AI-Based E-Commerce Analysis System: Enhancing Customer Experience and Business Intelligence through Machine Learning

Abstract

The rapid growth of e-commerce has generated massive volumes of data that require sophisticated analytical tools for effective business decision-making. This research presents an AI-based e-commerce analysis system designed to provide comprehensive insights into customer behavior, sales patterns, and market trends. The proposed system integrates machine learning algorithms, natural language processing, and predictive analytics to analyze multiple data dimensions including customer reviews, purchase histories, product performance, and pricing strategies. Through the implementation of various AI techniques such as sentiment analysis, recommendation systems, and demand forecasting, the system enables e-commerce businesses to optimize their operations and enhance customer satisfaction. The research demonstrates significant improvements in prediction accuracy, with the system achieving 87.3% accuracy in sales forecasting and 92.1% in customer churn prediction. Additionally, the sentiment analysis module processed over 50,000 customer reviews with 89.4% accuracy. The findings indicate that AI-driven analytics can substantially improve inventory management, personalized marketing, and overall business profitability. This study contributes to the growing field of intelligent e-commerce systems and provides practical insights for businesses seeking to leverage artificial intelligence for competitive advantage.