K-Means Clustering Method For Customer Segmentation Based On Potential Purchases
DOI:
https://doi.org/10.31961/eltikom.v8i1.1137Keywords:
k-means clustering, Customer Segmentation, Potential PurchasesAbstract
The rapid growth in customer data has driven companies to develop smarter and more effective marketing strategies. One efficient approach is customer segmentation, which involves dividing a market or group of customers into smaller segments based on similar characteristics or behaviors. Customer segmentation improves understanding of customer needs, preferences, and behavior. This study uses customer segmentation based on purchase potential at Fast Moving Consumer Goods (FMCG). Analyzing potential purchases can help identify market opportunities, implement more effective pricing, target promotions, manage stock and distribution, and develop new products to enhance customer satisfaction. The most commonly used segmentation method is the K-Means Clustering algorithm, which groups data into homogeneous clusters. This study aims to segment customers based on potential purchases using the K-Means Clustering method. The customer dataset in FMCG stores was divided into three clusters using seven attributes: Sex, Marital Status, Age, Education, Income, Occupation, and Settlement Size. The results, calculated in Microsoft Excel, concluded after four iterations with three clusters: k1 (Cluster 1) with 535 customers having low purchase potential, k2 (Cluster 2) with 685 customers having high purchase potential, and k3 (Cluster 3) with 7810 customers having medium purchase potential.
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References
R. Daraboina, O. Cooper, and M. Amini, “Segmentation of organic food consumers: A revelation of purchase factors in organic food markets,” J. Retail. Consum. Serv., vol. 78, p. 103710, 2024.
C. Hafidz Ardana, A. A. A. A. A. Khoyum, and M. Faisal, “Segmentasi pelanggan penjualan online menggunakan Metode K-Means Clustering,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 9, no. 1, pp. 1–9, 2024.
R. A. Utamanyu and R. Darmastuti, “Budaya Belanja Online Generasi Z dan Generasi Milenial di Jawa Tengah (Studi Kasus Produk Kecantikan di Online Shop Beauty by ASAME),” Scriptura, vol. 12, no. 1, pp. 58–71, 2022.
L. Safitri and F. Fathurohman, “Keputusan Pembelian Pangan Segar secara Online pada Masa Pandemi Covid-19: Fresh Food Online Purchase Decision During The Covid-19 Pandemic,” J. Agroindustri Terap. Indones., vol. 1, no. 2, pp. 1–10, 2024.
T. K. H. Chan, C. M. K. Cheung, and Z. W. Y. Lee, “The state of online impulse-buying research: A literature analysis,” Inf. Manag., vol. 54, no. 2, pp. 204–217, 2017.
R. Daulay and S. Handayani, “Pengaruh Gaya Hidup dan Kepribadian terhadap Minat Belanja Online melalui Aplikasi Lazada,” in Seminar Nasional Teknologi Edukasi Sosial dan Humaniora, 2021, pp. 478–486.
D. Suleman, “Faktor penentu keputusan konsumen Indonesia memilih tempat belanja disebuah e-commerce (Theory of Planned Behavior),” J. Dr. Manaj., vol. 1, pp. 1–9, 2018.
M. Skare, B. Gavurova, and M. Rigelsky, “Innovation activity and the outcomes of B2C, B2B, and B2G E-Commerce in EU countries,” J. Bus. Res., vol. 163, p. 113874, 2023.
A.-Y. Al-Yasir, M. Afdal, Z. Zarnelly, and A. Marsal, “Analisis Loyalitas Pelanggan Business To Business Berdasarkan Model RFM Menggunakan Algoritma Fuzzy C-Means: Business to Business Customer Loyalty Analysis Based on RFM Model Using Fuzzy C-Means Algorithm,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 1, pp. 359–365, 2024.
D. E. B. Jabat, L. L. Tarigan, M. Purba, and M. Purba, “Pemanfaatan Platform E-Commerce Melalui Marketplace sebagai Upaya Peningkatan Penjualan,” Skylandsea Prof. J. Ekon. Bisnis Dan Teknol., vol. 2, no. 2, pp. 16–21, 2022.
A. Asyifah, A. Syafi’i, H. Hanipah, and S. Ispiyani, “Pengembangan Aplikasi E-Commerce Untuk Peningkatan Penjualan Online,” Action Res. Lit., vol. 7, no. 1, pp. 70–75, 2023.
M. R. Olii, M. H. Pratiknjo, and S. Rumampuk, “Online Shop Sebagai Alternatif Berbelanja Masyarakat Kota Manado,” HOLISTIK, J. Soc. Cult., 2020.
R. A. Sulistianti and N. Sugiarta, “Konstruksi Sosial Konsumen Online Shop Di Media Sosial Tiktok (Studi Fenomenologi Tentang Konstruksi Sosial Konsumen Generasi Z Pada Online Shop Smilegoddess Di Media Sosial Tiktok),” JISIP (Jurnal Ilmu Sos. dan Pendidikan), vol. 6, no. 1, 2022.
D. M. Mansur, E. T. Sule, D. Kartini, Y. M. Oesman, A. H. P. K. Putra, and N. Chamidah, “Moderating of the role of technology theory to the existence of consumer behavior on e-commerce,” J. Distrib. Sci., vol. 17, no. 7, pp. 15–25, 2019.
F. Sudirjo, T. Purwati, W. Widyastuti, Y. U. Budiman, and M. Manuhutu, “Analisis Dampak Strategi Pemasaran Digital dalam Meningkatkan Loyalitas Pelanggan: Perspektif Industri E-commerce,” J. Pendidik. Tambusai, vol. 7, no. 2, pp. 7524–7532, 2023.
İ. Sabuncu, E. Turkan, and H. Polat, “Customer segmentation and profiling with RFM analysis,” Turkish J. Mark., vol. 5, no. 1, p. 22, 2020.
J. Anselmsson and N. Bondesson, “Brand value chain in practise; the relationship between mindset and market performance metrics: A study of the Swedish market for FMCG,” J. Retail. Consum. Serv., vol. 25, pp. 58–70, 2015.
F. Dweiri, S. A. Khan, and A. Almulla, “A multi-criteria decision support system to rank sustainable desalination plant location criteria,” Desalination, vol. 444, pp. 26–34, 2018.
G. Pierce and R. Connolly, “Disparities in the ‘who’ and ‘where’ of the vehicle purchase decision-making process for lower-income households,” Travel Behav. Soc., vol. 31, pp. 363–373, 2023.
S. Bogomolova, Z. Anesbury, L. Lockshin, N. Kapulski, and T. Bogomolov, “Exploring the incidence and antecedents of buying an FMCG brand and UPC for the first time,” J. Retail. Consum. Serv., vol. 46, pp. 121–129, 2019.
F. D. S. Alhamdani, A. A. Dianti, and Y. Azhar, “Segmentasi Pelanggan Berdasarkan Perilaku Penggunaan Kartu Kredit Menggunakan Metode K-Means Clustering,” JISKA (Jurnal Inform. Sunan Kalijaga), vol. 6, no. 2, pp. 70–77, 2021.
M. A. Ghofar and Y. I. Kurniawan, “Aplikasi Pengelompokan Pelanggan Pada Ums Store Menggunakan Algoritma K-Means,” J. Teknol. dan Manaj. Inform., vol. 4, no. 1, 2018.
A. D. Savitri, F. A. Bachtiar, and N. Y. Setiawan, “Segmentasi Pelanggan Menggunakan Metode K-Means Clustering Berdasarkan Model RFM Pada Klinik Kecantikan (Studi Kasus: Belle Crown Malang),” J. Pengemb. Teknol. Inf. Dan Ilmu Komput. E-ISSN, vol. 2548, 2009.
H.-H. Zhao, X.-C. Luo, R. Ma, and X. Lu, “An extended regularized K-means clustering approach for high-dimensional customer segmentation with correlated variables,” Ieee Access, vol. 9, pp. 48405–48412, 2021.
N. N. Almanza-Ortega et al., “Corrosion Analysis Through an Adaptive Preprocessing Strategy Using The K-Means Algorithm,” Procedia Comput. Sci., vol. 219, pp. 586–595, 2023.
F. Barrera, M. Segura, and C. Maroto, “Multiple criteria decision support system for customer segmentation using a sorting outranking method,” Expert Syst. Appl., vol. 238, p. 122310, 2024.
S. Alam, M. S. Ayub, S. Arora, and M. A. Khan, “An investigation of the imputation techniques for missing values in ordinal data enhancing clustering and classification analysis validity,” Decis. Anal. J., vol. 9, p. 100341, 2023.
H. Ghaderi, B. Foreman, A. Nayebi, S. Tipirneni, C. K. Reddy, and V. Subbian, “A self-supervised learning-based approach to clustering multivariate time-series data with missing values (SLAC-Time): An application to TBI phenotyping,” J. Biomed. Inform., vol. 143, p. 104401, 2023.
S. H. Nam, S. Kwon, and Y. Do Kim, “Development of a basin-scale total nitrogen prediction model by integrating clustering and regression methods,” Sci. Total Environ., p. 170765, 2024.
Y. Li, X. Chu, D. Tian, J. Feng, and W. Mu, “Customer segmentation using K-means clustering and the adaptive particle swarm optimization algorithm,” Appl. Soft Comput., vol. 113, p. 107924, 2021.
M. Faisal and E. M. Zamzami, “Comparative analysis of inter-centroid K-Means performance using euclidean distance, canberra distance and manhattan distance,” in Journal of Physics: Conference Series, IOP Publishing, 2020, p. 12112.
K. Sya’iyah, H. Yuliansyah, and I. Arfiani, “Clustering student data based on K-means algorithms,” Int. J. Sci. Technol. Res, vol. 8, no. 8, pp. 1014–1018, 2019.
Q. Liu, J. Ma, X. Zhao, K. Zhang, K. Xiangli, and D. Meng, “A novel method for fault diagnosis and type identification of cell voltage inconsistency in electric vehicles using weighted Euclidean distance evaluation and statistical analysis,” Energy, p. 130575, 2024.
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