Customer Behaviour Analysis Using Data Mining Techniques with AIDriven Recommendations

Project Code :TCMAPY1819

Objective

The motive of this project is to explore customer behaviour patterns using data mining techniques combined with AI-driven recommendations to support better decision-making in marketing and customer relationship management. Traditional methods of understanding customers often rely on manual analysis and generic strategies, which fail to capture the complexity of modern consumer preferences. By leveraging data mining, the system can uncover hidden trends, classify customers based on their behaviour, and predict purchasing tendencies. AI-driven recommendations can then provide personalized offers, promotions, and product suggestions, helping businesses improve customer satisfaction, strengthen loyalty, and enhance overall profitability in a competitive market.

Abstract

The rapid growth of e-commerce and digital platforms has led to massive amounts of customer interaction data, providing opportunities to understand customer behavior and deliver personalized recommendations. This project, “Customer Behaviour Analysis Using Data Mining Techniques with AI-Driven Recommendations”, focuses on analyzing customer activity patterns, including purchase behavior, review ratings, subscription status, discount utilization, and promotional engagement, to predict customer preferences and generate actionable insights.

Keywords: Customer Behavior Analysis, Data Mining, AI-Driven Recommendations, Machine Learning, Decision Tree, Random Forest, XGBoost, Predictive Analytics, Customer Insights, E-Commerce.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.