In 2011, neuroscientist T. Sigi Hale PhD was principal researcher (known in academia as principal investigator) leading a National Institute of Health-funded lab at UCLA. He left academia to enter the market research industry (he’s a principal neuroscientist at Alpha-Diver) and has helped pioneer the advent of the behavioral science movement.
From his first encounter with the world of ‘marketing science’ to his experience over the decade to follow, he observed conventions, themes, habits, and some downright mistakes being made in the space. Sigi also emerged as a foremost expert in exploring and applying what ‘behavioral science’ truly has to offer marketing insights and strategy.
While many in the neuro-marketing space are drawn to the sparkle and hype of tech ‘gadgetry’ such as eye tracking and biomonitoring (e.g. via smartwatches), Sigi’s observations provide a far more tangible, durable view of the ways insights professionals can better diagnose human behavior – and dramatically improve their results in real-world activation.
As Sigi discovered the marketing science world, his experience revealed three core insights that can guide marketers to truer, more effective brand activation – and which dramatically simplify the seemingly mysterious applications of neuroscience principles in business.
Observation 1: market research is model-free; academic research is model-based
The most fundamental – and likely the most instructive – difference between the world of academic research and that of market research is the fundamental philosophical divide between “model-free” and “model-based” research.
The vast majority of conventional market research remains model free. Under this philosophy, researchers go forth into the world to collect data (whether physically or digitally, qualitatively or quantitatively). They do so without a model, simply observing consumer behavior, asking people why they do one thing over another, or compiling data points related to various behaviors of interest. Then, they return to their huddle rooms, laptops, and offices to analyze the data; in essence, the effort to reveal “the model” from the data.
This effort to divine the signal from the noise of all the data they’ve amassed is doomed to introduce subjectivity, bias, and inaccuracies. What’s more, this approach fails to leverage all that’s known from previous research or existing knowledge.
Academic research is different
Academic research looks much different. In academia (such as Sigi’s university lab, and others), the first stage is to identify a reliable, established model and cultivate an associated set of hypotheses that will frame the data collection. Then, field work is driven not by collecting lots of disparate data, but rather mapping data collection back to the model and hypotheses. In other words, the model provides the guide as to what the researcher is looking for. Not only does this dramatically reduce the churn which so often paralyzes market research analyses, it “stands on the shoulders” of all previous research and learning to instill confidence, and assure objectivity in the findings and implications. While this comparison between these two approaches to research sounds simple, it’s quite cathartic in the implications.
Consider an analogy from everyday life: imagine you wanted to go for a hike. There are two basic choices you have…
Choice 1: consult a trail map, and follow an established path the destinations you’d like to explore.
Choice 2: forge off into the woods and simply wander around.
Aside from the obvious perils of choice 2, most people would agree that having a map is a more reliable, rewarding experience. And yet, most market research projects look a lot more like wandering in the woods than they do consulting a map. Model free research amounts to wandering in the natural habitats of consumers (both physical and digital) and hoping to stumble upon new discoveries.
Model based research leverages the trails blazed by predecessors to much more reliably guide explorers to their desired destinations and discoveries.
Observation 2: human beings are incapable of explaining WHY they do things
In a recent interview at the IAB Brand Disruption Summit, Gopuff founder and co-CEO, Rafael Ilishayev discussed the notable popularity on the platform of having pints of ice-cream delivered. Axiom reporter Sara Fischer, who conducted the interview, noted that surely this was driven by Gopuff’s reliably speedy delivery service, and consumer assurance that their ice-cream would not melt in transit.
While Ilishayev agreed with this practical component of the ice-cream category performance, he shared a more insightful explanation of Gopuff’s performance in ice-cream: simply, the fact that ordering ice-cream for home delivery spares the shopper from social judgement.
Picture yourself walking into a convenience store and awkwardly carrying three pints of Chunky Monkey to the front counter. Not exactly an occasion you’d rush to share on your Instagram feed. The cashier might even raise his eyebrows as you tumble the pints onto the counter.
Now picture tapping and swiping your way to order not three but FOUR pints on Gopuff, and having them dropped on your doorstep minutes later. No judgment, embarrassment, or hesitation – just pure enjoyment.
And yet, if the Gopuff team were to gather a quorum of young consumers in a traditional setting, such as a focus group, they’d insist that ordering from Gopuff is simply more convenient and time saving; in other words, they’d cite the rational, plausible reasons for their engagement with this behavior.
Social aspect of behavior is key
But the social aspect of the behavior is much more accurate as to they WHY of the behavior. To wit, Gopuff actually sells more pints of ice-cream in a given order than traditional retailers. All of this is an excellent demonstration of the fallibility of our perceptions as to the causation of our own behavior. Said plainly, from the brain-science perspective, human beings are incapable of explaining WHY we do (or don’t do) things.
And yet, most market research techniques continue to ask them to do so. Regardless of methodology, most marketing science asks people what they like, don’t like, want more of, want less of, and so on. And while research respondents try in earnest to provide accurate answers, they’re actually providing highly-filtered narratives, alibis, and perceptions. In other words, they’re reporting what they THINK they think.
Neuroscience, leveraging decades of past research, seeks out more durable (and often subconscious) measures of the true factors that explain, and even predict human behavior.
Observation 3: behavior is predictable
This statement is a third-rail for many. And neuroscience does not proport to amount to ESP, predicting whether a shopper will turn left or right; choose Coke over Pepsi, etc.
Rather, this definition of predictability relates to the durable aspects of human behavior. Most everyone can relate to the experience of walking around a corner and being startled by someone unexpectedly appearing. When this happens, what does the person who’s startled do? You know the answer intuitively – they jump, flinch, or otherwise physically recoil from the unexpected surprise. This is predictable.
Likewise, there are predictable lenses through which a person views everyday decisions, including purchase decisions.
- The PRACTICAL lens – making decisions based on knowledge and comparing options.
- The SOCIAL lens – making choices based on tribal knowledge and collective social agreement.
- The EXPERIENTIAL lens – personally exploring and discovering new, exciting experiences.
- The INSTINCTUAL lens – simply following natural impulses to do what feels good in the moment.
Just as hiking with a map still requires endurance and effort, it takes some work to identify and quantify which of the above is driving consumer behavior within a given context – but they’re surely present. And, when you’re studying a marketspace with an established model of WHY people behave in the ways they do, business building discoveries become a much more reliable and enjoyable endeavor.
The intersection of neuroscience and human behavior offers potent guidance in serving consumers better as marketers create more effective activations. Teams need not chase the shiny objects of highly-technical approaches – whether AI, eye tracking, EEG, biometrics, or the like. And, while neuromarketing is simpler than many perceive, it does require discipline in changing bad habits of many legacy marketing research practices.