Ariel Telpaz, Ryan Webb, and Dino J. Levy. Using EEG to Predict Consumers' Future Choices

Ariel Telpaz, Ryan Webb, and Dino J. Levy. 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. 


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). 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. 2009; Levy et al. 2011; Smith et al. 2014; Tusche et al. 2010). We note that each of these “non-choice” studies also find activity in other areas, varying from the dorsomedial prefrontal cortex (dmPFC), the insula, the anterior and posterior cingulate cortex (ACC, PCC), hippocampus, and parietal cortex. However the mPFC and Striatum are the only regions common across these studies, and the only regions identified in the meta-studies referenced above (which include the “non-choice” studies). 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 Neural Random Utility Model (Webb et al. 2013). This model extends the choice prediction results of the familiar Random Utility framework (Becker et al. 1964; McFadden 1973) to neural measurements, with the important distinction that there are no unobservable latent variables. In doing so, it characterizes neural sources of the stochasticity observed in choice behavior (Huettel and Payne 2009; Yoon et al. 2009) and improves upon choice prediction results.

These results are in line with many studies demonstrating that activity in the mPFC and striatum correlate with various value-related attributes, and correlate with known methods for estimating the values subjects place on choice objects - ranging from consumable goods, to money lotteries, charitable donations, durable goods, social preferences, and political preferences (for reviews see: Bartra et al. 2013; Grabenhorst and Rolls 2011; Kable and Glimcher 2009; Levy and Glimcher 2012; Padoa-Schioppa 2011; Platt and Huettel 2008; Rushworth 2008). Importantly, these same areas are also active for the valuation of novel goods that the consumer has never before experienced (Barron et al. 2013).

However the applicability of these findings to consumer marketing research remains in question, with the current cost of obtaining and operating an fMRI scanner preventing their broad application. Most prominently, an fMRI scanner has a very large fixed cost component. It is expensive to purchase (~$1M-$2M), expensive to keep operational ($100K-$150K for insurance, maintenance, and support staff), expensive to locate (requiring a customized room/building), and immobile. Compared to the fixed-cost component, the marginal cost of running an fMRI experiment is relatively low, but still on the order of $500 per experiment. These relatively high costs severely limit the use of fMRI in both academic and commercial applications.

There are also technical limitations to fMRI, primarily a relatively low temporal resolution on the order of 2 seconds (Huettel et al. 2004). This resolution makes it difficult to examine the rapid dynamics of neural signals that are relevant for the neural mechanisms underlying value representation. A faster sampling rate might convey predictive information for consumers’ valuation and choice, information that is blurred by fMRI. For instance, consumers can make decisions for consumable goods in as little as a third of a second (Milosavljevic et al. 2011). It may well be the case that a particular, rapid, component of the neural signal has more indicative and predictive power for consumers’ preferences than the more global signal of fMRI.

To address these concerns, in our study we use an alternative neuroscientific tool called the electroencephalogram (EEG). From a fixed cost standpoint, EEG is orders of magnitude cheaper than fMRI (roughly $50K compared to $1-$2M per unit), requires little support and maintenance, and is widely available in neuroscience laboratories. The marginal cost of running an EEG experiment is only a few dollars, more than an order of magnitude cheaper than an fMRI experiment. From a technical standpoint, EEG also has a very high sampling rate (on the order of 1-2ms, Luck 2005) which enables identification of very fast changes in the neural signal over short time scales (on the order of 50ms, Luck 2005) that may carry strong predictive information about consumer preferences and choice behavior.

In this article, we rigorously examine if EEG measurements of neural activity — recorded while subjects view individual consumer goods on a computer screen without making any choices — can be used to predict both rank-ordered preference ratings and actual choices in a subsequent behavioral choice task. We demonstrate that this is indeed the case. We show that specific spatial and temporal components of the EEG signal correlate with subjects’ future rank- ordered preferences and can be used to predict subsequent choices. To our knowledge, this is the first EEG study to demonstrate a basic principle: we can use measured neural activations to predict choices without the need to ask consumers anything.

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Keywords: Neuromarketing

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