PREDICTIVE MODELING OF CUSTOMER BEHAVIOR IN RETAIL USING ADVANCED DATA MINING TECHNIQUES

The rapid growth of retail industries, particularly supermarket chains, has generated massive volumes of customer transaction data, offering valuable insights into consumer behavior patterns. Understanding these patterns is essential for enhancing customer satisfaction, optimizing inventory management, and improving business profitability. Traditional analytical methods often fail to capture complex relationships and dynamic purchasing behaviors present in large-scale retail datasets. This paper proposes a comprehensive data mining approach for customer behavior analysis and predictive modeling in supermarket retail environments. The framework leverages advanced machine learning and data mining techniques to analyze transactional data, identify purchasing trends, and predict future customer behavior. The proposed system operates through multiple stages, including data preprocessing, feature extraction, clustering, classification, and predictive modeling. Techniques such as association rule mining, decision trees, and ensemble learning are integrated to improve prediction accuracy and uncover hidden patterns in customer purchasing habits. Additionally, the model incorporates segmentation strategies to categorize customers based on buying preferences, enabling personalized marketing and targeted promotions. The experimental evaluation demonstrates that the proposed approach significantly improves prediction accuracy, customer segmentation quality, and recommendation effectiveness compared to traditional methods. The system also reduces operational inefficiencies by enabling data-driven decision-making in inventory control and sales forecasting. The results highlight the effectiveness of integrating data mining techniques with predictive analytics to transform raw retail data into actionable insights. This research contributes to the development of intelligent retail management systems capable of adapting to evolving customer preferences and market trends.