Published Paper Details:

In the modern retail environment, effective customer segmentation is essential for optimizing marketing strategies and enhancing customer experiences. This project utilizes advanced technologies, specifically K-Means Clustering, to segment customers in malls and businesses. By leveraging data analytics platforms, CRM systems, AI, and ML techniques, businesses can gain deeper insights into customer behavior and preferences. The project involves collecting extensive customer data from sources like transaction history, demographic information, and behavioral patterns. The K-Means Clustering algorithm identifies distinct customer segments based on shared characteristics, enabling personalized marketing, optimized in-store experiences, and improved customer satisfaction. Unlike traditional segmentation methods, this system incorporates real-time data analysis and predictive modeling, allowing dynamic adjustments to evolving customer behaviors. By integrating big data analytics, AI, and ML, the project provides a comprehensive solution for customer segmentation, offering actionable insights that drive business growth and customer loyalty in today's competitive retail landscape.