RFM Analysis Application for Customer Loyalty Identification in Bodywork PT. Bengawan Karya Sakti
Abstract
The bodywork industry in Indonesia is under high competitive pressure, requiring companies to be more adaptive in understanding customer behavior in order to maintain business continuity. PT. Bengawan Karya Sakti as one of the national bodywork companies, has not optimally utilized historical transaction data to assess customer loyalty. This study aims to identify customer loyalty segmentation through the application of the RFM (Recency, Frequency, Monetary) method, which is used to analyze sales transaction data in 2022 and 2023. The study uses the CRISP-DM approach which includes the stages of business understanding, data exploration, data cleaning and processing, modeling, evaluation, and implementation of results. The transaction data analyzed includes attributes of transaction date, customer, number of transactions, and transaction value, which are then processed into RFM scores based on the transaction year and classified into categories such as Very Loyal, Loyal, At Risk, and others. The segmentation results show an increase in the number of very loyal customers from 2022 to 2023, as well as a significant decrease in inactive and at-risk customers. The chi-square statistical test shows that the difference in customer distribution between years is statistically significant (p-value <0.05), indicating a real influence from the company's strategy or external factors. The main conclusion of this study is that the RFM method is effective in the bodywork industry to support data-based marketing decision making and more targeted customer retention strategies.
References
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[2] R. M. Fauzan and G. Alfian, “Segmentasi Pelanggan E-Commerce Menggunakan Fitur Recency, Frequency, Monetary (RFM) dan Algoritma Klasterisasi K-Means,” 2024.
[3] A. L. M. Tampubolon, T. M. E. Y. Butar Butar, and S. Rochimah, “Segmentasi Pelanggan Majalah pada Situs Web E-Commerce dengan K-Means++ dan Metode RFM,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 11, no. 6, pp. 1243–1252, Dec. 2024, doi: 10.25126/jtiik.2024118208.
[4] C. Rungruang, P. Riyapan, A. Intarasit, K. Chuarkham, and J. Muangprathub, “RFM model customer segmentation based on hierarchical approach using FCA [Formula presented],” Expert Syst Appl, vol. 237, Mar. 2024, doi: 10.1016/j.eswa.2023.121449.
[5] J. Zhao, “Customer segmentation application based on K-Means,” Applied and Computational Engineering, vol. 47, no. 1, pp. 242–247, Mar. 2024, doi: 10.54254/2755-2721/47/20241400.
[6] İ. Sabuncu, E. Turkan, and H. Polat, “Customer Segmentation and Profiling with RFM Anakysis,” Turkish Journal of Marketing, vol. 5, no. 1, pp. 22–36, Apr. 2020, doi: 10.30685/tujom.v5i1.84.
[7] Andy Hermawan, Nila Rusiardi Jayanti, Aji Saputra, Army Putera Parta, Muhammad Abizar Algiffary Thahir, and Taufiqurrahman Taufiqurrahman, “Leveraging the RFM Model for Customer Segmentation in a Software-as-a-Service (SaaS) Business Using Python,” Maeswara : Jurnal Riset Ilmu Manajemen dan Kewirausahaan, vol. 2, no. 5, pp. 77–89, Oct. 2024, doi: 10.61132/maeswara.v2i5.1283.
[8] S. Wang, L. Sun, and Y. Yu, “A dynamic customer segmentation approach by combining LRFMS and multivariate time series clustering,” Sci Rep, vol. 14, no. 1, Dec. 2024, doi: 10.1038/s41598-024-68621-2.
[9] F. M. Talaat, A. Aljadani, B. Alharthi, M. A. Farsi, M. Badawy, and M. Elhosseini, “A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing,” Mathematics, vol. 11, no. 18, Sep. 2023, doi: 10.3390/math11183930.
[10] A. Alamsyah et al., “Customer Segmentation Using the Integration of the Recency Frequency Monetary Model and the K-Means Cluster Algorithm,” Scientific Journal of Informatics, vol. 9, no. 2, pp. 189–196, Nov. 2022, doi: 10.15294/sji.v9i2.39437.
[11] O. P. Barus, C. Nathasya, and J. J. Pangaribuan, “The Implementation of RFM Analysis to Customer Profiling Using K-Means Clustering,” Mathematical Modelling of Engineering Problems, vol. 10, no. 1, pp. 298–303, 2023, doi: 10.18280/MMEP.100135.
[12] D. Singh and B. Singh, “Investigating the impact of data normalization on classification performance,” Appl Soft Comput, vol. 97, Dec. 2020, doi: 10.1016/j.asoc.2019.105524.
[13] A. S. Paramita and T. Hariguna, “Comparison of K-Means and DBSCAN Algorithms for Customer Segmentation in E-commerce,” Journal of Digital Market and Digital Currency, vol. 1, no. 1, pp. 43–62, Jun. 2024, doi: 10.47738/jdmdc.v1i1.3.
[14] 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, Oct. 2023, doi: 10.3390/analytics2040042.
[15] M. Z. Alam, T. Ahmad, and S. Parveen, “Assessing social media and influential marketing on brand perception and selection of higher educational institute in India,” International Journal of Data and Network Science, vol. 9, no. 1, pp. 27–36, 2025, doi: 10.5267/j.ijdns.2024.11.001.
[16] N. Zahro, N. A. Maori, G. Wahyu, N. Wibowo, and N. U. Jepara, “Integration of RFM Method and K-Means Clustering for Customer Segmentation Effectiveness,” Journal of Dinda Data Science, Information Technology, and Data Analytics, vol. 5, no. 1, pp. 12–21, 2025, [Online]. Available: http://journal.ittelkom-pwt.ac.id/index.php/dinda
[17] S. Hidayat, R. Rismayati, M. Tajuddin, and N. L. P. Merawati, “Segmentation of university customers loyalty based on RFM analysis using fuzzy c-means clustering,” Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 2, pp. 133–139, Apr. 2020, doi: 10.14710/jtsiskom.8.2.2020.133-139.
[18] E. Ernawati, S. S. K. Baharin, and F. Kasmin, “A review of data mining methods in RFM-based customer segmentation,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Apr. 2021. doi: 10.1088/1742-6596/1869/1/012085.
[19] R. Rajagukguk, “Implementation of Loyalty Program Theory Based on Recency Frequency Monetary Score in Information Systems to Increase Customer Loyalty,” Journal of Information System Exploration and Research, vol. 3, no. 1, pp. 45–52, Jan. 2025, doi: 10.52465/joiser.v3i1.538.
[20] 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.
[21] V. H. Antonius and D. Fitrianah, “Enhancing Customer Segmentation Insights by using RFM + Discount Proportion Model with Clustering Algorithms,” 2024. [Online]. Available: www.ijacsa.thesai.org
[22] D. A. Imanuel and G. Alfian, “Visualisasi Segmentasi Pelanggan Berdasarkan Atribut RFM Menggunakan Algoritma K-Means Untuk Memahami Karakteristik Pelanggan pada Toko Retail Online,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 12, no. 2, pp. 283–292, Apr. 2025, doi: 10.25126/jtiik.2025128619.
[23] A. H. L. Chen, Y. C. Liang, W. J. Chang, H. Y. Siauw, and V. Minanda, “RFM Model and K -Means Clustering Analysis of Transit Traveller Profiles: A Case Study,” J Adv Transp, vol. 2022, 2022, doi: 10.1155/2022/1108105.
[24] R. Siagian, P. S. Pahala Sirait, and A. Halima, “E-Commerce Customer Segmentation Using K-Means Algorithm and Length, Recency, Frequency, Monetary Model,” Journal of Informatics and Telecommunication Engineering, vol. 5, no. 1, pp. 21–30, Jul. 2021, doi: 10.31289/jite.v5i1.5182.
[25] R. Heldt, C. S. Silveira, and F. B. Luce, “Predicting customer value per product: From RFM to RFM/P,” J Bus Res, vol. 127, pp. 444–453, Apr. 2021, doi: 10.1016/j.jbusres.2019.05.001.
[26] U. Firdaus and D. N. Utama, “Development of bank’s customer segmentation model based on rfm+b approach,” ICIC Express Letters, Part B: Applications, vol. 12, no. 1, pp. 17–26, Jan. 2021, doi: 10.24507/icicelb.12.01.17.
[27] H. Abbasimehr and A. Bahrini, “An analytical framework based on the recency, frequency, and monetary model and time series clustering techniques for dynamic segmentation,” Expert Syst Appl, vol. 192, Apr. 2022, doi: 10.1016/j.eswa.2021.116373.
[28] T. Ernayanti, M. Mustafid, A. Rusgiyono, and A. R. Hakim, “Penggunaan Seleksi Fitur Chi-Square dan Algoritma Multinomial Naive Bayes Untuk Analisis Sentimen Pelanggan Tokopedia,” Jurnal Gaussian, vol. 11, no. 4, pp. 562–571, Feb. 2023, doi: 10.14710/j.gauss.11.4.562-571.
Published
2025-07-07
How to Cite
Purwadi, P., Yudanto, S., & Wibowo, A. (2025). RFM Analysis Application for Customer Loyalty Identification in Bodywork PT. Bengawan Karya Sakti. Dinamik, 30(2), 193-202. https://doi.org/10.35315/dinamik.v30i2.10204
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