An impressive body of work is building in attention measurement. The three winning-papers sessions preceding this panel revealed a work in progress with shared goals as well as differences in approaches. Moderator Earl Taylor of the ARF’s MSI division asked the speakers about their views on barriers to the process, and opportunities for further improving attention measures.
This presentation focused on the implications of attention for advertisers, and how we can start applying these insights from a planning and buying perspective. Although viewability has become a proxy for attention, just because an ad is considered viewable does not mean eyes on ads. Viewability isn’t necessarily a true indication of attention – it can be quite arbitrary, and we need to go beyond that to the better and more accurate representation of those natural attention behaviors.
Moderator Jorge (TikTok) asked the panelists three key questions:
This presentation, based on research conducted jointly by Activision Blizzard and Alter, looks at the power of gaming with emphasis on ROI. Jonathan provided the background of esports, which he defined as professional gaming organized across teams or leagues with a range of game types. Professional e-gaming has grown rapidly to 436 million global viewers as of 2020 and is a highly fragmented marketplace.
Jane Clarke (CIMM) followed up with each of this session’s presenters on the goals and data points of their discrete studies. The following are edited highlights from the discussions.
A new study raises more questions about the ability of the “NPS” to predict company growth.
At this Insights Studio, authors from three different continents showcase their recently published work—including the JAR Best Paper 2022 on how advertising expenditures drive consumers’ perceptions of ad and brand quality. Also featured are studies on deepfakes and AI reshaping the advertising industry, the success of using product endorsers who are actors known for roles portraying despicable characters, and KPI patterns of social media influencers across several platforms. Talking points in the concluding Q&A span the future of AI in advertising and influencer marketing, machine-driven decisions for choosing endorsers, and factors (product- and economic-related) affecting consumer perceptions of quality in TV ads and engagement in user-generated content.
Member Only AccessAnibal Casso of Ogilvy moderated a discussion with panelists representing different perspectives in the industry (brand, agency, research). They tackled best practices, current challenges and the future of creative effectiveness.
In this session, Anastasia Leng of CreativeX argued that marketing and creative have the power to change things. In the face of an increased ad pool, which has gone up 6x in the last 20 years, ads now have a smaller shelf-life and need to be created in a more customized manner. In large part, technology has created this challenging landscape in marketing and advertising, but technology can now also help to address these new challenges. She pointed to computer vision technology which can help by supplying data created from “micro-feedback,” by clustering this information and fusing this feedback to create more useful macro-data to base decisions on.
Ariane Le Port of Google explored the relationship between augmented intelligence and creative effectiveness on YouTube. She noted that in the past, measuring creative was a challenge that was “so nuanced and so complex” that people tended to shy away from measuring it. In this session, Ariane pointed to a six-year experiment on YouTube video ads to help brands understand what is most effective in mobile video. In the experiment, they conducted A/B testing and took into account a variety of areas, such as framing, pacing, audio and other areas to find patterns of creative effectiveness. These experiments led to a partnership between Google and Ipsos to create YouTube’s ABCDs (Guidelines) for creative effectiveness. YouTube and Ipsos studied 17,000 ads in an effort to identify the creative elements that have a measurable impact, using a human and machine learning (ML) approach. Leveraging machine learning (ML) enabled them to look at large and robust datasets to gain a deep understanding of what elements work best in creative. Ariane discussed their augmented intelligence methodology which included data scope and collection, human and machine creative coding, metrics and data modeling and insights and commercialization.