AI-driven personalization in e-commerce: Enhancing consumer engagement and conversion rates

AI personalization in e-commerce has profoundly changed the arena of consumer engagement and purchase behavior with the backing of Machine Learning and Data Analytics. This study is an assessment of the role that AI personalization plays in consumer interaction and conversion rate. It specifically looks into how personalized product recommendations, dynamic pricing, and predictive analysis enhance user experience and sales performance. Using the mixed-method research design, the survey integrates quantitative and qualitative approaches to study how AI-driven personalization-product recommendations, dynamic pricing, predictive analytics, and user experience personalization affect consumer engagement, conversion rates, and trust based on the Technology Acceptance Model (TAM) and the Stimulus-Organism-Response (SOR) framework. A diverse array of consumer insights comes from a stratified random sample of 500 survey respondents and 30 interviewees, and is further analyzed through descriptive statistics, regression, mediation analysis (SPSS), thematic analysis (NVivo), and sentiment analysis, revealing key relationships between AI personalization, satisfaction, and engagement while addressing trust and privacy concerns in e-commerce. The findings affirm AI personalization to be a significant contributor toward engagement and conversion, whereby user experience personalization and personalized product recommendations rise as the better predictors of engagement. Predictive analytics promote customer retention through expectation management; dynamic pricing appears to be a weaker predictor of engagement than others, but shows a positive correlation with engagement.