Authors : Achmad Fawwaz Bahaudin

Volume/Issue : Volume 9 - 2024, Issue 10 - October

Google Scholar : https://tinyurl.com/3bddvbd5

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DOI : https://doi.org/10.38124/ijisrt/IJISRT24OCT731

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Abstract : A furniture production company needs to categorize its customers, especially those who are involved in export. The company has analyzed export shipment records from June 6, 2015, to April 23, 2022. The analysis used the Recency, Frequency, and Monetary (RFM) variables, which have been widely used in the field of marketing. The k-means clustering algorithm was employed for the analysis, resulting in the division of customers into three clusters. Cluster 1 comprises customers with the highest and most valuable purchases, including those with the codes CL, FR, NC, and RE. Cluster 2 includes customers who excel in one of the RFM variables, with codes AN, AR, BN, IN, IT, KE, KR, LK, MU, MY, SA, SC, SM, TW, and UN. Customers in Cluster 3 have the fewest and least valuable purchases, including those with the codes GP, KN, NL, OM, PT, and TZ.

Keywords : Analysis, K-Means Clustering, RFM Model, Recency, Frequency, Monetary.

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