In a group project, we conducted both qualitative and quantitative research to explore how AI-driven product recommendations influence customer behavior and retention. The qualitative phase involved in-depth interviews to uncover user perceptions and attitudes, while the quantitative phase included a between-subjects experiment using mockups. We applied independent samples t-tests, Chi-Square tests, and logistic regression to measure purchase intent, trust, and engagement—finding a statistically significant increase in purchase intent among users exposed to AI-powered recommendations. Through principal component analysis and cluster analysis, we identified three key user segments: Loyal Adoptees, Cautious Explorers, and Skeptical Bystanders. Based on our findings, we recommended strategies for Amazon such as segment-based personalization, explainable AI, and interactive filtering to enhance engagement and conversion.
