Last month, it seemed the entire tech world descended on Las Vegas for CES 2023. One of the key talking points at the trade show was the attention economy. The general consensus amongst panelists was that attention metrics provide more value for marketers than viewability. However, while attention is almost universally seen as the future of measurement, there is still considerable debate about how attention should actually be measured.
Perhaps the most popular method of measuring attention currently is eye-tracking, which has its use cases, but can often also end up in ‘implied attention.’ This is because eye-tracking techniques, including the research studies attached to said techniques, only measure attention based on the part of the screen a user is viewing. While useful, (when complemented with other techniques), just because our eyes are drawn to a particular part of a screen does not guarantee positive ad recall.
Being intelligent about measuring attention
Eye-tracking may work well in some instances – particularly in linear TV – but, in the data-driven world of digital advertising, attention needs to be measured through more granular means.
To get attention measurement right, the consumer has to be at the heart of any technique the advertiser is using. It’s important therefore to capture data such as, “How much time has the user spent within the action areas of the ad? Was the sound playing?” Capturing data in this way and using Machine Learning learning techniques to model the relationship between them and the chosen outcome – such as purchase – offers far greater insight.
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Thankfully, advancements in AI, including Deep Learning technologies, have brought a new level of sophistication and automation to these techniques. Capable of understanding and managing the complex relationship between ad creative elements and their impact for different audiences. These techniques can also be used to model the relationship between game sequences, creative objects, text and how different combinations engage different audiences, as well as how this evolves over time.
A year of better measurement in measuring attention
Many techniques exist for measuring attention and/or engagement. Some only focus on a particular dimension of a campaign. The best of these will combine customer interactions, advanced Machine Learning techniques, and optimize toward an attention metric to deliver the best possible result. Taking a step further, the most powerful solutions will be those that can view attention metrics that have been derived from a very broad range of factors. These include bidding, publisher inventory, and audience profiles as well as the data directly associated with consumer actions.
This is a progression that makes sense for the digital advertising industry, as it utilizes the burgeoning capabilities of Machine Learning techniques – coupled with the huge amount of computing horsepower we now have available – to genuinely improve the attention gained by ads and, in turn, boost sales.
Using the right techniques to measure attention will be vital in 2023 and beyond. Brands can no longer rely on subjective measures and need to focus on hard data derived from actual consumer interactions that also measure the quality of that attention. This is certainly a battle for hearts and minds and not just eyeballs.
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