Segmentasi Pelanggan E-Commerce Menggunakan Fitur Recency, Frequency, Monetary (RFM) dan Algoritma Klasterisasi K-Means
DOI:
https://doi.org/10.14421/jiska.2024.9.3.170-177Keywords:
E-Commerce, Customer Segmentation, RFM, K-Means, Web ApplicationAbstract
The rapid growth in the e-commerce industry demands the development of smarter and more focused marketing strategies. One approach that can be applied is customer segmentation using various features such as Recency, Frequency, and Monetary (RFM), along with machine learning-based clustering methods. The objective of this study is to design and develop a web-based e-commerce customer segmentation application using a combination of RFM features and clustering methods. The study proposes the K-Means algorithm and compares it with K-Medoids and Fuzzy C Means using publicly available e-commerce datasets. Experimental results showed that the K-Means algorithm outperformed K-Medoids and Fuzzy C Means (FCM) based on the Silhouette Score of 0.67305, Davies Bouldin Index of 0.51435, and Calinski Harabasz Index of 5647.89. Through analysis and testing, the designed application has proven effective in grouping customers into relevant segments. These segments are divided into three categories: Loyal, Need Attention, and Promising, visualized in a web-based application dashboard using Streamlit. The developed application allows e-commerce business owners and users from the business, management, and marketing divisions to categorize customers based on transaction data. The results of this study are expected to provide valuable insights to e-commerce management and marketing professionals who are facing increasingly fierce competition.
References
Adiyanto, A., & Arie Wijaya, Y. (2023). PENERAPAN ALGORITMA K-MEANS PADA PENGELOMPOKAN DATA SET BAHAN PANGAN INDONESIA TAHUN 2022-2023. JATI (Jurnal Mahasiswa Teknik Informatika), 7(2), 1344–1350. https://doi.org/10.36040/jati.v7i2.6849
Agustino, D. P., & Budaya, I. G. B. A. (2023). Evaluasi Performa Segmentasi Pelanggan Tenant Inkubator Bisnis dengan Menggunakan Model Consensus Clustering. Prosiding CORISINDO 2023, 236–240. https://www.stmikpontianak.org/ojs/index.php/corisindo/article/view/62
Alzami, F., Sambasri, F. D., Nabila, M., Megantara, R. A., Akrom, A., Pramunendar, R. A., Prabowo, D. P., & Sulistiyawati, P. (2023). Implementation of RFM Method and K-Means Algorithm for Customer Segmentation in E-Commerce with Streamlit. ILKOM Jurnal Ilmiah, 15(1), 32–44. https://doi.org/10.33096/ilkom.v15i1.1524.32-44
Anitha, P., & Patil, M. M. (2022). RFM model for customer purchase behavior using K-Means algorithm. Journal of King Saud University - Computer and Information Sciences, 34(5), 1785–1792. https://doi.org/10.1016/j.jksuci.2019.12.011
Chen, A. H. L., Liang, Y.-C., Chang, W.-J., Siauw, H.-Y., & Minanda, V. (2022). RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study. Journal of Advanced Transportation, 2022(1), 1–14. https://doi.org/10.1155/2022/1108105
Hilmy, F. M., Nurhaliza, R. A., Huzyan Octava, M. Q., & Alfian, G. (2023). Web-based E-Commerce Customer Segmentation System Using RFM and K-Means Model. 2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), 83–87. https://doi.org/10.1109/3ICT60104.2023.10391650
Juhari, T., & Juarna, A. (2022). IMPLEMENTATION RFM ANALYSIS MODEL FOR CUSTOMER SEGMENTATION USING THE K-MEANS ALGORITHM CASE STUDY XYZ ONLINE BOOKSTORE. EXPLORE, 12(1), 107–118. https://doi.org/10.35200/explore.v12i1.548
Kim, S.-Y., Jung, T.-S., Suh, E.-H., & Hwang, H.-S. (2006). Customer segmentation and strategy development based on customer lifetime value: A case study. Expert Systems with Applications, 31(1), 101–107. https://doi.org/10.1016/j.eswa.2005.09.004
Li, W., Wu, X., Sun, Y., & Zhang, Q. (2010). Credit Card Customer Segmentation and Target Marketing Based on Data Mining. 2010 International Conference on Computational Intelligence and Security, 73–76. https://doi.org/10.1109/CIS.2010.23
Mahfuza, R., Islam, N., Toyeb, Md., Emon, M. A. F., Chowdhury, S. A., & Alam, Md. G. R. (2022). LRFMV: An efficient customer segmentation model for superstores. PLOS ONE, 17(12), e0279262. https://doi.org/10.1371/journal.pone.0279262
Molla, A., & Licker, P. S. (2005). eCommerce adoption in developing countries: a model and instrument. Information & Management, 42(6), 877–899. https://doi.org/10.1016/j.im.2004.09.002
Paembonan, S., & Abduh, H. (2021). Penerapan Metode Silhouette Coefficient untuk Evaluasi Clustering Obat. PENA TEKNIK: Jurnal Ilmiah Ilmu-Ilmu Teknik, 6(2), 48–54. https://doi.org/10.51557/pt_jiit.v6i2.659
Shirole, R., Salokhe, L., & Jadhav, S. (2021). Customer Segmentation using RFM Model and K-Means Clustering. International Journal of Scientific Research in Science and Technology, 591–597. https://doi.org/10.32628/IJSRST2183118
Sikana, A. M., & Wijayanto, A. W. (2021). Analisis Perbandingan Pengelompokan Indeks Pembangunan Manusia Indonesia Tahun 2019 dengan Metode Partitioning dan Hierarchical Clustering. Jurnal Ilmu Komputer, 14(2), 66–78. https://doi.org/10.24843/JIK.2021.v14.i02.p01
Sutresno, S. A., Iriani, A., & Sediyono, E. (2018). Metode K-Means Clustering dengan Atribut RFM untuk Mempertahankan Pelanggan. Jurnal Teknik Informatika Dan Sistem Informasi, 4(3), 433-440–433–440. https://doi.org/10.28932/jutisi.v4i3.878
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Reyhan Muhammad Fauzan , Ganjar Alfian
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms as stated in http://creativecommons.org/licenses/by-nc/4.0
a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.