Content Consumer Segmentation form Importance Feature Selection for Service Cancellation Using Hybrid Clustering
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Abstract
This research aims to develop a consumer segmentation model for churn prediction using hybrid clustering techniques and feature selection, addressing limitations in traditional methodologies that rely on demographics or single clustering methods, which often struggle with high-dimensional and non-linear data. The analysis involved 10,983 YouTube Premium users in the Bangkok metropolitan area. The methodology integrates stepwise feature selection via ANN, XGBoost, and LightGBM, alongside the creation of derived behavioral risk variables. Consumer segmentation was executed using GMM, HDBSCAN, and Spectral clustering, with performance assessed through Silhouette scores, DBI, and CHI. The results identify key predictors such as tenure (Period), contract type, watch time, content count, subscription count, payment issues, and support tickets. These findings align with brand loyalty and customer journey theories, where positive engagement increases switching costs, while issues with service recovery accelerate churn decisions. Derived features—including WatchTime_per_Content, Subscribed_Ratio, SupportTickets_per_Period and Churn_Risk—further enhanced classification granularity. Analysis showed that HDBSCAN provided the most balanced clustering (DBI = 1.782), while Spectral Clustering identified the highest-risk group (average Churn_Risk = 0.699). This research contributes to the field by synthesizing ensemble learning and hybrid clustering, expanding churn analysis to the streaming platform context, and providing a robust framework for proactive customer retention.
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References
Aggarwal, C. C., Han, J., Wang, J., & Yu, P. S. (2003). A framework for clustering evolving data streams. Proceedings of the 29th International Conference on Very Large Data Bases, 29, 81–92.
Agrawal, R., Gehrke, J., Gunopulos, D., & Raghavan, P. (1998). Automatic subspace clustering of high dimensional data for data mining applications. ACM SIGMOD Record, 27(2), 94–105. https://doi.org/10.1145/276305.276314
Ankerst, M., Breunig, M. M., Kriegel, H.-P., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. ACM SIGMOD Record, 28(2), 49–60.
Asia Video Industry Association. (2025). The Asia video industry report 2026. https://avia.org/wp-content/uploads/2025/12/Asia-Video-Industry-Report-2026.pdf
Bansal, K. (2023). Customer experience: Creating value through transforming customer journeys. International Journal of Advances in Engineering and Management (IJAEM), 5(9), 715–725.
Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008, Article P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008
Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-based clustering based on hierarchical density estimates. In J. Pei, V. S. Tseng, L. Cao, H. Motoda, & G. Xu (Eds.), Lecture notes in computer science: Vol. 7819. Advances in knowledge discovery and data mining, Part II (pp. 160–172). Springer.
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). Association for Computing Machinery.
Corcoran, A. (2023). Integrated digital marketing in practice. Cambridge University Press.
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1–38.
Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD) (pp. 226–231). AAAI Press.
Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Cluster analysis (5th ed.). Wiley.
Fujo, S. W., Subramanian, S., & Khder, M. A. (2022). Customer churn prediction in telecommunication industry using deep learning. Information Sciences Letters, 11(1), 185–198.
Godinho de Matos, M., & Ferreira, P. (2020). The effect of binge-watching on the subscription of video on demand: Results from randomized experiments. Information Systems Research, 31(4), 1337–1360.
Gomez-Uribe, C. A., & Hunt, N. (2016). The Netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems, 6(4), Article 13. https://doi.org/10.1145/2843948
Grönroos, C. (1989). Defining marketing: A market-oriented approach. European Journal of Marketing, 23(1), 52–60.
Gupta, G., & Ghosh, J. (2006). Bregman bubble clustering: A robust, scalable framework for locating multiple, dense regions in data. Proceedings of the Sixth IEEE International Conference on Data Mining (pp. 232–243). IEEE. https://doi.org/10.1109/ICDM.2006.32
Gupta, G., & Singharia, K. (2021). Consumption of OTT media streaming in COVID-19 lockdown: Insights from PLS analysis. Vision, 25(1), 36–46.
Hallowell, R. (1996). The relationships of customer satisfaction, customer loyalty, and profitability: An empirical study. International Journal of Service Industry Management, 7(4), 27–42.
Helgesen, Ø. (2006). Are loyal customers profitable? Customer satisfaction, customer (action) loyalty and customer profitability at the individual level. Journal of Marketing Management, 22(3–4), 245–266.
Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. John Wiley & Sons. https://doi.org/10.1002/9780470316801
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurIPS) (pp. 3149–3157). Curran Associates.
Khan, M. L. (2017). Social media engagement: What motivates user participation and consumption on YouTube? Computers in Human Behavior, 66, 236–247. https://doi.org/10.1016/j.chb.2016.09.024
Kirgiz, O. B., Kiygi-Calli, M., Cagliyor, S., & El Oraiby, M. (2024). Assessing the effectiveness of OTT services, branded apps, and gamified loyalty giveaways on mobile customer churn in the telecom industry: A machine-learning approach. Telecommunications Policy, 48(8), Article 102816. https://doi.org/10.1016/j.telpol.2024.102816
Kotler, P., & Armstrong, G. (2018). Principles of marketing (17th global ed.). Pearson.
Kumar, V., & Shah, D. (2009). Expanding the role of marketing: From customer equity to market capitalization. Journal of Marketing, 73(6), 119–136.
Kumar, V., Bhagwat, Y., & Zhang, X. (2015). Regaining “lost” customers: The predictive power of first-lifetime behavior, the reason for defection, and the nature of the win-back offer. Journal of Marketing, 79(4), 34–55.
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout the customer journey. Journal of Marketing, 80(6), 69–96.
Lin, C., & Bowman, D. (2022). The impact of introducing a customer loyalty program on category sales and profitability. Journal of Retailing and Consumer Services, 64, Article 102769. https://doi.org/10.1016/j.jretconser.2021.102769
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 4768–4777). Curran Associates.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In L. M. Le Cam, & J. Neyman (Eds.), Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability: Vol 1. (pp. 281–297). University of California Press.
Magnite, (2024, March 22). Streaming TV’s new era: Embracing ad-supported streaming across screens in Thailand. https://magnite.showpad.com/share/PDZT19bbOii84SYUKdNbK
McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical den sity based clustering. Journal of Open Source Software, 2(11), Article 205. https://doi.org/10.21105/joss.00205
McLachlan, G., & Peel, D. (2000). Finite mixture models. John Wiley & Sons. https://doi.org/10.1002/0471721182
Ng, A. Y., Jordan, M. I., & Weiss, Y. (2001). On spectral clustering: Analysis and an algorithm. In T. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Proceedings of the 15th International Conference on Neural Information Processing Systems: Natural and Synthetic (pp. 849–856). MIT Press.
Oliver, R. L. (1999). Whence consumer loyalty? Journal of Marketing, 63(4_suppl1), 33–44.
Palomba, A. (2022). Building OTT brand loyalty and brand equity: Impact of original series on OTT services. Telematics and Informatics, 66, Article 101733. https://doi.org/10.1016/j.tele.2021.101733
Poudel, S. S., Pokharel, S., & Timilsina, M. (2024). Explaining customer churn prediction in telecom industry using tabular machine learning models. Machine Learning with Applications, 17, Article 100567. https://doi.org/10.1016/j.mlwa.2024.100567
Qin, Z., Shuai, Q., Wang, G., Zhang, P., Cao, M., & Chen, M. (2022). E-commerce: Concepts, principles, and application. Springer Nature Singapore.
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?”: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939778
Rokach, L. (2019). Ensemble learning: Pattern classification using ensemble methods (2nd ed.). World Scientific.
SBI. (2025, January 6). Tearing down YouTube Premium's pricing strategy. https://sbigrowth.com/insights/youtube-premium-pricing
Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888–905.
Siahaan, V., & Sianipar, R. H. (2023). Customer segmentation, clustering, and prediction with Python (2nd ed). Balige Publishing.
Statista Research Department. (2024, September 27). Reasons for unsubscribing from subscription video-on-demand (SVoD) services in the next three months in Thailand as of June 2024, by age. Statista. https://www.statista.com/statistics/1261149/thailand-reasons-for-unsubscribing-from-subscription-video-on-demand-services-by-age/
The Bureau of Registration Administration. (2025). Monthly population statistics of Thailand, December 2024 (in Thai). Department of Provincial Administration, Ministry of Interior. Retrieved July 21, 2026, from https://stat.bora.dopa.go.th/stat/statnew/statMONTH/statmonth/#/mainpage
Traag, V. A., Waltman, L., & van Eck, N. J. (2019). From Louvain to Leiden: Guaranteeing well-connected communities. Scientific Reports, 9, Article 5233.
Von Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and Computing, 17, 395–416.
Wagh, S. K., Andhale, A. A., Wagh, K. S., Pansare, J. R., Ambadekar, S. P., & Gawande, S. H. (2024). Customer churn prediction in telecom sector using machine learning techniques. Results in Control and Optimization, 14, Article 100342. https://doi.org/10.1016/j.rico.2023.100342
Wedel, M., & Kamakura, W. A. (2000). Market segmentation: Conceptual and methodological foundations (2nd ed.). Kluwer Academic.
Wei, Y. (2024). Telco customer churn prediction. Highlights in Science, Engineering and Technology, 92, 218–226.
Wetherby, S. (2026, July 28) How Many People Use YouTube in 2026? [Users Data]. Limelight Digital. https://www.limelightdigital.co.uk/youtube-statistics/
Xie, J., Girshick, R., & Farhadi, A. (2016). Unsupervised deep embedding for clustering analysis. In M. F. Balcan, & K. Q. Weinberger (Eds.), Proceedings of the 33rd International Conference on Machine Learning (ICML): Vol. 48 (pp. 478–487). JMLR.org.
Xu, T., Ma, Y., Ao, C., Qu, M., & Meng, X. (2023). A novel telecom customer churn analysis system based on RFM model and feature importance ranking. Interdisciplinary Journal of Information, Knowledge, and Management, 18, 719–737.
YouTube. (n.d.). YouTube Premium. Retrieved July 21, 2026, from https://www.youtube.com/premium
