privacy

How to Calculate Reach and Frequency Using Virtual IDs (VIDs)

Current existing methods used to calculate reach and frequency of a campaign or media schedule are known to have deficiencies in measuring cross-device ad exposure. Restrictions to protect digital privacy complicate cross-platform exposure measurement even further. Multiple global research organizations have turned to a concept known as “virtual people,” to overcome these limitations in order to produce aggregate reach and frequency estimates. This report by the ARF Analytics Council provides a foundational overview of VIDs for a broad audience, providing ARF members with a stronger understanding of this vital topic.

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Demystifying Data Privacy: Understanding the Cookieless Future

  • YOUNG PROS

On May 9th, the ARF Young Pros hosted an event focused on data privacy and the implications of a cookieless future. Cole Strain from Samba TV started the event with an overview of data privacy fundamentals and the impending changes due to the deprecation of cookies. This was followed by a panel discussion featuring experts from Snapchat, Comscore, and Google, moderated by one of our Young Pros Advisory Board Members, Gillian Kenah from Tracksuit.

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  • Article

How to Balance Consumer Response to “Skimpflation”

Rising costs are an important issue for businesses. Many wonder how they should respond and how customers will react if they say, reduce product quality. Such “skimpflation” can be harder for consumers to detect. However, this Marketing Science Institute (MSI) working paper finds that those who do realize it consider the practice deceptive and unfair. Consumers, it seems, prefer reduced product size (shrinkflation) or increased prices to skimpflation, with price increases the most popular/least unpopular of these options.

Driving Greater Campaign Reach and Relevancy Across Formats

Sharmilan RayerGM, Amazon Publisher Cloud


Sharmilan Rayer of Amazon Publisher Cloud discussed an approach to empowering addressability as legacy identifiers (cookies and mobile IDs) fade. This approach, called durable addressability, includes the sharing of first-party signals across publishers, advertisers and third parties. Its three pillars are first-party signal investment, secure signal collaboration and machine learning (ML) powered modeling. The Amazon Marketing Cloud is their new advertiser clean room which takes this approach. It allows advertisers to combine their first-party signals with Amazon’s publisher ones and any third-party’s in a privacy compliant way. Key takeaways:
  • Durable addressability starts with each member investing in first-party data from a resource, funding and technology perspective.
  • Sixty percent of advertisers report planning to leverage first-party data for ad placements, and 47% of publishers say their first-party data is the answer to cookie deprecation.
  • The first-party data advertisers would bring to this strategy includes customer engagement, conversions and proprietary audiences.
  • Amazon has access to publisher first-party data across CTV, web, mobile and audio. Having access to this first-party data allows for determining which ad opportunities are best for a particular campaign.
  • As cookies deprecate, clean rooms will begin playing a more important role, according to Amazon.
  • Modeling by machine learning has increased reach 20-30% on unaddressable supply, Amazon claims.
  • A new product called Performance Plus combines Amazon Ads signals, advertiser conversion signals and machine learning to generate predictive segments. It has been observed boosting conversions 30-80%.

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OOH Measurement’s Game Has Changed

Christina RadiganSVP, Research & Insights, Outfront

Christina Radigan of Outfront explored the advantages of out-of-home advertising (OOH) and discussed advancements in its measurement techniques. Christina noted that with the loss of cookies and third-party data, contextual ad placement will see a renewed sense of importance, and in OOH, location is a proxy for context, driving content. She further indicated the benefits of OOH citing a recent study by Omnicom, using marketing mix modeling (MMM), which found that increased OOH spend drives revenue return on ad spend (RROAS). This research also highlighted that OOH is underfunded, representing only 4% to 5% of the total media marketplace. Following up on this, Christina pointed to attribution metrics, measuring the impact of OOH ad exposure on brand metrics and consumer behaviors, to demonstrate OOH's effectiveness at the campaign level. Expanding on their work in attribution, she noted changes stemming from the pandemic: Format proliferation and greater digitization, privacy-compliant mobile measurement ramping up (opt-in survey panel and SDK) and performance marketing and measurement becoming table stakes for budget allocations. New measurement opportunities from OOH intercepts included brand lift studies, footfall, website visitation, app download and app activity and tune in. Finally, she examined brand studies conducted for Nissan and Professional Bull Riders (PBR), showcasing the effectiveness of OOH advertising in driving recall, ticket sales and revenue. Key takeaways:
  • MMMs return to the forefront, as models become more campaign sensitive and are privacy compliant (powered by ML and AI).
  • A study from Omnicom, using MMM, found that optimizing OOH spend in automotive increased brand consideration (11%) and brand awareness (19%). In CPG food, optimizing OOH spend increased purchase intent (24%) and optimizing OOH spend in retail grocery increased awareness (9%).
  • OOH now represents a plethora of formats (e.g., roadside ads, rail and bus ads, digital and print) and has the ability to surround the consumer across their journey, providing the ability to measure up and down the funnel, in addition to fueling behavioral research.
  • Key factors for successful measurement in OOH: feasibility (e.g., scale and scope of the campaign, reach and frequency), the right KPIs (e.g., campaign goal) and creative best practices (Is the creative made for OOH?).
  • OOH advertising is yielding tangible outcomes by boosting consumer attention (+49%). Additionally, there has been a notable surge in advertiser engagement (+200%).
  • Ad recall rates in OOH continue to increase (e.g., 30% in 2020 vs. 44% in 2023).

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AUDIENCEXSCIENCE 2024

The ARF’s annual AUDIENCExSCIENCE conference highlighted the most critical audience measurement issues. Through keynotes, panels, debates and rigorously peer-reviewed research presentations, attendees learned about a wide array of new and evergreen industry topics, endemic to our industry changes. World-class thinkers joined us in NYC to share their perspectives on the future of advertising research and measurement, and how tomorrow’s technologies and data trends will impact advertising and media.

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Forecasting & Optimizing Reach in a PII Compliant Measurement Ecosystem

Spencer LambertVP, Product & Partnership Success, datafuelX

Matthew WeinmanSr. Director, Advanced Advertising Product Management, TelevisaUnivision

Reach and frequency planning requires access to unique viewership data, which has become increasingly restricted due to identity restrictions. However, challenges exist with panel-only measurement, including the undercounting of Hispanic and Spanish language coverage, stated Matthew Weinman (TelevisaUnivision). Panel data undercounts Hispanics audiences by upwards of 20%, even for broad demographics. The benefits of big data exist across audience planning, viewership measurement and outcomes. Excessive frequency can be limited while maintaining or expanding reach, as well as improving ROAS. However, there are barriers to working with big data, including PII compliance. Additionally, the size and scale of big data leads to lengthy ID forecast times and computing costs. Spencer Lambert (datafuelX) presented details of their approach to ID-level forecasting which included their reach and frequency clustering methodology. Key takeaways:
  • Advantages of clustering methodology over identity methodology for reach and frequency:
  • Efficiency and accuracy: Delivers comparable accuracy metrics
  • Lower error rates: Seven percent for cluster reach forecasts vs. 20% error rate on identity-scaled reach forecasts
  • Cross-platform reach and frequency: By scaling cluster assignments to digital IDs, this methodology can empower cross-platform management and optimization
  • Lower compute time and costs
  • PII compliant: Preserves the use of identity-level planning

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A Clean Room Incrementality Experiment – An Indeed Case Study

Joe ZuckerSenior Manager, Marketing Analytics, Indeed

Clean room experiments are challenging in an online marketplace, such as Indeed’s job site for employers and employees, due to potential online experimentation biases, including activity bias, ad server bias and base rate bias, according to Joe Zucker (Indeed). Control groups can be created in multiple ways with different degrees of technical setup or in some cases, external modeling. The five variations of control groups are ghost ads, publisher house ads, PSA ads, propensity score matching and intent to treat. A comparison indicated that each option has both pros and cons, including cost, the need for additional data or publisher support. Joe reminded the audience that there is “no free lunch.” Ghost ads would be preferred by Indeed to create the control group; however, this option has high technical set-up requirements, few publisher partners have this capability and there is low control over the analysis. There are also challenges related to interpreting experimental results, which include low match/conversion rates and the need to analyze experiments with different control group construction. Indeed was able to measure aggregate incrementality for their campaign metrics and prove the value of their advertising as a result of these clean room experiments. Key takeaways:
  • Despite the challenges of clean room experiments, these experiments are critical to the measurement of the incremental impact of advertising on KPIs.
  • Clean room experiments can ensure high quality continuous reporting with actionable analytics and insights while achieving user data privacy compliance.
  • Experimentation enabled Indeed to focus on new customers in a cookie-free, privacy-forward manner with the ability to verify advertiser data.

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