Despite many common beliefs about the inherently evil nature of marketing, the main objective of marketing is to help match prod- ucts with people. Marketing serves the dual goals of guiding the design and presentation of products such that they are more compat- ible with consumer preferences and facili- tating the choice process for the consumer. Marketers achieve these goals by providing product designers with information about what consumers value and want before a product is created. After a product emerges on the marketplace, marketers attempt to maximize sales by guiding the menu of offerings, choices, pricing, advertising and promotions. In their attempts to provide these types of inputs, marketers use a range of market research techniques, from focus groups and individual surveys to actual market tests ��� with many approaches in between (see Supplementary information S1 (box)). In general, the simpler approaches (focus groups and surveys) are easy and cheap to implement but they provide data that can include biases, and are therefore seen as not very accurate1���4. The approaches that are more complex and therefore harder to implement, such as market tests, provide more accurate data but incur a higher cost, and the product, production and distribu- tion systems have to be in place for market tests to be conducted. There are some compromise approaches between these two extremes, which include simulated markets, conjoint analyses, markets for information and incentive-compatible pricing studies (see Supplementary information S1 (box)). As in all compromises, these approaches provide solutions with intermediate levels of cost, simplicity, realism and quality of data (TABLE 1). The incorporation of neuroimaging into the decision-making sciences ��� for example, neuroeconomics ��� has spread to the realm of marketing. As a result, there are high hopes that neuroimaging technology could solve some of the problems that market- ers face. A prominent hope is that neuro- imaging will both streamline marketing processes and save money. Another hope is that neuroimaging will reveal information about consumer preferences that is unob- tainable through conventional methods. Of course, with such high expectations, there is the accompanying hype. Several popular books and articles have been published that push a neuromarketing agenda, and there are now a handful of companies that market neuromarketing itself 5. In this Perspective, we aim to distinguish the legitimate hopes from the marketing hype. As such, we hope that this article serves the dual purpose of rec- ognizing the real potential of neuro imaging in business and providing a guide for potential buyers and sellers of such services. Why use brain imaging for marketing? Marketers are excited about brain imaging for two main reasons. First, marketers hope that neuroimaging will provide a more effi- cient trade-off between costs and benefits. This hope is based on the assumptions that people cannot fully articulate their prefer- ences when asked to express them explicitly, and that consumers��� brains contain hidden information about their true preferences. Such hidden information could, in theory, be used to influence their buying behaviour, so that the cost of performing neuroimaging studies would be outweighed by the benefit of improved product design and increased sales. In theory, at least, brain imaging could illuminate not only what people like, but also what they will buy. Thus far, this approach to neuromarketing has focused on this post-design application, in particular on measuring the effective- ness of advertising campaigns. The general approach has been to show participants a product advertisement, either in the form of a print advertisement or commercial, and measure the brain���s response in the form of a blood oxygenation level-dependent (BOLD) measurement, which is taken as a proxy for neural activation. The second reason why marketers are excited about brain imaging is that they hope it will provide an accurate marketing research method that can be implemented even before a product exists (FIG. 1). The assumption is that neuroimaging data would give a more accurate indication of the underlying preferences than data from standard market research studies and would remain insensitive to the types of biases that are often a hallmark of subjective approaches to valuations. If this is indeed the case, product concepts could be tested rapidly, and those that are not promising eliminated early in the process. This would allow more efficient allocation of resources to develop only promising products. Thus, the issue of whether neuroimaging can play a useful part in any aspect of market- ing depends on three fundamental questions, which we will address in this paper. First, can neuromarketing reveal hidden information that is not apparent in other approaches? Second, can neuromarketing provide a more efficient cost���benefit trade-off than other marketing research approaches? Third, can neuromarketing provide early information about product design? Revealing hidden information Brain activity and preference measurement. Allowing for the assumption in neuro- marketing that the brain contains hidden information about preferences, it is reason- able to set aside, for the moment, the issue of ���hidden��� and ask what relationships are known to exist between brain activity and expressed (that is, not hidden) preference. As it turns out, different methods of eliciting a person���s preference often result in different estimations of that preference3,4,6,7. science and society Neuromarketing: the hope and hype of neuroimaging in business Dan Ariely and Gregory S. Berns Abstract | The application of neuroimaging methods to product marketing ��� neuromarketing ��� has recently gained considerable popularity. We propose that there are two main reasons for this trend. First, the possibility that neuroimaging will become cheaper and faster than other marketing methods and second, the hope that neuroimaging will provide marketers with information that is not obtainable through conventional marketing methods. Although neuroimaging is unlikely to be cheaper than other tools in the near future, there is growing evidence that it may provide hidden information about the consumer experience. The most promising application of neuroimaging methods to marketing may come before a product is even released ��� when it is just an idea being developed. Pers P ectives 284 | APrIL 2010 | VOLuMe 11 www.nature.com/reviews/neuro �� 2010 Macmillan Publishers Limited. All rights reserved

This makes it difficult to know which method provides the truest measure of ���decision utility��� (that is, the expected utility, which would ultimately drive choice in the marketplace). It is clear that market tests give the most accurate answer, but having to run a market test on every product would defeat the purpose of market research ��� namely, to provide early and cheap information. Similarly, we suspect (and economists are certain) that methods that are incentive compatible are better than methods that are not. Incentive-compatible elicitation methods are methods that encourage the participant to truthfully reveal what is being asked of him because to do so would maximize the participant���s satisfaction (for example, he would earn the most money or receive the product he likes the best). In other words, it is in the participant���s interest to answer product-related questions truth- fully. However, using such methods is not always possible. One important question for the potential of neuromarketing is whether the neural signal at the time of, or slightly before, the decision (assumed to be a measure of decision utility) can be a good predictor of the pleasure or reward at the time of consumption (the ���experienced utility���)8. A second question is whether the link between these two signals holds even when the preference elicitation methods are not incentive compatible. If the answer to both of these questions is positive, neuromarketing could become useful for measuring preferences. Measurements such as willingness to pay (WTP) have only recently come under functional MrI (fMrI) examination. In one experiment, subjects bid on the right to eat snacks during the experiment. The amount they were willing to pay (a measure of deci- sion utility) correlated with activity levels in the medial orbitofrontal cortex (OFC) and prefrontal cortex (PFC)9,10. Interestingly, similar activation in the OFC has been observed when subjects anticipate a pleasant taste11, look at pretty faces12, hear pleasant music13, receive money14,15 and experience a social reward16,17. Such generally close corre- spondence in regional brain activity between the anticipation of rewarding events, the consumption of enjoyable goods and the willingness to pay for them suggests that the representation of expected utility may rely, in part, on the systems that evaluate the quality of the consumption experience. The theme of common systems for expectation and experience also applies to things that are unpleasant or even painful (although this involves a different network including the insula)18���21. Such similarities suggest that neuroimaging can become a use- ful tool in measuring preferences, particularly when incentive compatibility is important but there is no easy way to achieve it (for example, when the products have not been created). However, such similarities do not necessarily mean that brain activation is the same across different elicitation methods, and there are differences between the neural activation representing decision utility and that representing experienced utility14,22,23. This caveat aside, the generally close corre- spondence does suggest that neural activity might be used as a proxy for WTP in situations in which WTP cannot easily be determined ��� although this has yet to be demonstrated. Reverse inference and reward. The practice of measuring an increase in BOLD activity in a region such as the ventral striatum or OFC and then concluding that a ���reward- related��� process was active has become increasingly common. This form of deduc- tive reasoning is known as ���reverse infer- ence���24,25. Given the readiness of many to interpret brain activation as evidence of a specific mental process, it is worth examin- ing this type of inference. using a Bayesian analysis, it is possible to estimate the spe- cificity of activation in a particular region of the brain for a specific cognitive process. For example, Poldrack used the BrainMap data- base to analyse the frequency of activation of Broca���s area in language studies24. He found that activation of Broca���s area implied a Bayes factor of 2.3 for language involvement, which means that taking brain activity into account can make a small but significant Table 1 | comparison of selected marketing research approaches Focus groups Preference questionnaires Simulated choice methods Market tests What is measured Open-ended answers, body language and behaviour not suitable for statistical analysis Importance weighting for various product attributes Choices among products Decision to buy and choice among products Type of response process Speculative, except when used to assess prototypes The respondent must try to determine his decision weightings through introspection, then map those weightings into the response scale A hypothetical choice, so the same process as the actual purchase ��� but without monetary consequences An actual choice, with customers��� own money, and therefore fully consequential Typical use in new-product development processes Early on to aid general product design at user interface design for usability studies Design phase, when determining customer trade-offs is important Design phase, when determining customer trade-offs is important may also be used as a forecasting tool End of process, to forecast sales and measure the response to other elements of marketing, such as price Cost and competitive risk Low cost risk comes only from misuse of data by the seller Moderate cost and some risk of alerting competitors Moderate cost (higher if using prototypes instead of descriptions) and some risk of alerting competitors High cost and high risk of alerting competitors, plus the risk of the product being reverse engineered before launch Technical skill required Moderation skills for inside the group and ethnographic skills for observers and analysts Questionnaire design and statistical analysis Experiment design and statistical analysis (including choice modelling) Running an instrumented market and forecasting (highly specialized) Pers P ectives nATure reVIeWS | NeuroSCieNCe VOLuMe 11 | APrIL 2010 | 285 �� 2010 Macmillan Publishers Limited. 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