Uncovering Consumer Behavior Insights in Retail: A Data Driven Clustering-Based Approach with Distance Metrics

Abstract: In today’s fast-changing business world, understanding analysis can help identify valuable customer groups. By applying the K-means clustering algorithm and using the Silhouette coefficient, this research aims to determine the ideal number of clusters for better segmentation. With the power of artificial intelligence (AI) and big data analytics, businesses can gain deeper insights into consumer behavior, allowing them to refine marketing strategies, predict sales trends, and enhance customer engagement. By analyzing both structured and unstructured data, companies can better understand shopping patterns and shifting market trends. AI-driven segmentation enables businesses to create personalized marketing campaigns, improve customer relationships, and boost retention rates. This research emphasizes the importance of data-driven strategies in optimizing customer interactions and staying ahead in a competitive market