<![CDATA[Improving marketing strategies in mini markets by applying the clustering method as the basis of the approach. By using the K-Means cluster algorithm on data on the number of transactions and total sales, this research aims to identify groups of customers who have similar purchasing patterns. This clustering is the basis for formulating a more targeted and efficient marketing strategy. The K-Means approach is used to group customers into segments that have similarities in transaction behavior. The results of this clustering are then used to develop more personalized marketing strategies, understand the unique needs of each customer group, and increase the effectiveness of marketing efforts. This research involves collecting data on the number of transactions and total sales from mini markets during a certain time period. The data is then analyzed using the K-Means algorithm to produce customer segments that have similar characteristics. The results of this analysis resulted in 4 clusters being formed, consisting of cluster 0, cluster 1, cluster 2, cluster 3 consisting of 7303 data that had gone through the preprocessing stage, divided into cluster 0 including low clusters and cluster 1 including high clusters and clusters 2 and 3 including Meanwhile, from these results, strategies can be concluded that can be implemented to improve minimarket performance by identifying these results.]]>
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