Published Paper Details:

This research investigates the role of AI-driven personalization in improving customer retention in the online retail sector. As digitization accelerates and customer expectations shift, online retailers confront the dual challenge of attracting new customers while keeping their existing ones. In this competitive environment, AI-powered personalization emerges as a game-changing solution. Technologies powered by AI--such as machine learning algorithms, recommendation systems, chatbots, and real-time behavior tracking--empower businesses to customize customer experiences with unparalleled scale and accuracy. These technologies facilitate dynamic customer segmentation, predictive modeling of customer behavior, and immediate content adjustment based on preferences and historical interactions. The research examines how these features impact crucial facets of customer retention, including satisfaction, trust, and the likelihood of repeat purchases. The study utilized a descriptive design approach, incorporating both primary and secondary data sources. A structured questionnaire was distributed among online retail consumers to gauge their views and behaviors regarding AI-driven personalization. The data were analyzed using techniques like frequency distribution, correlation analysis, and regression models to identify the connections between personalization methods and retention indicators. Results reveal a significant positive correlation between AI-enhanced personalization and customer retention. In particular, tailored product suggestions, timely email marketing, and AI-supported customer service markedly boosted customer loyalty and satisfaction. Furthermore, real-time personalization--made possible through data analytics and AI technologies-- was observed to decrease cart abandonment and elevate purchase rates. The study highlights the necessity for e-retailers to invest in responsible AI frameworks, consumer data privacy, and real-time personalization techniques to sustain a competitive edge and foster long-term customer loyalty. It also recognizes the possibilities presented by advanced AI innovations--such as emotion detection, computer vision, and hyperpersonalized marketing--to further transform the customer experience. In summary, this thesis not only offers empirical proof of the efficacy of AI-based personalization but also acts as a strategic resource for e-commerce leaders aiming to utilize AI to boost customer interaction and loyalty. Future research could delve deeper into the effectiveness of personalization across various cultural settings and investigate its long-term effects on brand value and consumer trust.