Authors: Dr. Raja Sekhar Koduru, Saranya P K
Abstract: As a result, the competition for consumers' attention has been increased because of the rapid development of digital marketplaces. At the same time, the current approaches to consumer analytics are based on the use of self-reported measures and do not reflect subconscious processes. Neuromarketing or neuroscience marketing can be described as the application of neuroscientific methods to understanding consumer behavior. In other words, neuromarketing can be used to investigate the mechanisms of making purchasing decisions. This paper introduces the neuromarketing analytics framework based on EEG, ET, and GSR technologies to predict purchase intent in digital marketplaces. Based on data collected from 120 participants who were shown e-commerce product listings, spectral EEG features (theta, alpha, beta, and gamma bands), ET measures (fixation duration, saccade amplitude, and pupil dilation), and GSR phasic responses have been extracted. The proposed deep learning model combines TCN and multi-head attention architecture and achieves 89.2% accuracy in predicting purchase intent. The performance of the proposed model significantly outperforms unimodal baseline models (EEG-based: 76.4%; ET-based: 78.1%; GSR-based: 71.2%). The most significant predictors of purchase intent are found to be gamma band power (30-45 Hz) during product exposure and pupil dilation change.
DOI: http://doi.org/10.5281/zenodo.20666451
