What if there was a way of counting people as they entered a store that didn’t involve making them walk through a metal turnstile? What if, moreover, there was a way of telling where in the store they lingered, what time they arrived and how long they stayed before they left? You’d need to convince people to wear tracking devices on their bodies. But what if it turns out that everyone already did?
That’s the premise behind Turnstyle Solutions, a Toronto startup that’s taking a leading-edge approach to giving merchants a sense of how people are interacting with their stores. The service uses the WiFi transmissions from smartphones to track – but not identify – customers as they enter and explore a store.
Devon Wright, who launched the firm as CEO in June, says that the goal is to help bricks-and-mortar retailers get the same kind of metrics that digital retailers have long benefited from.
“They have so much information to go on to understand traffic,” he says. Keeping digital metrics, on the other hand, can help traditional retailers get up to speed. “That’s really what it comes down to,” he adds.
Turnstyle works like this: Every smartphone is a chattering, multiband radio station, constantly sending out inquiring signals to the world around it, even when it’s in your pocket. If your phone is WiFi enabled, then it’s is constantly on the lookout for networks to join. As part of this process, it discloses a bit of information about itself: a unique identifier called a “Media Access Control” or MAC address.
Turnstyle’s system is essentially a listening post: a customized WiFi base station that listens in as smartphones broadcast their MAC addresses as they look for hotspots. In the interests of privacy, the system immediately forgets the code itself, “hashing” it into a unique identifier that can’t be traced to an individual. But it will remember if the same (anonymized) smartphone returns for another visit. If clients use multiple base stations, Turnstyle can use users’ relative signal strength to determine where in the store they are.
Merchants can then log into a software dashboard to get analytics on things like how many people have entered the store, when they arrived, how long they stayed, and how many of them were repeat visitors.
The key takeaway isn’t individual behaviour, but patterns, says Mr. Wright. “They’re not making decisions off single customers. It’s off the aggregate.”
Mr. Wright’s entry into location-based analytics actually came from his experience playing in a band with his co-founders, who he met in business school. Ever the business students, they he wanted a better way of telling which members of the audience were returning fans who should be rewarded with drinks and tickets.
He soon discovered that on one hand, the market for social-media analytics was saturated. On the other, it was difficult to extract meaningful metrics from location-based “check-in” services like FourSquare because not enough people were actually checking in. The result was a system that, in effect, automates the checking-in.
“There’s a lot of guys out there who can do the Twitter analytics, but there’s nobody who’s doing the location-based thing really well.”
Mr. Wright says he’s aware that customers are sensitive to privacy concerns and has designed a product that can’t track individual identities. All the same, he sees a growing awareness amongst consumers that the devices so many people carry in their pockets are in constant contact with the outside world – and stores have always had their eyes open.
“I think it’s naive to imagine that this information isn’t the sort of thing that isn’t being collected by the guy standing at the door.”Report Typo/Error
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