SEGMENTATION ANALYSIS AND CUSTOMER VALUE USING CLUSTER ANALYSIS METHOD, RFM, IN BABY NEEDS STORE
DOI:
https://doi.org/10.35261/barometer.v11i3.13249Abstract
The competitiveness of the baby retail business requires business owners to manage sales data optimally. XYZ Store still faces problems in the form of low sales turnover on several products which results in stock buildup and suboptimal inventory management. This study aims to segment products using the Recency, Frequency, and Monetary (RFM) approach combined with the K-Means Clustering method. The data used are sales transaction data for one year with a total of 1,204 product types. Before the clustering process, the data was normalized using the Z-Score method, while the optimal number of clusters was determined using the Elbow and Silhouette methods, which resulted in three clusters as the best number. The results show that Cluster 1 consists of 630 products (52.33%) with stable sales performance, Cluster 2 consists of 90 products (7.48%) which are superior products because they have the highest Frequency and Monetary values, while Cluster 3 consists of 484 products (40.20%) with a relatively low sales level that requires further evaluation. The results of the study show that the RFM and K-Means Clustering methods are able to group products based on their sales characteristics so that they can support inventory management strategies and decision making at XYZ Store.
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