I believe the framing of the question is at odds with the fundamental concern – can a machine, or data produced from such a machine, provide meaning? The answer to-date is “No.”
Why should “meaning” be the central question? In marketing we are trying to understand people and hopefully through our actions, inspire, convince and persuade them to perform a behavior, whether irrational or rational. “Meaning” is the connective tissue that forms the muscle of that behavior. Take for example children and hot stoves. In the kitchen, I can remember the countless times we would tell our daughter “watch the stove; it is hot!” However, why do some children heed this warning, while others only do so after they feel the burn? I believe the difference is in each individual’s richness of meaning – an understanding of what is being communicated, and how that relates to us and our ultimate behavior choice. This is easily seen in another context —why do some bystanders walk past a person screaming for help, while others jump into action to assist that person? Both groups receive the same message and have the same stimuli, but the meaning applied connects with each individual in a different way to drive a certain behavior.
There is an enlightening experiment and online video involving children and the temptation presented by marshmallows. In it, a researcher places a marshmallow in front of a child and tells the child not to eat it. If the child doesn’t eat it before the researcher returns, he or she gets a second one. The results found most children could not resist, and ate the marshmallow. This defies the logic of the researcher’s request, and the data that the child may be rewarded and punished for different behavior. What is overlooked in this scenario is that marshmallows are irresistible. It is a notion like this that big data, at least for the moment, cannot account for.
Back to the central question: does big data inspire or hinder creative thinking? Neither is the case. Creative thinking is the process of coming up with fresh perspectives or solutions. By its very nature, we provide meaning or context to a series of underlying components in our solution that ultimately create something new or different. Framing the problem is likely where big data can hinder creative thinking, but it is not big data’s fault. People that frame the problem are the ones that should be held responsible. Big data cannot create rules or meaning – it can provide insights or trends, but people provide the meaning for those facts and figures. So, does big data hinder creative thinking? No – people do.
As an example, remember back to the well-known Target example where the company’s algorithms correctly predicted that a teenage girl from Minnesota was pregnant based on her purchases of unscented lotion, cotton balls and mineral supplements. The big data application accurately predicted the pregnancy, but as we now know there was a considerable backlash against Target and the industry for this type of “spying” into our lives. The problem here was Target inadequately framed the challenge they were trying to solve (and showed negligence when it came to controls on the application of the algorithm). Target had framed the challenge around the life-changing event of having a baby as being one of only a few opportunities to change a person’s shopping behavior and location. Prior to this example, most retailers would key off of public birth records to then “hit” new parents with everything they could. I still remember the stacks and stack of direct mail in my mailbox when our daughter was born. Target asked an appropriate question: “Can we reach new parents earlier in the process?” The answer was undoubtedly “yes.” The problem, however, was they improperly framed the solution to address the likely scenario that teenage pregnant girls would also be identified. Big data opened up a new line of options for marketers, however poorly framing the challenge resulted in unintended damage.
Big data opened up a new line of options for marketers, however poorly framing the challenge resulted in unintended damage.
Framing can lead good people to bad conclusions. Take the ever-present competitive challenge today in consumer package goods (CPG) – private label brands. Now, let’s compound the issue by saying that your branded offering has a 70 percent share of the market. Over the last five years, you’ve slowly lost share to private label brand. Each year you try to protect your share and volume by pumping more money into trade through price promotions. Over enough time, this strategy has eroded your healthy 50 percent margin to now around 35 percent, and stockholders are not happy. What are you to do? Take cost out of the product either by using cheaper ingredients, or reducing the number of ounces of product? For most CPG companies this is a very real scenario. The issue here is the framing of the problem at each step. In the beginning, why is private label taking share? It is likely a perceived quality issue and private label is “good enough”. The difference between branded product and private label is no longer meaningful to the point people will buy you over the other guy when there is a $0.10-0.25 price difference.
What if instead the frame was “how to maintain a meaningful differentiation that consumers would desire for the cost of a gum ball?”
Think about that for a moment… $0.25 cents. Now, in each subsequent decision, the framing becomes more and more about how to beat private label competitors at the private label game. Before you know it, you have a product that is no different than private label, margins that are compressed, and what the retailer doesn’t take from you in market share with their private brand they take from you in trade concessions. What if instead the frame was “how to maintain a meaningful differentiation that consumers would desire for the cost of a gum ball?” From this new frame, we focus not on competition but instead on how to chart our own course to a place where consumers will follow us. Good people can be led astray by bad framing of the problem.
This article originally appeared on LinkedIn