DeePay: deep learning decodes EEG to predict consumer’s willingness to pay for neuromarketing

Frontiers in Human Neuroscience

Neuroscience

Itamar Golan¹  Sharon Yefet²  Adam Hakim³  Dino J. Levy⁴ 

[1] Amir Globerson Research Group, Blavatnik School of Computer Science, Tel Aviv-Yafo, Israel;Neuroeconomics and Neuromarketing Lab, Coller School of Management, Tel Aviv University, Tel Aviv-Yafo, Israel;Neuroeconomics and Neuromarketing Lab, Sagol School of Neuroscience, Tel Aviv University, Tel Aviv-Yafo, Israel;Neuroeconomics and Neuromarketing Lab, Sagol School of Neuroscience, Tel Aviv University, Tel Aviv-Yafo, Israel;Neuroeconomics and Neuromarketing Lab, Coller School of Management, Tel Aviv University, Tel Aviv-Yafo, Israel;

关键词: neuromarketing;    deep learning;    neuroscience;    machine learning;    electroencephalogram;    consumer neuroscience;    neural networks;    consumer behavior;   

DOI  :  10.3389/fnhum.2023.1153413

 received in 2023-01-29, accepted in 2023-05-16,  发布年份 2023

来源: Frontiers

【 摘 要 】

There is an increasing demand within consumer-neuroscience (or neuromarketing) for objective neural measures to quantify consumers’ subjective valuations and predict responses to marketing campaigns. However, the properties of EEG raise difficulties for these aims: small datasets, high dimensionality, elaborate manual feature extraction, intrinsic noise, and between-subject variations. We aimed to overcome these limitations by combining unique techniques of Deep Learning Networks (DLNs), while providing interpretable results for neuroscientific and decision-making insight. In this study, we developed a DLN to predict subjects’ willingness to pay (WTP) based on their EEG data. In each trial, 213 subjects observed a product’s image, from 72 possible products, and then reported their WTP for the product. The DLN employed EEG recordings from product observation to predict the corresponding reported WTP values. Our results showed 0.276 test root-mean-square-error and 75.09% test accuracy in predicting high vs. low WTP, surpassing other models and a manual feature extraction approach. Network visualizations provided the predictive frequencies of neural activity, their scalp distributions, and critical timepoints, shedding light on the neural mechanisms involved with evaluation. In conclusion, we show that DLNs may be the superior method to perform EEG-based predictions, to the benefit of decision-making researchers and marketing practitioners alike.

【 授权许可】

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Copyright © 2023 Hakim, Golan, Yefet and Levy.