Whether they’re small corner stores or multi-story pharmacies, retailers sit on huge amounts of data about consumers’ shopping habits.
If properly tracked and analyzed, this data might be invaluable to the manufacturers of the products on their shelves, as well as to the retailers themselves. But instead of combing through this data in search of potentially profitable insights, many business owners only make rudimentary use of it, tracking inventory on a week-to-week basis, reordering products as stocks run low.
SwiftIQ — a Chicago-based analytics startup that focuses specifically on the retail space — wants to help retailers unlock their data’s full potential. By automatically tracking sales down to the single-transaction level, they seek a deeper understanding of where and when certain products are purchased, who the buyers are — and how brands and retailers can use that information to their advantage.
To illustrate the nuance provided by such granular data, CEO and co-founder Jason Lobel points to an early discovery his team made while piloting their platform.
“Energy drinks are bought from 6 a.m. to 8 a.m. Monday through Friday in convenience stores,” said Lobel. “A lot of retailers run promotions on orange juice and breakfast sandwiches, but we found that the most relevant thing for a convenience store to promote with breakfast is energy drinks.”
Another discovery the team made was that 12-packs of beer are sold pretty much exclusively for 10 hours over the course of an entire week.
These kinds of insights can be leveraged by retailers to determine things like product placement and ordering schedules: do 12-packs of beer really need to take up valuable cooler space all week, or can they be moved to the back during off-peak hours? Will the current supply of 12-packs last through the weekend?
Brands, on their end, can target their marketing efforts by analyzing data from multiple retail locations across a market.
“One of our customers just launched a new product, and 90 percent of its competitor’s revenue was in 20 percent of the stores we tracked,” said Lobel. “So that brand didn’t really need to go advertise at 100 percent of the 500 stores in this chain. They only needed to understand which 20 percent were generating all the revenue.”
Cross-location data can also help retailers determine which items to stock their shelves with. While a typical grocery store may stock as many as 50,000 different products, convenience stores may only have room for a tenth of that. When faced with such limitations, optimizing the inventory can make a huge difference for the retailer’s bottom line.
Founded in 2011, SwiftIQ launched its current flagship product in August 2014. Since last year, the team has grown from four to 13, with plans for additional hires in upcoming months. Having raised $3 million, mostly from local angel investors, Lobel said his team’s focus has been on ensuring the technology and analytics side of the business are as scalable as possible.
The platform is currently in use at 8,500 retail locations, with over $55 billion in transactions analyzed. The company has also developed partnerships with a number of major national brands, including Anheuser-Busch InBev — the company behind beers like Budweiser and Modelo.
Close to the headquarters of some of America’s largest packaged consumer goods manufacturers, including Kraft Foods, Procter & Gamble, Miller Coors and Anheuser-Busch, as well as retail titans like Walgreens, Lobel finds Chicago the perfect stomping ground for a startup in the retail space.
“That, and we’ve had a lot of success with people out of the University of Chicago, Kellogg, and the University of Illinois,” he added. “So we’re sitting with a bed of talent near us and with a bunch of customers that we don’t have to travel across the world to go see.”
Images via SwiftIQ.
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