data quality

How Researchers Can Learn from Recent Political Polling Challenges (Event Summary)

This event was suggested by the ARF’s LA Media Research Council in the aftermath of the poor performance of last November’s election polls.  Council members felt that the discussions about polling could impact trust in consumer and media research and that we should explore what research suppliers are doing to implement Best Practices. Research Quality has always been a key issue for the ARF. Most recently, an ARF event about the November polls found that while some issues are unique to political polling, many impact all survey research, for example, obtaining representative samples while response rates are declining, validity of responses, and predicting behavior and attitude trends.

Contending with Algorithmic Bias

On March 16, 2022, the ARF Cultural Effectiveness Council hosted a discussion on bias in the algorithms and models used by organizations, particularly those in advertising and marketing, to make selection or recommendation decisions.  Speakers from Publicis Media, Twitter, Wunderman Thompson, Cassandra, and the University of Southern California shed light on why this issue arises, what its effects can be and how to contend with it.  The session was moderated by Council Co-Chair Janelle James of Ipsos.

New Research Insights on Viewers’ Behaviors and Attitudes 

A (virtual) event presented by the ARF’s LA Media Research Council took place on June 15. Titled ”New Research Insights on Viewer’s Behaviors and Attitudes” it featured four presentations focused on issues that the Council had identified as priorities: better data on viewers’ use of media and platforms, the growth of streaming and content discovery and promotion.

Implications of Changing Privacy Frameworks on Measurement & Marketing (Part II)

During Part 2 of our Insights Studio on Privacy experts from Neustar, Sallie Mae and the ARF examined what privacy changes mean for marketers. They discussed the importance of a mutually agreed upon value exchange between the consumer and the brand and the implications of the changing privacy frameworks for the targeting and measurement of advertising. Although there are challenges related to consumer privacy, regulatory issues and measurement, the panelists were optimistic about the potential for resolution.

Rebuilding MMM to Handle Fragmented Data: The Challenge of Retailer Media

Liz Riley (OLLY) and Mark Garratt (In4mation Insights) explored rebuilding and reimagining marketing mixed modeling (MMM) to better handle fragmented data, in the era of retail media networks. Mark lauded MMM as an effective technique that has contributed to financial success for many businesses. In light of data becoming increasingly fragmented, he suggested that “some reinvention of the fundamental model framework is going to be required in order to move this old venerable method into the future.” Mark and Liz examined the Bayesian approach to MMM in handling fragmented data. Mark noted that there will not be a situation “where all the data is the same granularity in one place at one time.” The Bayesian approach can “fill in the blanks” of missing or fragmented data using reasonable estimates, creating a more accurate picture, which traditional MMM falls short of in the retail media environment.

MRC’s Outcomes and Data Quality Standard

The MRC’s Ron Pinelli outlined the scope of the Outcomes and Data Quality Standard, recently completed in September 2022. Part of MRC’s mission is setting standards for high quality media and advertising measurement, and Ron walked through the phased approach and iterative process that included the ANA, the 4A’s and other industry authorities.

Complexities of Integrating Big Data and Probability Sample People Meter Data

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.

Harnessing the Superpower of Personalization in a Privacy-Safe World

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.

The Quality Media Framework

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.