PREDICTIVE MODELING OF CUSTOMER PURCHASE BEHAVIOR USING RETAIL DATA MINING METHODS

Abstract

This research examines the process of predicting consumer behavior by utilizing retail data mining to evaluate a substantial quantity of transactional and customer data in order to identify patterns that can be used to anticipate future purchases. This project employs a variety of sophisticated machine learning techniques, such as clustering, classification, and association rule mining, to accurately classify clients, ascertain their preferences, and forecast purchasing trends. The research demonstrates the potential of behavioral, demographic, and historical sales data to enhance targeted marketing, assist in strategic decision-making, and enhance inventory management. By utilizing predictive and educated data, this method enables retailers to increase consumer engagement and generate additional revenue.