A Review of Machine Learning Approaches for User Engagement Profiling in Online Retail: Integrating Google Analytics for Predictive Insights

Abstract

The digital marketplace has evolved from a transactional platform to an experiential ecosystem, making user engagement a paramount metric for success. This review paper synthesizes current knowledge and presents a novel framework for integrating Machine Learning (ML) with Google Analytics (GA) data to transition from descriptive analytics to predictive user engagement profiling in online retail. We begin by delineating the critical imperative of understanding user engagement in the modern e-commerce landscape, highlighting the limitations of traditional, descriptive analytics platforms like GA, which, despite providing rich behavioral data, lack inherent predictive capabilities. The paper then systematically reviews and compares prominent machine learning classification algorithms—specifically Decision Trees (DTs), Naïve Bayes (NB), and k-Nearest Neighbors (k-NN)—for their applicability in modeling and predicting user behavior. Drawing on a synthesized empirical study, we demonstrate that Decision Trees can achieve superior accuracy (97.98%) and, more importantly, offer high interpretability through actionable decision rules derived from features like event count and bounce rate. Furthermore, this review elaborates on the architectural considerations for building a robust ML-GA integration pipeline, encompassing data ingestion, feature engineering, model deployment, and continuous monitoring. Finally, we discuss practical applications, identify prevailing research gaps—such as handling class imbalance and the need for real-time, cross-platform profiling—and outline future research directions, including the incorporation of Explainable AI (XAI) and Generative AI. This paper concludes that the synergistic integration of ML and GA data represents the next frontier for enabling proactive, personalized, and data-driven digital marketing strategies.