There was a discussion on LinkedIn in June 2015 that asked about the use of Big Data in retailing. The comments focused on detecting real time trends, analyzing purchasing behavior, and using historical activity to gauge the viability of upcoming strategies. It was short-sighted. Big Data was being used to ensure consistent or incremental revenue and profitability, but it wasn’t being used to detect breakthrough opportunities that could radically improve a company’s fortunes.
Commenters ignored a critical Big Data capability that can reveal the unknowns reflected in a diagram called the Johari Window. The window, named for two psychologists named Joseph Luft and Harrington Ingham, was originally designed to define relationships between oneself and others, but it’s extremely appropriate for marketers, as well.
THE PROMISE OF THE UNKNOWN
The Johari Window has four quadrants –
In a hardware environment, everyone knows there’s a market for hammers and nails, but consumers may not recognize the hidden advantages of a hammer with a larger head that reduces the number of missed blows and bruised fingers. Likewise, a retailer may be blind to the fact that customers are using pliers to hold nails in place, missing the opportunity to stock, promote, and sell a device that’s designed for that task.
For marketers and the product developers who lean on marketing research, however, the last, i.e. Unknown, quadrant holds the greatest potential, and Big Data can exploit it.
Employing advanced query languages to construct fuzzy searches and using computers that can combine and process multiple types of structured and unstructured data (without the need for highly time-consuming optimization to make the data uniform), marketers, researchers, and developers can discover relationships that no one searched for... because nobody suspected they exist. They’re the correlations that are unknown to manufacturers and their channels, to the employees who market their wares, and to buyers.
It might be possible, for instance, to reveal that a product that was neither defective nor unable to perform its advertised functions had an unusually high number of returns; that it was not exchanged for a competitor’s version of the returned product; and that a full refund wasn’t issued... because consumers bought a different product that had a key feature that the returned product and its competition lacked.
For a manufacturer, the discovery could lead to a new product category. For retailers, it could help them promote the existing product(s) in ways that eliminate the returns of one and increase the sales of another.
Big Data can, in a very real way, detect the kind of connections that Steve Jobs became known for in developing the original Mac and, after his return to Apple, the iMac and, most famously, the iPod, iPhone, and iPad. He was able to extend an existing demand (for portable music, for example) and combine it with available digital capabilities in a package that extended the Mac’s as-simple-as-possible design approach to create new categories.
Traditional querying of Big Data might have suggested, instead, that consumers had no desire for digital music players because they were considered complicated, costly, and incapable of storing all the songs that they wanted. While that may have led to simpler, cheaper, higher capacity devices, it probably wouldn’t have detected the unsuspected need for an online store, integration with desktop/laptop music libraries, and a uniform pricing structure.
With more advanced fuzzy queries that could process more types of data far more quickly on multi-thread processors (such as IBM’s Watson and Cray’s Urika), Big Data might have beaten Apple to market... if it were around at the time. Now it has the potential to help marketers understand what their customers want... before their customers know it themselves.
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