The Role of Location in Attribution

Let’s face it—we are a long way from being able to show that digital campaigns, and most other advertising formats, resulted in specific in-store sales. There are simply too many unconnected data silos to stitch together meaningful and statistically relevant results. The ad seen on TV can’t inform your phone or laptop that it’s also been seen, while the point-of-sale system or online checkout can’t notify those previous touch points to confirm the sale occurred. Forthcoming state and potentially federal regulation on data collection and sharing will also complicate this process, but we believe it will ultimately be beneficial for attribution solutions and, of course, consumers.

There remain difficult challenges for location-based marketing and analytics companies to overcome in demonstrating attribution. GPS-sourced location data can be finely tuned to improve accuracy and precision, but hasn’t yet proven accurate enough to be relied upon as the sole metric for validating campaign results. Using other location data signals such as beacons and NFC greatly improves accuracy, but the scale of data remains so small it typically renders the results insignificant.

To overcome location’s challenges, many companies take the small results and extrapolate them to estimate what the potential market impact should be. These inflated numbers don’t provide true insight, and many marketers look at them with a range of emotions from skepticism to total disregard. As 5G implementation and adoption grows, analysts predict that accuracy of location data will improve significantly, but it’s likely this data will remain siloed inside the carriers that collect it.

So if the scale of accurate location data prevents it from being a true stand-alone solution for proving attribution, what role will it play in measurement?

Today we already see marketers evaluating location data in the broader context of all their campaigns, whether digital or traditional. When a major TV campaign launches, marketers examine location data to determine if they see increases in foot traffic at the national, regional, and local level. They adopt this same approach for radio, print, and of course digital campaigns. Marketers also evaluate how their percentage of foot traffic rises and falls compared to their competitors.

Instead of focusing on “did this particular ad result in sales of these particular products,” they’re adopting a macro-economic view of attribution when using location data, driven in part by the many challenges listed above. This is a very similar approach to how hedge funds evaluate location data in order to gauge a company’s performance prior to the release of quarterly results.

Because the data originates from mobile phones, there’s also promise that usage of mobile wallets will be able to connect the last mile, showing that a sale actually occurred. The challenge here is that this data may also be highly siloed behind the walled gardens of a few major banks and technology players.

Ultimately, location data will be the glue that stitches together many of the disparate data silos. It will demonstrate when and how frequently audiences show up at locations, allowing marketers one more critical piece of the puzzle in solving for attribution.

Brian Handly is currently the CEO of Reveal Mobile. He was previously a GM of Microsoft’s Online Services division and possesses more than 20 years of technical, operational, and executive management experience. Brian was co-founder and CEO of Accipiter, which was acquired by aQuantive in December of 2006 followed by the acquisition of aQuantive by Microsoft in 2007.

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