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The Emerging Marketplace for RFID Data Analytics, or Finding a Needle in a Haystack

Solution vendors should plan for changes in the economics of reselling hardware. Those who find ways to incorporate data analysis into their offerings will enjoy higher margins and a competitive advantage.
By Scot Stelter
Jul 06, 2014

At the grocery checkout, there are two printers. One prints your receipt, while the other outputs a strip of coupons just for you. Usually buy Barq's root beer? Try Mug for free next time. Most likely, Catalina owns the second printer and uses loyalty card data to target promotions, generating substantially higher redemption rates than other methods. The company claims to have the largest shopper history database in existence—some 2,500 terabytes of information.

Data mining for retailers and brand owners is a profitable business. Catalina generated $661 million in revenue during a recent 12-month period with an EBITDA (earnings before interest, taxes, depreciation, and amortization) of about $230 million.

ChainLink Research says that more than 8.5 billion passive RFID tags will be attached to or embedded in merchandise or other items this year, and projects that a cumulative 150 billion will have been deployed by 2020. In addition to basic inventory or asset information, the flow of data from the readers will conceal a rich stream of information valuable to anyone who has the mathematics necessary to extract it. Not everyone has the tools to extract this data, but there are mathematicians and software engineers who do. This article takes a look at this trend, and how it will affect the RFID ecosystem we know today.

For retailers, on-shelf inventory management is just the starting point, according to Stacey Shulman, Zensar Technologies' senior VP of global retail and former American Apparel CTO. "Any data is valuable if it's behavior-changing," she says. Fixed RFID reader infrastructure provides a way of visualizing activity. For example, "we can determine an optimal planogram based on traffic, activity and correlated sales." It doesn't have to be labor-intensive: Using reference RFID tags, the system can learn the baseline planogram automatically, after which A/B testing, in which experimental planograms are compared to the baseline, can reveal how to improve on the original.

Combining RFID data with other technologies brings out additional value. For example, Shulman says, "Using Wi-Fi beacons and geofencing, we can correlate a person with an item that has been picked up and infer a relationship. We know what's moving and what's selling."

Fixed infrastructure is a prerequisite for users to realize these benefits, says Caltech researcher Mike McCoy, the founder of Southern California RFID data analytics startup Cofacet Inc. "Data analytics is more restricted with handheld systems. There's value and information that can be gleaned from [handheld] systems; however, the continuous reads from fixed infrastructure lead to a much richer dataset to analyze."

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