Can Brain Waves Really Tell If a Product Will Be Purchased? Inferring Consumer Preferences From Single-Item Brain Potentials

Abstract

Recent research has shown that event-related brain potentials (ERPs) recorded while participants view lists of different consumer goods can be modulated by their preferences toward these products. However, it remains largely unknown whether ERP activity specific to a single consumer item can be informative about whether or not this item will be preferred in a shopping context. In this study, we examined whether single-item ERPs could reliably predict consumer preferences toward specific consumer goods. We recorded scalp EEG from 40 participants while they were viewing pictures of consumer goods and we subsequently asked them to indicate their preferences for each of these items. Replicating previous results, we found that ERP activity averaged over the six most preferred products was significantly differentiated from ERP activity averaged across the six least preferred products for three ERP components: The N200, the late positive potential (LPP) and positive slow waves (PSW). We also found that using single-item ERPs to infer behavioral preferences about specific consumer goods led to an overall predictive accuracy of 71%, although this figure varied according to which ERPs were targeted. Later positivities such as the LPP and PSW yielded relatively higher predictive accuracy rates than the frontal N200. Our results suggest that ERPs related to single consumer items can be relatively accurate predictors of behavioral preferences depending on which type of ERP effects are chosen by the researcher, and ultimately on the level of prediction errors that users choose to tolerate.

How Do Reference Points Influence the Representation of the N200 for Consumer Preference?

“…Contrary to our findings, several studies with a similar study paradigm to our conjoined-reference condition found that the N200 could represent preference ( Li et al, 2012 ; Gui et al, 2016 ; Goto et al, 2019 ). This inconsistency may be the result of participants using different reference points.…”

“… Goto et al (2017) found that both the N200 and late positive potential (LPP) could index consumer preference, with a more negative N200 and less positive LPP for less-preferred than for highly-preferred products. Goto et al (2019) further explored whether ERP components measured in response to a single consumer item could predict preference for this item. Their results showed that N200, LPP, and positive slow waves (PSW) could predict consumer preference for a product with an overall accuracy of 71%, although prediction accuracy varied for different components.…”

“…First, one product is viewed in each trial, and participants are asked to make responses according to their judgment ( Telpaz et al, 2015 ; Goto et al, 2017 ; Ma et al, 2018 ; Tyson-Carr et al, 2018 ). Second, two products are displayed simultaneously in each trial, and participants are required to choose which product they prefer ( Li et al, 2012 ; Larsen and O’Doherty, 2014 ; Gui et al, 2016 ; Goto et al, 2019 ).…”

Neuromarketing research in the last five years: a bibliometric analysis

“…The atmosphere in the store and the consumer's state impact the emotional fatigue differently; thereby, the footfall. 15 different retailers shops Goto, et al (Goto et al, 2019) There are remarkable differences in visual selective attention among young adults, wherein the strong brand had greater visual attention compared with others.…”

An Adaptive Machine Learning Methodology Applied to Neuromarketing Analysis: Prediction of Consumer Behaviour Regarding the Key Elements of the Packaging Design of an Educational Toy

“…The literature offers examples of very different analytical techniques. It is common to find classical statistical techniques such as component analysis from ANOVA for the prediction of consumption preferences (Goto et al 2019), or the use of Naive Bayes to predict the acquisition of products (Taqwa et al 2015). Since 2016, it has become increasingly frequent that studies on EEG data on neuromarketing (in general) and on advertisement scoring (in particular) have begun to incorporate predictive machine learning techniques, such as SVM in combination with random forest classifiers (Libert and Hulle 2019), C4.5 classifier together with ANN (Morillo et al 2016) or SVM (Wei et al 2018).…”