ML-Based Adaptive Session Management and Real-Time User Tracking for Web Optimization

Understanding the user behavior on websites is essential for improving engagement and optimizing performance. Traditional analytics tools often face limitations such as inadequate real-time insights, granular tracking, and ease of setup. This paper introduces a real-time user interaction tracking system designed for simplicity, scalability, and actionable insights. The system incorporates features like real-time page load analysis, session tracking, engagement monitoring, and heatmap visualization of user mouse movements to reveal navigation patterns. In addition to rule-based notifications, the system leverages a pre-trained Logistic Regression model to predict user abandonment probabilities based on interaction patterns, enabling proactive alerts for high drop-off rates, inactivity, and form abandonment. By converting raw interaction data into meaningful insights, our system enables website owners to make informed decisions, improving user retention, optimizing user experiences, and driving business outcomes. Through its modular architecture and focus on security with encrypted data handling, JWT authentication, and role-based access control, the proposed solution offers a robust approach to real-time analytics for dynamic web applications.