privacy

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Driving Greater Campaign Reach and Relevancy Across Formats

Sharmilan Rayer – GM, 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 Radigan – SVP, 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 Lambert – VP, Product & Partnership Success, datafuelX

Matthew Weinman – Sr. 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 Zucker – Senior 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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The Power of AI for Effective Advertising in an ID-free World

Rachel Gantz – Managing Director, Proximic by Comscore

Amidst heightened regulations in the advertising ecosystem, Rachel Gantz of Proximic by Comscore delved into a discussion of diverse AI applications and implementation tactics, in an increasingly ID-free environment, to effectively reach audiences. Rachel highlighted signal loss as a "massive industry challenge," to provide a framework for the research she examined. She remarked that the digital advertising environment was built on ID-based audience targeting, but with the loss of this data and the increase in privacy regulations, advertisers have placed their focus on first-party and contextual targeting (which includes predictive modeling). In her discussion, she focused on the many impacts predictive AI is having on contextual targeting, in a world increasingly void of third-party data, providing results from a supporting experiment. The research aimed to understand how the performance of AI-powered ID-free audience targeting tactics compared to their ID-based counterparts. The experiment considered audience reach, cost efficiency (eCPM), in-target accuracy and inventory placement quality. Key takeaways:

• Fifty to sixty percent of programmatic inventory has no IDs associated with it and that includes alternative IDs.
• Specific to mobile advertising, many advertisers saw 80% of their IOS scale disappear overnight.
• In an experiment, two groups were exposed to two simultaneous campaigns, focused on holiday shoppers. The first group (campaign A) was an ID-based audience, while the second group was an ID-free predictive audience.
  • Analyzing reach: ID-free targeting nearly doubled the advertisers’ reach, vs. the same audience, with ID-based tactics.
  • Results from cost efficiency (eCPM): ID-free AI-powered contextual audiences saw 32% lower eCPMs than ID-based counterparts.
  • In-target rate results: Significant accuracy was confirmed (84%) when validating if users reached with the ID-free audience matched the targeting criteria.
  • Inventory placement quality: ID-free audience ads appeared on higher quality inventory, compared to the same ID-based audience (ID-free 27% vs. ID-based 21%).

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How AI Will Make Us Smarter

• Posted: March 15, 2024

During a recent ARF Town Hall, Professor Russel Neuman explained why he thinks AI will have primarily positive effects. It will make us smarter and more productive. Furthermore, he does not think new regulations are needed to protect humanity from negative consequences of AI development. Read more »

• Research & Data Quality
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Next Generation Artificial Intelligence

• TOWN HALL

Professor Russ Newman of New York University does not believe that AI will cause humanity’s extinction. Instead, it should help enhance human intelligence and productivity and our quality of life. After putting the AI revolution into historical context, Prof. Newman discussed aligning AI with human values. At our current stage, he believes the regulatory mechanisms in place are sufficient. He explained how large language models work, what allowed them to come into existence and their future impact, describing the effect on marketing and advertising, as well as what the individual user experience will be like. A democratizing, hyper-personalized experience could take place where AI agents advocate on their owner’s behalf and negotiate each transaction with their owner’s preferences in mind. Over time, he sees a great diversification of models coming into being. Historically speaking, each groundbreaking technology that changed the world has been a net gain for humanity. What makes AI different is that if applied correctly, it could make us smarter. The question is, if AI gives us exceptional advice, will we take it?

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• Research & Data Quality
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Minimizing Risk and Inspiring Innovation When Using GenAI and LLMs

Generative AI (GenAI) and Large Language Models (LLMs) have reached a point in their development where they have become tools so powerful, they will quickly become too important to ignore. Proper use can drive greater efficiency, productivity, better automation and even become new revenue drivers, according to this concise, how-to guide written by Steven Millman, ARF board member and Global Head of Research & Data Science at Dynata. It describes best practices and potential pitfalls in data privacy and security, protecting IP, oversight and more.

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New Insights on Attitudes about Privacy

• Posted: January 18, 2024

The findings of the ARF’s Sixth Annual (2023) Privacy Study have just been released. It reveals changes in many consumers’ attitudes and awareness of privacy regulations and practices.     

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