Systematic Review of AI-Driven Data Integration for Enabling Smarter E-Commerce Analytics and Consumer Insights

This systematic review examines the transformative role of AI-driven data integration in enhancing e-commerce analytics and consumer insights. With the rapid growth of e-commerce, businesses are increasingly relying on artificial intelligence (AI) technologies such as machine learning, natural language processing, and deep learning to process vast amounts of data and derive actionable insights. This paper explores the key AI methods employed in e-commerce, including consumer behavior analysis, personalization through recommendation systems, and demand forecasting for inventory management. Additionally, it addresses the challenges faced by e-commerce businesses in integrating AI, such as data silos, scalability, and data quality issues. By analyzing the current state of AI applications in e-commerce, this review highlights the significant impact of AI on consumer insights, particularly through sentiment analysis, real-time insights, and customer segmentation. Furthermore, it discusses the practical implications of AI for e-commerce businesses, emphasizing the benefits of enhanced personalization, improved operational efficiency, and better decision-making. Finally, the paper identifies potential directions for future research, focusing on the development of more advanced AI algorithms, ethical concerns, and the integration of AI with emerging technologies like augmented and virtual reality. This review provides a comprehensive understanding of AI’s pivotal role in shaping the future of e-commerce analytics and consumer engagement.