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NYCU: The Future of Media Measurement

Here are edited highlights from two conversations at AUDIENCExSCIENCE on the future of media measurement and its challenges. First: between Bill Livek — CEO & Executive Vice Chairman, Comscore, Inc. and Scott McDonald, Ph.D. — CEO and President, The ARF.  Scott: From the standpoint of a measurement company, trying to get as granular as possible but still aggregating things up for the advertisers who find this difficult and complex to buy - how do you think about this in 2021? Bill: This really is THE question. There’s premium video and video from user-generated content (UGC). But UGC should not even be remotely close to the value that premium video does for an advertiser. Scott: At this conference the one category of papers that we saw that had a huge increase was around the direct measurement of attention. Bill: We need engagement impressions of all types. We need to define what their content is (not just with length)…the branded advertisers look at those characteristics and they have to filter into media buying, as they are already in planning. We see the great agencies that are hired by the premium brands do a remarkable job of planning off these other data sets. But when it’s distilled into a buying metric of age and gender, I am not sure if we are doing justice to the premium content. Scott: Limitations of panels - what’s the workaround if we don’t have panels, what’s the alternative way of reconciling that? Bill: In my career we’ve seen response rates deteriorate … response rates for radio and TV (decades ago) were in the 60’s or 70’s.  We now know that it’s in the single digits. Which essentially means that we are guessing. ….Panels are interesting to collect other information that are not in big data sets. And we should leave it there. Scott: Parting words of advice? Bill: Have an open mind as we look at the future of measurement… We have an opportunity now to fix all the different verticals and how they intersect on how the consumer is being entertained and informed. And let the advertiser have those tools and let’s empower the media companies so that they can price appropriately based on the content that they have. I think this is a great time to be in the business.

Here is a summary of Nielsen COO Karthik Rao’s perspective on these issues, from an AUDIENCExSCIENCE conversation with Scott McDonald, Ph.D. – President & CEO, The ARF. The major challenge to measurement is the vast amount of content to measure and the ability to deduplicate across many screens. Inclusiveness is the second major challenge – showing the face of America. But this is an opportunity also. The third is chasing identity and privacy at the same time. Karthik felt that interoperability will have to be a core piece of the identity future. Nielsen built its identity graph in 2020 – which is necessary for coverage of populations. There can be multiple ID systems dedicated to different purposes – targeting systems are for scale, while measurement is for coverage. What will the role of panels be in the future, with it being more difficult to recruit them and service them during a pandemic? Nielsen stressed that it has been using big data for a long time and foresees a role for both in future measurement. Karthik announced that in January, Nielsen will be showing impact data combining ACR, STB and panel data. The Census is one data set serving as a truth set. It is used to calibrate ratings, but Nielsen will be looking at other data sets to use for calibration. This may be data for outcome-based analysis. Nielsen is a big believer in the WFA initiative. It also thinks it can create cross-platform measurement by working with multiple, walled gardens. Key takeaways:
  • Challenges are the vast content viewed on multiple portable devices, inclusiveness of the population, and the balance between identity and privacy.
  • Panels and big data each have their own strong points. Panels are directly observable – you simply know much more about the home, while big data may be less representative, but its size affords stability.

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NYCU: How to Improve Demographic & Media Use Data

At AUDIENCExSCIENCE, Jon Puleston — VP, Innovation, Profiles Division, Kantar, discussed shortcomings in demographic and media use data and offered solutions.   Inaccurate responses to basic demographic and media consumption questions asked in surveys are very problematic, since these responses form the bedrock data points for all demographic research and media consumption measurement.  As a result of inaccurate responses, the industry has been mis-measuring basic demographics and media consumption for decades. Additionally, each company and country has its own methods of measuring basic demographics and media consumption. Growing levels of international cross-market research require accurate and consistent data. Cross-market experiments and desk research provide a framework to address some of these fundamental flaws and form the basis for Jon’s proposals:

  • Adopt more inclusive and precise approaches to how demographic and media questions are asked, to improve the accuracy of responses. Establish an ESOMAR committee to develop best practice methods made up of representations from the key panel supply companies, leading market research firms, and key buyers of international market research.
  • Upgrade how basic demographics and media consumption questions in surveys are measured. Pledges are needed from key players in the research industry to work to adopt and implement the use of these best practices.
  • Create an international measurement standard. Support is needed from advertising professionals and ARF members.

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Moment-to-Moment EEG Metrics Enhance Ad Effectiveness Analysis


A European research team is on a mission to advance the development and use of electroencephalogram (EEG)-based methods for evaluating advertising effectiveness. Although their research leaves open questions, it suggests that recent developments in this area—specifically moment-to-moment EEG-based indicators—provide a clearer view into emotional response and attention to ads than previous EEG methods.

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The Social Media Measurement Field Guide


If you are new to social media measurement or could use a refresher, this guide is for you. It covers the metrics used for different types of social media marketing, sources for those metrics, KPIs, descriptions of useful study types, use cases, challenges, and appendices listing resources, key metric definitions and more. The field guide is the product of the ARF Social Council, a body comprised of leaders in the field hailing from major social platforms, brands, agencies, and relevant startups. The council revealed the guide’s top ten takeaways at a June 29 event. Now, the ARF offers its members the full and complete version.

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NYCU: New Challenges for First-Party Data

Kantar argues that first-party data – essential for brands seeking to build better relationships and drive results – faces new challenges.   Kantar’s recently published study “Data strategies for brand growth,” discusses how advertisers can realize the full power of their own direct consumer relationships to drive brand growth. But while advertisers may love the promise of their first-party, customer data, they face significant CHALLENGES as they navigate the demise of the third-party cookie, along with other obstacles.

  • Importance of data and information: Some 60% of the advertisers we spoke to predict that enhancing their own data with data from other sources will become even more important in the coming years. Sixty percent expect insights from their own data to increase, rising to 74% among larger companies (10,000+ employees).
  • Data activation in a post-cookie world: The way forward for brand success is for brands to activate first-party data and combine it with other relevant data sources. This will guide relevant message formulation, better targeting and improve impact across the sales funnel.
Profiling target segments across the web for customer acquisition is useful when scale is needed. Yet the imminent demise of the cookie is threatening investments that advertisers have made in marketing technology and systems developed over many years. As a result, it’s no surprise that brands want to activate their own data by connecting it to other, external, data sources. 80% of the advertisers we spoke to say they want their own, in-house segmentations to enable media planning and activation. And 54% believe that integrating other data sources with audience segmentation will become even more crucial. In sum, this all places even more importance on high-quality data and trustworthiness. It’s the data dimension rated to grow most in prominence over the next few years, particularly for larger advertisers where more money is at risk from being hooked up to imprecise profiling data and inaccurate data sets. Source: McCarthy, J. (2021, September 7). Unlocking the power of first-party data for advertisers. Kantar.  

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NYCU: Audience Measurement at a Crossroads

As the industry debates the future of audience measurement, the ARF’s upcoming AUDIENCExSCIENCE conference puts this issue front and center.   ARF CEO Scott McDonald explains the current challenge: “Technology continues to promise ever-more-granular data. Yet, privacy concerns threaten the ability to get a unified view across the increasingly fragmented media landscape. “Some argue for interoperable unified ID solutions, but legal and organizational barriers persist. Some argue for renewed focus on representative panels to tame and calibrate large datasets of passively measured behaviors, but covid has undermined confidence in the efficacy of this approach. Some argue for “clean room” solutions that permit privacy-safe matching of ad exposure and outcome data – implicitly accepting a balkanized future of multiple walled gardens who grade their own homework.” The main challenge, therefore, is for industry bodies to bring forth solutions that meet the market’s need for credible, cross-platform audience measurement. For more details: AudiencexScience 2021  

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CMO Briefs: Understanding Biases in Panel and Census Research Using AI/ML

Numerous bias types can impact panel and census research. Artificial intelligence (AI) and machine learning (ML) algorithms can perpetuate them. This is true of surveys, panels and the big data sets that are often used to calibrate each other. The following report touches on some of the biases that can occur, where they may affect panel and census research that use AI/ML and mitigation efforts to account for them. Read the article.