Neuromarketing Techniques to Enhance Consumer Preference Prediction
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Date
2024-01-03
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923
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Abstract
This study evaluates the time-tested method of consumer self-reported measures against advanced neuromarketing algorithms to evaluate experience products. To do so, the authors utilize data from the public DEAP database, which contains both self-reports and EEG measurements of the same subjects. With self-reported measures of valence, arousal, and dominance, the authors then evaluate consumer liking, comparing effectiveness of three different methods: (1) the FFT-analysis of EEG, to (2) self-reported ratings, and (3) a combined method of EEG analysis with self-reported ratings. Results suggest that neuromarketing methods when combined with self-reported measures, will substantially increase accuracy, precision, recall, and F1 scores. Moreover, with the exception of utilizing self-reported valence, dominance and arousal combined, the FFT-analysis of EEG was a more powerful predictor of liking than self-reported measurements. Implications for digital marketing, management and business ethics are discussed.
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Cognitive and Neuroscience Research in IS, experiential products, neuromarketing, preference, self-report, sensors
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10 pages
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Proceedings of the 57th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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