Using EEG to Predict Consumers' Future ChoicesJournal of Marketing Research

About

Authors
Ariel Telpaz, Ryan Webb, Dino J. Levy
Year
2015
DOI
10.1509/jmr.13.0564
Subject
Marketing / Economics and Econometrics / Business and International Management

Similar

Effects of inflation on consumer choice

Authors:
Yehoshua Liebermann, Meyer Ungar
1983

Russian consumers’ motives for food choice

Authors:
Pirjo Honkanen, Lynn Frewer
2009

Bank Characteristics and Consumer Bank Choice

Authors:
Leanna Lawtor
1988

An Information Processing Theory of Consumer Choice

Authors:
Ivan Ross, James R. Bettman
1979

Zum Verhalten des Murmeltieres (Marmota marmota (L.))

Authors:
Detlev Müller-Using
1956

Text

© 2015, American Marketing Association 

Journal of Marketing Research 

PrePrint, Unedited 

All rights reserved. Cannot be reprinted without the express  permission of the American Marketing Association. 

Using EEG to predict consumers’ future choices

Ariel Telpaz1, Ryan Webb2, and Dino J Levy3, 4

Ariel Telpaz, PhD:

Affiliation: 1Faculty of Industrial engineering and management, Technion - Israel Institute of Technology

Address:

Technion City Haifa, 32000, Israel

Tel: 972-546969568

Email: arielt@tx.technion.ac.il

Ryan Webb, PhD:

Affiliation:

Rotman School of Management, University of Toronto

Address: 105 St. George St, Toronto, Ontario, Canada, M5S3E6

Tel: 4169784418

Email: ryan.webb@utoronto.ca

Dino J Levy, PhD:

Affiliation: 3Marketing Department, Recanati Business School and 4Sagol School of Neuroscience, Tel-Aviv

University

Address: 55 Haim Levanon st.

Tel Aviv University, Ramat Aviv 69978, Israel

Tel: 972-3--6409565

Email: dinolevy@post.tau.ac.il 2

Using EEG to predict consumers’ future choices

It is now established that neural imaging technology can predict preferences over consumer products. However the applicability of this method to consumer marketing research remains in question, partly because of the expense required. In this article, we demonstrate that neural measurements made with a relatively low-cost and widely available measurement method —

Electroencephalogram (EEG) — can predict future choices over consumer products. In our experiment, subjects viewed individual consumer products in isolation, without making any actual choices, while we measured their neural activity with EEG. After these measurements were taken, subjects then made choices between pairs of the same products. We find that neural activity measured from a mid-frontal electrode displays an increase in the N200 component and a weaker theta band power that correlates with a more preferred good. Using state-of-the-art techniques for relating neural measurements to choice prediction, we demonstrate that these measures predict subsequent choices. Moreover, the accuracy of prediction depends on both the ordinal and cardinal distance of the EEG data: the larger the difference in EEG activity between two goods, the better the predictive accuracy.

Keywords:

EEG, Choice prediction, Consumer Neuroscience, Theta power, N200 3

INTRODUCTION

Over the past 15 years, our understanding of the neuroscience underlying decision-making has advanced rapidly (see Glimcher 2011; Glimcher and Fehr 2013 for reviews), raising hopes that measurements of neural activity — and a deeper understanding of neural mechanisms — can be applied to marketing research. Two promising avenues for such a contribution have been previously identified (Ariely and Berns 2010). First, there is the possibility that insight from neuroscience might improve the marketing message for existing products. Second, there is the possibility that neuroscience can provide insight into how products are valued before they even exist in the marketplace, improving product design.

Both of these avenues rely on the proposal that neuroscience will reveal information about consumer preference that is unobtainable through conventional methods. There is certainly room for improvement. Previous studies have demonstrated that different preference elicitation methods can result in different subject responses (Buchanan and Henderson 1992; Day 1975;

Griffin and Hauser 1993; McDaniel et al. 1985). The use of questionnaires for evaluating consumers’ preferences, attitudes, and purchase intent can result in a biased or inaccurate result (Fisher 1993; Neeley and Cronley 2004). A verbal statement of preferences can also generate conscious or unconscious biases. In some cases, consumers decline to state their actual preferences (for reasons such as discretion or shame), and in other cases consumers cannot verbalize a justification for their preferences (Johansson et al. 2006; Nisbett and Wilson 1977). 4

It can also be difficult (or sometime impossible) to directly elicit a consumer’s preferences through choices. This may arise due to high product cost, ethical considerations, or the fact that the product does not yet exist. This forces the marketer to examine hypothetical choices with hypothetical rewards, yielding a potential bias in which responses are overstated compared to incentive-compatible choices (Blumenschein et al. 2008; Cummings et al. 1995; Johannesson et al. 1998; List and Gallet 2001; Murphy et al. 2005) or plans (Ariely and Wertenbroch 2002;

O'Donoghue and Rabin 2008; Tanner and Carlson 2009). These results are bolstered by neuroscientific evidence suggesting variations in value computations between real and hypothetical choice situations (Kang and Camerer 2013; Kang et al. 2011).

Since the marketing message in many campaigns is presented with the hope that it will affect consumers’ preferences, attitudes, and/or actual purchases sometime in the future, all the factors above confound the task of evaluating consumer preferences and limit the ability to predict choice at the time of the purchasing decision. Therefore, finding a cost-effective tool that can predict consumers’ future behavior in response to marketing messages and forecast future preferences over novel goods will be beneficial in consumer marketing applications.

Substantial recent progress directly addresses these two avenues for neuroscientific methods in marketing research. Evidence from functional magnetic resonance imaging (fMRI) suggests that the same brain areas that represent values in a choice situation – primarily the medial prefrontal cortex (mPFC) and striatum (for three recent meta-studies, see Bartra et al. 2013;

Clithero et al. 2009; Levy and Glimcher 2012) - also represent values when subjects are evaluating individual goods in the absence of choice behavior (Falk et al. 2012; Lebreton et al. 5 2009; Levy et al. 2011; Smith et al. 2014; Tusche et al. 2010).1 The magnitude of these signals correlate with the trial-by-trial likelihood that a consumer will choose a particular good, and can be used to predict subsequent choices using a fully cardinal choice model referred to as the