<![CDATA[Competition in the world of sales is becoming increasingly fierce, so store owners need the right strategy to understand customer behavior patterns and increase sales. One of the most widely used data analysis methods is K-Means Clustering, which can be used to find patterns and trends in sales data. This study was conducted with the aim of determining customer segmentation based on sales transaction data in order to obtain customer groups with similar characteristics. The method applied in this study was the K-Means algorithm on a sales dataset with a total of 1,289 customer data. Cluster quality was evaluated using the Davies-Bouldin index (DBI), with a DBI result of 0.077, indicating excellent cluster quality. The analysis resulted in three customer clusters, namely: the first cluster (C1) consisting of loyal buyers with 562 customers, the second cluster (C2) consisting of occasional buyers with 279 customers, and the third cluster (C3) consisting of buyers with an average purchase of 448 customers. The implication of these research results is that management can develop more appropriate marketing strategies, such as providing a personal approach to loyal customers and designing specific strategies to attract occasional buyers to become more loyal. Thus, these research results can serve as a basis for more effective marketing decision-making.]]>
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