effectiveness

Measuring Attention and Outcomes for Audio Advertising

Mike FollettCEO, Lumen

Joanne LeongGlobal Head of Planning, Dentsu

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.
 

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Foundations of Incrementality

Sophie MacIntyreAds Research Lead, Marketing Science, Meta

Randomized Control Trials (RCTs) are the gold standard for unbiased measurement of incrementality according to Sophie MacIntyre (Meta). However, there are situations where RCTs are not available so Meta explored other methods to improve the measurement of incrementality. Meta’s researchers wanted to know how close they could get to the experimental result by using non-experimental methods. The researchers were unable to accurately measure an ad campaign’s effect with sophisticated observational methods. Additionally, traditional non-experimental models like propensity score matching and double machine learning were difficult to use and resulted in large errors. Sophie presented incrementality as a ladder of options that get closer to measuring true business value as the ladder is ascended. The different rungs of the ladder are based on how well a particular measurement approach can isolate the effect of a campaign from any other factors. This research was undertaken in collaboration with the MMA and analyzed non-incremental models, quasi-experiments with incrementality models and randomized experiments. Meta revealed that incrementality could be achieved with modeling if the research included some RCTs. Using PIE (predictive incrementality by experimentation) estimates for decision making led to results similar to experiment-based decisions. Sophie stated that academic collaborations provide quantitative evidence of the value of incremental methods. Key takeaways:
  • Incrementality matters because it is the foundation of good business decisions and should be the “North Star.”
  • Randomized Control Trial (RCTs) are the gold standard for determining incrementality.
  • Using a significant amount of data and complex models can improve the performance of observational methods but does not accurately measure an ad campaign’s effect.
  • Using Predictive Incrementality by Experimentation (PIE) estimates for decision making leads to results similar to experiment-based decisions.

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The Power of Radio Through the Lenses of Emotional Engagement

Pedro AlmeidaCEO, MediaProbe

Pierre BouvardChief Insights Officer, Cumulus Media | Westwood One

The presentation focused on determining the emotional impact of AM/FM radio ads. MediaProbe was retained by Cumulus Media to measure second-by-second electrodermal activity (EDA)—a measure of the sympathetic nervous system, to see when it is activated, whether listeners were excited by the stimulus they heard. This is termed Emotional Impact Score (EIS)—an impact metric that can help understand how excited people are on a second-by-second basis and what are the elements that drive this emotion. This is an objective way of quantifying emotion in media and advertising content, capturing the emotional implicit data (what people feel). Throughout the session, participants can also dial those moments that they like/dislike—the conscious explicit capture of likes and dislikes, and are asked pre and post session questions to learn more about recall and purchase intent. Methodology: 36 AM/FM radio ads, in a simulated broadcast of 30 minutes across four genres (urban, news, adult contemporary and rock/oldies). Each “broadcast” had three ad breaks and the average commercial break had three ads. Also, 227 people participated. Each “broadcast” had a sample size of 75 people and consumers listened to at least three of the four broadcasts. Each ad was exposed to 225 people. Findings:
  1. AM/FM radio programming outperforms MediaProbe’s U.S. TV norms by 13%. Put differently, the emotional impact score is higher when listening to radio.
  2. Carry over effect: radio advertising commercial pods receive 12% higher Emotional Impact Score over TV advertising commercial pods, making radio a premium platform.
  3. Across genres, people are more engaged when listening to news—people are processing what is being said, they are paying attention. There is no valence contamination between what is being said on the news and the emotional engagement to ads.
  4. People are more engaged during radio advertising—4% more than radio content.
  5. Looking at 32 individual MediaProbe ads, there is on average a 5% higher emotional impact score in comparison to 4,670 individual MediaProbe TV ads. This research is consistent with other lab-based studies.
  6. MediaProbe also conducted a physical feature analysis of the creative to find that: 1) higher pitch contrast between programming content and ads leads to higher impact. If the content has low pitch, ads should be higher pitch and vice-versa; 2) louder ads lead to higher impact.
  7. Using a regression analysis, MediaProbe found the following best performing creative in radio ads: 1) female voiceover; 2) with jingles/with background music; 3) five brand mentions are optimal; 4) no disclaimers. This too is consistent with other research.
Key takeaways:
  • AM/FM radio programming is more engaging than TV, according to MediaProbe
  • They also found that AM/FM radio advertising outperforms TV advertising.
  • News is the most impactful genre as a high-quality contextual environment for advertising.
  • Sound contrast between radio programming and ads drives higher attention and brand recall.
  • Creative best practices: female voiceover, jingles, one voiceover and five brand mentions.
 

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How Co-viewing and Other Factors Impact Viewer Attention to CTV

Monica LongoriaHead of Marketing Insights, LG Ad Solutions

Tristan WebsterChief Product Officer, TVision

The research presented included an online survey of over 1,000 respondents incorporated with TVision’s 5,000+ U.S. home panel data. Questions asked: 1. Does CTV garner more attention? 2. Are consumers more likely to co-view CTV? 3. Does co-viewing negatively affect attention? TVision’s equipment includes their always-on panel, a webcam that can capture how many people are in the room and eyes on screen at a second by second, a router meter to understand which CTV device is on and detects apps. TVision measurement engine includes remote device management and ACR engine. Findings:
  1. CTV in general has 13% higher attention index. Attention increases due to purposeful watching. Co-viewing CTV has stronger impact in comparison to linear (75% higher).
  2. Streaming is a popular co-viewing experience with mostly a non-negative impact to attention. Households with kids are more likely to pay attention to streaming content and ads with 36% more likely to discuss what is seen on TV. There are three different types of co-viewing: family setup with different age group (increased attention depends on genre), adults only setup with similar gender and age (biggest impact on attention), mixed adults only setup.
  3. Streaming is gaining ground as a co-viewing method for watching sports. Watching sports is typically with other people.
Implications for brands and marketers:
  1. CTV offers opportunity to create more engaging ads with higher levels of attention. CTV has digital capabilities that garner more attention. There is a need to create ads that are specific for CTV (in contrast to linear).
  2. Co-viewing can be an opportunity to turn your brand into a discussion.
  3. Measurement providers give us new insights into viewer behavior.
Key takeaways:
  • There is a higher attention with CTV in comparison to linear.
  • Positive impact of co-viewing: Co-viewing on streaming platforms is popular and generally maintains or increases attention.
  • Streaming is increasingly preferred for watching sports in a co-viewing context, offering new opportunities for targeted advertising and engagement in sports content.
  • Implications for brands and advertisers: The engaging nature of CTV offers ample opportunities for more impactful ads. Co-viewing experiences can transform ads into discussion points among viewers, enhancing brand engagement.

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The Impact of Co-Viewing on Attention to Video Advertising

Duane Varan, Ph.D.CEO, MediaScience

Impressions are measured everywhere, however, not all impressions are equal, and as such, we need to think about how to appropriately weigh them. The problem with CTV is that there is more than one viewer, and the device itself doesn’t tell you this. The question, then, is how do we account for these added impressions. From a value point of view, we need to understand what is the value of these additional viewers. There was a meta-analysis of MediaScience studies (n=11) on co-viewing. This is not conclusive but rather exploratory because these studies were commissioned by clients. These are premium publishers and not all TV is at that level of quality. The conceptual model of co-viewing: device level exposure data à add additional co-viewers à estimated additional co-viewers. How do we know that these additional co-viewers have the same values? We need to factor for what could be a diminished add impact. To do this: we need to adjust audience (factoring for diminished ad impact) à adjusted additional co-viewers (by impact). Results:
  1. Attention and memory effects are the two areas that matter the most when addressing co-viewing. The attention sphere is a small effect, and there is not a lot of variability with that effect. The real story is in memory—if you’re talking to someone it is difficult to process the ad. Memory retrieval when co-viewing decreases by 15-52% depending on the content.
  2. Co-viewing composition effect: Mixed gender viewing has a more detrimental effect than same sex viewing (decrease by 27%).
  3. Age effects: There are big differences by age but not a lot of difference in terms of the decline that is associated with co-viewing by age.
  4. Program effects: Majority of variability is in the program effects—between 22% and 58%. The co-viewing problem cannot be solved by industry averaging, but we would need program-level measurement. For instance, effect is worse with sitcoms than it is with sports. One of the theories is that in sports, a lot of human interaction happens at the moment, whereas in comedy this is saved for the ad break.
  5. Number of co-viewers effects: What happens when you increase the number of people in the room? In the studies, the maximum co-viewing is two. Looking at TVision data, they saw that for three or more viewers and above that impacts level of visual attention—from 3% drop with two viewers, to 18% drop with three viewers and 23% drop with four viewers or more. However, this is not significant because 97% of TV viewing occurs with one or two viewers, and only 3% of TV viewing is with three or more viewers (TVision data).
  6. Implications in terms of value proposition—the worst-case scenario is a detrimental effect of 58%. The net effect of co-viewers is negative 40. Average scenario— detrimental effect of 15%; net of 140 viewers in value.
Future research will focus on second screen device usage. Hypothesis is that the scale of this problem is bigger than the scale of co-viewing. Key takeaways:
  • Focus on co-viewing to understand the value of additional viewers.
  • Effect is seen in memory domain rather than attention domain.
  • Issue of variability by program means that the equation will differ between programs.

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Mapping the Impact: When, How and Why TV Commercials Work Best

Jeff BanderPresident, eye square

Sandra Schümann Senior Advertising Researcher, RTL Data & Screenforce

Marvin VogtSenior Research Consultant, eye square

Screenforce conducted a series of studies beginning in 2020, examining reach, success, mapping moods and impact in relation to attention. They mapped the impact by investigating when does which type of communication work best and why? There were 8,304 ad contacts in-home, 285 participants in a natural way (living rooms). They also examined 64 brands in three countries. The largest media ethnographic study in Europe examined usage situations and scenarios. There were four different scenarios: 1) Busy Day scenario (2-6PM Mon-Fri, people are distracted and focused on other things), 2) Work is Done (after 6PM, first lower part of concentration, seeking for better mood), 3) Quality Time (8-10PM, prime time, high activation of quality time, “Super Bowl moment,” high focus on screen), 4) Dreaming Away (10PM-1AM, typically alone, before sleep, dreamlike situation). Each of the 64 ads was tested in all four scenarios. The study included a technical objective criteria, subjective feeling and creative approaches. Eye square found a way where no additional material is needed other than an instruction book, webcam and GSR. Key findings:
  1. Visual attention is highest at late night (86%). Recall for ads works best in evening (75% Quality Time and Dreaming Away). However, advertising is shown to fit better earlier in the day.
  2. Characteristics per scenario: spot liking rises when using brand jingle (audio) in Busy Day scenario. This is because during the Busy Day scenario people are distracted and the jingle can help retain their focus.
  3. On a Busy Day, use strong brands with strong branding. When work is done, use ads to create a good mood. During Quality Time, it’s time for the big stories. During Dreaming Away, less is more.
  4. In sum, it is possible to find out which scenario works best for the spot and optimize the ads and find the best possible time and spot to air the ad.
Key takeaways:
  • TV ads have a strong effect, but there are ways to improve this impact.
  • Usage scenarios of audience has impact on ad effectiveness.
  • TVs can achieve a higher effect if they take the usage scenario into account.

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Having In-Depth Research on Consumers’ Values Yields Tremendous Benefits

  • ARF Knowledge at Hand, CMO Brief

In decades past, demographic characteristics were considered the strongest predictors of consumers’ values, attitudes and purchasing behavior. Over time, however, they have grown to become weak predictors and correlates. In this Knowledge at Hand report, Dr. Horst Stipp, EVP at the ARF, summarizes the latest and most impactful research to date on consumer values and how researching them carefully can help shape effective business strategies and impactful ad messages.

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Too Much Attention?

One expert argues that attention alone does not bring ad success and that we should not forget the other important levels of the “ARF Model.”

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