Retail analytics provide retailers the data to make better decisions, taking the “guesswork” out of developing strategies and deploying tactics. So, why are retailers so keen on “making bets?”
Retail has long been more art then science, with store design, window fixtures, merchandising displays, and merchandise color palettes and the like influenced by the valued subjective expertise of experienced retail veterans. Since RetailNext ushered in the big data era of in-store retail analytics seven years ago, data is now generated from a growing number of data sources and streams, and it’s ready accessibility across the organization now allows science to complement art.
This week, the retail news highlighted two rebounding retailers planning to make operational changes for the upcoming holiday season. First, as reported by Katherine Boccaccio in Chain Store Age, Wal-Mart Stores is making a “checkout promise” to staff every cash register from Black Friday through just before Christmas during peak shopping times, in an effort to alleviate long checkout lines.
Right on the heels of the Wal-Mart announcement, Target announced more than half of its U.S. stores would stay open later, to 10 or 11 pm on Sundays, and until 11 pm or midnight on other shopping days, with hours varying by store. As described by Paul Ziobro in a post on MarketWatch, Target is “betting” that enough customers will shop the later hours to make then investment worthwhile.
Retailers no longer have to make blind bets and hope for success. Good thing too, because since when is hoping a sound business strategy?
Queue analytics offers a retailer an opportunity to analyze checkout lines in real time. Once a pre-determined threshold is met – say more than one shopper or shopping cart waiting in line in front of a POS terminal – an alert can be sent to management and staff, triggering the opening of another terminal. No more guesswork, no more inefficient misalignment of staffing.
Video analytics and other Traffic counting processes deliver accurate data on shoppers and store visits, and allows a retailer to not only determine which store hours are most productive, but also empowers it to better align staffing to Traffic swings, optimizing Conversion, and maximizing store performance.
With data so readily available, accessible, and reportable, retailers no longer have to rely on hunches and anecdotal observations. There’s still a place for those experienced-based, subjective judgments, but they’re used to balance out and perform a reality check on the data.
Just like a casino “stacks the deck” in its favor by using math to set the odds (ever wonder how the casino is so much bigger and nicer than the gamblers’ homes?), retailers can use data science and math to take the gamble out of their best played bets.
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