Neuromarketing for Companies: Can it help?

Neuromarketing is a relatively new field of marketing research which focuses on consumers' cognitive and affective response to marketing stimuli. Neuromarketing is actually a child of the eternal corporate need to sustain a decision by all possible means when the pressure is way over the possibility of a decident to fight failure. Google, Coca-Cola, BMW, Procter & Gamble, Motorola, CBS are a few of the companies who have experimented neuromarketing for the past years. We have previously referred to neuroscience and neuromarketing research here and here, yet academics are still sceptical when it comes to predicting the future of this new marketing method. As a matter of fact, when i asked Prof. Alan Wilson, University of Strathclyde, about neuromarketing research a couple of weeks ago, his cautious response brought me down to earth: "Well, can neuroscience and neuromarketing provide, in the long term, any unique additional value to marketeers, compared to other marketing methods?" Well, i think it's too early to know the answer, but, at least let's try to discover some opportunities that neuromarketing may provide for marketeers, if any.

Trust

Trust is an issue which has been increasing in prominence within marketing. However, while consumer trust in brands and products is off course vital, marketing research has investigated trust on many other levels. Inter-organisational dealings such as joint ventures, strategic alliances and B2B buyer-seller dyads depend on mutual trust between parties. On one hand, consumer trust in marketing claims is crucial if they are to be believed, and ultimately lead to purchase behavior from consumers. The social utility of trust is clear when one considers that firms selling ‘fair trade’, ‘organic’, or other socially beneficial products must rely on consumer trust in their claims for success. Furthermore, in an organisational context, relationships depend on mutual trust between the parties. Without trust, opportunistic behavior dominates interactions, negating the possibility of long-term relationships between parties and again leading to a suboptimal situation for all. Marketing research has commonly conceptualized trust as more than a simple rational economic calculation, and it seems likely that neuroscientific methods can provide considerable insight into the nature and development of trust.

Neuroeconomic research has begun to investigate concepts of trust beyond rationality in recent times. Neuromarketing research can also be insightful to the investigation of trust. First and foremost, it is clear that, despite the centrality of trust to marketing relationships at a number of levels, controversies over the very nature of trust still exist. Neuroimaging is likely to offer considerable insight here. Research suggests that the caudate nucleus, which is often active when learning about stimuli–response relations, is involved in experimental games requiring some kind of trust. Yet is trust a simple response to a repeated positive stimulus, or something more? More interestingly, is the trust a buyer says they have in a seller, or a consumer in a product claim, similar in terms of the nature and location of brain activity to the trust that individual says they have in a close friend or family member? 

In particular, measuring both the spatial and temporal characteristics of neuronal activity may be important. For example does trust in an advertising claim or new business partner require increased information processing effort and time than trust in a long-term friend? This will have important implications as to the nature of trust. Furthermore, is consumer trust in claims relating to a product similar to a purchasing agent's trust in a contract with a supplier, and in turn is this of the same nature as the purchasing agent's trust in the individual sales executive they have negotiated with? Can trust be transferred from an organisation to a representative of that organisation? Finally, does trust evolve throughout the course of an inter-organisational relationship, or with continuing loyalty of a consumer to a single brand? Is trust ever truly existent in short-term marketing relationships? Exploring and understanding such questions about the nature of trust will then lead to greater ability to explore the antecedent factors to trust, and an ability to enhance firms' ability to build trust with customers and collaborators for mutually beneficial outcomes.

Pricing

Pricing is a key tool used by organisations in the positioning of their products. Marketing research has investigated the effects of price on consumers. Despite the amount of academic knowledge available, companies appear to use little of it when setting prices, leading to suboptimal situations for both consumers and firms. Understanding the psychology of pricing is of crucial importance if firms are to make optimal decisions and in fact has considerable utility in a broader sense. Pricing research has implications for how we understand information processing in any decision context where resources and information are scarce and costs must be weighed against benefits. Recent behavioral research for example has explored errors made by consumers when they process prices ending in 0.99 rather than a whole number -suggesting that individuals pay less attention to later numbers in a sequence. At this stage however, almost all pricing research is behavioral in nature, and relies on ‘assumptions’ about what actually occurs when individuals process pricing information.


In fact, pricing seems to lend itself almost perfectly to neuroimaging research. For example, simultaneously exploring the temporal and spatial nature of brain activity may help us understand exactly why prices such as ‘$4.99’ are perceived as significantly cheaper than those such as ‘$5.00’. Do individuals really ignore the final two digits, or are they processed in a different manner or at a later time - for example only when detailed comparative decisions must be made? Furthermore, do time or other pressures influence the processing of prices? 

Furthermore, neuroimaging looks likely to provide considerable insight into the nature of price information. Is the price of products a purely rational piece of information, or does it have emotional and/or reward-based connotations? It seems likely that the price of a basic product such as sugar is very different in nature from the price of a conspicuous product such as a Nike sports shoe, or a BMW sports car, which should be evidenced in changes in the location of brain activity when these prices are viewed alongside their associations (Source:UCLA). Research such as this will allow us not only to understand how prices are processed, but will afford insight into all situations where seemingly rational information is processed in decision-making situations.

Source: Forbes

Here is a recent pricing example of neuromarketing research: Kai-Markus Müller of Stuttgart-based The Neuromarketing Labs, using EEG brain wave measurement, gauged the emotional reaction of consumers to different prices for a small cup of coffee, which costs €1.80 ($2.45) at a Stuttgart Starbucks.The firm claims their results show that our brains reject prices that are too low or too high as being unrealistic, and says that the optimal price point for that small coffee in Stuttgart would be €2.40 ($3.25). Starbucks shareholders might like the idea that at least some of the firm’s products could be priced higher, but some caution is in order. For commodity items like coffee, lower prices tend to increase sales while higher prices discourage them. It would be quite unexpected for a higher price to increase unit sales for this type of product (Source:Forbes).

Conclusion

Trust and pricing were just two examples where neuromarketing/neuroimaging tools can assist marketeers and organizations further understand consumers. Neuromarketing research itself is constantly evolving, both in terms of technology as well as insights into exactly what activity and processes in various areas of the brain actually mean. As technology evolves, we will be able to measure frequency, temporal, and spatial characteristics of brain activity more accurately and in a complimentary fashion, potentially leading to new insight into what were previously well-accepted brain functions and areas of activity. I hope that neuromarketing will offer marketeers much insight into how humans behave during what is a large part of our modern lives.

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Facebook Likes and Human Behavior

Earlier in 2013, the Psychometrics Centre of the University of Cambridge conducted an impressive research about how private attributes are predictable from digital records of human behavior, like Facebook likes. Digitally mediated behaviors like Facebook likes can easily be recorded and analyzed, fueling the emergence of computational social science and new services such as personalized search engines and targeted online marketing. However, the widespread availability of extensive records of individual behavior, together with the desire to learn more about customers and citizens, presents serious challenges related to privacy and data ownership. 

The researchers (Kosinski, Stinwell & Graepel) showed that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including sexual orientation,ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases.

Predicting individual traits and attributes based on various cues, such as samples of written text, answers to a psychometric test, or the appearance of spaces people inhabit, has a long history. Human migration to digital environment renders it possible to base such predictions on digital records of human behavior. It has been shown that age, gender, occupation, education level and even personality can be predicted from people’s Web site browsing logs. Similarly, it has been shown that personality can be predicted based on the contents of personal web sites, music collections, properties of Facebook or Twitter profiles such as the number of friends or the density of friendship networks, or language used by their users. Furthermore, location within a friendship network at Facebook was shown to be predictive of sexual orientation.

The research 

The Psychometrics Centre, University of Cambridge

The study was based on a sample of 58,466 volunteers from the United States, obtained through the my Personality Facebook application, which included their Facebook profile information, a list of their Likes (n = 170 Likes per person on average), psychometric test scores, and survey information. Users and their Likes were represented as a sparse user–Like matrix, the entries of which were set to 1 if there existed an association between a user and a Like and 0 otherwise. The dimensionility of the user–Like matrix was reduced using singular-value decomposition (SVD). 
Numeric variables such as age or intelligence were predicted using a linear regression model, whereas dichotomous variables such as gender or sexual orientation were predicted using logistic regression. In both cases,the researchers applied 10-fold cross-validation and used the k =100 top SVD components. For sexual orientation, parents’ relationship status, and drug consumption only k = 30 top SVD components were used because of the smaller number of users for which this information was available.

Prediction of Dichotomous Variables

The Psychometrics Centre, University of Cambridge

The aforementioned figure shows the prediction accuracy of dichotomous variables expressed in terms of the area under the receiver-operating characteristic curve (AUC), which is equivalent to the probability of correctly classifying two randomly selected users one from each class (e.g., male and female). The highest accuracy was achieved for ethnic origin and gender. African Americans and Caucasian Americans were correctly classified in 95% of cases, and males and females were correctly classified in 93% of cases, suggesting that patterns of online behavior as expressed by Likes significantly differ between those groups allowing for nearly perfect classification. Christians and Muslims were correctly classified in 82% of cases, and similar results were achieved for Democrats and Republicans (85%). Sexual orientation was easier to distinguish among males (88%) than females (75%), which may suggest a wider behavioral divide (as observed from online behavior) between hetero and homosexual males.
Good prediction accuracy was achieved for relationship status and substance use (between 65% and 73%). The relatively lower accuracy for relationship status may be explained by its temporal variability compared with other dichotomous variables (e.g., gender or sexual orientation).

Predictive Power of Likes

Individual traits and attributes can be predicted to a high degree of accuracy based on records of users’ Likes. The best predictors of high intelligence include “Thunderstorms,” “The Colbert Report,” “Science,” and “Curly Fries,” whereas low intelligence was indicated by “Sephora,” “I Love Being A Mom,” “Harley Davidson,” and “Lady Antebellum.” Good predictors of male homosexuality included “No H8 Campaign,” “Mac Cosmetics,” and “Wicked The Musical,” whereas strong predictors of male heterosexuality included “Wu-Tang Clan,” “Shaq,” and “Being Confused After Waking Up From Naps.” 

Accuracy of selected predictions as a function of the number of available Likes. Accuracy is expressed as AUC (gender) and Pearson’s correlation coefficient (age and openness). About 50% of users in this sample had at least 100 Likes and about 20% had at least 250 Likes. Note, that for gender (dichotomous variable) the random guessing baseline corresponds to an AUC = 0.50. The Psychometrics Centre, University of Cambridge.

Moreover, note that few users were associated with Likes explicitly revealing their attributes. For example, less than 5% of users labeled as gay were connected with explicitly gay groups, such as No H8 Campaign, “Being Gay,” “Gay Marriage,” “I love Being Gay,” “We Didn’t Choose To Be Gay We Were Chosen.” Consequently, predictions rely on less informative but more popular Likes, such as “Britney Spears” or “Desperate Housewives” (both moderately indicative of being gay).

Conclusions

Similarity between Facebook Likes and other widespread kinds of digital records, such as browsing histories, search queries, or purchase histories suggests that the potential to reveal users’ attributes is unlikely to be limited to Likes. Moreover, the wide variety of attributes predicted in this study indicates that, given appropriate training data, it may be possible to reveal other attributes as well.
Predicting users’ individual attributes and preferences can be used to improve numerous products and services. For instance, digital systems and devices (such as online stores or cars) could be designed to adjust their behavior to best fit each user’s inferred profile. Also, the relevance of marketing and product recommendations could be improved by adding psychological dimensions to current user models. For example, online insurance advertisements might emphasize security when facing emotionally unstable (neurotic) users but stress potential threats when dealing with emotionally stable ones. 

Moreover, digital records of behavior may provide a convenient and reliable way to measure psychological traits. Automated assessment based on large samples of behavior may not only be more accurate and less prone to cheating and misrepresentation but may also permit assessment across time to detect trends. Moreover, inference based on observations of digitally recorded behavior may open new doors for research in human psychology.

On the other hand, the predictability of individual attributes from digital records of behavior may have considerable negative implications, because it can easily be applied to large numbers of people without obtaining their individual consent and without them noticing. Commercial companies, governmental institutions, or even one’s Facebook friends could use software to infer attributes such as intelligence, sexual orientation, or political views that an individual may not have intended to share. One can imagine situations in which such predictions, even if incorrect, could pose a threat to an individual’s well-being, freedom, or even life. Importantly, given the ever-increasing amount of digital traces people leave behind, it becomes difficult for individuals to control which of their attributes are being revealed.

There is a risk that the growing awareness of digital exposure may negatively affect people’s experience of digital technologies, decrease their trust in online services, or even completely deter them from using digital technology. It is our hope, however, that the trust and goodwill among parties interacting in the digital environment can be maintained by providing users with transparency and control over their information, leading to an individually controlled balance between the promises and perils of the Digital Age.

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