<![CDATA[The pharmaceutical industry has experienced rapid growth, urging companies to leverage sales data effectively to enhance data-driven marketing strategies. However, utilizing sales data remains a challenge for XYZ company, a pharmaceutical distributor. This study aims to analyze customer purchasing patterns by applying the FP-Growth algorithm for association analysis, combined with customer segmentation using the K-Means algorithm based on RFM (Recency, Frequency, Monetary) analysis. The segmentation process resulted in four customer clusters: active and loyal customers (Cluster 1), passive customers (Cluster 2), less active customers (Cluster 3), and new customers (Cluster 4). FP-Growth analysis for each cluster revealed that Cluster 1 generated 10 significant association rules with a minimum support of 0.01 and confidence of 0.7, while Clusters 2, 3, and 4 produced 2, 3, and 4 association rules, respectively, with adjusted parameters. All rules showed a lift value > 1, indicating positive relationships between products. The findings of this study provide strategic insights for companies in designing data-driven marketing approaches, such as more targeted product offerings for loyal customers or retention strategies for passive customers, thereby optimizing sales and increasing profitability in each customer segment.]]>
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