<![CDATA[The tourism sector undeniably has a significant economic impact on a region. One of the activities of tourists is shopping, which can create an additional market share for retailers. Therefore, retailers need to seize this opportunity by implementing strategies tailored to the profiles of both local and tourist visitors. This article proposes a retail strategy based on the analysis of local and tourist visitor profiles, using transaction data from Point of Sales (POS) to classify visitors based on their distance from the retailer. Hypothesis testing is conducted to understand the differences in purchasing patterns between local and tourist visitors, along with K-means clustering techniques to group visitors based on distance, age, and purchase value. The interpretation of clustering results helps identify purchasing patterns in each group, enabling retailers to adjust marketing and promotional strategies according to customer profiles. Through clustering analysis in a case study of a retailer in Yogyakarta, Indonesia, three main customer groups were identified: inactive, active, and out-of-town visitors. Retailers should prioritize efforts on active groups, especially Cluster 4, with active members and high transaction values. Clusters 6 and 9 should also be targeted to increase the probability of becoming Cluster 4. Group 1 can be disregarded in marketing efforts, and retailers should tailor their strategies for Group 3 based on their visitation patterns.]]>
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