Brands are reallocating their marketing budgets away from traditional media and toward programmatically bought digital media, social media platforms and retail media networks. In doing so, are they “killing the goose that laid the golden eggs?” One advertising guru thinks it is time to rethink current strategies.
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.
On April 4, industry experts explored where out-of-home (OOH) advertising stands today, its current capabilities and its potential in the future. Attendees heard data-backed insights on how OOH has changed in the past decade, where it fits in a multi-channel campaign or media plan, and brands’ objectives in using it.
Prof. Rachel Kennedy – Associate Director (Product Development), Ehrenberg-Bass Institute for Marketing Science
Beginning her discussion, Rachel Kennedy (Ehrenberg-Bass Institute) noted that Artificial Intelligence (AI) and other developments in computational advertising could mean key media principles, developed for traditional advertising, no longer apply. She examined empirical evidence, primarily focused on traditional media, which validated the idea that for media to thrive, it must consistently reach category buyers with both continuity and recency. Nevertheless, she acknowledged the evolving landscape of media. Building on that notion, she detailed two field experiments using social media, conducted with Stephen Bellman and Zachary Anesbury, also from the Ehrenberg-Bass Institute. The experiments aimed to assess: (1) whether AI-based optimization outperformed simpler, evidence-based optimization methods by implementing algorithms on YouTube and Meta platforms and (2) whether bursting, compared to continuous advertising, was more effective in reaching category buyers. The experimental design considered matched cells (e.g., randomized zip codes, matched demographics, people per HH, median weekly income, monthly repayments, motor vehicles per dwelling, etc.). Additionally, there were equal budgets per cell. Rachel noted that the standing principles will likely still have a role, but the research aimed to understand which ones and how they contribute to the current media landscape. Results from the experiments tended to be uneven and varied, indicating room for improvement.
Key takeaways:
AI and ML in programmatic advertising are discovering and using new media principles that may generate results from a variety of data points, better than any human could.
Experiment 1 (platform optimizer vs. simple reach principle): AI-based optimization beat simpler, evidence-based reach optimization, considering results for impressions, clicks and reach, reported by the digital agency responsible for scheduling the media.
However,AI did not outperform the simple media principles.
These findings suggest that using traditional media placement strategies can be just as effective as AI-based strategies for certain goals.
Experiment 2: Bursting is better than continuous advertising for reaching as many category buyers as possible.
However, neither campaign performed significantly better than the unexposed control cell.
Overall results from these experiments were messy, indicating the need for improvement, particularly in tools on the platform end (e.g., inadequate capping options, high budget spending and the need for enhancements in forecasting tools).
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.
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?
At this Insights Studio, JAR authors in Australia, China and the U.S. presented their recently published research on topics that some in industry may consider controversial. One found evidence of brand safety risks in programmatic advertising when ads were placed in negative news environments, contradicting some industry research. Another discovered social targeting spillover effects that suggest advertisers rethink conventional targeting methods. Other work came with unexpected findings: that highly creative advertising—although important for attracting attention—can have harmful effects on familiar brands, while benefiting unfamiliar brands. In the concluding Q&A, panelists explored aspects of brand recall relevant to their research, and whether brand size and other media channels would affect their results.