Clustering Customer’s Internet Subscriptions in Apartment Using K-Means Clustering Algorithm
DOI:
https://doi.org/10.37481/jmeb.v5i3.1514Keywords:
Customer Segmentation, K-Means Clustering, Internet Subscribers, STP Framework, 7Ps MarketingAbstract
In Indonesia’s increasingly digital urban environment, internet service providers (ISPs) face challenges in delivering packages that suit the diverse needs of apartment residents. A one-size-fits-all strategy often leads to service mismatches and customer dissatisfaction. This study aims to address this gap by segmenting apartment-based internet subscribers in Jakarta, Surabaya, and Bandung using the K-Means clustering algorithm. The research utilized secondary data from 12,966 subscribers, incorporating key variables such as service package type, housing classification, payment method, and average revenue per user (ARPU). Through data preprocessing and cluster analysis using IBM SPSS, five distinct customer segments were identified, each representing specific behavioral traits and service preferences. These segments range from budget-conscious families to high-end users with complex service demands. Guided by the STP (Segmentation, Targeting, Positioning) framework and the 7Ps of marketing, two primary target segments Family Value Mid-Tier and Stream Pro Users were highlighted for their high market potential and alignment with strategic pricing. The findings contribute practical insights for ISPs in developing personalized broadband services and more efficient marketing strategies tailored to urban apartment markets. This research also demonstrates the value of data-driven segmentation in improving customer satisfaction and business competitiveness in the digital service sector.
References
Al Nuaimi, M., Alhazmi, A., & Alghamdi, A. (2022). Customer segmentation in telecom using K-means clustering: A case study of Saudi Arabia. International Journal of Advanced Computer Science and Applications, 13(4), 271–278.
Alshahrani, H., Alfarraj, O., & Alalwan, N. (2023). Telecom customer churn prediction using clustering and classification techniques. IEEE Access, 11, 17420–17432.
Ayodele, E., & Sodeinde, V. (2024). Customer segmentation using the K-Means clustering algorithm. Ilaro Journal of Science and Technology, 4(1), 1–6.
Chandrashekar, G., & Sahin, F. (2019). A survey on feature selection methods in clustering. International Journal of Computer Applications, 182(14), 1–7.
Chernev, A. (2020). Strategic marketing management (10th ed.). Cerebellum Press.
El-Sappagh, S., Alonso, J. M., Ali, F., & Kwak, K. S. (2021). A data analytics framework for customer segmentation in smart services. Future Generation Computer Systems, 118, 103–115.
Gupta, A., & Arora, A. (2020). Comparative analysis of clustering algorithms for customer segmentation in telecom. International Journal of Engineering Research and Applications, 10(2), 18–25.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2021). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 54(1), 101–115.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2021). When to use and how to report the results of PLS-SEM. European Business Review, 33(1), 2–24.
John, J. M., Shobayo, O., & Ogunleye, B. (2023). An exploration of clustering algorithms for customer segmentation in the UK retail market. Preprints, (October 2023).
Kominfo. (2023). Data Penyelenggaraan Pos dan Informatika. https://pelaporan.komdigi.go.id/file/filespublikasi/2024/02/bd674258-95a1-4baa-b877-2cdd7814cc49.pdf
Kuo, Y. F., Yang, C. Y., & Lin, C. Y. (2022). Customer segmentation in the telecom industry: An approach combining behavioral data and demographic variables. Telecommunications Policy, 46(4), 102267.
Lee, J., Park, Y., & Kim, H. (2021). A customer segmentation approach using data mining and customer lifetime value: Application to an online marketplace. Sustainability, 13(6), 3429.
Li, J., & Zhang, M. (2021). Personalized marketing strategy based on STP theory and machine learning: A case study in the telecom industry. Journal of Marketing Analytics, 9(3), 189–202.
Mohan, S., & Mishra, S. (2020). Exploring the relevance of the extended marketing mix (7Ps) in digital services. International Journal of Business and Management Invention, 9(7), 1–9.
Nadif, M., Govaert, G., & Wang, Y. (2023). Advances in clustering algorithms: Applications in customer analytics and behavior modeling. Data Mining and Knowledge Discovery, 37(3), 701–729.
OpenSignal. (2024). Indonesia Mobile Network Experience Report. https://www.opensignal.com/reports/2024/04/indonesia
Prasetyo, D. A., Surya, S. M., & Hardani, R. N. (2021). The role of internet access in the digital economy development of Indonesian urban households. Journal of ICT Research and Applications, 15(1), 45–58.
Qiu, H., Tang, Y., & Wu, D. (2020). Improved customer segmentation approach based on behavior analysis and data normalization. Applied Sciences, 10(12), 4094.
Rahman, A., Sultana, N., & Karim, M. (2022). Behavioral factors influencing internet service choice: Evidence from emerging markets. Asian Journal of Business Research, 12(1), 24–39.
Saha, M. (2020). Customer engagement with the 7 Ps of marketing. SSRN Electronic Journal.
Saxena, A., Prasad, M., & Bharill, N. (2021). Review of clustering techniques in customer segmentation. Neurocomputing, 421, 234–272.
Saxena, A., Prasad, M., Gupta, A., Bharill, N., Patel, O. P., Tiwari, A., … & Lin, C. T. (2021). A review of clustering techniques and developments. Neurocomputing, 421, 234–272.
Setiawan, H., & Kurniawan, A. (2019). The implementation of STP and marketing mix to improve competitiveness of broadband services. Jurnal Manajemen dan Pemasaran Jasa, 12(1), 45–60.
Singh, A., & Rana, A. (2019). A review on K-means clustering algorithm and its variants. International Journal of Computer Applications, 178(31), 12–16.
Tjhin, A. M., Santoso, S., & Nugroho, B. Y. (2022). Consumer behavior analysis in selecting internet packages during the pandemic in Indonesia. Journal of Business and Management Review, 3(7), 415–425.
Velmurugan, T. (2020). An enhanced analysis of customer segmentation using clustering algorithms in the telecommunications industry. Journal of Computer and Communications, 8(10), 15–29.
Wang, J., Chen, X., & Huang, Q. (2020). Segmentation strategies for online consumers using clustering techniques: A practical case in telecom. Electronic Commerce Research and Applications, 40, 100943.
Wijaya, A. R., & Nugroho, S. R. (2022). Internet service quality and customer satisfaction among apartment residents: An Indonesian case. TELKOMNIKA Telecommunication, Computing, Electronics and Control, 20(4), 757–764.
Xie, Y., Zhao, L., & Zhang, H. (2023). An adaptive method for determining the number of clusters in K-Means algorithm. Expert Systems with Applications, 212, 118668.
Xu, M., Zhang, Z., & He, W. (2021). Behavioral segmentation in digital service adoption: A clustering-based approach. Journal of Business Research, 135, 390–402.
Zhang, Y., Liu, C., & Xu, H. (2020). A K-means-based customer segmentation method for e-commerce recommender systems. Journal of Intelligent & Fuzzy Systems, 38(4), 4703–4712.
Zhou, W., & Zhou, J. (2021). Data-driven segmentation in marketing: Review, implications, and future directions. Journal of Business Research, 134, 548–561.
