Boosting in-store performance with retail data

The prevalence of digital commerce is perpetually changing the face of the retail landscape. As a result, brick-and-mortar stores are put in a situation where they have to come up with compelling ways to make shoppers get off their couch and step into the store rather than buy online.

“However, all hope is still not lost for physical retail stores and malls,” affirms Adam Ioakim, a veteran in the retail business landscape. The GM (Australia and South East Asia) of ShopperTrak—a leading-edge retail tech solution provider—Ioakim points out, “The good news is that 85-95 percent of all retail transactions continue to take place at physical stores, and not online.”

That said, in today’s world of commerce, the way people purchase has changed dramatically. Present-day shoppers like to research the details of a product, compare its pricing with other similar brands in the market, and check user reviews before purchasing. Thus, shop owners need to think out of the box to gratify their customers.

“And, that’s precisely what ShopperTrak helps retailers achieve through our in-store retail analytics solution,” exclaims Ioakim. Further, while explaining about the solution, Ioakim notes, “Our analytics modules constantly track a retail store’s traffic data and observe how the shoppers behave inside the store.”

The information is made available to store managers through a centralised dashboard that has all the requisite information—including the store’s performance, sales, and staff disputes—in a single window. These statistics help retail managers contrive a plan as to how and where they should place products in the store to ensure maximum visibility and a greater sales-conversion ratio.

“The data is also beneficial to understanding the busiest hours of the day. Our clients use that information to make their staffing arrangements for the week, well ahead of time,” adds Ioakim.

Currently, ShopperTrak monitors 40 billion shopper visits per year, which includes customers of small, temporary kiosks all the way up to large malls and concert venues. This holistic view of customer traffic data across the globe lets ShopperTrak provide weekly reports to clients, aiding them in bench marking their store’s performance based on geographic location or specific market domains.

These wide-ranging analytical reports have earned ShopperTrak numerous accolades from its clientele. Ioakim recalls one such instance, where they engaged with Godiva, the famous Belgian manufacturer of gourmet chocolates. The luxury chocolate company has one of its largest outlets in Regent Street, London.

“The senior management team of Godiva approached us to help them increase customer footfall at their flagship store,” says Ioakim.

Godiva already had ShopperTrak’s “people counting” technology in its U.S. stores and wanted to implement the same for its European stores as well. The solution proved effective in mapping the traffic to sales-conversion rates and analysing data trends. Soon after the installation, Godiva’s Regent Street store recorded a 10 percent increase in in-store transactions, and conversion rates went up from 24 percent to 26.5% in just six weeks.

Such outcomes prove ShopperTrak’s steadfastness to offer quick, measurable results. Treading into the future, the retail tech solution provider aspires to add more toolsets to its already robust analytics portfolio and launch a new “market intelligence” solution as well. With this, ShopperTrak aims to aggregate all their 40-billion (and growing) shopper traffic data and provide extensive, in-depth market analytics reports to its clients, through which businesses can drive meaningful changes in their customer engagement models.

Through our unique retail technology and analysis solutions, we can identify key in-store and online profit opportunities in areas such as customer conversions, queue management, sales, marketing and merchandising. Our insights support retail bench-marking between stores and sites. For more information, visit au.shoppertrak.com/about

 

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