Neurocognitive Approach for Assessing Visual Engagement in Neuromarketing

The rapid expansion of online shopping, accelerated by the COVID-19 pandemic, has underscored the importance of effective advertising in shaping consumer behavior. Businesses now allocate approximately $674 billion annually to digital advertising to enhance consumer engagement and drive sales. Traditional studies on user engagement (UE) often rely on self-reported data. This study introduces a novel approach using electroencephalography (EEG) to analyze user engagement with video advertisements and its impact on user purchase intention. By evaluating video advertisement with EEG, advertising agencies can save significant amounts of money by identifying non-engaging advertisement before they air on TV, thereby avoiding potential losses for both the agency and the company. In this study EEG data was collected from 52 participants as they watched video advertisement, with 20 statistical features extracted from each electrode. Labels were assigned based on the data, and correlation feature selection was employed. Using classification algorithms, particularly a random forest, an accuracy of 94.77% was achieved. This study provides a framework for assessing viewer engagement through EEG and demonstrates its effectiveness in enhancing our understanding of advertising impacts.