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Home-Improvement Retailer Uses RFID to Improve Store Layout
In the meantime, as the patron browses the store, wireless access points receive transmissions from the tag, and the Ekahau Positioning Engine software server—with an Intranet connection to Rautakesko's back-end system—collects that ID number and analyzes the tag data. Ekahau Vision software provides a display of the store's floor plan, with an icon representing each customer tag. The system locates those tags through Ekahau algorithms that rely on received signal strength indicators (RSSI). In an ideal environment, Rutanen says, Ekahau can achieve an accuracy of 1 to 3 meters (3.3 to 10 feet).
The staff can search one of multiple tags in real time, or utilize the data for marketing research, such as determining whether customers purchased items they stopped to view on display, which way traffic flows throughout the store, and at which times of the day the checkout counters are backed up on account of excess traffic or insufficient staff. They can also determine which demographic segments—such as age and gender—are interested in a particular product, and when that demographic shops.
The Ekahau system works with the stores' existing Wi-Fi networks, with approximately 20 LAN wireless access points in place for voice and data, says Antti Korhonen, Ekahau's president and CEO, thus saving the retailer time and money that would otherwise be spent installing RFID interrogators. In several cases, Rautakesko's stores did not yet have a Wi-Fi network, so Ekahau installed one for them.
The earliest studies, Kuokkanen says, simply involved testing the Ekahau hardware and monitoring traffic movement in real time. But in 2008, he adds, the company began to develop business analysis related to the studies.
Thus far, Kuokkanen reports, Rautakesko has been happy with the results. "We get valuable information for our store concept design and planning," he says, adding that in the future, the company intends to implement such RFID deployments at three or four stores per year.
"We hope that we can learn about time spent in our departments and new showroom—are there some showrooms that are more interesting, or do the showrooms attract customers better in some other location?" Kuokkanen states. "We hope that we can find some customer flow connections between some departments—for example, the bathroom showroom to the tiling department."
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The staff can search one of multiple tags in real time, or utilize the data for marketing research, such as determining whether customers purchased items they stopped to view on display, which way traffic flows throughout the store, and at which times of the day the checkout counters are backed up on account of excess traffic or insufficient staff. They can also determine which demographic segments—such as age and gender—are interested in a particular product, and when that demographic shops.
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| Konsta Kuokkanen |
The earliest studies, Kuokkanen says, simply involved testing the Ekahau hardware and monitoring traffic movement in real time. But in 2008, he adds, the company began to develop business analysis related to the studies.
"We hope that we can learn about time spent in our departments and new showroom—are there some showrooms that are more interesting, or do the showrooms attract customers better in some other location?" Kuokkanen states. "We hope that we can find some customer flow connections between some departments—for example, the bathroom showroom to the tiling department."
