Customer Segmentation in B2B Coffee Bean Supply Using RFM and K-Means Clustering - Kumala, Annisa Galuh Ratna; Nugroho, Dian Ari (Jurnal Kewirausahaan dan Inovasi, 2025)
Purpose – This study aims to identify and analyze B2B customer segments of CV Ijen Nusantara, a coffee bean supplier, by applying customer segmentation based on purchasing behavior to support more effective marketing strategies and resource allocation. Design/Methodology/Approach – This study employs a quantitative approach using primary transaction data from CV Ijen Nusantara’s customers in Greater Malang over a 12-month period. Customer purchasing behavior was analyzed using the Recency, Frequency, and Monetary (RFM) model, while the K-means clustering method was applied to group customers based on similarities in purchasing characteristics. From a total population of 314 customers, 87 customers were selected and analyzed to identify optimal market clusters. Findings – The results reveal that K-means clustering successfully identifies three optimal customer segments with distinct purchasing characteristics. These clusters provide valuable insights into customer behavior patterns, enabling the company to develop more targeted marketing strategies and allocate business resources more efficiently. The findings demonstrate the importance of data-driven customer segmentation in improving competitiveness within the B2B coffee bean supply industry.   Originality/Value This research is original in combining the RFM model with K-means clustering to segment B2B customers in the coffee bean distribution sector. The findings provide practical value by delivering data-driven customer groupings that support targeted marketing strategies and improved business decision-making.