• by Chuck Martin , Staff Writer, July 25, 2013

The world of data tracking for mobile commerce is getting much more precise.

The phone knows where the phone goes, as we all know. And that knowledge can be used to help provide better services to those carrying them.

Any driver using Google Navigation, for example, gets the benefit of other phones being tracked to identify bottlenecks on roads ahead. The next step was for Navigation to automatically re-route your trip to avoid the traffic jam, so the benefit became seamless.

The tracking of phones at retail also is being used in efforts to provide a better shopping experience.

In these cases, the value comes from the data about the phone being tracked, not information about the person.

This is about the use of customers as data rather than data about the customer.

This data about phone movements already is being used at hundreds of stores ranging from small mom-and-pop shops to national chains and shopping centers.

The data tracking engine is from Euclid, a three-year-old California company that likens what it does to Google analytics but for the physical world.

Rather than tracking phones by apps, sign-ins, GPS or cell tower, Euclid installs sensors at stores to capture MAC addresses, which are part of every smartphone.

The company doesn’t capture any information about the person, just the identification of smartphones that are on with Wi-Fi enabled.

The idea is to map shopper traffic and analyze how stores can become more effective. The large volume of aggregated data of phone traffic patterns is what provides the value.

Euclid tracks more than 20 million shopping events a month, says George Kwon, director of product at Euclid. An event could be observations of how many people walk by the store to determine traffic patterns or the volume on sidewalks or malls, says Kwon.

The company targets specialty retailers, including those for men’s and women’s apparel, accessories and home furnishings.

The traffic analysis is what marries the outside and inside the store activity.

For example, the system can associate how many people spent time at which outside window display with who came in to the store after.

It can associate repeat visits, such as of those who shopped during the holidays, how much later they came back to the store and where in the store they went.

The data shared with the retailers is in the aggregate, not by individual phone, says Kwon.

This could be a case of using some very big mobile data to make commerce a little better.