Customer Behaviour Analysis Using Data Mining Techniques With AI Driven Recommendations
DOI:
https://doi.org/10.46647/rdems0204016Keywords:
Customer Behaviour Analysis, Data Mining, Artificial Intelligence (AI), Recommendation Systems, Machine Learning, Predictive Analytics, Customer Segmentation, Association Rule Mining, Classification Techniques, Clustering Algorithms, Personalized Recommendations, Big Data Analytics, Consumer Insights, Decision Support Systems, Market Basket Analysis.Abstract
The rapid growth of digital transactions and online platforms has generated vast amounts of customer data, creating opportunities to analyze and understand consumer behavior more effectively. The project titled “Customer Behaviour Analysis Using Data Mining Techniques with AI Driven Recommendations” focuses on leveraging data mining and artificial intelligence to extract meaningful insights from customer data and provide personalized recommendations. By applying techniques such as clustering, classification, and association rule mining, the system identifies patterns in customer preferences, purchasing habits, and interaction behavior, enabling businesses to make informed decisions. The proposed system integrates AI-driven recommendation mechanisms to deliver customized product suggestions based on user behavior and historical data. It enhances customer engagement, improves satisfaction, and increases business profitability by targeting the right audience with relevant offerings. Additionally, the system supports real-time analysis and adaptive learning, allowing recommendations to evolve with changing user preferences. Overall, this project demonstrates how the combination of data mining and AI can transform raw data into actionable insights, leading to smarter marketing strategies and improved customer relationship management.