Mall Customer Segmentation System for Retail Analytics and Personalized Marketing

-This paper presents a comprehensive customer segmentation system for retail businesses, specifically designed for shopping mall environments. Using advanced clustering techniques and RFM (Recency, Frequency, Monetary) analysis, we develop a robust framework for identifying distinct customer segments with similar purchasing behaviors. The system processes transactional data to create meaningful customer profiles, enabling businesses to implement targeted marketing strategies and improve customer relationship management. Our approach integrates data preprocessing, feature engineering, clustering algorithms, and interactive visualization to provide actionable insights. The implemented dashboard facilitates segment comparison, geographical distribution analysis, and automated personalized email campaigns tailored to each segment’s preferences. Experimental results demonstrate the effectiveness of this approach in identifying five key customer segments with distinct behavioral patterns. The system’s practical application is validated through its ability to generate segment-specific marketing recommendations and predict customer preferences, leading to more efficient resource allocation and potentially increased customer engagement. This research contributes to both the theoretical understanding of customer behavior modeling and provides a practical tool for retail analytics in real-world business environments.