AI-Enhanced Emotional Intelligence for Hyper-Personalized E-Commerce Experiences and Its Impact on Consumer Loyalty

Abstract

In the evolving landscape of e-commerce, AI-driven personalization has significantly enhanced consumer experiences, yet existing models primarily rely on behavioral and transactional data. The AI-driven emotional Intelligence (AI-EI) Model is an innovative approach to building an AI-driven hyper-personalized shopping experience by integrating real-time sentiment analysis, micro-expression recognition, and adaptive learning, and this study proposes one of them. With consent, e-commerce platforms can use AI to decipher consumer emotions via voice, text, or facial cues to recommend products, concepts, and user interactions that match those of emotional shoppers. In the case of emotion-based AI personalization, the research investigates its effect on consumer loyalty, trust, and purchasing behavior, while addressing data privacy concerns about a concern regarding ethical AI implementation. The study assesses the consumer perceptions of emotion-driven recommendations, their willingness to interact with AI-enhanced shopping experiences, and their longer-term effect on brand affinity through survey-based methodology. This study will enable valuable results for businesses straining to approach deeper customer relationships using intelligent and emotion-empathic personalization to augment customer satisfaction and retention. This is an addition to the debate on AI ethics in e-commerce that departs from the current engagement with the system that trades innovation and consumer trust in the digital marketplace.