CLUSTERING USING K-MEANS ALGORITHM AND RECENCY, FREQUENCY, MONETARY MODEL FOR CUSTOMER SEGMENTATION
DOI:
https://doi.org/10.31328/jsae.v8i2.7302Keywords:
K-Means, RFM Model, Customer Segmentation, ClusteringAbstract
Understanding customer behavior is a critical component in developing effective and sustainable marketing strategies. This study aims to segment customers of Rayu Manis, a culinary business based in Surabaya, by implementing the Recency, Frequency, Monetary (RFM) model in combination with the K-Means clustering algorithm. Transactional data collected from February 2023 to October 2024 underwent several processing stages, including data preprocessing, RFM scoring, logarithmic transformation, normalization, determination of the optimal number of clusters using the Elbow method, and evaluation using the Silhouette Score and Davies-Bouldin Index. The clustering results revealed that the Sheet Order dataset formed two clusters with a Silhouette Score of 0.51285, while the Sheet Rayu Manis dataset yielded three clusters with a Silhouette Score of 0.656. The resulting segmentation identified groups of loyal, potential, and at-risk customers, providing a data-driven foundation for targeted marketing strategies and supporting strategic decision-making within the context of small and medium-sized enterprises.
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