The rise of machine learning in buying algorithms is helping to debunk some of advertising’s commonly held truths.
The assumption that high viewability equates to high quality is often misleading. When campaign goals are lower funnel and there is no constraint to purchasing only highly viewable placements, AI frequently reveals that lower-viewability placements – as measured by third-party vendors – can yield superior performance results when purchased at the right moment.
With this in mind, I have been following the progress the IAB is making developing new attention standards, which are purported to help advertisers compare and measure attention more easily across different partners and channels.
The IAB Attention Measurement Task Force and the Media Rating Council just announced a joint effort to accredit attention measurement vendors and plan to release guidelines. It remains to be seen how much flexibility attention vendors will have or if attention will be simplified into a common approach, as was the case with viewability.
If attention is treated like viewability 2.0, high or low attention could become a blanket requirement for a campaign and hinder performance.
The danger of overreliance on attention
It’s important to remember why advertisers adopted viewability in the first place. Early in programmatic advertising, it became clear that many purchased ads were not actually in view on the page. Viewability addressed this issue effectively, becoming a default requirement to avoid fraud.
However, there is no rampant “inattention fraud” that a high-attention checkbox could solve. What captures attention is more than just page placement; it’s the message, the creative and the viewer’s current mindset.
If attention becomes just another checkbox, it could artificially decrease supply and increase yield, potentially unfairly, as a campaign could still perform based on other metrics, such as CPA or ROAS. Instead of relying solely on attention scores, we should use technology intelligently to consider all contextual and behavioral factors.
The complexity of attention
Defining a “viewable” impression was relatively simple: 50% of the ad’s pixels must be visible in the browser window for one continuous second. Attention, however, is far more complex. It can be measured by various means, from mouse movement and cursor hover time to eye-tracking and engagement.
The simplicity of viewability allowed it to become universal, as any ad tech provider or publisher could measure and deliver it. Attention, on the other hand, involves attributes that are difficult to measure uniformly across different ad formats and layouts.
Standardizing attention metrics would require significant compromises, either on the complexity of the current offerings or on the scale of the data being used. Some critics consider eye-tracking invasive, which means it is unlikely to become universal. If the IAB accepts any solution using an opt-in panel for eye-tracking, advertisers would need to accept such panel-based data as estimates, like they do with linear gross rating points, because they don’t have impression-by-impression data.
Imagine trying to fit the nuances of audiences and their interests, lifestyle and current mindset into categories like “high” or “low” attention. Enabling attention to be complex leans into the capabilities that programmatic technology didn’t have when viewability standards were created.
Letting data decide
Instead of trying to aggregate attention scores and treating them as binary, advertisers should use more dynamic measurements as one of many inputs in their media mix. Sometimes the attention score of an ad unit will be important, but sometimes the user or the creative will matter more. Strong creative and accurate targeting may reduce the importance of a high attention score.
Consider an eye-catching ad that people only glance at but drives high awareness. A default high attention requirement would block this placement. However, an advertiser allowing ad tech providers to test attention elements without being limited by them would still be able to use the creative to meet awareness campaign goals.
Advertisers have more data than ever before, making it beneficial to start embracing the complexity of attention. Allowing different providers to offer different solutions and definitions of attention might be inconvenient for publishers, but it gives advertisers multiple lenses through which to test their campaigns. Rather than pushing for standardized attention segments, we should focus on testing standards that enable advertisers to maximize performance based on their chosen inputs.
While attention metrics hold significant potential, they should be integrated as a feature in buying algorithms and models rather than as a standalone requirement. This approach will enable advertisers to tap into the full power of AI and data-driven decision-making to optimize their campaigns effectively.
“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
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