An Advanced Forecasting Model Leveraging Emotion-Gesture Correlation to Predict Returning Visitors Surpasses Visit Duration as a Predictive Factor

Abstract

This research paper explores the effectiveness of user emotional experience as a predictor for future returning of the user to the website. Traditional web analytics have been limited in their ability to accurately capture the nuances of user experiences. Methods like eye tracking, speech tracking, and surveys, while insightful, often suffer from being overly intrusive, leading to biased results. This study introduces a state of the art, nonintrusive method of measuring user experience: touch-gesture based emotion measurement. This technique leverages the subconscious nature of touch gestures to gather emotional data, allowing for a more authentic and unbiased user interaction with websites. To leverage this method, we first gained explicit data collection consent and gathered browsing data from 164,527 users across a 1-year period on a Canadian e-commerce website visited from a touchscreen device. While the sample size is significantly large, the sample is primarily made up of visitors from Canada, which could limit generalization of the findings. Using this data, we implemented an AI model which predicts whether a user is likely to return to the website or not, primarily based on their emotional touch gestures on their first visit with an accuracy of 91.7%. This approach not only enhances our understanding of user engagement but also opens new avenues for optimizing user experience in untested digital spaces such as e-learning and mental health.