This article is part of the Digiday Partner Program and is brought to you by OpenX. Matt Reid is vice president of marketing at OpenX.
It’s the digital ad world’s costliest word problem: If consumers are spending 12 percent of their time on mobile devices but advertisers are spending just 3 percent of their budgets there, how much money is being left on the table?
The answer, of course, is a lot. If ad spending had kept pace with mobile usage, the industry would be enjoying a $20 billion market. Instead, according to a report by eMarketer, that spending will likely top out at just over $7 billion this year. That is a 44 percent increase over last year but still not where the market could be.
We are being held back because mobile simply doesn’t offer the key capabilities marketers have come to rely on: tracking, capping, targeting and retargeting. In fact, according to a survey of mobile marketers conducted by the Mobile Marketing Association, 48 percent of respondents cited tracking and measurement as their greatest mobile marketing concern.
Traditionally, online advertising has relied on what we’re calling “deterministic device identification”— cookies, Android IDs and IFA. These solutions won’t work in mobile and, increasingly, on desktops. UDID is over. Android IDs have fragmentation issues. And privacy concerns are driving governments and Internet search giants like Mozilla to limit third-party cookies.
Without a solid tracking and measurement system, marketers can’t justify moving money from an established channel to an emerging one. Mobile has to reinvent the wheel. For our part, we’re backing “probabilistic device identification.”
Here’s how it works. When someone visits a website, non-sensitive information about their device is collected, including device type, OS type, time zone and system language. These parameters are used to create a device profile and then “hashed” to anonymize the visitor.
That hashed profile is stored on the marketer’s server. When the device visits the site again, the data is collected once more and matched to the stored profile, allowing the marketer to target ads based on the profile’s past behavior.
Taken together, probabilistic identification allows marketers to target their messages with almost 80 percent accuracy, more than enough precision for most use cases. Moreover, it overcomes privacy concerns for several reasons: They collect no personally identifiable information, satisfying consumer advocates; they leave no code residue on anyone’s device; and finally, they function equally well on any device or use case (including the traditional Web.)
Finally, we’re backing this method because it dovetails with RTB, one of our core capabilities. Existing device-identification methods were not fast enough or effective enough to recognize users in multiple-use cases.
So there you have it. Privacy concerns and technological limitations are limiting mobile advertising’s potential. We must find a way forward.
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