AI-Powered Predictive Analytics for E-commerce: Enhancing User Experience and Business Decision Making
Abstract
In the rapidly evolving e-commerce landscape, delivering personalized customer experiences and making informed business decisions are paramount for success. This paper explores the application of AI-powered predictive analytics to enhance both user experience and business decision-making in e-commerce platforms. By utilizing machine learning algorithms such as collaborative filtering, regression models, and deep learning techniques, the proposed framework predicts user preferences, optimizes product recommendations, and anticipates demand patterns. Additionally, AI-driven insights support dynamic pricing, inventory management, and personalized marketing campaigns, improving customer satisfaction and operational efficiency. The system’s ability to analyze large datasets in real-time allows businesses to anticipate market trends, reduce churn, and increase conversion rates. Experimental results from a real-world e-commerce platform demonstrate the effectiveness of AI in driving revenue growth and providing users with tailored shopping experiences. This approach highlights the power of predictive analytics in transforming e-commerce strategies and decision-making processes.
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