Artificial Intelligence is revolutionizing the way advertisers find and influence consumers, how success is measured, and the strategies and tools used for experimentation. On July 10, panelists from Google and HP led a discussion on the changing landscape of measurement tools, shifting mindsets from precision to prediction, and leveraging AI-driven tools to achieve marketing and business outcomes.
On June 25, OptiMine dove into the specifics of Google’s privacy changes and revealed how these changes will impact the marketing and measurement landscape. Traditional forms of measurement will no longer be viable as privacy-driven data loss continues to diminish accuracy, coverage and reliability. Attendees gained applicable strategies on how their brands can adapt before it’s too late.
How do positive and negative appeals in charity advertising shape donors' choices between immediate and long-term assistance? Discover strategic approaches that charities can adopt to optimize their advertising and maximize donor engagement and contributions.
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.
The ARF Knowledge Center provides secondary research services for ARF and ARF-MSI members on a broad range of topics. Analysis of ARF member questions posed to the Knowledge Center in 2023 shows distinct trends and interests posed by different constituent members. As always, most questions tend to be specific to the unique business needs of each member, but there were some overarching trends, which highlight the evolving landscape of the marketing and advertising industry. This FAQ discusses these trends alongside sample member questions and Knowledge Center research reports.
At SHOPPER 2024, practitioners and academics shared research-based insights on retail media networks (RMN), AI, influencer marketing and live shopping. The industry’s leading experts revealed which tools, technologies and trends are shaping the ever-evolving shopper landscape and what brands need to know to stay ahead.
Research has demonstrated that developing messages, creative and targeting based only on demographic characteristics is not optimal. Investing in research on consumers’ values and emotions is likely to increase marketing effectiveness. Read more »
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%.
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%).
Representing the Alliance for Video Level Contextual Advertising (AVCA), Rohan Castelino (IRIS.TV) and Mike Treon (PMG) examined research conducted with eye tracking and attention computing company, Tobii. The research endeavor focused on the impact of AI-enabled contextual targeting on viewer attention and brand perception in CTV. Beginning the discussion, Rohan examined challenges with CTV advertising. He noted that advances in machine learning (ML) have empowered advertisers to explore AI enabled contextual targeting, which analyzes video frame by frame, uses computer vision, natural language, understanding, sentiment analysis, etc., to create standardized contextual and brand suitability segments. Highlighting a study of participants in U.S. households, the research specifically aimed to understand if AI-enabled contextual targeting outperformed standard demo and pub-declared metadata in CTV. Additionally, they wanted to understand if brand suitability had an impact on CTV viewers’ attention and brand perception. Results from the research found that AI-enabled contextual targeting outperformed standard demo and pub-declared metadata in CTV and increased viewer engagement. In closing, Mike provided the marketers’ perspective on the use of AI-enabled contextual targeted ads and its practical applications.
Key takeaways:
Challenges with CTV advertising: Ads can be repetitive, offensive and sometimes irrelevant, in addition to ads being placed in problematic context.
In addition,buyers are unsure who saw the ad or what type of content the ad appeared within. A recent study by GumGum showed that 20% of CTV ad breaks in children’s content were illegal (e.g., ads shown for alcohol and casino gambling).
Advertisers have begun experimentation with contextual targeting in CTV, as a path to relevance.
A study conducted with U.S. participants that examined the effects of watching 90 minutes of control and test advertisements, using a combination of eye tracking, microphones, interviews and surveys to gather data found that:
AI-enabled contextual targeting attracts and holds attention (e.g., 4x fewer ads missed, 22% more ads seen from the beginning and 15% more total ad attention).
AI-enabled contextual targeting increases brand interest (e.g., 42% more interested in the product, 38% gained a deeper understanding).
Research to understand if brand suitability had an impact on CTV viewers’ attention and brand perception found that:
Poor brand suitability makes CTV viewers tune out ads and reduces brand favorability (e.g., 54% were less interested in the product, 31% liked the brand less).
AI-enabled contextual targeted ads are as engaging as the show.
