The rapid advancement of artificial intelligence (AI) is reshaping how U.S. e-commerce businesses manage customer relationships, allowing for more personalized and proactive retention strategies. This study examines how AI-powered predictive analytics can identify at-risk customers and strengthen long-term loyalty by analyzing real-time behavioral data such as browsing habits, purchase history, engagement levels, and cart abandonment. By leveraging these insights, American e-commerce platforms can anticipate customer needs and respond with timely, targeted actions—such as personalized discounts, reminders, or service outreach. As online competition intensifies across the U.S. retail landscape, retaining existing customers has become just as vital as attracting new ones. This paper emphasizes the potential of real-time AI systems to lower churn rates, increase customer satisfaction, and promote sustainable growth. It also highlights the critical role of datadriven strategies in creating customer-centric experiences and helping U.S. e-commerce firms remain agile in a fast-evolving market. Overall, the findings suggest that integrating AI-enabled behavioral analytics into customer engagement practices offers a scalable and effective pathway to building brand loyalty and enhancing business performance.