A Monetization Model for a Telemedicine Platform Based on Customer Segmentation using the K-Means Algorithm and Willingness-To-Pay

Yusrinnatul Jinana; Robi Sunggara; Didin Muhidin

doi:10.59934/jaiea.v5i3.2518

Authors

Yusrinnatul Jinana Universitas Bhakti Husada Indonesia
Robi Sunggara Universitas Bhakti Husada Indonesia
Didin Muhidin Universitas Bhakti Husada Indonesia

DOI:

https://doi.org/10.59934/jaiea.v5i3.2518

Keywords:

Telemedicine, Customer Segmentation, K-Means Clustering, Willingness-to-Pay, Monetization Model, Tiered Subscription

Abstract

Telemedicine platforms in Indonesia face post-pandemic monetization challenges, where the freemium model has not been effective in converting free users into paying customers. This study aims to design a customer segmentation-based monetization model using the K-Means Clustering algorithm and Willingness-to-Pay (WTP) analysis in the suburban area of Kuningan Regency. Data were collected through a survey of 264 respondents and analyzed using K-Means with a Silhouette Score evaluation (0.42 at *k*=3) and the Kruskal-Wallis test for differences in WTP between segments. The clustering results identified three customer segments: the Digital Light Generation, Health Care Professionals, and Chronic Patients Needing Monitoring, with significantly different WTP (p<0.001). Revenue simulations recommended an optimal tiered subscription model: Basic Package Rp25,000/month, Premium Rp75,000/month, and Family Rp130,000/month. This model is able to maximize revenue while maintaining inclusiveness of service access, bridging business sustainability and affordability for suburban communities, and contributing to the Precision in Health Care approach in developing digital health businesses..

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References

IMARC Group. (2024). Online medical service market report by service type, platform type, application, end user, and region 2025–2033. https://www.imarcgroup.com/online-medical-service-market

Saputri, M. E., & Lukiarti, M. M. (2024). The rise of telemedicine services in Indonesia: What factors determine customers' repurchase intentions? Procedia Computer Science, *234*, 1205–1213. https://doi.org/10.1016/j.procs.2024.03.108

CNN Indonesia. (2022, May 4). Global telemedicine startup stumbles, valuation plummets 90%. https://www.cnnindonesia.com/technology/20220504120000-185-793555/startup-telemedicine-global-terseok-seok-valuasi-anjlok-90

Velayati, F., Ayatollahi, H., Hemmat, M., & Dehghan, R. (2022). Telehealth business models and their components: Systematic review. Journal of Medical Internet Research, *24*(3), e33128. https://doi.org/10.2196/33128

Lin, S. (2023). Consumer preferences and willingness to pay for attributes of telemedicine service: A case study in Indonesia [Master's thesis, Tsinghua University]. Tsinghua University Digital Thesis Repository.

University of Indonesia. (2025). Teleconsultation costs in Indonesia: A qualitative stakeholder study. Kesmas: National Public Health Journal, *19*(1), 45–54.

BPS Kuningan Regency. (2024). Kuningan in numbers 2024. Kuningan Regency Central Statistics Agency.

APJII. (2024). Indonesia internet penetration survey 2024. Indonesian Internet Service Providers Association.

Saunders, M., Lewis, P., & Thornhill, A. (2023). Research methods for business students (9th ed.). Pearson.

Malhotra, N. K., Nunan, D., & Birks, D. F. (2020). Marketing research: An applied approach (6th ed.). Pearson.

Campbell, S., Greenwood, M., Prior, S., Shearer, T., Walkem, K., Young, S., Bywaters, D., & Walker, K. (2020). Purposive sampling: Complex or simple? Research case examples. Journal of Research in Nursing, *25*(8), 652–661. https://doi.org/10.1177/1744987120927206

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage

Geron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O'Reilly Media.

UNESCO. (2018). A global framework of reference on digital literacy skills for indicator 4.4.2. UNESCO Institute for Statistics.

McKinney, W. (2022). Python for data analysis: Data wrangling with pandas, NumPy, and Jupyter (3rd ed.). O'Reilly Media.

Ezugwu, A. E., Shukla, A. K., Agbaje, M. B., Oyelade, O. N., José-García, A., & Agushaka, J. O. (2020). Automatic clustering algorithms: A systematic review and bibliometric analysis of relevant literature. Neural Computing and Applications, *33*, 6247–6306. https://doi.org/10.1007/s00521-020-05395-4

Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2), 224–227. https://doi.org/10.1109/TPAMI.1979.4766909

Dunn, O. J. (1964). Multiple comparisons using rank sums. Technometrics, *6*(3), 241–252. https://doi.org/10.1080/00401706.1964.10490181

Amit, R., & Zott, C. (2020). Business model innovation strategy: Transformational concepts and tools for entrepreneurial leaders. Wiley.

Cengil, A. B., Eksioglu, B., Eksioglu, S., Eswaran, H., Hayes, C. J., & Bogulski, C. A. (2026). Resource use patterns in US telehealth services: Machine learning and clustering analysis across 4 specialties. Journal of Medical Internet Research, *28*(1), e56789. https://doi.org/10.2196/56789

Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. Wiley.