Pengaruh Analisis Customer Lifetime Value Menggunakan RFM Dan K-Means Clustering Terhadap Strategi Pemasaran Industri E-Commerce

Authors

  • Juhairiah Juhairiah Universitas Sangga Buana YPKP
  • Taufik Zulfikar Universitas Sangga Buana YPKP

DOI:

https://doi.org/10.29408/jpek.v10i2.35573

Keywords:

Customer Lifetime Value; Customer Segmentation; E-Commerce; K-Means Clustering; RFM

Abstract

The rapid growth of the e-commerce industry has intensified competition in customer retention and business profitability. Therefore, companies need to understand customer characteristics to develop targeted marketing strategies. This study aims to analyze Customer Lifetime Value (CLV) using the Recency, Frequency, Monetary (RFM) approach and K-Means Clustering to support marketing decision-making. The research methodology follows the CRISP-DM framework utilizing the Online Retail Dataset consisting of 4,338 customers. The K-Means algorithm successfully classified customers into five main segments: Potential Customers, Hibernating Customers, Champions, Loyal Customers, and VIP Customers. The VIP Customers segment achieved the highest average CLV of 572,425.61, while Hibernating Customers had the lowest average CLV of 1,435.94. Furthermore, Potential Customers contributed the largest share of revenue due to their dominant population size. The integration of RFM, K-Means, and CLV provides deeper insights into customer behavior to support more effective marketing strategies.

References

[1] M. Ray and B. K. Mangaraj, “AHP Based Data Mining for Customer Segmentation Based on Customer Lifetime Value,” Int. J. Data Min. Tech. Appl., vol. 5, no. 1, pp. 28–34, 2016, doi: 10.20894/ijdmta.102.005.001.007.

[2] D. W. Maulida and M. Dachyar, “Customer Loyalty Strategy Using Customer Lifetime Value (A Case Study of Jamu and Herbal Products SME)”, doi: 10.46254/an12.20220195.

[3] Y. Shih and C.-Y. Liu, “A Method for Customer Lifetime Value Ranking — Combining the Analytic Hierarchy Process and Clustering Analysis,” J. Database Mark. Cust. Strategy Manag., vol. 11, no. 2, pp. 159–172, 2003, doi: 10.1057/palgrave.dbm.3240216.

[4] G. M. Díaz and D. Gómez, “RFID: A Fuzzy Linguistic Model to Manage Customers From the Perspective of Their Interactions With the Contact Center,” Mathematics, vol. 9, no. 19, p. 2362, 2021, doi: 10.3390/math9192362.

[5] F. Safari, N. Safari, and G. A. Montazer, “Customer Lifetime Value Determination Based on RFM Model,” Mark. Intell. Plan., vol. 34, no. 4, pp. 446–461, 2016, doi: 10.1108/mip-03-2015-0060.

[6] B. C. Laksono and I. Y. Wulansari, “Estimating Customer Lifetime Value in the E-Commerce Industry Using Multivariate Analysis,” Proc. Int. Conf. Data Sci. Off. Stat., vol. 2021, no. 1, pp. 507–518, 2022, doi: 10.34123/icdsos.v2021i1.161.

[7] C. Hsu, Y. Chang, T. Chen, T. Chang, and Y. Lin, “Who Donates on Line? Segmentation Analysis and Marketing Strategies Based on Machine Learning for Online Charitable Donations in Taiwan,” Ieee Access, vol. 9, pp. 52728–52740, 2021, doi: 10.1109/access.2021.3066713.

[8] C. Yan, H. Sun, W. Liu, and J. Chen, “An Integrated Method Based on Hesitant Fuzzy Theory and RFM Model to Insurance Customers’ Segmentation and Lifetime Value Determination,” J. Intell. Fuzzy Syst., vol. 35, no. 1, pp. 159–169, 2018, doi: 10.3233/jifs-169577.

[9] B. C. Laksono and I. Y. Wulansari, “Pemodelan Dan Penerapan Metode RFM Pada Estimasi Nilai Konsumen (Customer Lifetime Value) Menggunakan K-Means Clustering Machine Learning,” Semin. Nas. Off. Stat., vol. 2020, no. 1, pp. 1277–1285, 2021, doi: 10.34123/semnasoffstat.v2020i1.689.

[10] M. A. A. Kazemi, N. Estemdad, and A. Poorebrahimi, “Customer Behavior in Banking Industry: Comparison of Data Mining Techniques,” J. Adv. Manag. Sci., pp. 13–16, 2015, doi: 10.12720/joams.3.1.13-16.

[11] G. ASLANTAŞ, M. GENÇGÜL, M. RUMELLİ, M. ÖZSARAÇ, and G. BAKIRLI, “Customer Segmentation Using K-Means Clustering Algorithm and RFM Model,” Dokuz Eylül Üniversitesi Mühendis. Fakültesi Fen Ve Mühendis. Derg., vol. 25, no. 74, pp. 491–503, 2023, doi: 10.21205/deufmd.2023257418.

[12] D. Aslan and M. Tolon, “Comparing Customer Segmentation With CLV Using Data Mining and Statistics: A Case Study,” J. Bus. Res. - Turk, vol. 10, no. 4, pp. 887–900, 2018, doi: 10.20491/isarder.2018.554.

[13] R. Riyanto, “Modelling Customers Lifetime Value for Non-Contractual Business,” Ijiis Int. J. Inform. Inf. Syst., vol. 4, no. 1, pp. 55–62, 2021, doi: 10.47738/ijiis.v4i1.77.

[14] S. M. H. Hasheminejad and M. Khorrami, “Clustering of Bank Customers Based on Lifetime Value Using Data Mining Methods,” Intell. Decis. Technol., vol. 14, no. 4, pp. 507–515, 2021, doi: 10.3233/idt-190176.

[15] K. Kumaran, P. Pathak, R. Haque, and P. Stynes, “A Machine And Deep Learning Framework To Retain Customers Based On Their Lifetime Value,” pp. 91–103, 2022, doi: 10.1007/978-3-031-24094-2_6.

[16] ‪Fitri Marisa, S. S. S. Ahmad, Z. I. M. Yusof, F. Fachrudin, and T. M. Akhriza, “Segmentation Model of Customer Lifetime Value in Small and Medium Enterprise (SMEs) Using K-Means Clustering and LRFM Model,” Int. J. Integr. Eng., vol. 11, no. 3, 2019, doi: 10.30880/ijie.2019.11.03.018.‬‬

[17] N. A. Ejgerdi and M. Kazerooni, “A Stacked Ensemble Learning Method for Customer Lifetime Value Prediction,” Kybernetes, vol. 53, no. 7, pp. 2342–2360, 2023, doi: 10.1108/k-12-2022-1676.

[18] S. Chavhan, R. C. Dharmik, S. Jain, and K. Kamble, “RFM Analysis for Customer Segmentation Using Machine Learning: A Survey of a Decade of Research,” 3c Tic Cuad. Desarro. Apl. Las Tic, vol. 11, no. 2, pp. 166–173, 2022, doi: 10.17993/3ctic.2022.112.166-173.

[19] E. F. L. Awalina and W. I. Rahayu, “Optimalisasi Strategi Pemasaran Dengan Segmentasi Pelanggan Menggunakan Penerapan K-Means Clustering Pada Transaksi Online Retail,” J. Teknol. Dan Inf., vol. 13, no. 2, pp. 122–137, 2023, doi: 10.34010/jati.v13i2.10090.

[20] R. Siagian, P. S. P. Sirait, and A. Halima, “E-Commerce Customer Segmentation Using K-Means Algorithm and Length, Recency, Frequency, Monetary Model,” J. Inform. Telecommun. Eng., vol. 5, no. 1, pp. 21–30, 2021, doi: 10.31289/jite.v5i1.5182.

[21] T. A. Afifah, R. Novita, T. K. Ahsyar, and Z. Zarnelly, “Penerapan Algoritma K-Means Menggunakan Model LRFM Dalam Klasterisasi Nilai Hidup Pelanggan,” J. Media Inform. Budidarma, vol. 8, no. 2, p. 1010, 2024, doi: 10.30865/mib.v8i2.7605.

[22] A. P. Pramudiansyah, “Segmentasi Pelanggan Menggunakan Algoritma K-Means Berdasarkan Model Recency Frequency Monetary,” J. Ilm. Ilmu Komput., vol. 7, no. 2, pp. 06–19, 2021, doi: 10.35329/jiik.v7i2.201.

[23] I. Maskanah, “Segmentasi Pelanggan Toko Purnama Dengan Algoritma K-Means Dan Model RFM Untuk Perancangan Strategi Pemasaran,” Inovtek Polbeng - Seri Inform., vol. 5, no. 2, p. 218, 2020, doi: 10.35314/isi.v5i2.1443.

[24] A. A. D. Sulistyawati and M. Sadikin, “Penerapan Algoritma K-Medoids Untuk Menentukan Segmentasi Pelanggan,” Sistemasi, vol. 10, no. 3, p. 516, 2021, doi: 10.32520/stmsi.v10i3.1332.

[25] F. Juniati, S. Monalisa, R. Zafa, and M. Muslim, “KLASTERISASI CUSTOMER LIFETIME VALUE DENGAN MODEL LRFM MENGGUNAKAN ALGORITMA Fuzzy C-Means,” J. Ilm. Rekayasa Dan Manaj. Sist. Inf., vol. 6, no. 1, p. 38, 2020, doi: 10.24014/rmsi.v6i1.8635.

[26] B. T. Kristanti, A. Junaidi, and E. P. Mandyartha, “Implementasi K-Means Clustering Dalam Segmentasi Pelanggan Berdasarkan Usia, Pendapatan, Dan Model RFM (Studi Kasus: Lantikya Store Jombang),” J. Inform. Dan Tek. Elektro Terap., vol. 12, no. 3, 2024, doi: 10.23960/jitet.v12i3.4677.

[27] T. L. Afandi, B. Warsito, and R. Santoso, “Implementasi K-Medoids Dan Model Weighted-Length Recency Frequency Monetary (W-Lrfm) Untuk Segmentasi Pelanggan Dilengkapi Gui R,” J. Gaussian, vol. 11, no. 3, pp. 429–438, 2022, doi: 10.14710/j.gauss.11.3.429-438.

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Published

2026-08-28

Issue

Vol. 10 No. 2 (2026): JPEK (Jurnal Pendidikan Ekonomi dan Kewirausahaan)

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