Explore the latest findings from the ARF Attention Measurement Validation Initiative. The phase two report is a comprehensive examination of various attention measurement methods used in creative testing. It concludes with reflections on the challenges of attention measurement, as well as some suggestions for advertisers on how to choose and evaluate attention measurement providers.
Privacy regulations have served as the impetus for a renewed interest in contextual targeting. To be effective, an ad must be related to its context but different enough to stand out. This working paper from the Marketing Science Institute (MSI) at the ARF presents a comprehensive model leveraging eye-tracking data and XGBoost algorithms to forecast the effectiveness of ad placements in real time.
ATTENTION 2024 showcased findings from Phase 2 of the ARF’s Attention Measurement Validation Initiative – aimed at understanding various attention measurement tools’ validity, reliability, and application in advertising. From analyses of ad creative to brand building, industry leaders gathered to discuss and debate the fascinating and fast-changing field of measurement.
The ARF Knowledge Center provides secondary research services for ARF and ARF-MSI members on a broad range of topics. Analysis of ARF member questions posed to the Knowledge Center in 2023 shows distinct trends and interests posed by different constituent members. As always, most questions tend to be specific to the unique business needs of each member, but there were some overarching trends, which highlight the evolving landscape of the marketing and advertising industry. This FAQ discusses these trends alongside sample member questions and Knowledge Center research reports.
Attention metrics have drawn a high degree of energy in the last few years, for many reasons, including the loss of behavioral signals due to privacy restrictions, growing frustration with ad viewability and its perceived limitations, attention metrics’ impact on the cross-platform measurement debate and that biometric technologies can now be applied “in the wild,” rather than just in labs.
The ARF’s Attention Measurement Validation Initiative aims to describe the attention measurement space in detail, illuminating this nascent sector. The Phase One findings include a comprehensive literature review and a report that maps out the vendor landscape in this increasingly diverse specialty. The report includes two sections. The first section describes what methods are being used, what these companies report and how and what they measure, be it ad creative or the media environment. The second section includes in-depth overviews of the 29 participating attention measurement companies. The Phase One Report is a must-read for anyone interested in attention metrics or what companies are operating in the space.
Aarti Bhaskaran – Global Head of Research & Insights, Snap
Kara Louis – Group Research Manager, Snap
Aarti Bhaskaran and Kara Louis of Snap presented their amalgamation of work on augmented reality (AR) with key data and client case studies from the last two years. Showcasing the growth of the AR landscape, Aarti and Kara featured how consumers are gravitating towards AR and the expanding number of opportunities available for advertisers in reaching new audiences and utilizing within the media mix. Case studies include brands using AR try-on technology from Champs Sports and Clearly eyeglasses.
Key takeaways:
AR usage is widespread and growing, from Boomers to GenZ. By the year 2025 there will be approximately 4.3 billion AR users across all generations.
Almost all marketers (91%) think consumers use AR for fun, but 67% of consumers prefer using AR for shopping over fun (53%).
Interacting with products that have AR experiences leads to a 94% higher purchase conversion rate, as individuals can better assess them and feel connected with brands. Certain AR applications can substitute physical shopping with different features varying across the customer journey.
Interactive and personalized shopping experiences reach Gen Z—92% are interested in using AR for shopping, with over half of Gen Z saying they’d be more likely to pay attention to ads using AR. Gen Zs are also twice as likely to buy items that they have experienced first using AR than those who don’t.
AR lenses on Snapchat outperformed all other media formats. Other platforms would need 14-20 ads to generate the same level of attention as Snapchat lenses.
AR not only drives short-term impact with higher purchase intent and brand preference, but it also improves brand opinion, influences implicit associations and increases likelihood to purchase and recommend.
The creative attributes that include logo and product branding, complexity, messaging and user experience show a significant relationship with AR performance in brand lift.
Context matters—not all reach is equal, and so, we need a way to qualify each impression and valuate each of these impressions. Metric of valuation needs to be valid, reliable and have predictive power for business outcomes. The research focus: 1) What can we say about the value of emotional engagement (EE)? 2) Can we model the value of EE via its impact on memory? 3) Can we use EE to optimize and valuate content and ad positions? How?
Methodology: MediaProbe used Galvanic Skin Response with participants who were exposed to content through a MediaProbe panel (U.S., 2,700 households). Data gets delivered second by second and data extracted goes toward creating an impact measure of how much people are reacting to what they are watching. The platform calculates an impact value that enables comparisons across media platforms. There was an added layer to see whether participants are leaning into the content and are engaged. U.S. TV dataset includes over 45,000 participants, reaching over 85,000 hours. More than 1,000 TV hours are monitored and over 42,500 ads. Using a subset of 16,351 ads and 329 “premium pod” formats, participants watch content and are then asked which ads they remember.
Findings:
Enhancing the emotional impact of an ad in 150 EIS points equates to adding a second 30’ ad unit. This will increase probability of brand recall by 15%. For each 100 points, this increases probability of brand recall by 10%.
Single best predictor of whether someone will respond to an ad is how much a person was engaged with the content prior to the ad. EE carries over to the ad break. It’s more engaging pre-break, in earlier breaks and earlier position in break, which leads to higher ad impact.
However, this is different across genres. Genre moderates pre-break emotional patterns. This is further differentiated within genres. For instance, people will react differently to ad breaks when watching soccer vs. some other sport. MediaProbe shows that there is 66% similarity between various award shows in terms of EE to ad breaks. They use this data to realize the value of different ads placed in different breaks (1st, 2nd, etc. break) and pods. Emotional engagement helps better predict ads performance.
Additional findings show that first-in-break still rules and that premium pods deliver higher recall.
Key takeaways:
Ad EIS is systematically associated with ad recall.
It is possible to optimize ads for estimated impact by advertising in the most engaging content and being present after the most engaging moments.
Different genres tend to have typical pre-break engagement morphologies. This allows to estimate the delivered value of each pod position (and order in break when relevant).
Audio is a daily ritual at the heart of the day. With the richness of audio experiences, should brands strive for contextual alignment? But what is contextual alignment? There are two types: Genre based—aligning with audio content genre that is contextually relevant to the brand; Ritual based—aligning with audio ritual/behavior that is contextually relevant to the brand. Methodology: a controlled test to quantify the impact of genre and ritual-based contextual alignment; recruitment of weekly audio listeners from a representative online panel, listening to content that they chose for roughly 30 mins. Listeners then answered brand metric questions to determine ad effectiveness.
Findings:
Ads in context perform better. The brands feel more relevant.
Audio with Rituals in context taps into purchase and genuine interest in the product.
Listeners feel more connected to the brand when hearing contextually aligned ads.
Listeners who felt energized or excited were more receptive to the ad. Audio during rituals get people motivated and more open to noticing ads.
Implications:
Ensure contextual targeting is a part of your digital audio planning to drive transactional next steps.
Explore rituals to reach a highly engaged audience and amplify the effectiveness of your audio.
Audacy came out with a campaign to promote the Audacy app across radio stations: four markets, 22 stations, 20 unique promos, during six weeks of media. Findings show that the rituals campaign worked—increases in app downloads are directly attributable to the rituals campaign. The campaign particularly influenced heavy radio listeners, parents, 35-54 and cross-platform listeners.
Key takeaways:
Audio rituals works.
Audio rituals targeting works.
There is a way to further slice and be more precise with audio.
Lumen and Dentsu measured attention in audio. Audio is obviously a key component, but the main challenge is how to create attention metrics for audio that can be comparable to visual? Can eye tracking be applied to audio, and if so how? Previous research shows that ads have to be noticed to drive results. Not necessarily looked at. There is a need for some form of attention to make ads work. Seventy percent of viewable ads are not viewed and as such do not sell. Research also shows that longer ads drive better outcomes in terms of prompted recall and choice uplift. Visual eye movements are a part of this but only the first part of the process that may lift to memory and action. At Lumen they measure 1) how many ads are viewable for the user; 2) whether they are viewable (=MRC); 3) % viewed; 4) view time in seconds; 5) APM in seconds; 6) cost per attentive impression. Eye tracking works by taking videos of eyes while on screen—simple behavioral metric. After this they ask questions and understand the relationship between eye movements and other measures.
Audio works differently. We lack information about the percent of people who listened and average listening time, but we can infer from visual attention. This is, thus, an audio-visual equivalence. How much visual attention would generate same recall from audio? According to the presenters, inferential model seems to work quite well. They infer likely levels of audio attention from several factors: exposure time, brand recall, choice uplift, forced vs. voluntary.
Methodology: They measured people listening to radio, podcasts and streaming audio services. There were three forms of audio advertisements, thousands of people from whom to collect audio and recall data and infer how much visual attention would have been needed to do the same.
Finding: Attention metrics are equivalent for audio. This data is built into Dentsu’s planning tools when their trading teams are contemplating which media to buy. The research shows that audio generates attention at a lower cost. In a digital world, it is about measuring live campaigns, and planners and clients are used to getting impression-level data about viewability or audibility. Audio industry has the ability to supply this data. Individual data on podcasts and streaming could help demonstrate the true power of audio campaigns. Challenge to industry: now that the potential power has been demonstrated we need to get impression level data to be able to measure live campaigns.
Key takeaways:
Radio is an extremely cost-effective way of reaching people and driving outcomes.
We have benchmarks, we want measurement, we need impression-level data.
Combine attention data with outcomes data to tell a compelling story.
