Research has demonstrated that developing messages, creative and targeting based only on demographic characteristics is not optimal. Investing in research on consumers’ values and emotions is likely to increase marketing effectiveness. Read more »
Manuel Garcia-Garcia, Ph.D. – Global Lead of Neuroscience, Ipsos
Ariane Pol – Global Head of Research for Creative Works, Google
Geraldine Rodriguez – Client Manager Applied Research, Ipsos
Can YouTube help drive long-term brand building? How do you measure long-term brand building? When brands want to air strategic long-term campaigns, they typically revert to traditional media. Most people are not in need of a brand’s immediate offering, but they represent the biggest sales opportunity. Ten years ago, the IPA demonstrated that campaigns whose primary focus was emotional were the most effective. Emotions are the fuel that allow high conversion over time. Brands should tap into emotions of consumers that may not be interested in a product now but may be relevant in the future. Ipsos partnered with Google Creative Works to study the observed and declared behaviors. Methodology: A triangulation of methods were used. They were Creative/Spark (market validated KPIs of creative impact); Ipsos Bayesian Nets (models the impact of emotion); Ipsos Emotion Framework (captures emotional responses). Ipsos Emotion Framework defines emotions as physiological changes we experience in response to the environment. These are complex emotions that are heavily driven by culture and context, and they are therefore, not universal. This complicates measuring emotions. While emotions are not universal, we can explain emotions based on valence, arousal and control. This maintains the cultural authenticity but can be compared across cultures. The experimental approach to measuring long-term brand growth included a brand relationship index (BRI), comprised of brand performance = how would you rate [brand] in terms of what you are looking for in a [category] + brand closeness = how close do you feel to [brand]? Findings:
Pedro Almeida – CEO, MediaProbe
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:
Prayushi Amin – Associate Director, Magna Global
Idil Cakim – SVP, Research & Insights, Audacy
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:
Mike Follett – CEO, Lumen
Joanne Leong – Global 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:
Sophie MacIntyre – Ads 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:
Pedro Almeida – CEO, MediaProbe
Pierre Bouvard – Chief 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:
Monica Longoria – Head of Marketing Insights, LG Ad Solutions
Tristan Webster – Chief 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:
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:
Jeff Bander – President, eye square
Sandra Schümann – Senior Advertising Researcher, RTL Data & Screenforce
Marvin Vogt – Senior 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: