Customer Segmentation Using K-Means Clustering Algorithm: A Data-Driven Approach to Marketing Optimization

Customer segmentation is a fundamental strategy in modern marketing that enables businesses to categorize their customer base into distinct groups sharing similar characteristics, behaviors, and purchasing patterns. This research paper presents a comprehensive implementation and analysis of the K-Means clustering algorithm applied to customer segmentation, utilizing the Mall Customers Dataset. We identify five distinct customer segments and provide actionable marketing recommendations. Experimental results demonstrate that K-Means clustering achieves an average silhouette score of 0.55.