Jessica Miles, Integral Ad Science’s (IAS) ANZ country manager, explains in this op-ed that harnessing attention as a metric for digital ad campaigns will yield big bottom-end lifts and make for a better understanding of media quality.
Attention is one of the most complex and widely discussed topics in digital media. Defining it has proven difficult, but industry experts unanimously agree that harnessing attention is pivotal to executing a successful advertising campaign. With this content explosion and an increasing number of platforms and channels available, marketers have found it challenging to break through the noise with their messages, making attention an appealing and essential concept in advertising.
Fragmented and fleeting consumer attention
In digital advertising, attention refers to the degree of focus and engagement users have with an ad. The more attention an ad receives, the more likely it is to drive desired outcomes, such as increased brand awareness or conversions. Therefore, measuring attention is essential in understanding an ad campaign’s effectiveness and making data-driven optimisation decisions.
Currently, methods for measuring attention in digital advertising tend to rely on a small sample of media quality metrics, like viewability or time-in-view. While these metrics can deliver information about whether or not a consumer has seen an ad, the picture of attention remains blurry. Eye-tracking methods, which monitor consumer eye movement while viewing an ad, can provide valuable insight into the deeply human aspect of attention by understanding where consumers focus their gaze — but this method is often costly and time-consuming.
On the other hand, signal-based measurements are generated when a consumer interacts with an ad, like user-initiated scrolls or click-to-play. This method is more cost-effective and less time-consuming than eye-tracking measurement, but it provides less detail about whether a user has actually seen or focused on the ad.
Given the dynamic nature of digital advertising, marketers may begin turning to hybrid measurement, which combines both signals and eye-tracking. This approach can provide a more comprehensive understanding of user engagement by combining the detailed information of eye-tracking measurement with the real-time data provided by signal-based measurement.
Measuring — and capturing — consumer attention can be daunting and confusing for marketers. However, one of the most straightforward steps a brand can take to increase the likelihood of attention is to deploy strategies focused on media quality metrics and contextual relevance.
Gauging consumer attention with viewability and media quality metrics
Viewability is critical for measuring overall consumer attention levels because it measures whether an ad was in-view and therefore had the opportunity to be seen by a user. After all, if an ad isn’t viewable, it cannot drive attention.
Advertisers can use viewability metrics to identify which ad placements and formats most effectively capture attention. They can also use this data to optimise their media buying strategy by prioritising placements with higher viewability rates.
IAS recently conducted a study with a major consumer packaged goods (CPG) brand to determine the impact of in-view ads on attention and outcomes. The IAS in-view ads resulted in a three times higher return on ad spend (ROAS) than ads that were not in-view, highlighting the importance of ensuring that ads are seen.
Media quality metrics are essential to the end-to-end ad journey and can be used in tandem with other measurement frameworks to help drive better outcomes. Another study used these metrics to show how an increase in viewability and brand safety also increased time-in-view (with an average 57% uptick in conversions).
Optimising media quality can help increase time-in-view, but there’s a balance that marketers are looking to find in these processes. In the CPG brand study, ads that remained in view between three and 10 seconds over-indexed at driving incremental sales compared to shorter and longer time-in-view ranges. This suggests marketers can use an ideal time-in-view range to gain attention and provide the most opportunity to drive incremental sales.
Holding audience attention with contextual relevance
Contextual relevance refers to the correlation between the ad and the context of the surrounding content. Advertisers can use contextual targeting to identify the most appropriate content and placements for their ads, increasing the chances of the ad being seen and engaged with by the target audience.
In a recent ad experiment, IAS tracked user eyesight onscreen to gauge how much time consumers spent with a display ad for HP laptops in conflicting content environments. One environment was contextually aligned with the ad, and the other was not.
After analysing these variables, the study concluded that consumers were four times more likely to remember the HP brand in the contextually relevant scenario versus the misaligned content environment. Moreover, consumers reported being 14% more likely to purchase from HP after seeing the in-context ad than the out-of-context ad.
As privacy regulations are implemented and third-party cookies fade away, more and more brands are leveraging contextual targeting technology to ensure their campaigns align with user preferences and that marketers are reaching an audience interested in what’s being advertised.
Contextual relevance captures attention, while media quality metrics measure attention
As consumers’ attention becomes increasingly fragmented and scarce, marketers look to different forms of attention measurement and optimisation tools to drive greater engagement and outcomes. Some of the most accessible metrics available are media quality metrics.
With the upcoming deprecation of third-party cookies, contextual approaches and attention metrics will become even more critical. This approach is marketing by mindset. Measuring what resonates with audiences based on multiple data points — and drives business outcomes — will be essential. Simply pulling together a random assortment of metrics and slapping an ‘attention’ label or only focusing on one to the exclusion of all others does not provide real value. Successful marketers are tapping into these instincts to fuel more effective campaign outcomes.
If you want to learn more about attention, IAS has published a whitepaper and first-of-its-kind model that predicts the likelihood an impression will lead to a result.
