Neuroscience is more than emotion
I recently attended and spoke at the NIMF (Non-conscious Impact Measurement Forum, http://nimf.insightinnovation.org/) in NYC on November 6th and was delighted that the focus was on how to maximize the advances in neuro-related technology with proper research design and validation. Many of the key players and clients in the field of “neuromarketing” were in attendance and this may be an indication of a shift that is much needed to help legitimize and advance the field. I see myself as a neuroscientist first and market researcher second. And as such, I’m not a fan of the term "neuromarketing”. I prefer to use "applied consumer neuroscience" to describe the application of neuroscience to business. The field is definitely maturing and I do believe there is a real place for neuroscience, done properly, in business and industry.
But something else also caught my attention at the meeting and is something I notice in nearly all uses of neuroscience in market research and industry… the focus on emotion.
And of course, it makes sense. We are using neuroscience tools and methods to uncover the “non-conscious” and non-cognitive motivators behind purchase intent and product liking, etc. And yes, much of that can be emotionally driven motivators. But are we “neuromarketers”, putting all of our eggs in one basket and missing the larger picture of opportunity that neuroscience can offer?
I think we need to take a step back. Neuroscience isn’t about emotions. Neuroscience is more the study of the mechanisms behind behavior which may include emotion, but is definitely not limited to it. In fact, Wikipedia.com says “Neuroscience (http://en.wikipedia.org/wiki/Neuroscience) is the scientific study of the nervous system.” No mention of emotion there. So if we take a step back and reframe how we see neuroscience can be used in market research, what do we have? If not emotion, then how can looking at the brain and physiology help design better products and communications for consumers?
One trend in “neuromarketing” is the neuro-measurement of a “realistic” shopping experience. This can be problematic on many levels, including the amount of noise that is generated in a “real-world” shopping experience that will be difficult, if not impossible to tease out of sensitive physiological measures like EEG, HR, EDA, etc. But a bigger problem is that since there is no experimental control of the situation, it is impossible to make any conclusions from the data. Essentially, each shopper measured is their own study, meaning not generalizable for a larger population. The result is much like case studies (http://en.wikipedia.org/wiki/Case_study) in medicine or psychology, where a doctor may write up a paper on his or her experience with one patient with one disease. While the information may be helpful in designing real studies using larger sample sizes, the information you can obtain from one person is very limited.
It’s a question of validity: are you measuring what you think you are measuring? But even further, is what you are measuring useful to the question?
In looking at shopper experience, is it useful to analyze the “brainwaves” (associated with emotion) of the overall experience? Or is it more useful to take a step back and examine the points of interest that are common for all consumers? For example, if we were trying to sell candy and wanted to know why a shopper might not buy our candy on their trip to the grocery store, will getting the brainwaves of the consumer tell us why they aren’t picking up our candy? Probably not. But if we step back and think about the barriers preventing the shopper from adding candy to their cart (“Not in my budget”, “Didn’t see it”, “Not on my shopping list”, etc.) we can deep dive into the why. And that is what neuroscience and psychology is good for in market research: deep diving into the drivers of behavior. With a properly designed study, it is possible to examine these “penetration barriers” when it comes to shopper behavior.
Emotionally speaking (http://visual.ly/color-emotion-guide), blue has been associated with trust and red with excitement. But how does this help you choose your bottle color? And if you neuro-measure (I’ll use this term to encompass EEG or biometrics or eye tracking, etc) a person’s response to seeing blue or red bottles, you may see some difference, but not much. And what do those differences really mean?
So again, if we take a step back and rethink the question and the situation, we see that the real question is whether the color blue or red help convey a message to the consumer. This message could be about the brand or the concept. But the important part is that the messages (either from the color or the label or the brand or concept) must all be congruent. When they aren’t, this can be confusing to the consumer. For example, neon colors and exclamation points on packaging for a relaxing lotion may confuse the consumer and discourage him/her from buying it.
But if we combine behavioral research design with neuro-measures, we can better explain and examine the congruency of packaging design. For example, through psychological concepts like “priming” and/or implicit association testing, we can see if a red bottle better connects to the concept and brand than a blue bottle. So the color blue in one context might mean “trust” while in another context it might mean “calm”. Context is very important when considering how packaging affects the perception of products.
A large part of neuromarketing work is around advertising and media communications. How does the brain react to tv commercials, print ads, online ads, etc .? The real importance of this work is the time-locking of neuro-measure graphs (peaks and valleys) to particular events in the stimulus. For example, what was the brain’s reaction when the brand logo appeared? What were they feeling when they saw the product or the spokesperson? Many people talk about the emotional bond generated by advertisements that translates to liking.
Neuro-measures being used examine the emotional impact of advertisements. But it’s important to understand the context and meaning behind these changes in “emotional brainwaves” (or whatever you’re measuring and – no, it’s not clear that these brainwaves are emotion specific). If we focus only on emotion, then we are missing a big piece of the consumer experience when it comes to commercials. Not all commercials are happy and a human reaction is typically more complicated than any single emotion. Therefore, higher-order psychological understanding of consumer response is needed for real interpretation for all elements of the stimulus (for example the overall context of the ad). Like what does “engagement” mean during a “branding moment” and does the “branding moment” have “stopping power”. Higher-order psychological constructs require more work to study – it’s not as clear as looking at a graph. And this can make high-throughput neuromarketing research difficult. The push for “faster, cheaper, smaller” neuromarketing research results in faster, cheaper and smaller results.
Product consumer research examines how consumers perceive products holistically as well as by attributes. The importance of being able to tease apart product attribute effects is that this understanding can help with product innovation. Understanding the impact of taste, smell, sight, sound and feel on overall product perception can help in product design. And perception isn’t all about emotion. For example, how will this flavor innovation affect brand perception? And is it congruent to packaging design? Is the skin feel of this lotion giving benefits that signal moisturization? Does this smell connote clean? How does my fragranced product perform against my competitor’s fragranced product?
In a larger viewpoint, you can see how understanding consumer needs for products can help build better products (a top-down as opposed to a bottom-up approach to research) and consumer technical models for innovation. If we start with understanding our consumer using a combination of qualitative and quantitative research with applied consumer neuroscience, then we can build a real story into the drivers of behavior and liking of consumer products.
Choice modeling (http://en.wikipedia.org/wiki/Choice_modelling) is something that market researchers, psychologists and statisticians use to understand the decision process that people go through when they encounter products or experiences. The most powerful piece of choice modeling is proper experimental design (laying out how different choices are experienced by the consumer). But then the question becomes how do we understand their choice? For example, what is the greatest driver of purchase? Is it cost or function? Is it brand recognition or promotion? Emotion is definitely a big driver of decision making, but not the only one. Experience, perception and context can also play a large role in driving decision behavior. And so, seeing the emotional reaction to product choice is not enough.
Quantitative researchers can use tools like Max-Diff (Maximum Difference Preference Scaling), Conjoint Analysis and Bayesian Analysis to help model consumer behavior in making choices. Psychologists use tools like implicit association testing (IAT), priming paradigms and other cognitive measures to understand drivers of behavior. When these tools are used in conjunction with neuroscience, real insights into the mechanisms of that behavior can be uncovered. For example, if we find what price works best with a product via conjoint analysis, how best is that presented to the consumer? How does that affect brand perception? Combining these powerful tools results in powerful insights into communications with consumers.
The avenues for application of neuroscience in business are countless. And the overarching theme is that if you start with the question (what you want to know about your product, communication or consumer) then you can design a proper study that will answer that question in a way that is useful and actionable to your business needs.
My big take away from the NIMF meeting is that if we research providers take a step back and really listen to the questions that clients are asking, then we can help to design methodologies that answer real questions to help real innovation and industrial decisions.