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Cross-Channel Measurement: Why Attribution Is the Wrong Path

From the beginning, advertisers have struggled to accurately measure the value of their ads. With the dawn of advertising’s digital age—and a wealth of available data promising the Internet’s inherent trackability—many marketers thought the advertising measurement problem would be solved at last. They were partially right.

For intention-based buying channels like search, where the bulk of the value shows up within the channel itself, measuring value is relatively easy. However, the same cannot be said for early funnel brand advertising, such as display, where most of the value is typically found in other, down-funnel channels such as search. Direct, in-channel measurement of early funnel display advertising doesn’t tell the full story. Relying on it alone is akin to flying half-blind; at best, it prevents an advertiser from seeing all the value available within a channel; at worst, it can lead him/her to trouble, misallocating precious ad budget and under-delivering on ROI.

So how does a marketer measure how much an ad on one channel influences purchases through another channel?

The allure of attribution

In recent years, attribution—cookie-based path analysis—has been a much-talked-about process promising to solve the cross-channel measurement challenge.

In an ideal world, attribution would be a marketer’s dream. Third-party cookies track an individual’s path through interactions with ads over time and, if that individual makes a purchase, the resulting value is allocated across all of the ads he/she encountered. Sounds good, doesn’t it? But in today’s multi-device world where, according to 2012 research from Google/Ipsos/Sterling, 90% of consumers move between multiple devices to accomplish online goals, attribution is fatally flawed because third-party cookies cannot track consumers as they move from one device to another. The result: incomplete purchase paths, misattributed value and ill-informed budgeting decisions based on flawed data. The bottom line is that using attribution to measure ad value doesn’t hold up in our multi, multi, multi-device world.

Tried and true: an audience approach  

In contrast, an audience approach to measuring cross-channel ad value bypasses the issues with third-party cookie-based path analysis by going back to advertising measurement basics. Rather than attempting to track individuals—which not only doesn’t work but raises privacy concerns to boot—an audience approach measures the value of an ad’s views or impressions.

This, of course, is nothing new. For ages, advertisers have used an audience approach as a measurement marker for broadcast, print, and outdoor ads. As it turns out, applying this approach from the offline world to the digital domain solves many digital-spawned issues such as multiple device and browser usage. Rather than following one individual’s digital path, the audience approach measures the value of an ad’s views by answering the question, “How does each display ad impact the performance of each keyword and with what magnitude?”

Unlike attribution’s third-party cookie data foundation—which crumbles in our multi-device world—audience-based measurement taps impression data that not only spans devices, browsers, and users, but is also readily available and matches the branding intent of the channel. This is the only viable approach in today’s multi-device, multi-browser, and multi-user world.

So why isn’t everyone doing it?

Given that impression data sources are readily available, why isn’t everyone running down the audience-based measurement road already? The challenge is in the modeling, but the good news is that more mature digital channels, like paid search, have led the way in the development of sophisticated modeling techniques. Measuring value using impression data requires advanced modeling at the most granular, or atomic, level to analyze the potentially millions of relationships between individual display ads and individual search keywords. Like digging through an impossibly massive digital haystack, atomic-level modeling identifies those cross-channel, ad-to-keyword connections that matter—those that, in sum, add up to big bottom-line value.

By revealing those value-driving ad-to-keyword connections, an ad’s unique value per impression, or VPI, can be measured and translated into actionable terms that guide advertisers to bid and buy display ads based on the cross-channel value of each ad in the right places and at the right levels. Continuous re-measurement and optimization based on the cross-channel value of ads allows advertisers to not only tap previously hidden value in early funnel channels like display—it enables them to consistently drive higher ROI across digital channels.

Jim Moar is CEO of OptiMine Software.

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