Customer Segmentation Using K-Means Clustering for Data-Driven Business Analytics

In the era of digital transformation, organizations generate vast volumes of customer data through online transactions, customer relationship management systems, and social media interactions. Analyzing this data is essential for understanding customer behavior and improving business decision-making. Customer segmentation is a widely used analytical technique that divides customers into meaningful groups based on shared characteristics. Machine learning techniques, particularly clustering algorithms, have significantly enhanced the effectiveness of segmentation methods. Among these techniques, the K-Means clustering algorithm is one of the most popular due to its simplicity, efficiency, and scalability. This study presents a comprehensive analysis of customer segmentation using K-Means clustering. The proposed methodology involves data collection, preprocessing, feature selection, clustering, and cluster evaluation. The study also discusses the workflow of customer segmentation, its applications across various industries, and the limitations associated with clustering algorithms. The results demonstrate that K-Means clustering provides valuable insights into customer behaviour, enabling organizations to design targeted marketing strategies and improve customer relationship management. The research highlights the practical significance of clustering techniques in modern business analytics and decision-support systems.