DeePay: Deep Learning Decodes EEG to Predict Consumer’s Willingness to
Pay for Neuromarketing
Abstract
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.