Designing User Experience Improvement and User Behavior Pattern Recognition Algorithms in Design Operation

Enhancing user experience (UX) is a key component in customer retention and sales promotion in e-commerce platforms. To build an effective UX model it is necessary to predict the user behavior more accurately and develop UX model that is tailored based on those behavior patterns. Existing models lack the ability to integrate advanced Machine Learning (ML) models to address the challenges. This study is an attempt to tackle these limitations that employs advanced AI tools to predict user behavior so that to construct an more effective UX model. The study involved 80 users from China who were aged 26 to 52, with diverse backgrounds in education, occupation, and tech proficiency. The work have employed Google Analytics, Hotjar, and FullStory to collect the user interactions and by using Generalized Sequential Pattern (GSP) algorithm, Decision Trees (DT), and Logistic Regression (LR) the work attempts to accurately predict the user behavior patterns. The results show that the model achieved better accuracy of 0.8795 and an F1 Score of 0.8610 on the test dataset. It also excelled in conversion rate (12.34%) and bounce rate (28.65%) which show effectiveness in retaining users and converting visits into actions.