Analisis Segmentasi Pelanggan pada Bisnis dengan Menggunakan Metode K-Means Clustering pada Model Data RFM

  • Sisilia Fhelly Djun Universitas Pendidikan Ganesha
  • I Gede Aris Gunadi Universitas Pendidikan Ganesha
  • Sariyasa Sariyasa Universitas Pendidikan Ganesha
Keywords: Business Intelligence, Customer Clustering, Business Development Strategy, Customer Value Pyramid.


The development of business strategies, particularly in the marketing of SMEs, requires the utilization of business intelligence as the foundation for objective decision-making. This research aims to develop a business intelligence scheme for SMEs and design targeted assistance strategies for SME support institutions. The implementation of business intelligence involves leveraging transactional data from SMEs to ascertain customer segmentation and correlating it with Customer Relationship Management (CRM) strategies. Transactional data is processed into a Recency, Frequency, Monetary (RFM) data model. Customer segmentation is achieved through a clustering process using the K-Means algorithm, and the results yield distinct profiles for SME customers. Evaluation processes are conducted to determine the optimal solution for the number of customer segments. Evaluation methods, including the Elbow Method, Silhouette Scores, and Davies–Bouldin Index, are employed to determine the optimum cluster. The evaluation results indicate that the optimum cluster is 3, with the best Silhouette Score being 0.548 and Davies–Bouldin Index at 0.76. The first customer segment exhibits the highest shopping frequency and monetary value, categorizing them as active and profitable customers. Special loyalty services are recommended for this segment. The second segment, despite having the largest number of customers, exhibits a shopping frequency of only 1-2 times, with an average recency of approximately the last 2 months. These customers require effective after-sales service. The third segment consists of customers who last shopped more than 6 months ago, making them a low-priority segment. Re-engagement strategies, such as email marketing, are suggested for this segment. Support institutions can focus on CRM assistance targeting these three identified segments.


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K. Sedyastuti, “Analisis pemberdayaan UMKM dan peningkatan daya saing dalam kancah pasar global,” INOBIS: Jurnal Inovasi Bisnis dan Manajemen Indonesia, vol. 2, no. 1, pp. 117–127, 2018,

L. M. M. Hewitt and L. J. J. Van Rensburg, “The role of business incubators in creating sustainable small and medium enterprises,” The Southern African Journal of Entrepreneurship and Small Business Management, vol. 12, no. 1, p. 9, 2020,

A. Sandra, H. Hanif, R. I. Arfianti, and P. Apriwenni, “Pendampingan Pajak UMKM: Masalah dan Solusinya,” Academics in Action Journal of Community Empowerment, vol. 1, no. 1, pp. 1–7, 2019,

N. Alinsari, “Peningkatan literasi keuangan pada umkm melalui pelatihan dan pendampingan pembukuan seder-hana,” Magistrorum et Scholarium: Jurnal Pengabdian Masyarakat, vol. 1, no. 2, pp. 256–268, 2020,


I. G. B. A. Budaya, D. P. Agustino, and G. I. R. Martha, “Digital Marketing Literacy for Food Product Dewi Catur Women Farmer Group,” WIDYABHAKTI Jurnal Ilmiah Populer, vol. 4, no. 3, pp. 69–74, 2022,

I. G. B. A. Budaya, G. I. R. Martha, D. P. Agustino, I. M. P. P. Wijaya, and I. G. Harsemadi, “Prediction Model for the Tenant’s Potential Failure from Business Incubation Process during COVID-19 Period Using Supervised Learning,” in 2021 3rd International Conference on Cybernetics and Intelligent System (ICORIS), IEEE, 2021, pp. 1–5,

Q. Zhang, H. Yamashita, K. Mikawa, and M. Goto, “Analysis of purchase history data based on a new latent class model for RFM analysis,” Industrial Engineering & Management Systems, vol. 19, no. 2, pp. 476–483, 2020,

M. Tavakoli, M. Molavi, V. Masoumi, M. Mobini, S. Etemad, and R. Rahmani, “Customer segmentation and strategy development based on user behavior analysis, RFM model and data mining techniques: a case study,” in 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE), IEEE, 2018, pp. 119–126,

I. G. Harsemadi, D. P. Agustino, and I. G. B. A. Budaya, “Klasterisasi Pelanggan Tenant Inkubator Bisnis STIKOM Bali Untuk Strategi Manajemen Relasi Dengan Menggunakan Fuzzy C-Means,” JTIM: Jurnal Teknologi Informasi dan Mul-timedia, vol. 4, no. 4, pp. 232–243, 2023,

P. Anitha and M. M. Patil, “RFM model for customer purchase behavior using K-Means algorithm,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 5, pp. 1785–1792, May 2022, doi: 10.1016/j.jksuci.2019.12.011.

L. P. W. Widhyastuti, I. N. Sukajaya, and K. Y. E. Aryanto, “The Customer Profiling berdasarkan Model RFM dengan Metode K-Means pada Institusi Pendidikan untuk menunjang Strategi Bisnis di Masa Pandemi Covid-19,” JTIM: Jurnal Teknologi Informasi dan Multimedia, vol. 4, no. 2, pp. 94–108, 2022,

M. M. D. Alam, R. Al Karim, and W. Habiba, “The relationship between CRM and customer loyalty: The moderating role of customer trust,” International Journal of Bank Marketing, vol. 39, no. 7, pp. 1248–1272, 2021.

H. Rangriz and Z. Bayrami Shahrivar, “The impact of E-CRM on customer loyalty using data mining techniques,” BI Management Studies, vol. 7, no. 27, pp. 175–205, 2019,

R. U. Khan, Y. Salamzadeh, Q. Iqbal, and S. Yang, “The impact of customer relationship management and company reputation on customer loyalty: The mediating role of customer satisfaction,” Journal of Relationship Marketing, vol. 21, no. 1, pp. 1–26, 2022,

A. Nastasoiu and M. Vandenbosch, “Competing with loyalty: How to design successful customer loyalty reward programs,” Bus Horiz, vol. 62, no. 2, pp. 207–214, 2019,

C. M. Durugbo, “After-sales services and aftermarket support: a systematic review, theory and future research di-rections,” Int J Prod Res, vol. 58, no. 6, pp. 1857–1892, 2020,

J. S. Thomas, C. Chen, and D. Iacobucci, “Email Marketing as a Tool for Strategic Persuasion,” Journal of Interactive Marketing, vol. 57, no. 3, pp. 377–392, May 2022, doi: 10.1177/10949968221095552.

C. E. Khedkar and A. E. Khedkar, “Email Marketing: A Cost-Effective Marketing Method,” Vidyabharati International Interdisciplinary Research Journal 13 (1), 2021.

A. T. Widiyanto and A. Witanti, “Segmentasi Pelanggan Berdasarkan Analisis RFM Menggunakan Algoritma K-Means Sebagai Dasar Strategi Pemasaran (Studi Kasus PT Coversuper Indonesia Global),” KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, vol. 1, no. 1, pp. 204–215, 2021,

How to Cite
S. F. Djun, I. G. A. Gunadi, and S. Sariyasa, “Analisis Segmentasi Pelanggan pada Bisnis dengan Menggunakan Metode K-Means Clustering pada Model Data RFM”, jtim, vol. 5, no. 4, pp. 354-364, Feb. 2024.