Božidar Jaković. "Churn management in telecommunications: Hybrid approach using cluster analysis and decision trees." Journal of Risk and Financial Management 14.11 (2021): 544.

[17] binti Oseman, Khalida, Norazrina Abu Haris, and Faizin bin Abu Bakar. "Data mining in churn analysis model for telecommunication industry." Journal of Statistical Modeling and Analytics Vol 1.19-27 (2010).

[18] Rahman, Hafiz, et al. "IBM Telco Customer Churn Prediction with Survival Analysis." Proceedings of the International Conference on Advanced Technology and Multidiscipline (ICATAM 2024). Vol. 245. Springer Nature, 2024.

[19] Tamuka, Nyashadzashe, and Khulumani Sibanda. "Real time customer churn scoring model for the telecommunications industry." 2020 2nd International Multidisciplinary Information Technology and Engineering Conference (IMITEC). IEEE, 2020.