E-COMMERCE OPERATIONS WITH AI-POWERED DATA WAREHOUSES: A CASE STUDY ON CUSTOMER BEHAVIOR ANALYSIS

Abstract

This paper presents a comprehensive systematic review of the impact of AI-powered data warehouses on e-commerce operations, with a particular focus on customer behavior analysis and operational efficiency. Leveraging the PRISMA methodology, the study synthesizes findings from 70 research articles spanning multiple countries, including the United States, China, India, the United Kingdom, and Germany. The review highlights the transformative role of AI-driven data warehouses in enabling real-time, predictive analytics, which significantly enhances the ability of e-commerce businesses to understand and respond to customer preferences, optimize inventory management, and implement dynamic pricing strategies. While the benefits of AI integration are substantial, the study also identifies persistent challenges, such as data privacy concerns, high implementation costs, and integration complexities, that may hinder widespread adoption. The paper concludes with recommendations for businesses to strategically approach the implementation of AI-powered data warehouses, emphasizing the need for scalable solutions, robust data governance, and ongoing investment in technology and training. This research underscores the potential of AI-powered data warehouses to drive innovation and growth in the e-commerce sector, while also calling for continued exploration of solutions to address the existing challenges.

Keywords: Artificial Intelligence, Data Warehousing, Customer Behavior Analysis, Operational Efficiency, Customer Engagement, Business Growth