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An Insider's Guide to Device Matching: Myths and Reality

The stunning proliferation of smartphones and tablets brings with it a whole array of opportunities and frustrations for digital marketers. With most of a marketer's prospective customers carrying a geo-enabled computer in their pocket at all times, the old time and space constraints of marketing messages come unhinged. In addition, the new stream of user data that emanates from mobile devices offers a quantum leap in opportunities to learn about and engage with customers.

The downside of this flood of new data is that consumer identities are now split between screens. And it's not just the smartphone added to multiple browsers on two or more laptops, but the huge penetration of tablets, with connected TVs close behind. For marketers, the task of finding an audience has become geometrically more complicated as they need to find that audience many times over, and somehow coordinate messaging across all of these screens.

So along comes the industry's new trendy technology: device matching. Device matching is a set of predictive technologies that connects multiple devices used by a single consumer. It enables data gathering and advertising that moves seamlessly between screens, allowing marketers to target consumers without redundancy and waste. Device matching also helps address cookie clearing, as any given browser is persistently associated with a given smartphone or tablet.

Sounds like an ideal solution, right? But there's a rub. The inevitable question that arises from device matching involves the match rate -- how often do we correctly associate Mary's phone with Mary's computer. If we can't prove that we've got a match a large majority of the time, is there any value in the technology?

The reality of device matching is that there is no reliable truth set. Claims about match rates tend to be arbitrary. And, surprisingly, match rate may not even be the important issue. The whole point of identifying a consumer across devices is to deliver marketing messages to the right audience based on predictive analytics. And predictive analytics is a statistical undertaking where precision is often the enemy of scale.

In that context, match rates are far less critical than examining your consumers' cross-screen behaviors. For instance, an 80 percent match rate (which has gained currency as the standard) is great, but not if you miss badly in your estimation of customer intent. Do devices that physically appear at Best Buy stores correlate to a higher incidence of "showrooming," with the actual purchase being made on Amazon.com? How often do Amazon shoppers also frequent independent bookstores? Do those consumers also make e-book purchases?

Asking questions specific to the brand and campaign enables marketers to create a more complete picture of their prospective consumers. CrossWalk marketing -- enabled by predictive device matching -- is one component of marketing solutions that capture the full value of the digital ecosystem. The CrossWalk is a great innovation, but a means to an end. It enables all of that mobile intelligence to be injected into display campaigns, and all of that browsing intelligence to contribute to mobile campaigns. In fact, those who boast about match rates may have very little else to back up their claims. As we like to say, the real value is in matching audiences to your brand, not in matching devices to each other.

It's time to move beyond the simplistic inquiry into tracking consumers as they move across smartphones, tablets and computers. The real challenge in our multi-platform world is to develop the insights and analytics to identify and engage the consumers -- at scale -- who are most likely to be receptive to your messaging and ultimately make a purchase.

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