Pemetaan Konsumen Berdasarkan Perilaku Transaksi Produk Ritel Pada Distributor XYZ Semarang
DOI:
https://doi.org/10.32492/jeetech.v3i2.3201Keywords:
Customer segmentation, Retail products, K-Means clustering, RFM Analysis, Data miningAbstract
Customer segmentation is essential for any business to better understand their customers, to maintain customer satisfaction, and to develop product and service needs. The purpose of customer segmentation is to determine how to handle customers in each category to increase each customer's profit for the business. Distributor XYZ Semarang is a multi-branched company engaged in the sale of consumer retail products to serve customers from small (retail) to large (wholesale) businesses. The database used in this study was taken from one branch with a transaction period of 2 years. The purpose of this research is to build a customer segmentation model based on customer demographics and transaction behavior and to help businesses better understand their customers to support marketing strategies. The proposed segmentation model is regarding customer demographic data regarding Recency, Frequency, and Monetary (RFM) resulting from purchasing behavior, customers have been segmented using the K-means grouping technique into various groups based on their similarities, and profiles for each group are identified based on their characteristics. . Thus, companies can find out the behavior of customers from the results of grouping based on how much value they spend, how often they make purchases and what products are their shopping concerns where this can be used as an evaluation basis for marketing strategies and further analysis..
References
Xin-an Lai (2009), Segmentation Study on Enterprise Customers Based on Data Mining Technology, IEEE First International Workshop on Database Technology and Applications, pp. 247 – 250
] F. Nursa, H. Hardisman, and R. Semiarty, Analisis Segmentasi dan Penentuan Target Pasar Pelanggan Instalasi Rawat Jalan Rumah Sakit Universitas Andalas, J. Kesehat. Andalas, vol. 8, no. 3, pp. 650–660, 2019
] Eko Prasetyo, Data Mining Mengolah Data Menjadi Informasi Menggunakan Matlab. Yogyakarta: ANDI, 2014.
] K. K. Tsiptsis and A. Chorianopoulos, Data Mining Techniques in CRM: Inside Customer Segmentation. New Jersey: John Wiley & Sons, 2011.
] 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, 433.
] Pete Chapman, Julian Clinton, Randy Kerber, Thomas Khabaza, Thomas Reinartz, Colin Shearer, and Rüdiger Wirth (2000); CRISP-DM 1.0 Step-by-step data mining guides
] Mariscal, Gonzalo, Oscar Marban, and Covadonga Fernandez. 2010. "A Survey of Data Mining and Knowledge Discovery Process Models and Methodologies." The Knowledge Engineering Review, Cambridge University Press 25:2(2010): 137–66.
] Y. Chen, G. Zhang, D. Hu, and S. Wang, "Customer Segmentation in Customer Relationship Management Based on Data Mining," vol. 207, pp. 288–293, 2006.
] N. Kurinjivendhan and K. Thangadurai, "Modified k-means algorithm and genetic approach for cluster optimization," Proc. 2016 Int. Conf. Data Min. Adv. Comput. SAPIENCE 2016, pp. 53–56, 2016
] J. Wei, S. Lin, and H. Wu, "A review of the application of RFM model," African J. Bus. Manag., vol. 4, no. 19, pp. 4199–4206, 2010
] A. X. Yang, "How to develop new approaches to RFM segmentation," J. Targeting, Meas. Anal. Mark., vol. 13, no. 1, pp. 50–60, 2004
] R. Ait Daoud, A. Amine, B. Bouikhalene, and R. Lbibb, "Combining RFM model and clustering techniques for customer value analysis of a company selling online," Proc. IEEE/ACS Int. Conf. Comput. Syst. Appl. AICCSA, vol. 2016–July, 2016
] D. Birant, "Data Mining Using RFM Analysis," in KnowledgeOriented Applications in Data Mining, Turkey: KImito Funatso, 2011, pp. 91–108
] I. Pranata and G. Skinner, "Segmenting and targeting customers through clusters selection & analysis,: ICACSIS 2015-2015 Int. Conf. Adv. Comput. Sci. Inf. Syst. Proc., pp. 303–308, 2016
] T. Hardiani, S. Sulistyo, and R. Hartanto, "Segmentasi Nasabah Tabungan Menggunakan Model RFM(Recency,Frequency,Monetary) dan K-Means Pada Lembaga Keuangan Mikro ISBN:979-26-0280-1 ISBN: 979-26-0280-1," Semin.Nas. Teknol. Inf. dan Komun. Terap., no. May 2017, pp. 463–468, 2015
Published
How to Cite
Issue
Section
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
