As a growing number of advertisers, publishers and brands look to target consumers across devices, many issues in the cross-targeting space are yet to be resolved.
Marketers want to maintain consumer relationships across mobile, desktop, wearables and other devices but due to cookies’ impotency outside desktop, data fragmentation remains a key technical challenge.
“Data fragmentation is a complicated challenge for the industry because there’s no single unique identifier across devices,” says Jasme Bantens, managing director of digital insights for Mindshare North America.
One option for marketers is piecing together various signals across some channels, such as Set Top Box, PC, OTT, and mobile, says Bantens.
However, a discrepancy remains between what advertisers want to do and what they actually can do.
An eMarketer study shows that few cross-device targeting solutions are effective, which leads to the question: Is cross-device targeting, still just a fairy tale?
“The reality is you can target across different devices, but there are few options to target across different devices knowing that it’s the same user for the most part,” says Michael Kaushansky, executive vice president and chief data officer for Havas Media.
Kaushansky explains the challenge with two cross-device targeting models as examples: “probabilistic” and “deterministic.”
The “deterministic” methodology leverages users’ data such as social log-ins, email address or e-commerce. For example, Facebook’s Atlas ad server allows marketers to syndicate Facebook log-ins and target users across mobile, tablet, desktop and laptop.
Although “deterministic data” is a strong indicator, it’s not 100 percent accurate. Chances are a user may log-in to a platform from another person’s device or that user may not be a subscriber, and thus there’s no log-in detail available.
On the “probabilistic” side, marketers rely on devices rather than users’ log-in data.
“Companies like Drawbridge and Tapad collect device IDs to solve cookies’ lack of tracking functionality outside desktop. With the device IDs, they are able to identify associated cookies to a device, using WiFi signals, location, etc. and probabilistically match the device to a user,” Kaushansky explains.
For marketers who are looking to track consumers across devices, “deterministic” could be a preferred choice because it not only covers a broader cross-section of a campaign but also measures the backend at a user level, adds Kaushansky.
And they are likely to do so via an application like Facebook Atlas or Google’s DoubleClick platform.
Vivian Chang, vice president and general manager of data business for cross-device marketing company Tapad, looks at cross-targeting differently. Since both “deterministic” and “probabilistic” have their own drawbacks, marketers should experiment with combined modelling instead of taking an either-or approach.
“We start with probabilistic data. First, our algorithm processes device data for connection clues, it then analyzes those clues to make a prediction about whether these devices are associated with the same user, we then use a deterministic data set to verify the accuracy and scale of these probabilistic connections,” she says.
“It is not used in targeting; it is exclusively used as verification. In this way, we get the best of both worlds – scale with a high degree of accuracy in a privacy-safe setting,” she explains.
Chang’s company has used this combined approach to deliver effective cross-device campaigns for brands like Dell and National Geographic Channel. Regardless of the effectiveness, Chang admits that there are still many challenges in the cross-device targeting space. One big hurdle is data sharing and ad budget allocation.
Data sharing and budget fluidity have been the big issues to date, she says.
“Traditionally, agencies were created to work on siloed teams with siloed budgets and siloed results. In this setting, it was hard to assess the interrelationship between channels: how mobile might affect desktop, or TV or even brick-and-mortar. This is changing as agencies restructure accordingly but it’s a complicated workflow to change,” Chang explains.
Another obstacle is the “walled garden” created by the likes of Google, Facebook and Twitter.
“The idea of a walled garden is another big challenge. When advertisers work with large companies who deal in deterministic data, they have to remain inside that ecosystem, giving marketers much less control over their ad spend and technology choices. This exacerbates the problem of fragmentation, the exact problem that cross-device is meant to solve,” says Chang.
*Image via Shutterstock
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