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.
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
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.
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%).
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 »
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?
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.
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.
One of the ARF’s most popular reports for membership and the press, the 6th Annual Privacy Study has now been released. The study surveyed 1,329 American consumers in the spring of 2023 on a Qualtrics online sample and platform. The report contains perennial questions regarding device usage, trust in institutions and how well privacy terms are understood. Last and this year’s versions also investigated what changes in information the public is willing to share and under what circumstances they are willing to share it. A new aspect to this year’s study is that it contains longitudinal findings across all six years.
