Neurophysiological methods to study consumer perceptions of television advertising content

Березка, С. М.; Шерешева, Марина

doi:10.21638/11701/spbu08.2019.202

Cited by 2 publications

References 56 publications

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A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research

et al. 2022

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Introduction The present paper discusses the findings of a systematic review of EEG measures in neuromarketing, identifying which EEG measures are the most robust predictor of customer preference in neuromarketing. The review investigated which TF effect (e.g., theta-band power), and ERP component (e.g., N400) was most consistently reflective of self-reported preference. Machine-learning prediction also investigated, along with the use of EEG when combined with physiological measures such as eye-tracking. Methods Search terms ‘neuromarketing’ and ‘consumer neuroscience’ identified papers that used EEG measures. Publications were excluded if they were primarily written in a language other than English or were not published as journal articles (e.g., book chapters). 174 papers were included in the present review. Results Frontal alpha asymmetry (FAA) was the most reliable TF signal of preference and was able to differentiate positive from negative consumer responses. Similarly, the late positive potential (LPP) was the most reliable ERP component, reflecting conscious emotional evaluation of products and advertising. However, there was limited consistency across papers, with each measure showing mixed results when related to preference and purchase behaviour. Conclusions and implications FAA and the LPP were the most consistent markers of emotional responses to marketing stimuli, consumer preference and purchase intention. Predictive accuracy of FAA and the LPP was greatly improved through the use of machine-learning prediction, especially when combined with eye-tracking or facial expression analyses.

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A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research

et al. 2022

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A Study in Understanding the Critical Factors Influencing the Markers in Implementing Neuromarketing Concepts in Understanding Consumer Behavior

Reepu,

Kumar

2024

Advances in E-Business Research

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Marketers in current business environments invest large amounts of money in understanding customers' needs and requirements; for this purpose, they use novel tools like artificial intelligence, the Internet of Things, big data analytics, and other technological tools. However, Neuromarketing is one innovative method that has gained more attention, and it is mainly involved in understanding the brain's response towards marketing and advertisement campaigns. Customers are confronted with an overwhelming number of advertisements, products, and services in today's market, which can be confusing. Organizations that are interested in modifying their tactics should have an understanding of the factors that influence the decisions that customers make and the mechanisms that shape their responses to marketing initiatives. Surveys and focus groups, two classic marketing research methods, rely on self-reported data, which can be biased and cannot adequately discover the essential elements that influence consumer behaviour.

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Neurophysiological methods to study consumer perceptions of television advertising content

Cited by 2 publications

References 56 publications

A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research

A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research

A Study in Understanding the Critical Factors Influencing the Markers in Implementing Neuromarketing Concepts in Understanding Consumer Behavior

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