Mind Control 101: A Neuromarketing Primer, Part 2

This post also appears on Wearable.ai, a news summary and intelligence gathering service for the emerging wearable computing industry. For inquires, please email interviewer and publisher Mark Brooks.

This is Part 2 of a two-part Q&A with author and neuromarketing expert Darren Bridger. For Part 1, click here.

Neuromarketing involves the study of how the human brain responds to particular stimuli in distinct ways, and and how businesses can employ that information to connect with customers and increase their business. 

Its foundation on neuroresearch offers a route to deeper insights and better design. The following covers ways this strategy can accelerate business, and the opportunities it presents for Internet of Things (IoT) and wearables initiatives.

See also: Mind Control 101: A Neuromarketing Primer, Part 1

MB: How is neuromarketing using wearable computing and Internet of Things. What are the challenges and opportunities?

DB: The most obvious place is with wireless/portable wearable sensors. The main ones are wireless EEG caps, heart rate sensors, GSR (skin conductance) sensors, and wearable eye-trackers.

At the moment, most of the devices used for research are specialized kit, and not the typical consumer wearables. As devices like smart-watches become more widely used and have better sensors and become more web-connected, the potential could be there to tap into data from consumer wearables.

There are more challenges with collecting brain/physiological data out in the real world than in-home or lab. For example, if you want to understand how people respond to a real world experience you could send them out with portable sensors, but for each person within your test-group you then need to correlate where their head and gaze were pointing at each second. It’s just a more time/computational intensive exercise compared to sitting them in front of a video-simulation, where you know exactly what every person saw second-by-second, so you can more easily compare and aggregate their responses.

More computational power and larger testing groups could solve this problem. This is why I think there is the potential for tech/software companies to move into this field. A consumer wearable that is being worn by millions of people provides a big sample base, and then it just becomes a challenge of (a) gaining access to that user data, and (b) crunching the numbers.

Even a simple sensor like a camera has the potential to collect useful information on people’s emotions and behaviors when combined with the right algorithms. For example, posters with built-in eye-trackers have already been trialed. As cameras and computing drop in price, you could have more eye-trackers embedded around shops, buildings and public places that understand where people are looking and adjust displays accordingly.

You can also extract information from cameras on people’s emotions: from facial emotional expressions, and even heart-rate. As blood pumps around our bodies our faces are slightly, imperceptibly ‘flushing’ different colors. We can’t see this, but point a camera at someone’s face then amplify the visual changes in hue and it becomes measurable. There are also more exotic camera-based applications such as extracting information from a person’s gait and movements, or eye-trackers built into smartphones.

As we interact more with screens and “smart devices” there will undoubtedly be increasing demand for those devices to understand human responses, to optimize themselves to our levels of interest, emotions, and behavior. This pressure to make emotions and attention “machine-readable” will likely expand these applications out of the domain of market research into other sectors.

MB: How would you recommend startups on a budget get early access to neuromarketing labs?

DB: I see neuroresearch tech at a point analogous to computing in the late 1970s: poised to move from being a big/expensive lab application to something more accessible to a far wider range of organizations.

The tech and computing power to run more lab-based studies are falling in price in many cases and most of these tests don’t require many other fancy features. Usually just a quiet office room is enough. However, the software algorithms, and know-how to compute, analyze and interpret the results are still a scarcer resource. I would recommend smaller organizations like startups get some specialized advice before just buying sensors and using the software that comes with them. Often this can lead to misleading results.

A sister discipline to neuromarketing – behavioral economics – offers even more affordable options.

Making a Better Wearable

MB: Do you use any wearable devices? How would you advise them to improve, through the application of behavioral science? How could they do better?

DB: We use GSR, which is a measurement of emotional arousal based on skin conductance level changes, similar to the lie detector test. We also use wireless EEG to measure brain activity, although these currently tend to be more medical than consumer grade.

Behavioral science has two main useful applications in this area:

Firstly, knowledge from the behavioral sciences could be used and enhanced through ‘quantified self’ type applications. People already use sensors on watches and other devices to learn about their physical fitness. Other applications could give people feedback and insights on their own behavior and thinking and point to ways to improve their lives. Technology that learns and improves the more we use it is very appealing and could encourage more people to adopt wearables.

Secondly, behavioral sciences have a lot to add in terms of improving the usability and appeal of such devices. An obvious area of opportunity is to make them more intuitive and easy-to-use. Here there are many insights to be gleaned from neuroscience on attention and behavior.

For example, we all have limited attention, and there are increasing demands placed on it. Wearables can help deliver information to us in more easily digested ways, through things like better ways of filtering and delivering complex information to us in real-time, or through using communicating information to us through an underused channel. A field called Embodied Cognition can offer insights on making gestures and device interactions more intuitive. Sensory neuroscience can offer insights on making devices more comfortable to wear and more emotionally appealing and desirable. 

Lead photo by Dierk Schaefer

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