AI-Driven Predictive Analytics for Customer Retention in E-Commerce Platforms using Real-Time Behavioral Tracking

Abstract

The rapid evolution of artificial intelligence (AI) has transformed customer relationship management within the e-commerce sector, enabling more proactive and personalized strategies for enhancing customer retention. This study explores the role of AI-driven predictive analytics in identifying at-risk customers and fostering long-term loyalty through the real-time tracking of consumer behavior. By analyzing dynamic data points such as browsing history, purchase patterns, engagement frequency, and cart abandonment trends, e-commerce platforms can anticipate customer needs and intervene with targeted retention strategies. The integration of AI algorithms with behavioral tracking tools enables platforms to detect subtle shifts in consumer activity, allowing timely responses such as personalized offers, reminders, or service interventions. As competition in the digital marketplace intensifies, retaining existing customers has become as critical as acquiring new ones. This paper underscores the potential of real-time predictive systems to reduce churn, enhance user satisfaction, and drive sustainable growth. Furthermore, the study highlights the strategic importance of data-driven insights in shaping customer-centric experiences and enabling e-commerce firms to remain agile in an ever-changing market. Ultimately, the findings suggest that embedding AI-powered behavioral analytics within customer engagement models offers a scalable and intelligent approach to fostering brand loyalty and improving overall business performance.