DeePay: deep learning decodes EEG to predict consumer’s willingness to pay for neuromarketing
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 dimensionalit...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Human Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnhum.2023.1153413/full |
| _version_ | 1797810969306464256 |
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| author | Adam Hakim Itamar Golan Sharon Yefet Dino J. Levy Dino J. Levy |
| author_facet | Adam Hakim Itamar Golan Sharon Yefet Dino J. Levy Dino J. Levy |
| author_sort | Adam Hakim |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T07:16:42Z |
| format | Article |
| id | doaj.art-118c9bb36d6c4e44a1b3f98a7a27be44 |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-03-13T07:16:42Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-118c9bb36d6c4e44a1b3f98a7a27be442023-06-05T04:25:50ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612023-06-011710.3389/fnhum.2023.11534131153413DeePay: deep learning decodes EEG to predict consumer’s willingness to pay for neuromarketingAdam Hakim0Itamar Golan1Sharon Yefet2Dino J. Levy3Dino J. Levy4Neuroeconomics and Neuromarketing Lab, Sagol School of Neuroscience, Tel Aviv University, Tel Aviv-Yafo, IsraelAmir Globerson Research Group, Blavatnik School of Computer Science, Tel Aviv-Yafo, IsraelNeuroeconomics and Neuromarketing Lab, Coller School of Management, Tel Aviv University, Tel Aviv-Yafo, IsraelNeuroeconomics and Neuromarketing Lab, Sagol School of Neuroscience, Tel Aviv University, Tel Aviv-Yafo, IsraelNeuroeconomics and Neuromarketing Lab, Coller School of Management, Tel Aviv University, Tel Aviv-Yafo, IsraelThere 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.https://www.frontiersin.org/articles/10.3389/fnhum.2023.1153413/fullneuromarketingdeep learningneurosciencemachine learningelectroencephalogramconsumer neuroscience |
| spellingShingle | Adam Hakim Itamar Golan Sharon Yefet Dino J. Levy Dino J. Levy DeePay: deep learning decodes EEG to predict consumer’s willingness to pay for neuromarketing Frontiers in Human Neuroscience neuromarketing deep learning neuroscience machine learning electroencephalogram consumer neuroscience |
| title | DeePay: deep learning decodes EEG to predict consumer’s willingness to pay for neuromarketing |
| title_full | DeePay: deep learning decodes EEG to predict consumer’s willingness to pay for neuromarketing |
| title_fullStr | DeePay: deep learning decodes EEG to predict consumer’s willingness to pay for neuromarketing |
| title_full_unstemmed | DeePay: deep learning decodes EEG to predict consumer’s willingness to pay for neuromarketing |
| title_short | DeePay: deep learning decodes EEG to predict consumer’s willingness to pay for neuromarketing |
| title_sort | deepay deep learning decodes eeg to predict consumer s willingness to pay for neuromarketing |
| topic | neuromarketing deep learning neuroscience machine learning electroencephalogram consumer neuroscience |
| url | https://www.frontiersin.org/articles/10.3389/fnhum.2023.1153413/full |
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