Customer Segmentation in an Internet Service Provider: A K-Means Case Study of Telecommunication Company
DOI:
https://doi.org/10.63158/journalisi.v8i3.1631Keywords:
Customer Segmentation, Internet Service Provider, K-Means Clustering, Silhouette Coefficient, Elbow Method, Telecommunication Customer AnalyticsAbstract
PT Lintas Jaringan Nusantara, an internet service provider, faces challenges in utilizing customer data, which is mainly used for administrative purposes such as billing and support, limiting deeper analysis. This study applies K-Means clustering under the CRISP-DM framework for customer segmentation-based service-oriented attributes: internet package, price, and NAS location, using 972 customer records. Categorical attributes were transformed using frequency encoding and manual mapping. Model evaluation using the Elbow Method suggested 3 clusters, while the Silhouette Coefficient indicated that 10 clusters were optimal, improving the score from 0.5471 to 0.7704. The resulting clusters show variations in customer characteristics and provide an exploratory overview of grouping patterns. However, the 10 clusters solution should not yet operationally validated, as stakeholder validation involving marketing and customer service teams is still required to assess interpretability, business relevance, and practical applicability. Further validation using additional customer data or alternative datasets is also recommended. Overall, the findings serve as an initial analytical step to support future data-driven decision-making.
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[1] G. E. Corazza et al., “A glimpse on the futures of telecommunication networks: From market, technology and regulation trends to strategic foresight,” J. Open Innov. Technol. Mark. Complex., vol. 12, no. 1, Mar. 2026, doi: 10.1016/j.joitmc.2026.100735.
[2] H. Ribeiro, B. Barbosa, A. C. Moreira, and R. G. Rodrigues, “Determinants of churn in telecommunication services: a systematic literature review,” Manag. Rev. Q., vol. 74, no. 3, pp. 1327–1364, Sep. 2024, doi: 10.1007/s11301-023-00335-7.
[3] A. H. Abushar, “Factors Affecting Brand Switching Behaviour in the Palestinian Telecommunications Industry in the Gaza Strip,” in Stud. Syst. Decis. Control, vol. 516, 2024, pp. 291–302. doi: 10.1007/978-3-031-49544-1_26.
[4] J. S. Harini, A. Anusuya, P. Kanimozhi, and T. Ananthkumar, “Churn Prediction and Factor Identification in Telecommunication Industry Using Deep Learning,” in Proc. Int. Conf. Emerg. Technol. Eng. Appl. (ICETEA), 2025. doi: 10.1109/ICETEA64585.2025.11099737.
[5] S. Sayuti, B. Berman, D. Sirya, and J. Heikal, “Clustering Customer’s Internet Subscriptions in Apartment Using K-Means Clustering Algorithm,” 2025, doi: 10.37481/jmeb.v5i3.1514
[6] E. Eslami, N. Razi, M. Lonbani, and J. Rezazadeh, “Unveiling IoT Customer Behaviour: Segmentation and Insights for Enhanced IoT-CRM Strategies: A Real Case Study,” Sensors, vol. 24, no. 4, Feb. 2024, doi: 10.3390/s24041050.
[7] C. Rungruang et al., “RFM model customer segmentation based on hierarchical approach using FCA,” Expert Syst. Appl., vol. 237, Mar. 2024, doi: 10.1016/j.eswa.2023.121449.
[8] J. M. John, O. Shobayo, and B. Ogunleye, “An Exploration of Clustering Algorithms for Customer Segmentation in the UK Retail Market,” Analytics, vol. 2, no. 4, pp. 809–823, Dec. 2023, doi: 10.3390/analytics2040042.
[9] K. Tabianan et al., “K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data,” Sustainability, vol. 14, no. 12, Jun. 2022, doi: 10.3390/su14127243.
[10] J. V. Kristian, T. A. Munandar, and D. B. Srisulistiowati, “Exclusive Clustering Technique for Customer Segmentation in National Telecommunications Companies,” 2023, doi: 10.58776/ijitcsa.v1i1.19
[11] A. M. Ikotun et al., “K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data,” Inf. Sci., vol. 622, pp. 178–210, Apr. 2023, doi: 10.1016/j.ins.2022.11.139.
[12] R. H. Bemthuis et al., “A CRISP-DM-based methodology for assessing agent-based simulation models using process mining,” J. Simul., 2025, doi: 10.1080/17477778.2025.2508245.
[13] E. H. Sharaf Addin et al., “Customer Mobile Behavioral Segmentation and Analysis in Telecom Using Machine Learning,” Appl. Artif. Intell., vol. 36, no. 1, 2022, doi: 10.1080/08839514.2021.2009223.
[14] P. Ramesh and P. T. V. Bhuvaneswari, “Machine learning driven clustering for silhouetting 5G network throughput,” Sci. Rep., vol. 16, no. 1, Dec. 2026, doi: 10.1038/s41598-026-45902-6.
[15] M. Mustaqim et al., “Analisis Data Pelanggan dengan Algoritma K-Means untuk Peningkatan Penjualan Layanan ICONET DIBANGKA BELITUNG,” J. Akad. Ekon. Manaj., vol. 2, pp. 50–60, Dec. 2025, doi: 10.61722/jaem.v2i4.7106
[16] H. Huang et al., “Feature Selection for Unsupervised Machine Learning,” in Proc. IEEE 8th Int. Conf. Smart Cloud (SmartCloud), 2023, pp. 164–169. doi: 10.1109/SmartCloud58862.2023.00036.
[17] M. F. Fachri and L. Zahrotun, “Centroid Optimization of K-Means Using Ant Colony Optimization for Culinary MSME Clustering,” J. Inf. Syst. Inform., vol. 8, no. 1, pp. 860–888, Mar. 2026, doi: 10.63158/journalisi.v8i1.1443.
[18] L. Bai and J. Liang, “A categorical data clustering framework on graph representation,” Pattern Recognit., vol. 128, Aug. 2022, doi: 10.1016/j.patcog.2022.108694.
[19] D. Choi et al., “Deep Clustering for Mixed-type Data with Frequency Encoding and Doubly Weighted Cross Entropy Loss,” in Proc. ITC-CSCC, 2022, pp. 141–144. doi: 10.1109/ITC-CSCC55581.2022.9894964.
[20] A. M. Ikotun et al., “Benchmarking validity indices for evolutionary K-means clustering performance,” Sci. Rep., vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-08473-6.
[21] N. Huda Ahsina et al., “Analisis segmentasi pelanggan bank berdasarkan pengambilan kredit dengan menggunakan metode K-means clustering,” 2022, doi: 10.33197/jitter.vol8.iss3.2022.883.
[22] A. Y. Raya-Tapia et al., “Machine Learning and Clustering for a Sustainable Future: Applications in Engineering and Environmental Science,” in Stud. Comput. Intell., vol. 1233, 2025, pp. 1–351. doi: 10.1007/978-3-032-03876-0.
[23] A. S. Nyamawe et al., “Practical Machine Learning; A Beginner’s Guide with Ethical Insights,” 2025. doi: 10.1201/9781003486817-1.
[24] G. Vardakas, I. Papakostas, and A. Likas, “Deep Clustering Using the Soft Silhouette Score: Towards Compact and Well-Separated Clusters,” arXiv preprint, Feb. 2024, doi: 10.1007/s10994-026-07026-w.
[25] S. Kumar et al., “Customer segmentation in e-commerce: K-means vs hierarchical clustering,” Telkomnika, vol. 23, no. 1, pp. 119–128, Feb. 2025, doi: 10.12928/TELKOMNIKA.v23i1.26384.
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