Integral Ad Science’s Quality Attention and the Attention Payoff With Yannis Dosios, CCO of IAS

Yannis Dosios, CCO at Integral Ad Science, introduces the groundbreaking Quality Attention product, set to revolutionize how brands measure and optimize attention in an era of declining identifiers and oversaturation of online ads.

Can I have your attention? My goal was to capture your attention on this page, just as IAS designed Quality Attention to accomplish. Did it work?  

The waning reliance on traditional identifiers and the escalating challenge of capturing consumer attention as the digital landscape becomes oversaturated with ads fueled the rise of attention metrics. Brands now gauge attention metrics through various parameters such as viewability, creative size, interaction, ad position, time of day, publisher or program, audibility, page clutter, device frequency, and eye tracking.

We spoke with Yannis Dosios, Integral Ad Science’s CCO, to understand why attention metrics is the new hot commodity, the development of IAS’ Quality Attention Product, and how they plan to future-proof the tech. 

Andrew Byrd: The industry has discussed the importance of attention metrics for a while now. From your perspective, why has attention become such a critical metric?

Yannis Dosios: The advertising ecosystem is very complex, with an ever-growing number of channels, publishers, and formats for advertising spend. Cookie deprecation, now underway in 2024, further complicates this. In this challenging environment, marketers need a metric that helps them understand how much their advertising campaign resonates with consumers and how it can be optimized to maximize performance. 

Attention, when measured accurately, can be that metric. It can predict how much an advertisement resonates with a consumer and how likely it will lead to an advertiser’s desired business result. By combining visibility, situational context, and interaction data signals, IAS’s Quality Attention can predict whether an impression will lead to business results such as sales, conversions, or brand impact.

AB: What motivated Integral Ad Science to develop Quality Attention, and what challenges does it address for the industry?

YD: Innovation is a top priority at Integral Ad Science (IAS). Our customer’s needs drive our product roadmap, and an attention measurement solution that offers transparent metrics was high on their list.

In our report The Attention Payoff, we surveyed media experts who told us that media quality was the top KPI monitored by attention users. So, Quality Attention had to help advertisers focus on getting better inventory quality and improving media KPIs.

One significant need by marketers today is to demonstrate a clearer link between their advertising strategies and business results. What excites me most about Quality Attention: it consistently delivers the link to results. Campaigns with high IAS Quality Attention show a 130% lift in conversion rates, 91% higher brand consideration, and 166% higher purchase intent compared to low attention impressions.

AB: Could you delve into the technology behind Quality Attention, particularly the role of machine learning and the utilization of eye-tracking data from Lumen Research?

YD: IAS is the first company to combine one of the world’s largest consumer attention biometric data sets with media quality metrics to provide the most accurate picture of attention for global advertisers. 

Lumen Research is a leading attention technology company. They use eye-tracking data to build robust predictive models of attention for planning, buying, and reporting on digital media. The Lumen attention panels are the world’s largest continuous eye tracking panels, with over 650k+ panelist data collected globally over the years, all training the Lumen Predictive Attention Model.

IAS’s Quality Attention uses advanced machine learning, actionable data from Lumen Research’s eye-tracking technology, and various signals obtained as part of IAS’s core products. The technology is trained based on a pool of billions of impressions and millions of conversion events to generate the most accurate and insightful picture of attention for global advertisers.

AB: What specific factors and signals does IAS’s attention model consider, and how are they weighted to calculate an impression’s attention score?

YD: After analyzing billions of impressions, we established in IAS’s Taking Action on Attention white paper that attention is a function of three key categories: Visibility, Situation, and Interaction.

Visibility signals measure impressions’ validity, including metrics such as viewability and time-in-view. Situation signals describe the environment in which impressions are served, including metrics such as ad density and ad share on screen. Interaction signals measure consumer activity in the presence of ads and include metrics such as scrolling, ad pausing, and eye tracking.

Quality Attention uses advanced AI and ML to weigh and combine these signals into a single attention score, designed to evaluate if an impression is more likely to lead to a business result, including awareness, consideration, and conversion.

AB: Can you elaborate on the partnership between IAS and Publicis? How has Quality Attention assisted them in improving their business outcomes?

YD: We are excited about our IAS partnership with Publicis, breaking new ground with Quality Attention. As one of the first to use Quality Attention, Publicis is leveraging Quality Attention to reduce ad fatigue, identify better inventory quality, and improve media KPIs.

AB: The partnership between IAS and Lumen Research aims to change the measurement of digital advertising impressions. How does this collaboration enhance the accuracy of tracking ad impressions and predicting attention?

YD: By bringing Lumen Research’s cutting-edge eye-tracking data to IAS’s attention model, this partnership helps give advertisers access to the most robust predictive attention models at scale.

We were excited to partner with Lumen Research and be the first company to unify media quality with human attention. Their interaction signals can help identify human preference, measuring activities like scrolling, interacting with ads, and where the consumer’s gaze and fixation are on the page.

AB: There are always learning curves when testing and implementing new tech. What challenges have you come across? How are you looking to future-proof the tech?

YD: The biggest challenge we faced was ensuring that we could demonstrate the link between attention and proven results. So, early partners like Publicis were critical in the testing phase. The world is moving towards a place where we expect marketers to demonstrate their effectiveness, so we were pleased to see how Quality Attention was able to show performance and proven results. We are delighted to have a strong and growing set of success stories linking high-quality attention with solid business results, such as a 130% lift in conversion rates and 166% higher purchase intent when comparing high with Quality Attention campaigns. 

We are always looking for ways to improve our solutions to bring our customers and partners the best in measurement and optimization. As new platforms emerge and popular platforms, like social media, evolve alongside AI/ML technology, we intend to continue modernizing Quality Attention to serve marketers’ needs. We are also working to enable advertisers to use quality attention in their programmatic buys to proactively optimize their campaigns for performance.