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Neurons is the world’s leading consumer neuroscience company. Since 2013, Neurons has worked with Fortune-500 companies such as Google, Coca-Cola, Mercedes, Capital One, and Ikea to optimize every part of their customer journey, including advertising, retail, user experience, innovation, tech, and beyond. 
 
 
Neurons offers a suite of neuroscience products that comprise Predict, a consumer behavior prediction AI tool, and Explore, a continuous discovery platform for testing the commercial performance of assets with real user data. Neurons’ tools assess and measure the attention, emotional resonance, cognition, and memory that any asset exacts in any digital context. Visit neuronsinc.com for more.

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Neurons is the world’s leading consumer neuroscience company, combining neuroscience and AI to predict consumer responses. 

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<img decoding="async" class="alignnone size-full wp-image-13272" src="https://wp.greenbook.org/mr/wp-content/uploads/2014/12/neurobiz.jpg" alt="neurobiz" width="718" height="314" />
By Thomas Zoëga Ramsøy
Is brain scanning data invalid? A recent meta-analysis study published in the esteemed journal PNAS (<a href="http://www.pnas.org/content/113/28/7900.abstract">http://www.pnas.org/content/113/28/7900.abstract</a>) has suggested that decades of brain research studies should be questioned! An estimated 40,000 studies using the established and used method fMRI (or functional Magnetic Resonance Imaging) may be hampered by a software bug that has gone undetected for too long.
In the study, several brain scanning studies of the so-called “resting-state data” suggest that as many as 70% of “positive findings” may indeed be false. That is, 7 out of 10 reported “brain blobs” found when people are resting inside a brain scanner may in fact never be there!
This is a very interesting finding indeed. It sure reminds us of the need for replications in science, a part of scientific housecleaning that is never honoured as part of a scientist&#8217;s career. It is worth noting that the finding pertains to only one type of neuroimaging studies, fMRI (and most likely only the so-called blood oxygenation level dependent, or BOLD, fMRI), and not other methods such as electroencephalography (EEG) or magnetoencephalography (MEG). Still, the problem could be substantial, as many of our insights about the brain may be unsupported.
But is it really that bad? There is a saying that “extraordinary claims require extraordinary evidence” and here, we have a problem. Indeed, one major caveat of the study is the assumption that the resting-state data, used in this meta-analysis, can be treated as equal between individual. After all, when asking people to rest (often between more active tasks), we can assume that the brain at rest is similar for us all, correct? Instead, decades of psychological studies of the mind at rest – including mind-wandering and Task Unrelated Images and Thoughts – have shown extreme degrees of variation both within and between individuals. What follows are inspired by a yet unpublished manuscript that I have co-authored with Prof. Bernard J Baars from the Neuroscience Institute, which I hope can help put a perspective to the intended killing of fMRI.
The restless resting state
Considerable attention has been called to a putative “resting state” of the brain, observed during designated rest breaks in neuroimaging experiments. Robust brain differences have been found between task-related (TR) and task-unrelated (rest break) conditions. Some scientists speculate that TU brain activity may reflect a special state of the brain, sometimes called a “resting state,” “default mode,” or a “baseline condition”. We suggest that the explanatory use of these terms is premature. Instead, a large empirical literature points to an alternative account: people during rest breaks are reverting to their normal, spontaneous stream of thought, which is subjectively rich and self-relevant, highly variable, multimodal, often explicitly goal-directed, and probably functional. Even the word “state” may be premature, since it suggests a stable condition of the brain. Instead, some five decades of psychological studies shows TU activities to be dynamic, heterogeneous, shaped by emotional and motivational primes, and focused on current life concerns. The term “spontaneous thought” might therefore be a more accurate label for task-unrelated brain activity.
The spontaneous brain
Scientists tend to be cautious about self-reported experiences, but some facts about consciousness are as predictable as objects falling in earth gravity. The entire field of psychophysics relates precisely controlled stimuli to reliable subjective reports. Even endogenous events can be reported reliably, as in experimental studies of verbal rehearsal and visual imagery, and their brain correlates. Less well-known is almost a half century of thought-sampling studies, using real-time reports under known conditions. One of the oldest psychological demonstrations is to simply close one&#8217;s eyes and try to stop the flow of thought. We can read sources over some 26 centuries reporting how difficult that is to accomplish. The flow of spontaneous experience appears to have its own persistence and &#8220;urgency,&#8221; as William James wrote a century ago. Some five decades of systematic psychological research supports the notion that spontaneous cognition is not random, but reflects “current, personal concerns”. There is also evidence for repetitive long-term themes in spontaneous mentation, influenced by major life events, traumatic experiences, and implicit goals. In everyday life, spontaneous, apparently unstructured thinking may be the most common kind of goal-directed thought.
What happens when people go from a focused, externally instructed cognitive task to a condition that is not heavily structured by external demands?  To many subjects, designated rest breaks may be a chance to get back to a normal, spontaneous, self-relevant, and active stream of thought. In the 1960’s and 1970’s a number of studies in experimental psychology focused on internally generated images and thoughts, which showed that depriving the mind from sensory information stimulated the occurrence of internally generated experiences. TU thoughts were studied in a more elaborate and specific research program called Task Unrelated Images and Thoughts (TUITS). Here, subjects were asked to report with regular intervals the content of their thoughts. This could either be performed in laboratory settings, in which subjects were given a tasks of varying difficulty and attentional load, or they could focus on more everyday settings, where subjects were interrupted at random times during a day, and were to write down their ongoing thoughts. One of the general findings from this research was that there is a continuous shifting of attention between externally and internally generated sources of information. Furthermore, spontaneous thoughts were found to be rather repetitive and predictable, always returning to “current concerns”. The content of thought was found to become increasingly unrelated to external events as these external events become more static and predictable. In this sense, the more boring the task, the more did people spend time (during testing) on task-irrelevant thoughts.
More recently, the study of TUITS has re-emerged in the scientific literature. A portion of this research has focused on detailed observations about the influence of TUITS on cognitive performance and a detailed examination of the intrusiveness of the task unrelated thoughts on both ongoing and later performance. Other studies have begun to couple the occurrence of TUITS to measurable physiological changes such as increased heart rate during TUITS. As with the original TUIT studies, these results confirm that increases in task difficulty, altering the attentional load, make TUITS become significantly less frequent. On the other hand, the easier the task – leading to more automatic behaviour – the more TUITS are reported by subjects. In this sense, a rest state is only at one extreme of how much attentional load is put on a subject’s mind. At the other end are highly energy- and attention-demanding tasks such as working memory 2-back or 3-back tasks. The conditions we are comparing in a resting state study are vital to our interpretation of the results.
Daydreaming was also studied in the same period as TUITS was explored. Using the “Imaginal processes inventory” it was shown that people were aware of some daydreaming every day, and that daydreams ranged from “obvious wishful thinking to elaborate and complex visions of frightening or guilty encounters”. Furthermore, factor analyses have revealed three major types of daydreaming: positive-constructive; guilty-dysphoric; and a poor attentional control pattern characterized by fleeting thoughts and an inability to focus on extended fantasy. At the same time, the test-retest reliability of daydreaming reports has been found to be high. As such, the literature on TUITS and daydreaming, both highly relevant inputs to the study of RS, is both rich in number of studies and in information about the richness of conscious content during such periods.
The non-death of fMRI findings
So what may initially seem like a failure of replication of brain scanning data, may in fact be a failure to understand the human mind. Many neuroscientists just don&#8217;t know the psychological literature enough to see that that the human mind does not &#8220;do anything&#8221; when asked to rest and relax. Indeed, as we have seen above, the mind is never at rest, and when not given an explicit task, it will defer to an inner state of &#8220;current concerns.&#8221;
So the basic assumption of this meta-analysis is flawed, and we should treat the conclusion accordingly. Analysing resting state brain activity and assuming that the results will be convergent is the same as claiming that every human mind at any time is thinking about the same things.
That said, we still replications of brain scanning studies, as we would need whether one scanning method is valid. Here, neuroscience fortunately has a vast toolbox, and now classical work from leading figures such as Nicos Logosthetis has provided clear links between fMRI studies and other measures of brain activity. For example, Logosthetis’ work has clearly shown that fMRI activation “blobs” are related to dendritic activity, rather than axonal activity. Still, there is nothing in these data to support a claim that fMRI data are invalid. Indeed, extraordinary claims have not been supported by extraordinary evidence in this case.
__________
Interested in learning more? Sign up for Thomas Zoëga Ramsøy&#8217;s <a href="https://www.coursera.org/learn/neuromarketing">free Coursera course on neuromarketing and consumer neuroscience</a>.

The Restless Resting State, and Why Brain Scanning is Still Valid

<blockquote>Editor&#8217;s Note: The business of market research is changing rapidly, and nonconscious measurement techniques are growing ever more important as a class of consumer insight techniques. Nonconscious methods are an area exhibiting rapid cycles of innovation and growth. Certainly adoption has been slow for a variety of reasons, but the impact across many categories has been high. In today&#8217;s post one of the preeminent applied neuroscientists in the world today, Dr. Thomas Ramsoy, gives a bit more insight into why this is such an important and topic and what traditional market researchers need to understand about the science behind the techniques.
We think this is such an important topic that we&#8217;re launching a unique one day event in November that is designed to advance the conversation and increase collaboration among corporate clients, market research consultants and technology providers.
Save the date for the <a href="http://nimf.insightinnovation.org/" target="_blank">Nonconscious Impact Measurement Forum (#NIMF) </a>on November 6th! Brought to you by GreenBook and the Burke Institute, NIMF is an intensive day of learning and collaboration around the business impact generated by the dynamic and growing field of nonconscious measurement. You will learn the business case for and be inspired by nonconscious measurement methods, including applied neuroscience, implicit approaches, behavioral economics techniques, biometric measurement and holistic nonconscious models, and have the opportunity to test out many methods being discussed for yourself!
Today&#8217;s post is a great lead in for the type of bigger conversations that will be happening at NIMF.</blockquote>
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By Thomas Ramsoy
One of the key criticisms and concerns one hears towards applied neuroscience and neuromarketing is the sample size. Is it really valid, let alone representative, to test 40 people? In traditional methods, such as surveys, interviews and even focus groups, the number of test persons tested usually runs in the hundreds or thousands. In applied neuroscience tests we usually see test of around 100 people in nation-wide samples. In studies in mobile settings, we can see test with as few as 40 people. Surely, that is a problem?
Maybe the reason that applied neuroscience is not testing hundreds or thousands of people is that it will be too expensive or too hard to scale? Testing a single person with functional Magnetic Resonance Imaging (or fMRI) often costs something like $2,000. Even with other methods such as EEG (electroencephalography), although the price can be down to $100 per person or lower, it may be hard to scale, since every person you test simultaneously requires a full hardware and software setup.
Things have changed dramatically. In several neuroscience studies, it has been found that neuroscience measures can predict behaviour. Even in studies using very small samples, neuroscience can predict not only individual choice, but effects at a cultural level. For example, in a recent study by Dmochowski and colleagues (Dmochowski et al., 2014), brain responses of only 16 participants could predict effects at the cultural level. By using EEG, the researchers developed a novel method that assessed how consistently the brain activation was across the group. Higher group consistency to TV series and commercials was related to both higher social activation (the number of Tweets) as well as stated reference (Nielsen rating).
Other studies have found a similar pattern. In a study by Berns and Moore (2012) the researchers went back to older fMRI data in a study of responses to music. Here, they found that activation of a deep brain structure – the nucleus accumbens – predicted whether the music had become a massive cultural hit, far better than self-reports from the same people. These and other studies have hinted that there may be some common brain activations in a group that is representative for how a whole culture will respond.
So the criticism towards applied neuroscience on sample size is erroneous, and the concerns are unwarranted. One part of the problem with these attitudes is that they assume that traditional research methods are the golden standard. Think of it this way: in traditional methods, each person contributes with only a few data points, as given by their answers. Furthermore, traditional methods only assess measures that a person is privy to, and is willing and able to share. In neuroscience measures, an fMRI scan divides the brain into 3D boxes (voxels) of an approximate size of 3x3x3 millimeters, thus measuring in several thousands of voxels across the brain. Each voxel value is assessed about every other second, and thus a test lasting 20 minutes provides a raw data value of 2.1 million data points per person. In EEG, we see that each electrode samples the signal with a millisecond resolution. This signal is then divided into several frequencies (e.g., alpha, beta, delta, gamma, theta), and therefore a single person can contribute with an order of magnitude more in data power than in fMRI, with 60 million data points or more per person. By merely contrasting the data load of traditional methods and applied neuroscience, we can see that we’re in a completely different ball game.
Crucially, neuroscience measures do not only assess a limited set of responses that a person has conscious access to. These measures provide deep insights into what people are attending and missing; how consumers’ emotional responses occur within milliseconds after seeing a message, a brand or a product; and how particular motivational responses drive attention, desire and ultimately choice.
Today, the scientific foundation of applied neuroscience is unparalleled, and continues to grow both in our insights and application. For those who employ and adapt these methods (with the premise that they are used correctly) consistently see an improved understanding of their consumers, increase their communication effects, and thereby increased their revenue.
Today, it is fair to claim that the obstacles for adopting applied neuroscience now lies not in the science or the methods, but within the market research industry.
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REFERENCES
Berns, Gregory S, and Sara E Moore. &#8220;A Neural Predictor of Cultural Popularity.&#8221; Journal of Consumer Psychology 22, no. 1 (2012): doi:10.1016/j.jcps.2011.05.001.
Dmochowski, Jacek P, Matthew A Bezdek, Brian P Abelson, John S Johnson, Eric H Schumacher, and Lucas C Parra. &#8220;Audience Preferences Are Predicted by Temporal Reliability of Neural Processing.&#8221; Nature communications 5 (2014): doi:10.1038/ncomms5567.

Brain Scans, Cultural Popularity And Sample Sizes

<blockquote>
Editor&rsquo;s Note: I just got my copy of Neuromarketing for Dummies last week and was planning to write a review. Good thing for all of us that an eminently more credible&nbsp; reviewer,&nbsp; neuroscientist Dr. Thomas Zo&euml;ga Rams&oslash;y, stepped up and did it instead. I haven&rsquo;t had a chance to do more than skim the book so far, but I can&rsquo;t wait to dig in. I&rsquo;ve known one of the authors, Dr. Steve Genco, for several years and expect that this will be a VERY impactful read. Judging from Thomas&rsquo;s review below, it sure sounds like it is.
</blockquote>
By Dr. Thomas Zo&euml;ga Rams&oslash;y
Neuromarketing is a dual edged sword. On the one hand, we have realized that we cannot do with traditional measures. People simply do not know, remember or want to convey their emotional responses. With our minds being great interpreters and post-hoc sense makers, asking our conscious minds are in many ways like asking your dog to bake you an apple pie. On the other hand, establishing a common knowledge and best practices for non-traditional methods has become harder than once thought.
In many ways, we live in an illusion of naive realism &ndash; the idea that what you see is what you get. But at any instance of sentience, our experience of the world is seen through a filter. That filter is still you, but it&rsquo;s the unconscious side of you. Where to step next, breath or not breath, look right or left, and even what to pay attention to and evaluate something. This is all something going on automatically and without you being privy to it.
So why are we still pretending that asking people about their habits, wants and likes are the only reliable means of understanding them? For decades, psychologists and biologists have observed humans and animals using other tools. In fact, such measures are several orders of magnitude better at determining what we look at and how we respond, compared to asking people. Our conscious minds are just the tip of the iceberg of what our minds compute. Asking the conscious mind will only give you a tip of the iceberg idea of what&rsquo;s going on.
So we need new approaches, and neuromarketing has for years been a potent candidate. It is arguable whether the discipline has been successful in living up to those expectations. For one thing, the topic is altogether so new, that we need a whole new language to understand consumers. Consumers are biological beings. How do you bring that into business?
Furthermore, many neuromarketing providers have solved this problem by inserting simplifying (or: simplistic) accounts, and hidden their actual operations behind a fog of neurobabble. This has proven not to be the way in which you&rsquo;d like to forward a discipline.
Finally, several neuromarketing books have appeared on the market, some good but others terribly misleading. These books have mostly been driven by an honest inspiration and awe for the insights that neurobiology can bring to our understanding of consumer psychology and behavior. But they have often fallen short of providing a coherent picture, let alone actionable insights.
But now fortune is upon us all. We need to wait no more. Finally, a book has arrived that offers a refreshing take on neuromarketing. In the book humorously entitled Neuromarketing for Dummies (<a href="http://goo.gl/HmnTVu">http://goo.gl/HmnTVu</a>), authors Stephen Genco, Andrew Pohlman and Peter Steidl have brought together a book that will soon become a standard frame of reference for neuromarketers and marketers alike. If you want to understand &ndash; or have other understand &ndash; what neuromarketing is really about, this is the book.
Several things about the book are worth bringing to the fore. First of all, it provides an excellent introduction to the actual reasons for going into neuromarketing. By comparing the Rational Consumer Model and the Intuitive Consumer Model, the reader is given a road map for both understanding our naive beliefs in our own and consumers&rsquo; rationality as well as a new way to understand consumers.
By drawing on a combination of science and best practices, the volume provide an overview of the current state of neuromarketing. Even better, it does so without falling prey to the usual lofty talks about what this new science can bring. It talks about what the insights already are, and how they both can be and already are being used.
A recurring theme in the book is about the different practices we are now seeing within neuromarketing. In particular, it is well observed that there already now is a difference between neuromarketing service vendors, and neuromarketing consultancy. Vendors includes companies that do the actual testing of participants, and bring their own specialties and flavors. Consultancies are more or less a new breed that provide advice both on the selection and conduct of neuromarketing studies, as well as providing their interpretation of the results for the client.
If you don&rsquo;t know how neuromarketing studies are conducted, this is also covered in the book. By showing very simply how a study consists of different phases, how the data are analyzed and interpreted, the authors demystify neuromarketing.
The authors have succeeded in making a book that combines nice to know with need to know, along with an overall gist of the actual thinking that needs to be brought with this field. Neuromarketing is, after all, not just business as usual. It is a very new way of thinking and acting. Seeing that consumers are biological creatures implies a whole new set of ways to understand their thinking and acting. Neuromarketing for Dummies provides a rock solid foundation from which we can start establishing a long needed benchmark for neuromarketing and consumer neuroscience.
A book for dummies? Nah, you&rsquo;ll be genuinely brain-wise after reading this.

The latest entry to the pile of neuromarketing books is the one to rule them all. Why is neuromarketing important? Why should you use brains in marketing? A Neuromarketing for Dummies book was what it took!

Neuromarketing for Dummies

<a href="https://wp.greenbook.org/mr/wp-content/uploads/2013/07/neuro-data.png"><img decoding="async" class="alignnone size-full wp-image-10218" alt="neuro data" src="https://wp.greenbook.org/mr/wp-content/uploads/2013/07/neuro-data.png" width="681" height="341" /></a>
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By <a title="View all posts by Thomas Zoëga Ramsøy" href="http://greenbookblog.org/author/thomasramsoy/">Thomas Zoëga Ramsøy</a>
It&#8217;s on everybody&#8217;s lips these days. BIG DATA!!! How can you make the most of it, how can you analyze those data, how can you make sense of them?
But before you embark on the big data adventure, maybe it&#8217;s a good idea to check with others who have dealt with big data for a long time already. This time, big data inspiration comes from an unexpected side: neuroscience.
Yes, neuroscience has dealt with big data for a long time, and the data sizes are becoming larger by each year.
Put it this way, a typical data set for a functional MRI scan takes up over 500 MB per person, providing a new data point in thousands of minuscule subregions of the brain every 2 seconds or so. With an EEG, you get a new data point every 1 millisecond or so for typically 10 to 128 electrodes, and you can look at five or more different frequencies, producing millions of data points per person. If you want to think big data, neuroscience can take you there.
But with big data, you also get big challenges. This has been one of the largest issues in neuroscience. As any statistician will tell you, if you have an enormous statistical power, any test you run can easily turn out to be significant. Everything is significant in the land of Big Data!
Take one example, a scientific paper entitled &#8220;Neural Correlates of Interspecies Perspective Taking in the Post-Mortem Atlantic Salmon: An Argument For Proper Multiple Comparisons Correction&#8221; (and winner of the 2012 IgNobel Prize). Here, researchers reported that they could find brain activation during interspecies social interaction – in a dead salmon. You read right: brain activation in a really dead salmon! Just by doing the analysis wrong and not correcting the data, the researchers got a strong false positive effect.
<a href="https://wp.greenbook.org/mr/wp-content/uploads/2013/06/salmon.png"><img decoding="async" class="alignnone size-full wp-image-10147" alt="salmon" src="https://wp.greenbook.org/mr/wp-content/uploads/2013/06/salmon.png" width="549" height="299" /></a>
Obviously, Big Data can also become Big Problems. So what do we do?
Based on the experiences from neuroscience, here&#8217;s a few rules of thumb.
Kill the dead salmon!
Seems harder than you might think. When you have big data, you need to correct for multiple comparisons. The traditional method is to use Bonferroni correction. In neuroimaging we can use, e.g. Family-Wise Error correction. Find the appropriate way of minimizing the chances that your significant results could have happened by pure chance!
Look at the smaller samples!
This is a very simple rule. If you cannot find your effect in a smaller sample, is it really interesting? If you need to include thousands of people to find a significant effect, chances are that it&#8217;s not a very interesting phenomenon&#8230;
Look at the extremes!
A classical way in neuroscience is to look at aberrant behavior. If you observe an effect in a normal sample, what about the extremes? Do experts, niches, special segments, all behave according to your newfound law? When does the rule break down? Sometimes you might learn more from the extremes than the boring mean.
Use different sources!
If you&#8217;re using surveys, try other measures. Combine methods. If the phenomenon is robust with one method, you should be able to find it in many different sources. If your results are only for one particular domain, what does that mean? While using surveys, why not see if neuromarketing measures show the same results?
Kill your ideas!
A long forgotten ethos in commercial research is the idea of trying to kill your darlings. With Big Data, this becomes more important than ever. If you are pursuing an idea that X is related to Y, don&#8217;t try to find that relationship – try to kill the whole idea! Throw your worst tests on it, beat it with a bat, fry it and throw it out. If it sticks, chances are you are on to something.
Enjoy your Big Data!

Spoiler alert! Big Data can be dry, but there's some nice pesci in here!

Big Data, Neuroscience and, Yes, Dead Salmons

p style=&#8221;text-align: left;&#8221;>The recent use of neuroscience in business tends to divide the waters between the yay and the nay sayers. And maybe it’s no wonder. Today, we are constantly bombarded with news items that there is no God spot in the brain; that deep, unconscious structures of the brain are responsible for most of our choices; that we have specific neurons that are used to recognize intentions and emotions in others (so-called mirror neurons).
The problem is that this scattered information only leaves us with a patchwork of information, but with no coherent picture. How, one might wonder, can we use this knowledge to affect our business? Today’s steady stream of news from neuroscience still has a very low actionable value.
Nevertheless, there is an irrefutable fact that business is facing a challenge that only neuroscience can help solve. As any behavioral economist will tell you, human are irrational creatures. We deviate from what are the normative best solutions. We make decisions based on incomplete information. We are affected by contextual information and make mental shortcuts.
However, behavioral economics does not explain why we err in the first place. Here, neuroscience has demonstrated added value on two counts. First, it provides a causal explanation – we are loss aversive because our risk decisions unfortunately engage structures that typically response to either threat or rewards. Second, neuroscience provides an entire palette of methods that can be used to assess unconscious mental processes that a person is not even privy to.
In short, neuroscience allows us to go straight into the engine room of the mind. When done right, this is a tremendously powerful approach to understanding what drives our choices. When done wrong, we will be more confused, misguided and frustrated by neuroscience than we were before we started.
This leaves us in a dilemma: we need neuroscience to understand and measure the unconscious facets of our behavior, but does neuroscience need to be so darn hard to understand?
The short answer is: of course not!
We really do not need to make it this hard on ourselves. To produce clear, actionable insights, we need not understand the nitty gritty. It is not really important to understand the significance of mirror neurons in empathy; the nucleus accumbens in consumer choice; or the uncinate fasciculus in the control of emotions. This is semantic babbling that only at best can impress, but so often confuse the recipient.
The problem is that so many proponents of neuromarketing are using the neuroscience jargon. Fancy, colorful brain images accompanied by strange jargon seems so often to be what people communicate.
Worst of all are solutions where clients pay for neuromarketing tests but the actual metrics are secret, and all is hidden in a mist of neuro-oxymoron jargon. As neuroscience has progressed, we already have good markers of emotion, working memory, attention and motivation. There is no secret sauce.
What we need is a common language that provide actionable insights, but without a need for a PhD in neuroscience to make sense of it. In the case of neuromarketing, one should focus on terms that we all understand, such as attention, cognition, emotion and motivation. Do customers see or miss a particular piece of information? How do they respond emotionally to it? Does an ad contain too much information? Are customers motivated to purchase the product?
These are all insights that all marketers can relate to, and that you cannot reliably get from more traditional self-report measures. When done right, neuromarketing is extremely valuable in going behind the scenes of the conscious mind.
Neuromarketing needs a place to be communicated and synthesized, explained and debunked. That is exactly why we are starting this blog at GreenBook. This is the spot, and I hope you’ll dial in as much as possible.

Neuroscience Doesn’t Have To Be That Complicated!

As behavioral scientists, we meticulously craft our strategies to steer consumer behavior towards desired outcomes. Yet, even the most expertly devised nudges can fail to make their mark&mdash;not because they lack quality, but because they either go unnoticed or are misinterpreted by the audience. It's like confidently dressing for a sunny day only to be caught off-guard by an unforeseen downpour&mdash;the weather forecast went unchecked.
<h2 class="p3">The Need for Pre-Testing</h2>
Pre-testing in marketing and behavioral strategies serves as our essential weather forecast. Traditionally, acquiring insights into a campaign's potential effectiveness before its launch has posed significant challenges&mdash;it's often costly, complex, and time-consuming. These barriers frequently render comprehensive pre-testing a rarity, particularly when constrained by tight budgets or urgent deadlines.
[related-article title="Insights and Analytics in the AI Era" url="https://www.greenbook.org/insights/the-prompt-ai/insights-and-analytics-in-the-ai-era"]
<h2 class="p3">Enter Predictive AI</h2>
However, the landscape is evolving with the rise of predictive AI technologies. These tools are transforming the way we approach pre-testing, making it both more accessible and more profound. We are now witnessing the emergence of at least three distinct types of AI solutions:
<ul class="ul1">
<li class="li1">Predictive AI represents a specialized category of AI models focused on forecasting behavior based on historical data, thus eliminating the need for expensive and time-intensive direct measurements of consumer responses during critical planning stages.</li>
<li class="li1">In contrast, suggestive or copilot AI loads all essential information about a brand, its industry, and customer demographics upfront, using this data to interpret and recommend actions based on the outputs from predictive AI. This form of AI offers strategic guidance on how to leverage predictions, embedding deep insights into the decision-making process.</li>
<li class="li1">Meanwhile, generative AI, which is currently extremely popular, operates by generating responses or content based on the input it receives. Although it excels at creating new content from existing patterns, it is not inherently capable of predicting future behaviors or outcomes. Because generative AI relies solely on historical and publicly available data, it tends to replicate past trends and biases, thereby perpetuating existing issues rather than providing fresh, unbiased predictions. This limitation renders generative AI unsuitable for tasks that require accurate forecasts of future human behaviors, as it lacks the ability to recognize emerging trends or shifts in consumer sentiment that have yet to be documented.</li>
</ul>
In the realm of marketing and design operations, predictive AI leverages vast datasets to understand how potential consumers react to stimuli visually, emotionally, and cognitively. This capability enables us to predict the impact of our nudges and refine them to more effectively resonate with our target audience before they are even implemented.
<img style="display: block; margin-left: auto; margin-right: auto;" src="https://greenbook-ent.directus.app/assets/bc175445-dc4c-4c53-b2f4-c80d0293439c?width=1204&amp;height=496" alt="Nudge Wisely: Predictive AI for Precision Impact">
Predictive AI solutions have long succeeded in predicting visual attention, and are currently showing success in predicting other types of consumer responses, such as emotions and comprehension. This image shows how an AI model (bottom row) accurately predicts eye-tracking results (top row).
<h2 class="p3">Working with Predictive AI</h2>
Imagine you're orchestrating a campaign aimed at promoting healthier eating habits. You've crafted several creative concepts designed to nudge people towards making healthier food choices. Instead of simply relying on historical data and crossing your fingers, you turn to predictive AI.
This AI sifts through a comprehensive database of behavioral patterns, analyzing how similar initiatives have fared in the past. It scrutinizes visual attention patterns, emotional responses, and cognitive engagement related to your concepts. Within seconds, you receive detailed feedback&mdash;not only on the potential effectiveness of your ideas but also insights into why they might succeed, which aspects are most compelling, and how certain elements could be misinterpreted or overlooked.
Armed with this data&mdash;which might seem like something out of a sci-fi novel&mdash;you can significantly refine your campaign. This process ensures your messages capture better attention and are understood more clearly, all while maintaining the strong emotional resonance of your original concepts.
<h2 class="p3">How is it possible to predict human responses?</h2>
The efficacy of AI in predicting human behaviors hinges on a series of critical steps. Initially, we have honed our ability to measure human responses and pinpoint those that reliably indicate real-world outcomes. Neuroscience has consistently demonstrated that responses from a small sample group can forecast broader market reactions&mdash;ranging from advertising impacts to sales volumes, and even box office success.
Secondly, by concentrating on responses that exhibit a high degree of consistency across individuals, we identify prime candidates for predictive modeling. Take, for instance, the universal reaction of moviegoers jumping during a horror film scene. While the intensity of reactions may vary, the collective response is remarkably uniform. Recognizing these consistent behaviors is crucial for developing effective predictive AI models.
The third step involves the aggregation of vast datasets. The construction of systems capable of amassing such data is essential, as AI model training demands substantial quantities and quality of data.
Finally, the actual training and implementation of these models enable the widespread use of predictive AI, including its integration into platforms as DIY tools. This process is far from mystical; it is a rigorous, methodically controlled, and deeply scientific endeavor. Training an AI model with inadequate metrics or insufficient data might produce some results, but these are unlikely to be robust or broadly applicable.
<h2 class="p3">Adapting to Predictive AI</h2>
To seamlessly integrate predictive AI into our strategic development processes, we must embrace a new mindset. Similar to how checking the weather becomes a routine part of planning a day, using predictive AI tools should evolve into a standard practice in campaign planning. This transition requires not only training teams to interpret and apply AI insights but also adjusting project timelines to include AI-driven testing phases.
Incorporating predictive AI in our pre-testing strategies does more than just help us sidestep potential pitfalls&mdash;we're significantly boosting the potential impact of our initiatives. This technology goes beyond merely predicting outcomes; it delves into the consumer psyche, providing a more transparent overview of the behavioral landscape we aim to navigate.
Predictive AI enables us to fine-tune our nudges for not only greater engagement but also to ensure they uphold ethical standards and cultural relevance. This enhances the overall efficacy of our interventions, ensuring they resonate more profoundly with our target audience.
<img src="https://greenbook-ent.directus.app/assets/9370a163-82ba-41af-9367-983d3584679c?width=1482&amp;height=878" alt="Nudge Wisely: Predictive AI for Precision Impact 2">
This example shows how a Predictive AI solution both provides insights into what customers are likely to see and miss, as well as their emotional and cognitive responses. Industry benchmarks (bottom) are provided to show how the asset score relative to the industry category. On the right side is a Suggestive AI-based Copilot that understands the metrics, provides an overall assessment, and provides recommendations for how you can improve your asset. From <a href="http://www.neuronsinc.com">www.neuronsinc.com</a>&nbsp;
<h2 class="p3">Conclusion</h2>
As we integrate predictive AI into our toolkit, we equip ourselves to ensure that our marketing strategies are not only noticed but also resonate deeply, effectively navigating the tumult of consumer attention and unpredictability. This effort extends beyond merely sidestepping potential setbacks; it involves mastering the ability to forecast and thrive in any condition, fundamentally transforming how we ensure our strategies excel in the ever-changing landscape of consumer behavior.
In today's world, where attention is a precious resource and consumer behavior continuously evolves, predictive AI is not merely beneficial&mdash;it's indispensable. It transforms speculation into a precise science, ensuring that our nudges are as impactful in reality as they are idealized in concept. This fusion of technology and behavioral science ushers in a new age of accuracy in behavioral insights, crafting an era where each nudge is judiciously planned and implemented.

Discover the art of behavioral science strategies that aim to influence consumer behavior. Learn how even the most well-crafted nudges can be overlooked.

Nudge Wisely: Predictive AI for Precision Impact

In the high-stakes world of marketing, understanding your consumers is paramount. But what if I told you that some of the most common marketing strategies could be slowly killing your brand? Recent advances in neuroscience have revealed that four key consumer responses, all occurring within a fraction of a second, can make or break your marketing success.
In an age where 82% of consumers actively ignore ads, it&rsquo;s a wonder that brands survive at all. The digital landscape is a battlefield, and the weapons of choice are attention, emotion, cognition, and memory. Misuse any of these, and you&rsquo;re effectively killing your brand.
<h2>The Gatekeeper: Attention</h2>
<a href="https://www.greenbook.org/insights/gain-retain/cant-keep-my-eyes-off-you-attention-part-one">Attention is the gatekeeper of all ads</a>. It&rsquo;s a power that operates within the blink of an eye, even as briefly as within the first 100 milliseconds of ad-watching. Yet, despite its speed, it&rsquo;s astonishingly selective. For instance, 50% of all ads place the brand in the bottom right corner or at the end of a commercial, but less than 5% of viewers actually notice it there.
<div class="post-related-post">
<h3>Related</h3>
<h4><a href="https://www.greenbook.org/insights/behavioral-insights-academy/using-behavioral-science-to-create-effective-marketing-strategies-an-analogy-of-a-road-trip">Using Behavioral Science to Create Effective Marketing Strategies: An Analogy of a Road Trip</a></h4>
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Consider the case of the World Wildlife Foundation. They continuously create highly visually appealing ads, but they constantly keep the brand in the bottom right corner, making less than 5% of viewers actually notice who the sender is. With the ads being highly metaphorical, it is hard to get the message across if people don&rsquo;t see the brand.
The solution? Understand the visual hierarchy of your ad and ensure your brand isn&rsquo;t sidelined. Place your brand where it can be easily seen and remembered. Build the brand as a natural part of the ad narrative.
<h2>The Relevance Barometer: Emotion</h2>
Emotion is the relevance barometer of your ad. A positive emotional response can boost your brand impact by 400%, but fail to produce an emotional &ldquo;bump&rdquo; within the first 400 milliseconds, and your ad will likely be ignored. In fact, excessive ad intrusion can even trigger negative emotions.
Take the case of Sony Bravia. They created an ad where thousands of colorful balls were jumping down San Francisco streets. Everybody loved the beautiful scenery, and both ad liking and ad memory were high, but brand memory was extremely low. The take-home message: Ad liking and emotions can be at the cost of brand communication.
The key is to create ads that resonate emotionally with consumers, making them feel seen, understood, and valued, and naturally creating a bridge to the brand. Strive to create a positive emotional connection that enhances brand affinity, and build the brand as a natural part of the storyline.
<h2>The Information Overload: Cognition</h2>
Cognition, particularly cognitive load, is another crucial factor. It&rsquo;s a delicate balance: too much information can overwhelm and confuse viewers, causing them to look away. This can have a negative halo effect on your brand.
Consider the series of studies done by Neurons for Ericsson and Vodafone. It was found that ads that were seen in the context of mobile delays and noise led to negative brand emotions, meaning that negative brand experiences, even when by proxy, can have a negative halo effect on the brand.
The antidote? Keep it simple, clear, and contextually relevant. Less is more when it comes to information in ads. In the increasing exposure and pace of digital ads, cognitive load is already high. Build simple messages with this in mind.
<h2>The Forgotten Hero: Memory</h2>
Finally, there&rsquo;s memory. Is your ad remembered at the cost of the brand? Does it bridge the right associations to the brand? It&rsquo;s a sobering fact that across multiple studies, less than 1% of all ads are remembered.
Consider the use of influencers. The use of influencers is highly popular but also contested. In one series of ads for Intel, world-class swimmer Michael Phelps starred in Intel ads, but people only remembered Phelps but not the Intel brand.
The goal is to create ads that not only stick in the consumer&rsquo;s memory but also reinforce the brand&rsquo;s identity and values. Ensure that your ad and brand work in harmony, not in competition.
<h2>Conclusion</h2>
These four powers&mdash;attention, emotion, cognition, and memory&mdash;can make or break your brand. But how can you harness them effectively? The answer lies in the ability to test and iterate rapidly. Recent advances in neuroscience, particularly in neuromarketing and consumer <a href="https://www.greenbook.org/insights/behavioral-science">neuroscience</a>, have shown that the four powers can be measured, understood, and used to improve your assets.
But these methods are typically expensive, time-consuming, and can be hard to translate directly to actions. Recent advances in online implicit measures and AI solutions now allow marketing teams to quickly test and iterate on their designs and campaigns. In this way, it is becoming clear that a new breed of neuro+AI solutions dramatically boosts the way that marketing teams operate, improving ROI by over 20% and reducing time costs by more than 20%. Indeed, by understanding, predicting, and addressing these key consumer responses, you can not only avoid killing your brand but also breathe new life into it.

In the world of marketing, understanding consumers is paramount. But what if some of the most common marketing strategies could be slowly killing your brand?

4 Ways to Kill Your Brand (And How Neuroscience Can Avoid It)

Neurons is the world’s leading consumer neuroscience company, combining
neuroscience and AI to predict consumer responses. Taastrup, Denmark.

Neurons Inc.

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