Incorporating biometric data in models of consumer choice

The use of neuro-physiological data in models of consumer choice is gaining popularity. This article presents some of the benefits of using psycho-physiological data in analyzing consumer valuation and choice. Eye-tracking, facial expressions, and electroencephalography (EEG) data were used to const...

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Bibliographic Details
Published in:Applied economics 2019-03, Vol.51 (14), p.1514-1531
Main Authors: Huseynov, Samir, Kassas, Bachir, Segovia, Michelle S., Palma, Marco A.
Format: Article
Language:English
Subjects:
Biometric data
Biometrics
Brain
choice
Consumer behavior
Decision making models
Economic analysis
Economic theory
Electroencephalography
Eye tracking
Facial expressions
Lasso
Physiology
Popularity
Power
predictive power
Tracking
Valuation
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Summary:The use of neuro-physiological data in models of consumer choice is gaining popularity. This article presents some of the benefits of using psycho-physiological data in analyzing consumer valuation and choice. Eye-tracking, facial expressions, and electroencephalography (EEG) data were used to construct three non-conventional choice models, namely, eye-tracking, emotion and brain model. The predictive performance of the non-conventional models was compared to a baseline model, which was based entirely on conventional data. While the emotion and brain models proved to be as good as conventional data in explaining and predicting consumer choice, the eye-tracking model generated superior predictions. Moreover, we document a significant increase in predictive power when biometric data from different sources were combined into a mixed model. Finally, we utilize a machine learning technique to sparse the data and enhance out-of-sample prediction, thus showcasing the compatibility of biometric data with well-established statistical and econometric methods.
ISSN:0003-6846
1466-4283
DOI:10.1080/00036846.2018.1527460