Machine Learning Models for Customer Segmentation in Telecom

Authors

  • Naveen Bagam Independent Researcher, USA

DOI:

https://doi.org/10.36676/j.sust.sol.v1.i4.42

Keywords:

Customer Segmentation, Telecom, Machine Learning, Supervised Learning, Clustering, Data Preprocessing

Abstract

This paper throws light upon the role of machine learning in refining customer segmentation for telecom organizations. Customer segmentation becomes a critical initiative for telecom companies to tailor respective services and customer experience. It discusses the distinction between traditional and advanced machine learning models, including supervised, unsupervised, and hybrid models that are compared in terms of segmentation methods, data requirements, and model evaluation techniques. The article delves into machine learning capabilities to help differentiate customers with enhanced accuracy, thus illuminating insights into business strategies, implementation challenges, and future trends in telecom analytics.

References

Alkhayrat, M., Aljnidi, M., & Aljoumaa, K. (2020). A comparative dimensionality reduction study in telecom customer segmentation using deep learning and PCA. Journal of Big Data, 7(1), 9. DOI: https://doi.org/10.1186/s40537-020-0286-0

Dullaghan, C., & Rozaki, E. (2017). Integration of machine learning techniques to evaluate dynamic customer segmentation analysis for mobile customers. arXiv preprint arXiv:1702.02215. DOI: https://doi.org/10.5121/ijdkp.2017.7102

Wu, S., Yau, W. C., Ong, T. S., & Chong, S. C. (2021). Integrated churn prediction and customer segmentation framework for telco business. Ieee Access, 9, 62118-62136. DOI: https://doi.org/10.1109/ACCESS.2021.3073776

Routray, S. K. (2021). Marketing strategy through machine learning techniques: A case study at telecom industry. International Journal of Innovation Engineering and Science Research, 5(3), 21-30.

Omonge, J. (2021). A Customer segmentation model using logistic regression: a case of Telkom Kenya (Doctoral dissertation, Strathmore University).

Zhang, T., Moro, S., & Ramos, R. F. (2022). A data-driven approach to improve customer churn prediction based on telecom customer segmentation. Future Internet, 14(3), 94. DOI: https://doi.org/10.3390/fi14030094

Bayissa, F. (2019). TELECOM CUSTOMER SEGMMENTATION USING DATA MINING TECHNIQUES (Doctoral dissertation, St. Mary’s University).

Ullah, I., Raza, B., Malik, A. K., Imran, M., Islam, S. U., & Kim, S. W. (2019). A churn prediction model using random forest: analysis of machine learning techniques for churn prediction and factor identification in telecom sector. IEEE access, 7, 60134-60149. DOI: https://doi.org/10.1109/ACCESS.2019.2914999

Jothi, R., & Muthukumaran, K. (2022). Telecom Customer Segmentation Using Deep Embedded Clustering Algorithm. In Machine Learning and Data Analytics for Solving Business Problems: Methods, Applications, and Case Studies (pp. 75-88). Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-031-18483-3_5

Chornous, G., & Fareniuk, Y. (2022). Optimization of Marketing Decisions Based on Machine Learning: Case for Telecommunications. In IT&I (pp. 112-124).

Sujah, A. M. A., & Rathnayaka, R. M. K. T. (2019). Mining profitability of telecommunication customers and customer segmentation with novel data mining approach.

Zadoo, A., Jagtap, T., Khule, N., Kedari, A., & Khedkar, S. (2022, May). A review on churn prediction and customer segmentation using machine learning. In 2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COM-IT-CON) (Vol. 1, pp. 174-178). IEEE. DOI: https://doi.org/10.1109/COM-IT-CON54601.2022.9850924

Dong, Y. (2024). Potential Customer Prediction of Telecom Marketing based on Machine Learning. Highlights in Science, Engineering and Technology, 92, 138-145. DOI: https://doi.org/10.54097/bbqe4m48

Choi, C. (2018). Predicting customer complaints in mobile telecom industry using machine learning algorithms. Purdue University.

Qureshi, S. A., Rehman, A. S., Qamar, A. M., Kamal, A., & Rehman, A. (2013, September). Telecommunication subscribers' churn prediction model using machine learning. In Eighth international conference on digital information management (ICDIM 2013) (pp. 131-136). IEEE. DOI: https://doi.org/10.1109/ICDIM.2013.6693977

Zhao, Y., Shao, Z., Zhao, W., Han, J., Zheng, Q., & Jing, R. (2023). Combining unsupervised and supervised classification for customer value discovery in the telecom industry: a deep learning approach. Computing, 105(7), 1395-1417. DOI: https://doi.org/10.1007/s00607-023-01150-4

Namvar, A., Ghazanfari, M., & Naderpour, M. (2017, November). A customer segmentation framework for targeted marketing in telecommunication. In 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/ISKE.2017.8258803

Mahmoud, H. H., & Asyhari, A. T. (2024, July). Customer Segmentation for Telecommunication Using Machine Learning. In International Conference on Knowledge Science, Engineering and Management (pp. 144-154). Singapore: Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-97-5489-2_13

Prathyusha Nama, Manoj Bhoyar, & Swetha Chinta. (2024). AI-Powered Edge Computing in Cloud Ecosystems: Enhancing Latency Reduction and Real-Time Decision-Making in Distributed Networks. Well Testing Journal, 33(S2), 354–379. Retrieved from https://welltestingjournal.com/index.php/WT/article/view/109

Prathyusha Nama, Manoj Bhoyar, & Swetha Chinta. (2024). Autonomous Test Oracles: Integrating AI for Intelligent Decision-Making in Automated Software Testing. Well Testing Journal, 33(S2), 326–353. Retrieved from https://welltestingjournal.com/index.php/WT/article/view/108

Nama, P. (2024). Integrating AI in testing automation: Enhancing test coverage and predictive analysis for improved software quality. World Jou…

Nama, P., Reddy, P., & Pattanayak, S. K. (2024). Artificial intelligence for self-healing automation testing frameworks: Real-time fault prediction and recovery. CINEFORUM, 64(3S), 111-141

Harish Goud Kola. (2024). Real-Time Data Engineering in the Financial Sector. International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 3(3), 382–396. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/143

Harish Goud Kola. (2024). Real-Time Data Engineering in the Financial Sector. International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 3(3), 382–396. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/143

Naveen Bagam. (2024). Data Integration Across Platforms: A Comprehensive Analysis of Techniques, Challenges, and Future Directions. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 902–919. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/7062

Bagam, N., Shiramshetty, S. K., Mothey, M., Annam, S. N., & Bussa, S. (2024). Machine Learning Applications in Telecom and Banking. Integrated Journal for Research in Arts and Humanities, 4(6), 57–69. https://doi.org/10.55544/ijrah.4.6.8

Sai Krishna Shiramshetty. (2024). Enhancing SQL Performance for Real-Time Business Intelligence Applications. International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 3(3),

Mouna Mothey. (2022). Automation in Quality Assurance: Tools and Techniques for Modern IT. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 11(1), 346–364. Retrieved from https://eduzonejournal.com/index.php/eiprmj/article/view/694282–297. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/138

Mothey, M. (2023). Artificial Intelligence in Automated Testing Environments. Stallion Journal for Multidisciplinary Associated Research Studies, 2(4), 41–54. https://doi.org/10.55544/sjmars.2.4.5

Mouna Mothey. (2024). Test Automation Frameworks for Data-Driven Applications. International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 3(3), 361–381. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/142

SQL in Data Engineering: Techniques for Large Datasets. (2023). International Journal of Open Publication and Exploration, ISSN: 3006-2853, 11(2), 36-51. https://ijope.com/index.php/home/article/view/165

Data Integration Strategies in Cloud-Based ETL Systems. (2023). International Journal of Transcontinental Discoveries, ISSN: 3006-628X, 10(1), 48-62. https://internationaljournals.org/index.php/ijtd/article/view/116

Naveen Bagam, Sai Krishna Shiramshetty, Mouna Mothey, Harish Goud Kola, Sri Nikhil Annam, & Santhosh Bussa. (2024). Advancements in Quality Assurance and Testing in Data Analytics. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 860–878. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/1487

Shiramshetty, S. K. (2023). Advanced SQL Query Techniques for Data Analysis in Healthcare. Journal for Research in Applied Sciences and Biotechnology, 2(4), 248–258. https://doi.org/10.55544/jrasb.2.4.33

Sai Krishna Shiramshetty "Integrating SQL with Machine Learning for Predictive Insights" Iconic Research And Engineering Journals Volume 1 Issue 10 2018 Page 287-292

Sai Krishna Shiramshetty, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.3, March- 2023, pg. 49-62 DOI: https://doi.org/10.47760/ijcsmc.2023.v12i03.006

Sai Krishna Shiramshetty. (2022). Predictive Analytics Using SQL for Operations Management. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 11(2), 433–448. Retrieved from https://eduzonejournal.com/index.php/eiprmj/article/view/693

Sai Krishna Shiramshetty, " Data Integration Techniques for Cross-Platform Analytics, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 4, pp.593-599, July-August-2020. Available at doi : https://doi.org/10.32628/CSEIT2064139 DOI: https://doi.org/10.32628/CSEIT2064139

Sai Krishna Shiramshetty, "Big Data Analytics in Civil Engineering : Use Cases and Techniques", International Journal of Scientific Research in Civil Engineering (IJSRCE), ISSN : 2456-6667, Volume 3, Issue 1, pp.39-46, January-February.2019 DOI: https://doi.org/10.32628/IJSRCE19318

Mothey, M. (2022). Leveraging Digital Science for Improved QA Methodologies. Stallion Journal for Multidisciplinary Associated Research Studies, 1(6), 35–53. https://doi.org/10.55544/sjmars.1.6.7

Mothey, M. (2023). Artificial Intelligence in Automated Testing Environments. Stallion Journal for Multidisciplinary Associated Research Studies, 2(4), 41–54. https://doi.org/10.55544/sjmars.2.4.5

Mouna Mothey. (2024). Test Automation Frameworks for Data-Driven Applications. International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 3(3), 361–381. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/142

Kola, H. G. (2024). Optimizing ETL Processes for Big Data Applications. International Journal of Engineering and Management Research, 14(5), 99-112.

SQL in Data Engineering: Techniques for Large Datasets. (2023). International Journal of Open Publication and Exploration, ISSN: 3006-2853, 11(2), 36-51. https://ijope.com/index.php/home/article/view/165

Data Integration Strategies in Cloud-Based ETL Systems. (2023). International Journal of Transcontinental Discoveries, ISSN: 3006-628X, 10(1), 48-62. https://internationaljournals.org/index.php/ijtd/article/view/116

Harish Goud Kola. (2024). Real-Time Data Engineering in the Financial Sector. International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 3(3), 382–396. Retrieved fromhttps://ijmirm.com/index.php/ijmirm/article/view/143

Downloads

Published

26-11-2024

How to Cite

Naveen Bagam. (2024). Machine Learning Models for Customer Segmentation in Telecom. Journal of Sustainable Solutions, 1(4), 101–115. https://doi.org/10.36676/j.sust.sol.v1.i4.42

Issue

Section

Original Research Articles