- Neuroforecasting is the practice of using neurophysiology to predict population-wide behaviors from small groups of people.
- Neurophysiological predictions regularly contradict self-reported preferences.
- We can leverage neural data to create more compelling content.
Imagine. It’s Friday evening. You’ve worked hard all week and want nothing more than to sink into your sofa and watch a good movie on Netflix. The only problem is that as you scroll through the roster of films, you’re overwhelmed by the array of choices.
Watching the trailers isn’t helping you to decide either. Sighing, you put on a sleek-looking neuro-gadget, power it on, and resume scrolling through the movie choices.
Pretty soon, an app on your phone notifies you that the spy thriller you passed over without a second’s thought is actually the best choice for you this evening, based on how you and others like you resonated with it while watching the trailer. You’re surprised. Spy thrillers aren’t normally your thing, and this one stars an actor you’re not a big fan of.
With a healthy dose of skepticism, you press “play” for the spy thriller, expecting that 15 minutes into it, you’ll stop and change to a different movie. But the thought soon vanishes as you find yourself pulled into the thriller’s surprisingly gripping story and on the edge of your proverbial seat, loving every minute of it.
Welcome to the world of neuroforecasting. Does it sound like science fiction? It’s actually science reality, and the day when everyday consumers will be able to tap into this kind of wearable technology may not be so far away.
What Is Neuroforecasting?
Simply put, neuroforecasting is the practice of using evidence-backed methods and technology from the field of neuroscience to forecast the choices and behavior of large groups of people. This can be done by measuring the brain activity of small groups of individuals, usually using fMRI technology. At the same time, they are engaged in specific behaviors such as viewing specific content or using a specific product. Typically, while this is done, subjects will also be asked questions about the content or product to gauge their conscious thoughts and feelings.
Two key points are worth noting here: First, neuroforecasting makes it possible to predict the behaviors of large groups of people, even entire populations, by studying a small group of participants. The data collected from the small group can be used quite accurately to predict the behavior of the broader population. This is more efficient and less prone to error than traditional research methods such as surveys and interviews in which large, diverse samples need to be studied.
Second, and this might surprise a lot of people, what they consciously think and say about a specific product or content often conflicts with what their physiological data tells us. For example, let’s say you show a group of 10 people two different movie trailers and ask them which movie they’d prefer to watch. To keep it simple, let’s say that all 10 say they’d prefer Movie #1.
In a neuroforecasting scenario, the data might tell us that all 10 people would more likely watch Movie #2. So then why would they say they prefer Movie #1? There’s a range of possible reasons. They may have thought it was the more socially acceptable choice to make, for example, or maybe they genuinely believed in their conscious mind that they would have preferred Movie #1.
The brain data, however, doesn’t lie. This aligns with what we know from neuroscience and psychology in that there isn’t always consistency between what people consciously think or say and what they do. Unconscious mental and physiological processes often drive people.
How Does It Work?
The easiest way to illustrate how the technology of neuroforecasting works is by explaining synchrony, and the easiest way to do that is to use heart rate as an example. Imagine that Person A and Person B watch Movie #1 and Movie #2 together. While watching Movie #1, their heart rates are synchronized and reflect the appropriate kinds of emotion (excitement, fear, sadness) in the scenes that are supposed to arouse those emotions.
This means that they’re enjoying the movie and that others will most likely enjoy it. But while watching Movie #2, their heart rates are not in sync. This indicates that key parts of the movie are not landing as they should or that the viewers are simply bored and have their attention elsewhere. In this case, they’re not enjoying the movie, and others are not likely to enjoy it either. While this is a simplification, it’s basically how it works.
Compared to traditional consumer research methods, the ease and efficiency with which consumer behavior can be predicted using neuroforecasting technology naturally makes it attractive to marketers. If you’re a movie studio, for example, and you have two films that you could potentially release but only have the budget and resources to market one properly, then you’re going to want to make the right choice about which film to release.
Traditionally, market research would rely on focus groups and surveys, but as pointed out, what people tell you doesn’t always concur with what they do. In the earlier scenario, for example, all 10 participants told the researchers that they preferred Movie #1. If the studio were to release Movie #1, it would flop. In contrast, the neuroforecasting data would have told them that every one of the research participants would not have gone to see Movie #1 but rather Movie #2. You can see why organizations would want to take advantage of this kind of technology.
The idea of neuroforecasting being used for marketing purposes may concern some people due to the problems we’ve seen with social media platforms, for example, and their effects on mental health and polarization.
Certainly, with any technology, there’s a potential of it being misused. Still, unlike conventional marketing tactics, which may cause people to consume content and products they don’t enjoy, neuroforecasting creates the potential for providing content and products that people enjoy, leading to a win-win situation for both consumers and companies alike.
Creating Better Public Service Announcements
Aside from its potential for marketers and consumers, neuroforecasting also has the capacity to help organizations create more effective public service announcements (PSAs). As with consumer decision-making, what people tell you isn’t as indicative as what their brain data tells you.
Research shows great promise in harnessing the power of neuroforecasting for important public health issues such as smoking cessation, both in terms of what works and what doesn’t. However, as with consumer goods, technology is underutilized. It’s hard not to wonder how much more effective public health messaging for the Covid-19 pandemic could have been, for example, had neuroforecasting been better utilized earlier.
But the pandemic isn’t over yet, nor is it likely to be the last one. We face other challenges to public health and safety, such as climate change, misinformation, and extreme polarization. Given that some of these problems are made worse by powerful technologies, maybe it’s time that we use equally powerful technology to help solve them.
Jorge Barraza, Ph.D., Program Director and Assistant Professor in the online Master of Science in Applied Psychology program at the University of Southern California.