DeePay: Deep Learning Decodes EEG to Predict Consumer’s Willingness to Pay for Neuromarketing
2021
There is an increasing
demand within consumer-neuroscience (or neuromarketing) for objective neural
measures to quantify consumers’ preferences and predict responses to marketing
campaigns. However, the properties of EEG datasets raise various difficulties
when performing predictions on them, such as the small size of data sets, high
dimensionality, the need for elaborate feature extraction, intrinsic noise, and
unpredictable between-subject variations. We aimed to overcome these
limitations by combining unique techniques within a Deep Learning (DL) framework,
while providing interpretable results for neuroscientific and decision-making
insight. In this study, we developed a DL model to predict subject-specific
preferences based on their EEG data. In each trial, 213 subjects observed a
product’s image, out of 72 possible products, and then reported how much they
were willing to pay (WTP) for the product. The DL employed EEG recordings from
product observation to predict the corresponding reported WTP values. Our
results showed 75.09% accuracy in
predicting high vs. low WTP, surpassing other models and a manual feature
extraction approach. Meanwhile, network visualizations provided the predictive
frequencies of neural activity and their scalp distributions, shedding light on
the neural mechanism 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.
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