No major advances in mobile location technology have emerged. In the last couple of months, however, quite a few very different approaches to location tracking have emerged. These range from leveraging the earth's magnetic field to piggybacking the data already used by mobile ads, tracking a combination of Wi-Fi and Bluetooth signals, and riding the audio signals from existing music speakers. One app even reacts to light patterns from specially enhanced LED bulbs.
But none of these approaches, thus far, has proven effective at delivering truly precise locations. For example, a customer is looking for a very specific cereal, such as multigrain oats and honey flavored Special K. With item-level product tracking, the system could go far beyond directing the shopper to the cereal aisle. It would offer mobile app-based directions right to the Special K area and then navigate (up one shelf, three inches to the right, third row) the shopper to the exact SKU.
It's a given that this nirvana app has already checked real-time inventory to make sure the specific product really is in stock and then used video or RFID tracking to verify the product is indeed where it's supposed to be. (Come to think of it, a truly impressive demo would be if this app directed the shopper to where the one box of that flavor of cereal had been misplaced by another shopper.)
Now that the resistance to using such customer-tracking approaches appears to be slightly melting (well, maybe not), it's something that IT shops are going to need to examine more thoroughly.
Alas, location services don't come close to that level of capability yet, under any of these approaches. But it should happen soon. (Recent trials by Google and a more limited exploration by grocery giant Meijer have amply demonstrated that we're far from the goal.)
To get there, of course, will require major improvements in tracking all three of the elements needed: the shopper, the desired product and the inventory data, so a non-existent product can be immediately flagged—ideally with a message saying, "I'm so sorry but we're out of stock at the moment. Here are five suggestions of similar products that we do have in stock. Here's a button to special order the original product right now, and we'll E-mail or message you when it's in. Or click on this button and we'll ship it to you through our E-Commerce operation."
Beyond the upsell potential—which Walmart is working hard to use for its in-aisle mobile checkout trial—there is, of course, the huge CRM potential of knowing everything a customer is even thinking about buying (through a barcode scan) and then marrying that information to the shopper's location.
Let's look at some of the location options:
A Finland-based vendor called IndoorAtlas said that the nature of many retail buildings makes them natural for magnetic mapping.
"Steel masses inside buildings twist Mother Earth's magnetic field such that every spot produces a unique pattern. Each building, floor and corridor creates a distinct magnetic field disturbance that can be measured to identify a location and generate a map," the vendor says on its site.
This approach currently delivers roughly one-meter accuracy (half-meter in either direction) with current handsets, said IndoorAtlas CEO Janne Haverinen. But he said with sensor improvements expected within the next two to three years, he believes accuracy will improve 10 times—to about 10 centimeters (or approximately 4 inches).
To set it up initially requires a detailed mapping of the store, something that Haverinen said typically takes about two-and-one-half hours for a 10,000-square meter (or about 110,000 square feet) store. Given the magnetic nature of it, retailers considering such an approach need to be aware of anything metal (specifically, anything that sticks to a magnet). When any such metal is changed, the store—or at least the portion of the store where the change has happened—needs to be remapped. "So if you remove or rearrange metal shelves, it will change the magnetic field," Haverinen said.
Metal shopping carts pose a smaller issue. "If someone passes you with a metal cart, it might have a small effect. It might give you some bad readings," he said, adding that the software could—theoretically—filter that out. Either way, Haverinen said, the effect would be very short-lived.
His pricing approach, though, could be more disruptive. Instead of a flat rate, IndoorAtlas wants to charge retailers for every time any shopper in the store makes a location interaction. Presumably, it could also be triggered in reverse, where the store activates a "where is the customer now?" inquiry. For a high-volume store, the CEO said the per interaction cost would be "a small fraction of a cent" and then, when asked, said "one-millionth of a penny is close."
The common thread in almost all these approaches is to work with something that already exists in the mobile or retail environment. What a few vendors are doing is trying to piggyback location information on top of the existing back-and-forth signals from the networks that decide which mobile ads to post. Those data exchanges already identify the shopper in various ways, including the number associated with specific hardware (that shopper's phone), the specific application, the phone's OS and other attributes. Those small snippets of ad-serving codes can be picked up by anyone with access to that ad network, said Sense Networks CEO David Petersen. "These are true IDs, analogous to cookies. When we see that, it bounces that location against local points of interests" and looks for a match, he said.
The problem is that this system is almost exclusively designed for ad transmission, so the location precision is not going to be especially precise. But retailers building atop such a system could work around that issue.
Although not related to location, our personal favorite example of leveraging mobile data to identify the phone came from a Seattle vendor in February. It avoided a PIN by grabbing whatever data about the phone it could, including the list of installed apps and the names of the five most frequently called friends. Other attributes grabbed include the operating system version, an app cookie, the SD card, the nature of a Wi-Fi connection, and carrier and CPU performance.
Mobile vendor LoyalBlocks just finished a pilot of very small New York City-based merchants where the store has an Android unit (provided by the vendor, if necessary) that actively searches for any other device using either wireless or, interestingly enough, Bluetooth.
"We're using the Android device as a sort of radar that detects the customers as they walk in. The wireless module of that Android device is used to create an active scanning area within" the store, said the vendor's CEO, Ido Gaver. "When the customer launches the app for the first time, aside from creating the user account, it's associating their mobile device parameters with their profile."
LoyalBlocks estimates that it can deliver accuracy of about five inches, a spokesperson for the vendor, Loren Pomerantz, said.
Mood Media, the owner of famed elevator music generator Muzak, launched a wonderfully clever move with Macy's this summer. The program, as announced, is a way of enabling Macy's—and other chains—to launch ShopKick chainwide by riding over the existing sound speakers.
But Mood also spoke of its ability to send tones out from those speakers and have an app listening for these high-frequency bursts of sound. Given that each sound will announce which speaker it's coming from—some stores have hundreds of these speakers in each location—and that the app can detect the relative strength of those sounds and, therefore, extrapolate the relative position to each speaker, it can theoretically locate shoppers with an unusually high degree of precision.
Even better, given that these speakers already exist within quite a few of the largest chains—Mood customers include Walmart, Target, Home Depot, Sears, Toys"R"Us, Foot Locker, Abercrombie & Fitch, TJX, CVS and Macy's—this has some practical, relatively low-cost potential.
A startup called ByteLight embeds its technology within a standard LED light, which sends out a light pattern that its application can recognize. The strength of the received pattern indicates where the shopper is standing within one meter, according to the vendor's CTO, Dan Ryan, when it's triangulated with patterns from nearby lights.
Downsides: The phone not only has to be activated with the app launched, but it can't be in a pocket or anywhere else where the light would be blocked. That means the shopper can't accidentally hold it with a finger covering the phone's camera lens. For phones that have two lens—front and back—the finger-blocking is less of an issue, because the app should work as long as both lens are not blocked.
On the plus side: The phone can be in airplane mode—no network—and this light-based app should still work.
For what it's worth, Ryan said he's not concerned about his offering's limitations. He envisions it being used to track shoppers while they are actively interacting with the mobile app. If that's the case, then there's no reason why the phone would be off or in someone's pocket or for a finger to be covering the lens (or both lens).
That's a valid approach, and it would certainly apply to the Special K scenario described at the beginning of this story. But it underscores the differences in the approaches and how retailers need to think through exactly how they want interactions to happen. The fully effortless scene of a customer walking into a store and being identified when his or her phone is in a pocket is just one possibility.
And to be fair, there are a huge number of caveats with all these approaches. First, the legal issues surrounding such shopper tracking are still unclear. One federal appellate panel has ruled, but we have yet to hear from the full circuit. Nor has the U.S. Supreme Court, which already has a case relating to this topic before it, weighed in. There are tons of state legislatures, too, not to mention thoughts from Congress and various federal agencies.
Then there is the question of how much tolerance—or resistance—for such efforts shoppers will offer. Given how little people seem to care about privacy—through their purchase actions, not what they tell people taking surveys—it's most likely that retailers will have free reign to take location tracking as far as each chain wants.
Critically, this assumes such tracking is used to deliver true benefits to shoppers. Amazon has always been the gold example of this tactic. The E-tailer customizes its pages and sends tons of E-mail pitches but is so focused on truly appearing helpful that it generates almost no resistance. If this helps customers get in and out of stores more quickly and helps to rapidly locate hard-to-find products, this could be a huge win. Assuming, of course, at least one of these technologies proves to actually work in the field.