Jon Watts (CIMM) led a conversation with the CEOs of an organization that is helping to manage the JIC (OpenAP) and one that participates in it (the VAB), the EVP of an organization that does not belong to the JIC but has met with it and the CEO of the MRC. The participants clarified their relationships with each other, discussed Nielsen and expressed their hope for the future of television measurement.
Robert L. Santos of the U.S. Census Bureau detailed how the organization is evolving itself, as well as its foundational statistics about America’s people and economy, in preparation for the Decennial Census in 2030.
Nielsen compared the implied ratings from ACR data and STB data in homes where they also have meters. The correlation was quite high, though panel adjustments raised the rating levels by about 1%. Big Data are limited in different ways: not all sets in a house provide ACR or STB data, they are devoid of persons information and STB’s are often powered up but the TV is off. Nielsen presented how a panel of 40,000 homes can be used to correct those biases. A critical finding was that projection of MVPD data outside of its geographic footprint significantly changed network shares. That said, Big Data can significantly improve local market data where samples are necessarily much smaller.
Key Takeaways
Nielsen presented their view on how to best use Big Data. Nielsen uses a wide variety of data sets as part of its Big Data solution. It identified several gaps in the use of ACR data, including when native apps are used and in cases where channels are not monitored.
Nielsen has about 30 million RPD and ACR homes and a panel of 40,000 homes which it uses to adjust its Big Data. Where possible, it matches its panel homes to RPD/ACR in those same homes. The r2 with the panel data is .98 and .96 respectively, which is quite good. However, using the panel to adjust anticipated missing data, raises the overall viewing levels by about 1%.
However, there are other TVs in the homes that are not ACR capable and there is no persons data associated with ACR only homes. The panel and Experian are used to model who is in the household and the panel and Gracenote are used to model who among them would be viewing.
Similar modeling and correction procedures are used for STB data. However, one of the most significant adjustments of STB data is modeling when the TV is off but the STP is still powered on.
Nielsen uses a fusion process to conduct its viewer assignment, the process of modeling who is viewing when the set is known to be tuned. A simple way to understand this is to look for a similar panel home and assign the viewing characteristics of the panel home to the ACR or RPD home. The use of Gracenote has made a significant improvement in the viewer assignment model.
Nielsen showed data on the shifts in share that occur when you take a Big Data set like an MVPD’s RPD data and project it beyond the footprint of that MVPD. The share shifts are much greater than when one limits the projection to the geographic footprint of that MVPD.
Statistically, when one compares the differences between the shares projected within and not within the footprint, 28% of the shares when projected beyond the footprint of the MVPD were different from the panel at the .05 level of statistical significance.
However, Nielsen showed how much Big Data can eliminate quarter-hours with zero ratings within local market (where zero ratings reduced from 62 to 0 in one market) by including Big Data in local market ratings.
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.
Michael Siewert – Programmatic Director, Colgate-Palmolive Co.
Michael Siewert (Colgate-Palmolive) and Souptik Datta (GroupM) presented how their companies worked together to combine measurement data for building custom solutions around bidding in the programmatic space. Colgate wanted to create their own quality definition for their inventory and be able to benchmark at a scalable cost benefit. Building a framework around their definition of quality and creating their own “qCPM” metric allowed them to understand the details of performance at a baseline and optimize with machine learning across 80+ markets and varying formats.
Key Takeaways: Mapping their quality journey involved
Defining the quality metric for Colgate led Souptik (GroupM) to create a menu of foundational elements (verification, clarity) and Colgate’s CPMs, KPIs, goals and values that constituted quality for them, which then informed a custom formula. Their qCPM metric is formulated from cost, quality and business effectiveness KPIs.
Reporting and benchmarking: Using multiple DSPs, clean rooms and viewability partners to build a global reporting scalable cloud-based system, Colgate’s interactive dashboard was able to measure benchmarks and put dollar values to opportunity sizes (underperforming and extra mile) for the first time.
Automating custom bidding to optimize for quality: Because it’s not possible to optimize manually, Colgate used its own AI algorithm to spot inefficiencies, seeing a nearly 20% lift in small-scale tests. They also implemented their own custom bidding tests to monitor and analyze to see how much improvement they could make.
The ARF hosted its annual flagship conference, AUDIENCExSCIENCE 2023, on April 25-26, 2023. The industry’s biggest names and brightest minds came together to share new insights on the impact of changing consumer behavior on brands, insights into TV consumption, campaign measurement and effectiveness, whether all impressions are equal, join-up solutions across multiple media, the validity, reliability and predictive power of Attention measures, targeting diverse audiences, privacy’s effect on advertising and the impact of advertising in new formats. Keynotes were presented by Tim Hwang, author of Subprime Attention Crisis, Robert L. Santos of the U.S. Census Bureau, Brian Wieser of Madison and Wall, LLC and Andrea Zapata of Warner Bros. Discovery.
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