CUSTOMER BEHAVIOUR ANALYSIS USING DATA MINING TECHNIQUES WITH AI-DRIVEN RECOMMENDATIONS

Abstract

In the rapidly evolving business environment, understanding customer behavior has become essential for companies striving to improve customer satisfaction, optimize marketing strategies, and increase profitability. This project employs advanced data mining techniques to analyze vast and complex customer datasets, uncovering hidden patterns and trends related to purchasing habits, preferences, and engagement levels. By utilizing clustering, classification, and association rule mining, the system effectively segments customers into meaningful groups, enabling targeted marketing efforts. Additionally, the project integrates artificial intelligence, particularly machine learning algorithms, to develop an AI-driven recommendation engine that provides personalized product or service suggestions tailored to individual customer profiles. This personalized approach enhances the relevance of recommendations, thereby increasing customer engagement and loyalty. The combined application of data mining and AI not only facilitates deeper insights into customer behavior but also automates the recommendation process, enabling businesses to respond swiftly to changing customer needs. The implementation demonstrates a comprehensive framework that supports data-driven decision-making, helps businesses identify new opportunities, and ultimately drives revenue growth by delivering a superior customer experience. This integrated system highlights the transformative potential of leveraging data analytics and AI technologies in modern customer relationship management.