Customer segmentation is a crucial strategy for businesses seeking to optimize marketing efforts and enhancecustomerexperiences. This study explores the use of the K-means clustering algorithm tosegment customers based ontheironline shopping behavior. By analyzing data from a large dataset of online transactions, we identify key features suchas purchase frequency, total spending, recency of purchases, and product category preferences for clustering. Using the Kmeans algorithm, we group customers into distinct segments, each exhibiting unique shopping behaviors and preferences.The analysis reveals valuable insights into customer profiles, enabling targeted marketing strategies and personalizedrecommendations. We evaluate the clustering results using metrics such as silhouette score and visualize the segments fora comprehensive understanding of customer groups. The findings provide actionable strategies for businesses to engagewith customers more effectively, improve customer satisfaction, and drive sales growth.Overall, this study demonstrates the potential of leveraging the K-means algorithm for customer segmentationin thecontext of online shopping behavior, offering businesses a pathway to achieve better customer engagement and optimizemarketing campaigns.