#5266. Machines learn neuromarketing: Improving preference prediction from self-reports using multiple EEG measures and machine learning

August 2026publication date
Proposal available till 12-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Marketing;
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Abstract:
A basic aim of marketing research is to predict consumers’ preferences and the success of marketing campaigns at the population-level. However, traditional marketing tools have various limitations, calling for novel measures to improve predictive power. In this study, we use multiple types of measures extracted from electroencephalography (EEG) recordings and machine learning (ML) algorithms to improve preference prediction based on self-reports alone. Subjects watched video commercials of six food products as we recorded their EEG activity, after which they responded to a questionnaire that served as a self-report benchmark measure. Thereafter, subjects made binary choices over the food products. We attempted to predict within-sample and population level preferences, based on subjects’ questionnaire responses and EEG measures extracted during the commercial viewings.
Keywords:
Consumer neuroscience; Electroencephalography (EEG); Forecasting; Machine learning; Neuromarketing; Preference prediction

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