A decade and more ago, when “e-commerce” was all the rage, technology innovation experts focused on online-only retailers like Amazon.com.
In addition to the still-novel customer experience of purchasing goods online, there was a data-driven reason for this attention. By comparison, physical stores—which represented, then as now, the lion’s share of retail activity—were comparatively opaque from a data standpoint. In the brick-and-mortar world, it’s much harder to do the sort of real-time marketing analytics or inventory optimization possible on a store’s Web site.
But that’s changing—fast. The potential for connecting data sensors on retail racks, in shoppers’ hands—everywhere the in-person consumer experience takes place—suggests both big opportunities for growth and a challenge for retailers to build connections among varied systems, said Jon Stine, director, retail industry for Cisco Consulting Services. “I’d suggest we take the next step beyond automatic data capture and the structured dataset, and we move into next generation,” Stine said.
Stine spoke to Data Informed in September, just ahead of Cisco’s release of a study, which focuses on the impact on retail of what the company calls the “Internet of Everything.”
Connecting those everythings is a theme for Cisco. CEO John Chambers, speaking at Interop New York earlier this month underscored the connected-device explosion. By Cisco’s own estimates, the number of connected devices will mushroom from 10 billion in 2010 to 50 billion by 2020. “And that number is probably way too low,” Chambers remarked.
When it comes to retailers, Cisco believes companies could have realized an additional $99 billion in sales in 2013 if they were more connected across their operations. That’s like adding another Costco Wholesale (or one and a half Amazon.coms) in revenues to the retail sector.
Among other suggestions, Cisco says retailers can exploit this data and device opportunity by connecting previously unused (or “dark”) data assets, such as video surveillance cameras, social media, the Internet and customers’ mobile signals. The result? Better prediction of new trends, employee empowerment and improved profitability. (Also among the suggestions are building trust with shoppers by showing what they have to gain by sharing data—a consideration that other technologists and consumer privacy experts echo.)
While existing technologies like bar coding and, more recently, radio-frequency identification (RFID) already let retailers monitor their inventories, what Stine proposes sounds more ambitious. It requires real-time data analysis of what he broadly defines as “events” taking place inside the store.
These events can involve physical items—five garments hanging near each other, and how this proximity impacts sales—to “contextual” ones, such as a shopper turning on his or her smartphone and accessing a shopping app or a social media site.
Knowing that a specific person is in a particular aisle at a specific time has phenomenal possibilities for retailers ready to pull together the networking and database infrastructures, Stine said.
“It’ll impact our ability to better predict behavior, inventory and turn,” Stine said.
Real-time analysis is the Holy Grail for much of this, although Stine was quick to note several data management challenges. “There’s data capture, data connection and, more importantly, data filtering,” he said, adding, “Do you want to analyze the needles or the haystack?”
Stine, whose discussion focused on technology implementation issues, said he believes retailers have little choice but to pursue these concepts. “The functionality in ERP has been commoditized,” he said, meaning the only way for a retailer to differentiate itself is how it captures, processes and drives decisions with data.
An Accent on Customer Experience
Emphasizing data does not mean shoving numbers in front of shoppers. Retailers at the cutting edge of this technological shift emphasize customer experience over raw technology.
Take Hointer, which has pilot clothing stores in Palo Alto and Seattle.
Hointer stores use a combination of smartphone apps and back-end automation to deliver clothing to a shopper’s fitting room, and so provide many of the efficiencies and data-collection aspects of online shopping with the high-touch of an in-store retail experience. In a Hointer store, for example, a shopper sees pants on the rack with a 3-D barcode on a tag. The shopper points her smartphone using a Hointer app at the clothing, selects it, and is then told which fitting room to visit. The selected clothes arrive there, passing through a chute.
While this makes Hointer stores more efficient than traditional apparel retailers (in part because they obviate the need for sales associates to sort and restock store shelves), the real goal is to improve the customer experience, company CEO Nadia Shouraboura told Data Informed.
“The cost-benefit savings are phenomenal, but to me they’re secondary,” Shouraboura said.
Then again, data is at the heart of the equation.
“We collect more data than traditional store,” Shouraboura said. “We know what you like, what you didn’t like, what you discarded, and if you needed alterations.” In summary, “We know what you have in your hands and on your hips,” she says.
The database also logs what suggestions the customer accepted from the “stylist”—a tablet-equipped salesperson in Hointer parlance—so style suggestions will improve over time. (In the near future, tablets will be added to the fitting rooms themselves, offering cross-sell and up-sell suggestions to shoppers.)
And that’s precisely the point, Shouraboura said. Improving the customer experience means people will try more items, more quickly, and go home with a higher percentage of them in a shopping bag.