<![CDATA[This study aims to segment customers of a veterinary clinic using a combination of Recency, Frequency, Monetary (RFM) analysis and the K-Means clustering algorithm. Transactional data from December 2024 to April 2025 were processed to generate key customer features, followed by min-max normalization to ensure comparability across variables. The segmentation was conducted in AI Studio 2025, with cluster quality evaluated using cluster distance performance and Davies-Bouldin Index. The analysis resulted in four distinct customer segments: the majority were passive customers with low transaction frequency and spending, while a smaller group showed high purchasing activity and made a significant economic contribution. This study demonstrates the effectiveness of automated data mining tools in uncovering meaningful customer profiles in a veterinary service context. The results provide a practical basis for targeted marketing, customer retention strategies, and service improvement in veterinary clinics. This approach offers valuable insights for data-driven decision making and represents a novelty for veterinary service management in Indonesia. Keywords: Customer Segmentation, RFM Analysis, K-Means Clustering, Veterinary Clinic, Data Mining.]]>
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