Audrey Steele (FOX) introduced this presentation by highlighting the objectivity of the years-long study focused on the relative value of different platforms and impression quality, with the brands involved amassing close to $3 trillion in sales. While many in the industry are focusing on maximum reach, this study looked at sales as the most important measure of impressions, quality and value between media platforms.
Michael Tscherwinski (Circana) and Greg Younkie (Kraft Heinz) explored how personalization of quality data can take performance up to the next level and shared learnings from a two year journey that found personalized impressions supported by the right data can drive stronger impact.
Kyle Holtzman – Business Lead Restaurant Vertical, Google
Bennett M. Kaufman – Cross-Media Measurement Lead, Google/YouTube
Michelle Smiley – Analytical Lead Restaurant Vertical, Google
In this session, speakers Bennett M. Kaufman, Kyle Holtzman and Michelle Smiley of Google explored a two-pronged approach to cross-media measurement and planning that considered the full-funnel impact across traditional TV and streaming video (YouTube), to make sense of all the "disparate forms of data and measurement." The approach considered a geo-based experiment and audience incrementality to demonstrate and solve the following challenges: to retain current loyal customers, to age down the brand and to appeal to new consumers (Generation Z). The speakers presented a study done by Google in partnership with Burger King to test a new experimentation strategy to understand and measure the relationship between Linear TV and YouTube. The speakers touted the benefits of this method as repeatable and customizable across a variety of media channels, in addition to being timely, omni-channel and privacy safe.
Key Takeaways
The geo-based experiment addressed the understanding of changing behavior in the physical stores for Burger King, through increased sales related to media spend. This technique gave the ability to measure the uplift between control and treatment to understand media impact. The geo-experiment focused on three KPIs: store sales, store transactions and deal take rate (promotion featured in the ad).
Results from the geo-experiment indicated:
Store sales generated by linear TV were flat but store sales increased in views from YouTube.
Store transactions generated by linear TV decreased while YouTube views increased store transactions.
In terms of the deal take rate (deal shown in the ad) the take rate was higher generated by linear TV, though it still generated positive returns from YouTube.
Audience incrementality testing was conducted by Comscore (3rd party incrementality validation). Through this process, they wanted to understand if they were reaching a new target audience and if their message was reaching anyone that may not have heard their message on linear TV alone.
Audience incrementality testing resulted in the following:
Accounting for the target audience of adults 18-49 was critical in the short and long term.
YouTube reached 78 million adults ages 18-49. In addition, 34 million of the viewers were YouTube-only, unique viewers.
There were 43,365,489 cross-platform unique viewers.
20,680,526 were unique linear TV-only viewers with 64 million total linear TV viewers.
Greg Pharo – Sr. Global Director, Holistic Communications & Marketing Effectiveness, The Coca-Cola Company
This presentation discussed cooperation between Coca-Cola and Adelaide. Adelaide created a metric AU—a single metric for brands to ensure their media gets the most attention. They conducted a campaign with Diet Coke to understand how attention metrics could be incorporated into a campaign measurement system to improve efficiency and to understand what could drive success. Coca-Cola has an end-to-end framework for measuring their campaigns. The E2E metrics has key components that measure human impact: head metric—all different measures if an experience was noticed and recalled; heart—resonance and relevance; hand—purchase/shopping; mouth—consumption. AU is part of the “head” component. An earlier trial in Europe took two campaigns—one with Aquarius and one with Coke. Half of media was optimized based on attention and the other on exposure. Clear convincing results showed that there is higher ad recall, recognition and impact based on attention as opposed to viewability.
The Diet Coke campaign focused on the following questions: Can attention metrics offer insights into media? What level of attention is needed to increase brand lift? How can we gain real time insights? Can we leverage attention metrics to reduce ad waste?
Methodology—3 stages:
1. A/B test: The campaign was split into two groups, AU-optimized and BAU optimized to VCR and CTR. Findings show consistent results when optimizing attention. Half to viewability and half to attention.
2. Max AU analysis: This considers the single highest AU impression for a respondent to control for frequency. It uses actual response data to gauge lift. This suggests the level of attention at which single impressions are impactful. Findings show exposure to media above 35 AU resulted in higher ad recall, purchase intent and favorability among consumers.
3. AU flight control: This considers the relationship between the frequency of exposures and Lucid survey results at different levels of media quality. Suggests the AU above which media is cumulatively impactful. They conducted regression analysis to find the minimum AU to use to drive consistent outcomes. The correlation at given frequency between ad exposures and purchase intent increases above 20 AU.
For the Diet Coke campaign, optimal AU increased above 20 AU and is strongest above 29 AU, peaking at 38 AU. There is an opportunity to drive incremental lift:
1. Exposure to high AU media drives brand lift indicating AU is a proxy for Coca-Cola’s KPIs.
2. Identifying the minimum level of AU required for a KPI uncovers significant efficiencies.
3. Attention metrics provide a real-time window into brand performance.
Next steps: How to measure AU everywhere; To explore leveraging high AU PMPs to provide targeting opportunities.
Key Takeaways
Exposure to high AU (Adelaide’s Attention Metric) drives brand lift indicating AU is a proxy for Cola’s KPIs.
Identifying the minimum level of AU required for a KPI uncovers significant efficiencies.
Attention metrics provide a real-time window into brand performance.
Johanna Welch – Global Mars Horizon Comms Lab Senior Manager, Mars
Mars and Realeyes prove connection between creative attention and sales performance. Mars’ Agile Creative Expertise (ACE) tool tracks visual attention and emotion responses to digital video ads. Visual attention AI and facial coding measures how long participants watch a video and how their attention changes as they watch. Proven this model to work—optimizing content, lifting sales up to 18% in 19 markets, $30 million in ad optimizations in 18 months.
ACE can 1. Predict sales accurately while learning how consumers behave and think. 2. Optimize—improve performance through creative selection. 3. Scale—establish a fast scalable solution. The model links attention, emotion and memory. Accordingly:
1. Attention is access to the brain and enables the brand to enter into consciousness.
2. Facial reactions—build memories.
3. Impact—higher consideration, conversions and sales.
ACE solution:
1. Participant exposure: 24-48 completion, 150-500 viewers from pool of +200m people. Observe people.
2. Attention detection: deep learning, collect viewer attention through natural viewer experience.
3. Actionable scores: ML and AI analytics to assess performance and deliver scores.
Company impact: validated predictions proved connections to behavioral and sales data via over 4,000 sales/ads data points and benchmarks. They also used ACE to improve performances for TikTok, Facebook, Instagram, YouTube, achieving 18% cumulative sales lift. Global scale—scored over 1,000 creatives in 18 months. In conclusion, ACE is the biggest attention database, received U.S. patent for visual attention detection. Mars hopes to share ACE with other companies. And the next step is how to take a pre-testing tool to in-flight content and to examine brand equity.
Key Takeaways
Establishing the connection between creative attention and sales performance is key.
Mars’ Agile Creative Expertise (ACE) tool tracks visual attention as well as emotional responses to digital video ads.
Proven this model to work—optimizing content, lifting sales.
Yannis Pavlidis – VP, Data Science and Analytics, DISQO
In this session, Yannis Pavlidis of consumer insights and CX firm DISQO tackled the challenges of benchmarking, cross-media outcomes and brand lift due to incomplete data from siloed platforms and media channels. In the opening, Yannis provided a refresher on the importance of benchmarks and obstacles from existing approaches to benchmarking (e.g., inconsistent methodologies, outdated data and collection techniques). The discussion examined solutions to address issues in data collection concerning benchmarking ad impact, which streamlines the process using consented, single-source data. The presentation also examined calculating benchmarks based on data taken from one source group (rather than two unaffiliated groups), considered the recency of the campaign used and subsequent behavior(s) which then can be correlated with survey responses. The advantage of using consented single-source data is that it can lead to more insightful, relevant and consistent outcomes in benchmarks.
Key Takeaways
Challenges with existing approaches to benchmarks included the following:
Inconsistent methodologies across social networks make data comparison difficult when assessing cross-media campaigns.
Behavioral data is often aggregated from more than one source, making data triangulation inefficient and unreliable (e.g., comparing audiences that are not the same).
Outdated benchmarking data often fails to capture more recent substantial changes in the U.S. consumer landscape and the introduction of Generation Z to the consumer marketplace.
Inefficiencies in the benchmarking process are addressed by using the same audience and methodologies across social platforms. Data and information gleaned from surveys and behaviors of consumers come from a single source. In addition, results from campaigns focus on the past three years, creating recency and relevancy.
Calculating benchmarks are based on campaigns no further than March 2021. The median lift score is calculated using the difference between the exposed group and the control group.
Different categories are considered when specific benchmarks are calculated. In addition, a threshold of 15 brands was implemented to create variety and statistical significance.
Audiences surveyed are opt-in and tracked using metered data to assess ad exposure and downstream data. Surveys are provided to exposed and matched control individuals to assess attitudinal changes. Additionally, surveys and behavior can be correlated.
Lloyd Darbonne – Senior Director Research, Insights, & Strategy, FOX Corp.
Bill Harvey – Executive Chairman, Bill Harvey Consulting, Inc.
Audrey Steele – EVP Sales Research & Strategy, FOX Corp.
Audrey Steele (FOX) introduced this presentation by highlighting the objectivity of the years-long study focused on the relative value of different platforms and impression quality, with the brands involved amassing close to $3 trillion in sales. While many in the industry are focusing on maximum reach, this study looked at sales as the most important measure of impressions, quality and value between media platforms.
Bill Harvey (BHC) detailed the study’s methodology of implementing a standard multiple regression analysis with ROI optimization using SMI’s real ad spend numbers and Circana’s and S&P Global’s sales spend across the top ten brands in each of QSR, CPG and Auto verticals over nine years of data.
Lloyd Darbonne (FOX) covered how the thousands of iterations of their ROI optimizer selected the media mix that predicted the highest share for each company studied. Concentrating on entertainment (inclusive of TV sports & news, TV cable entertainment, TV Big 4 entertainment and premium digital video TV), the optimizer then measured the optimal allocation for maximum ROI in each vertical. Results across verticals documented higher ROIs with significant reallocations and rebalancing of ad spends in TV and premium contexts.
Key Takeaways
Brands that increased their spend in non-premium digital lost sales and market share, much of it due to misallocation of advertising spend. There are opportunities for 20-40% ROI increases by reallocating non-premium digital dollars to TV.
TV has 2.6x the sales effect of non-premium digital. There is a 14.6% incremental sales lift added by advertising, on top of the baseline 85% sales without advertising. TV generated 69% of the added 14% across the combined three verticals, with non-premium digital at 27%. In all three verticals studied, broadcast entertainment still has a good amount of headroom—increasing share of ad spend will increase sales effects.
Buyer focus on CPM and rush to oversaturated lower-priced media and non-premium digital inventory has served to suppress the sales effects of overall campaigns.
Focusing on ROAS instead of reach, and using standard multiple regression analysis gives advertisers an advantage over slower-moving competitors.
Gregory Younkie – Sr Data Scientist & Data Strategy, Kraft Heinz
Michael Tscherwinski (Circana) and Greg Younkie (Kraft Heinz) explored how personalization of quality data can take performance up to the next level and shared learnings from a two year journey that found personalized impressions supported by the right data can drive stronger impact.
Kraft started with its CRM recipe-focused data and grew it 17% with sweeps and games. Its in-house consumer insights platform, Kraft-O-Matic, had three core competencies with its consumer database incorporating 1P (first-party), 2P (second-party) and 3P (third-party) data, insights and analytics and agile marketing. Using identity resolution to match 1P data to devices and content, and enriching 3P data to create high-value audiences, Kraft then activated personalized marketing campaigns with speed to capture engagement and ROAS.
Driving 1P acquisition, data enrichment, more personalized activation and uplift measurement resulted in a 93% lift in ROAS impact and secured an increase in media spend from leadership.
Key Takeaways
Enriching datasets with demographics, psychographics and purchase-based data powered their modeling for different machine learning techniques across the consumer funnel from awareness to performance marketing.
Within CPG, purchase-based data proved to be the best predictor of future performance.
Utilize HH transaction-level to enable more sophisticated media approaches and get deeper insights about your consumers.
Select the right clean room partner for your goals.
Attention, AI and machine learning stand at the top of the Journal of Advertising Research’s inaugural list of research priorities, developed in close consultation with the advertising industry—a move intended to deepen the journal's commitment to addressing practitioners’ greatest research needs.