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David Kepron

Brain Food: The Predictive Power of Dopamine

How retailers can keep customers’ minds engaged




As we experience and interact in our world with myriad digital interfaces, we leave behind a “Hansel and Gretel” trail of ones and zeros that trace a path of connections to people, places and things. The Internet of Things (IoT) suggests we weave a web of connection points extending ourselves to a global cognitive coalition of like-minded brand loyalists and inanimate objects that share our digital life story. We are knitted into this digital tapestry that hovers above the world of embodied experiences, and we can’t detach ourselves from it. Even when we delete SNS posts, emails and online accounts, they don’t fully go away. Parts of us have been extended and shared, distributed among the denizens of the digisphere.

When we leave a digital imprint of our experiences, it remains for examination, and use, by others. The crystal-ball-work that is done with data collected from shoppers’ digital histories is called “predictive analytics.” Statistical analysis of our data trails by way of computer modeling algorithms seeks to find patterns in our transactional data to provide customers opportunities, with the presumption that future shopping will be more enjoyable and relevant.

While customers shop online, the hundreds of marketing messages presented to them are directed to their individual needs and wants. This, of course, is not coincidence. It is targeted messaging that is created from analyzing a series of web searches, Internet purchases and digital communications that customers may have engaged in recently. Gathering digital information and offering opportunities for products or services in real-time will be increasingly important, especially with a generation of shoppers for whom “now” means literally right now, in the moment where they are standing in the aisle, at a shelf, trying to decide.

The idea that our digital histories are held in gigantic databases is like imagining that there are other versions of ourselves – avatars of sorts – digitally constructed, in suspended animation within the digital realm. They may be like the “name on the tip of your tongue,” not quiet fully formed, but coalescing given the right circumstances, into the image and memories of the character that eluded you a moment earlier. These constructions are not separate from us; we are connected because they mirror our digitally mediated embodied experiences.

However, relying exclusively on “avatar analytics” to predict future opportunities will promote manufactured shopping experiences that are good at predicting needs and wants based on transactional history, but not good at providing experience that come about serendipitously – those chance encounters with people, places and things that we could have never anticipated.

When retailers and brands continue to refine their manipulation of data and use it in the service of manufacturing simplicity in a world of digital distraction, will curated experience be the death of novelty? Will everything we do in shopping places be constructed to satisfy our individual needs and wants to the extent that there will no longer be any discovery in the process?


This is the dichotomy in the use of predictive analytics; we will be able to know what individual shoppers are likely to need, want or do, based on their digital life-stream, and intervene to provide shopping experiences crafted to their unique needs. But, as retailers make shopping “perfect” for them, they will run the risk of boring their customers, unless they are also creative at introducing moments of discovery and novelty into the experience.

Using predictive analytics in creating memorable shopping moments is like Wolfram Schultz’s monkey-and-juice study. After being exposed to a repeated pattern of stimuli, the monkey’s brain came to predict the arrival of juice and released dopamine prior to the actual arrival of the rewarding squirt of juice into its mouth. When the juice didn’t arrive as expected, the brain took notice and reduced dopamine production until it began to learn a new pattern, upon which, its dopamine production increased. As prediction of the pattern became easier, dopamine production again leveled off. In short, dopamine reached peak production only when the monkey was experiencing something new.

The same is true of shoppers and predictive-analytic shopping. The brain becomes “bored” with what it already knows; it wants novelty. The challenge with predictive analytics is to avoid providing experiences that are, well, predictable.

Using big data to get a sense of what the customer may do in the future, helps decision-making in a complex retail environment of abundant choice. However, relying on it to continually present the shopper with the best set of options runs the risk of creating shopping experiences that verge on the banal.

In using predictive analytics, it is possible that a shopping trip of curated products or services actually makes the experience less interesting. Removing the unexpected is like holding on to the expected pattern of the delivery of juice in the Schultz study. Unless the predictions of future shopping behavior are comingled with other seemingly tangential opportunities, then removing choice also removes the novelty our brains are designed to seek out.

If a shopper buys new shoes, the predictive power of computer algorithms may suggest that she should buy new shoelaces, shoe care products, other shoes or even associated products in the fashion accessories category such as handbags. On the other hand, along with those items, the algorithms could suggest a podiatry appointment or foot massage, or a ballroom dancing lesson, or a fancy restaurant, perhaps even a connection to a local running club and jogging routes. These adjacencies are not so predictable, though tangentially connected to the idea of a new shoe purchase and help to introduce novel selection options.


Understanding big data and putting it to relevant use by employing predictive analytics is an extremely powerful tool in the brand or retailer’s toolbox. If retailers and brands are to keep in step with shoppers’ needs and wants in the future, they will come to rely on parsing big data into chunks that make it effective in predicting behavior and providing simplicity in the shopping aisle.

But prediction of buying behavior alone will not be enough.

As our ability to capture even more granular bits of information about customer habits grows, retail will have to provide excitement through exploration and novelty. This will require creativity, invention and intuition, as well as good math skills.

David Kepron is Vice President – Global Design Strategies with Marriott International. His focus is on the creation of compelling customer experiences within a unique group of Marriott brands called the “Lifestyle Collection,” including Autograph, Renaissance and Moxy hotels. As a frequently requested speaker to retailers, hoteliers and design professionals nationally and internationally, David shares his expertise on subjects ranging from consumer behaviors and trends, brain science and buying behavior, store design and visual merchandising as well as creativity and innovation. David is also author of “Retail (r)Evolution: Why Creating Right-Brain Stores will Shape the Future of Shopping in a Digitally Driven World,” published by ST Media Group Intl. and available online from ST Books. @davidkepron;



MasterClass: ‘Re-Sparkling’ Retail: Using Store Design to Build Trust, Faith and Brand Loyalty

HOW CAN WE EMPOWER and inspire senior leaders to see design as an investment for future retail growth? This session, led by retail design expert Ian Johnston from Quinine Design, explores how physical stores remain unmatched in the ability to build trust, faith, and loyalty with your customers, ultimately driving shareholder value.

Presented by:
Ian Johnston
Founder and Creative Director, Quinine Design

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