Predicting user engagement levels through emotion-based gesture analysis of initial impressions

This study validates the predictive power of emotion-based gesture analysis in determining user engagement levels based on their initial impressions of a website. To achieve the objective of this research, we conducted experiments for 53 weeks by capturing the data from 3,797 unique visitors engaging with an e-commerce website. Gesture-based emotion analysis was employed to capture users' initial impressions, encompassing gestures like clicks, scrolls, swipes, and taps on mobile touch surfaces. Emaww AI’s proprietary first impression metric captures the initial emotional response to a product or service and this initial impression can have a significant impact on their overall impression of the site. The result of this research demonstrates a strong correlation between five levels of users’ first impression to the subsequent engagement during their visit to a website. The validated regression model demonstrated commendable performance, as indicated by a Mean Absolute Error (MAE) score of 1.60. This relatively low MAE suggests that the model's predictions closely align with the actual values, reflecting a high level of accuracy. The implications for diverse users highlight the importance of aligning website content and aesthetic design with users' emotions to drive engagement.