Traditional media quality metrics have created perverse incentives in advertising. Without precise quality measures, advertisers default to cost-based decisions despite performance risks, while publishers struggle to balance cost, quality, and volume—often accepting low CPMs for premium inventory.
Metrics like viewability and VCR have proven inadequate and gameable, flooding the market with technically compliant but ineffective inventory that fails to drive positive outcomes for brands and consumers.
Other industries have solved similar challenges using better quality data. The used car market, for example, was transformed by services like Carfax, which helped buyers identify value and enabled quality sellers to command fair prices.
In advertising, attention-based metrics have emerged as the most reliable indicators of quality, paving the way for a healthier and more transparent market. Using precise metrics that correlate directly to advertising outcomes, brands can make smarter media investment decisions and achieve substantial performance gains.
Although quality takes different forms in every medium, attention metrics like Adelaide’s AU generally incentivize publishers to create fewer, higher-quality ad placements while empowering buyers to place greater value in them. By raising the demand for high-quality media and motivating publishers to enhance their offerings, attention metrics fuel better outcomes for all stakeholders.
A novel approach to measuring media quality brings several unique concepts that merit closer examination. We’ve compiled this glossary to provide an overview of the practice and theory behind attention metrics.
The term “attention economy” describes how humans manage the wealth of information at their disposal. In advertising, it typically refers to the growing competition for consumers’ eyes and ears as attention becomes increasingly scarce and highly sought-after.
Data points or signals used to model consumer focus on advertisements. Attention metrics range from loose proxies like viewability to precise measures like panel-based eye-tracking, which records gaze and focus.
Introduced in 2020 by Adelaide and the Association of National Advertisers (ANA), The Attention Pathway illustrates how media, creative, and audience work together over the course of an impression to drive attention and subsequent impact.
The Attention Pathway involves three distinct phases:
High-quality media placements offer high attention potential
Interesting creative attracts and holds attention
Strong branding ensures the ad makes a lasting impression
Attention to advertising is ephemeral. It waxes and wanes over the arc of an impression, with media placements creating an opportunity for attention that creative holding it for as long as the audience likes what they’re seeing. Measures of media quality should extract the impacts of creative and audience, focusing solely on a placement’s likelihood of attention versus how long it sustains attention. This ensures precision and mitigates the effects of The Attentive Audience Paradox.
A measure of quality characterized by a media placement’s likelihood of driving attention and, ideally, subsequent outcomes. Attention metrics focused on the probability of attention aim to remove “noise” introduced by audience relevance and creative quality. In doing so, they offer a more precise understanding of media quality and can be used by both buyers and sellers.
A phenomenon outlining the risks associated with using duration-based attention metrics (DBAMs) as advertising KPIs.
On the surface, DBAMs appear logical for assessing media quality: intuitively, the longer someone spends with an ad, the better the outcome. However, factors beyond media quality, including creative quality and audience, influence the duration of attention. Research indicates that optimizing media for the duration of attention—also known as maximizing attention—can lead to unintended consequences due to its effects on creative selection and audience targeting.
For example, this approach may create a bias for eye-catching or salacious creative that effectively holds attention but does not necessarily drive positive brand outcomes. Optimizing impressions toward the audiences most likely to pay attention longer is even riskier, as research shows that older demographics and people who are already brand-aware tend to spend more time viewing content than others.
Read our WARC article to explore this research further and discover solutions to avoid the pitfalls of The Attentive Audience Paradox.
Adelaide’s omnichannel media quality metric, which rates the quality of media placements using a machine-learning model powered by attention metrics, device signals, and full-funnel outcome data. AU predicts any placement’s likelihood of attention and subsequent outcomes and covers approximately 95% of an advertiser’s spending across channels, including digital, online video (OLV), social, connected TV (CTV), linear TV, cinema, audio, and digital out-of-home (DOOH). Visit our blog to learn more about how we use data at Adelaide.
AU helps many of the world’s largest brands make smarter media investment decisions. Campaigns optimized with AU consistently achieve remarkable results. Our 2024 Outcomes Guide features 45 case studies across 18 industries, demonstrating how AU has helped advertisers drive an average of 40% upper-funnel lift, 53% lower-funnel lift, and 37% cost savings.
A 1998 Rice University usability study concluded that test subjects either consciously or unconsciously ignored content and navigational elements contained within large, brightly colored banners that resembled banner ads. The researchers dubbed the effect “banner blindness.”
A relative measure of ad density on a given page. It is determined by comparing the pixels-in-view for the given ad to the total pixels-in-view for all ads on a page. In media environments such as connected TV (CTV) and podcasts, ad load is similar to clutter and refers to the number of competing ads within an episode.
In digital environments, we’ve observed that a single ad in view offers a significantly higher likelihood of attention and impact versus an ad in a highly cluttered environment. As clutter increases, all ads on a page suffer.
Data about the genre of podcasts and count, duration, and positions of ads within podcast episodes. Adelaide licenses data from podcast transcription providers to incorporate this information into its media quality scoring model.
A mutual relationship or direct connection between two or more things. Effective media quality metrics correlate to business outcomes through the funnel—from brand awareness to sales and conversions. Correlation is measured on a -1 to +1 scale, with negative numbers representing negative correlation and vice versa. A correlation coefficient of 0 indicates no correlation.
In advertising, creative is the ad itself.
One of the most interesting applications of attention metrics is evaluating creative effectiveness and performance by holding media quality constant.
Alternatively, certain duration-based attention metrics (DBAMs) can be useful in creative analysis. Some Adelaide clients leverage creative insights from our partner Realeyes, which uses computer vision and eye tracking to measure and predict creative’s ability to hold attention. When combined with media quality insights from Adelaide, advertisers can strategically target placements where their creative will perform the best.
Duration-based attention metrics (DBAMs), which emerged around 2015, measure or predict attention in seconds. Faced with the near-complete gaming of viewability, researchers and vendors began working on technology to measure attention paid to advertising and its resulting impact, leading to the creation of DBAMs.
However, duration is a multifaceted measure, and you can break it down into at least five distinct types—all with different use cases in advertising. Here are the primary types of duration-based metrics and when to use them:
The evaluation of eye position and movement to assess visual attention. Eye-tracking vendors collect data through eye-tracking devices, like webcams or front-facing phone cameras, or through lab-based setups. These tests are conducted across a statistically significant group of participants. Then the results are averaged to determine how much time people typically spend looking at advertisements in a given scenario. They can also be used within algorithms to predict additional attention types. Eye tracking results are often presented as “heat maps” or “gaze plots” overlaid on the visual that was viewed.
In media and advertising, the dispersion of audiences across numerous channels and platforms, making it difficult for advertisers to reach large, unified audiences.
The proliferation of media options, including digital, social, and traditional media, drives fragmentation, resulting in more segmented and niche audiences. Fragmentation also makes it increasingly challenging to measure and compare media quality across channels, platforms, and formats.
A combined measure of pixels-in-view and time spent with a given ad. Adelaide continuously tracks in-view duration throughout every session to determine the percentage of an ad in view and for how long. An advertisement with a longer in-view duration generally offers a higher probability of attention. Still, advertisers can't rely on viewable/in-view duration as a standalone attention proxy.
A set of features that help determine an ad’s location on a page or screen. Page geometry includes other ads on the page, content blocks, navigation, footers, side rails, and other standard elements found on both the open web and within walled gardens. Adelaide utilizes Page Geometry to understand the centrality of a given ad on a page relative to other elements. Depending on screen size, a more centrally located ad generally indicates a higher probability of attention.
The pod group that an ad appears in (e.g., pre-, mid-, or post-roll). Not to be confused with the term “podcast,” which is a portmanteau of “iPod” and “broadcast.” “Pod,” in this case, means an “independent grouping.”
“Quality” CPM, wherein an impression only counts if it meets advertiser-specific criteria, such as viewability, brand safety, and frequency-capping requirements. These requirements vary by the advertiser and analytics provider, so the actual “quality” of the impression is indeterminate. While a step above the low end of the viewability threshold, viewability is just one factor affecting placement quality.
Viewability, a measure of whether an ad has the potential to be seen, was defined by the Media Rating Council (MRC) and the Interactive Advertising Bureau (IAB) in 2014. The requirement for viewability specifies that display ads must have 50% of pixels within the viewport for at least one continuous second; video ads must meet the same pixel requirement but with a longer time requirement of at least two seconds.
Over the last decade, the standard has impacted online advertising to an immense degree — with smaller ads often fixed to the screen to maximize viewability.
Custom bidding algorithms informed by attention metrics, which maximize media value dynamically by intelligently bidding up higher-quality placements and reducing bids for lower-quality inventory.
Attention-based custom bidding algorithms enable advertisers to increase the delivery of high-attention formats and secure higher-quality media for a fair price.
Pre-bid segments are grouped into high, medium, and low-attention sets representing varying levels of media quality. Advertisers simply select the levels of media quality they’d like to focus their budgets on to ensure their programmatic investments drive efficient attention.
Supply-side platforms (SSPs) and publishers can curate inventory based on attention data and expose different tranches of quality as separate deal IDs. This method of programmatic activation allows advertisers to focus their budgets on high-attention inventory and easily layer on additional targeting tactics, such as audience and contextual targeting configurations. Media buyers can access high-attention PMPs from seats on all major demand-side platforms (DSPs).
Explore the pros and cons of each programmatic strategy by downloading our programmatic activation guide, created in partnership with the ANA—or check out our “Attention 101” page.
A Media Mix Model that leverages attention metrics to increase the fidelity of impression quality, resulting in greater accuracy and more precise recommendations.
MMM aims to assess each channel’s contribution to sales for optimal calibration of the media mix, but it often overlooks quality differences among placements. This oversight can lead to underestimating exceptionally high-quality placements, while placements that garner little attention may receive too much credit. For context, in 2021, Google published a study with Nielsen examining the variance in placement quality that MMM didn’t consider and found that ROAS from placements typically treated as uniform by MMM varied by up to 48%.
Attention metrics add a meaningful quality differentiation between placements. By integrating them into media mix models, brands can more accurately gauge each placement and channel’s true contribution to the overall impact of their media investments. These models short-circuit the cycle of prioritizing low prices, steering brands toward media placements that deliver the highest value relative to cost.
Download our free guide, Modernize Your MMM with Attention Metrics, to learn more about the advantages of attention-weighted media mix models.
Adelaide’s suite of tools designed to help advertisers pinpoint the optimal media quality minimums for their specific objectives and then plan, optimize, and benchmark against them.
By placing business outcomes at the heart of the campaign lifecycle, Flight Control transforms Adelaide’s AU metric into a dynamic solution for enhancing media strategies, from planning and launch to analysis and activation. Media agencies have started integrating custom Flight Control calculators into their proprietary planning tools to ensure each campaign starts with clear and quantifiable media quality targets. Programmatic partners leverage it to inform custom algorithms and media curation.
Learn more about Flight Control from our Director of Analytics and partners at GroupM Nexus.
When a media seller or publisher and an advertiser align on a target media quality average for a campaign. In other words, when media sellers offer advertisers the opportunity to transact on total guaranteed attention rather than total viewable impressions.
Attention metrics offer incredible potential as a unit of currency. Several publishers are partnering with early adopters of attention metrics to construct more profitable campaigns that drive predictable results. EssenceMediacom and Kargo launched the first digital campaign guaranteeing media quality with Adelaide’s AU—find out how.
