Neuro marketing perspective on online purchase decision making for decoding the digital consumer

Abstract

Online purchase decisions are becoming more complicated and are impacted by subconscious cognitive and emotional processes as a result of the rapid rise of e-commerce. These underlying elements are frequently missed by traditional marketing research, underscoring the necessity of a neuromarketing-based strategy. Through the integration of EEG, eye-tracking, and galvanic skin response (GSR) measurements with behavioral surveys, this research seeks to decipher digital consumer behavior by examining the effects of online stimuli, including visual design, website layout, and interactive elements, on emotional arousal, satisfaction, trust, attention, and purchase intention. To gather survey and neuromarketing data, 50–100 participants interacted with e-commerce platforms as part of a mixed-method research approach. To find patterns and connections between physiological reactions, visual attention, and behavioral outcomes, quantitative analysis was done using correlation, descriptive statistics, predictive modeling, and regression modeling. F1-score, precision, accuracy, and recall were used to assess the efficiency of machine learning techniques, such as Support Vector Machines and Random Forest, which were used to forecast purchase behavior based on a combination of neuromarketing and survey variables. The findings show the influence of visual and emotional signals on consumer engagement and insight into the unconscious factors that influence online purchase decisions. Results indicate that neuromarketing metrics provide a robust predictive method for comprehending digital customer behavior when paired with behavioral data. By matching the online experience with the emotional and cognitive behaviors of customers, this research helps designers create user-centered e-commerce interfaces and evidence-based digital marketing tactics that maximize trust, engagement, and conversion rates.