Analyzing the Role of Artificial Emotional Intelligence in Personalizing Human Brand Interactions: A Mixed-Methods Approach

Abstract Objective: This study investigates the role of artificial emotional intelligence in personalizing human brand interactions. Methods: A mixed-methods approach was employed, combining quantitative and qualitative data analysis. In the quantitative phase, online interaction data from 500 human brands with their audiences were collected over 6 months and analyzed using machine learning algorithms. The qualitative phase involved in-depth interviews with 25 branding experts and 50 consumers. Results: Quantitative findings revealed that the use of artificial emotional intelligence led to a 37% increase in engagement rates and a 28% increase in audience satisfaction (p < 0.001). Thematic analysis of qualitative data showed that artificial emotional intelligence strengthens the emotional connection between human brands and their audiences by creating personalized interactions. Conclusions: This research contributes to existing literature by presenting a novel conceptual model for integrating artificial emotional intelligence into personal branding strategies. It provides valuable guidance for professionals in leveraging emerging technologies to create more effective communications with audiences.