AI-Driven Personalization Strategies for Enhancing E-Commerce Customer Engagement

Thesis Abstract

The rapid proliferation of e-commerce platforms has intensified the need for personalized customer experiences to foster engagement, loyalty, and increased sales, posing significant challenges for online retailers seeking scalable and effective strategies. This study aims to develop and evaluate AI-driven personalization strategies to enhance customer engagement within e-commerce environments, focusing on the integration of advanced machine learning algorithms and real-time data analytics. The specific objectives include identifying key drivers of customer engagement, designing personalized recommendation models grounded in AI techniques, and assessing the impact of these strategies on user engagement metrics and purchase behavior. Employing a mixed-methods research design, the study combines quantitative analysis of customer interaction data with qualitative insights from consumer focus groups. The population comprises active users of a leading global e-commerce platform operating in North America, totaling approximately 100,000 registered customers. A stratified random sampling technique was used to select 1,200 respondents to ensure representation across demographics such as age, gender, and purchasing habits. Data collection was conducted through structured online surveys measuring perceived personalization, engagement levels, and satisfaction, complemented by transaction and browsing data derived from the platform's backend systems over a six-month period. Quantitative data were analyzed using multiple regression analysis and structural equation modeling (SEM) to ascertain the relationship between personalization variables and customer engagement outcomes. The qualitative data from focus groups underwent thematic analysis to explore consumer perceptions of AI-driven recommendations and personalization effectiveness. The findings are expected to demonstrate that AI-powered personalization significantly correlates with increased engagement metrics, such as session duration, click-through rates, and repeat purchase intentions, mediated by perceived relevance and trust in the system. This research anticipates revealing that effective personalization depends heavily on the deployment of adaptive machine learning models such as collaborative filtering, content-based filtering, and hybrid approaches, aligned with the Theory of Planned Behavior and the Technology Acceptance Model. The study's contribution to knowledge lies in providing empirical evidence on the efficacy of particular AI algorithms in the context of e-commerce personalization and developing a conceptual framework linking AI-driven strategies with consumer engagement constructs. Additionally, it advances understanding of consumer trust and perceived relevance as critical mediators in the personalization process. The study concludes that AI-driven personalization strategies hold substantial potential for transforming e-commerce customer engagement by fostering perceived value and trust. It recommends that online retailers invest in scalable machine learning infrastructures capable of delivering real-time, context-aware recommendations and prioritize transparency in AI algorithms to enhance consumer trust. Future research avenues include longitudinal studies to examine the long-term effects of personalization and exploring personalization applied within emerging fields such as augmented reality shopping and voice-activated commerce. Overall, the research provides a comprehensive model for implementing AI-based personalization strategies, informing practitioners and academic researchers seeking to optimize customer engagement through innovative, data-driven solutions in the rapidly evolving digital marketplace.