Integration of Marketing Analytics and Neuromarketing for Predicting Purchase Behavior

Advances in marketing analytics and neuromarketing have independently improved the understanding of consumer purchase behavior. However, limited empirical research has integrated traditional data-driven analytics with neurophysiological measures to enhance predictive accuracy. This study proposes and empirically tests an integrated framework combining marketing analytics indicators (clickstream data, engagement metrics, and past purchase behavior) with neuromarketing measures (eye-tracking attention, emotional arousal, and cognitive load) to predict purchase intention. Using a quantitative, multi-method research design, data were collected from 360 consumers exposed to digital product stimuli. Structural Equation Modeling (SEM) results indicate that neuromarketing variables significantly enhance predictive power beyond traditional analytics. The findings contribute to predictive marketing theory and offer actionable insights for data-driven decision-making.