IAS expands Quality Attention™ to Mobile In-App, enhancing ad measurement

Integral Ad Science (IAS) yesterday announced a significant expansion of its Quality Attention™ measurement product. The update extends the product's capabilities to include support for mobile in-app environments, marking a crucial advancement in digital advertising measurement technology. This enhancement aims to provide advertisers with a more comprehensive tool to assess the effectiveness of their digital campaigns across an increasingly diverse range of platforms and formats.

According to the press release from IAS, Quality Attention™ is the first measurement product in the industry to unify media quality metrics with eye tracking data, leveraging machine learning to deliver actionable insights. The expansion into mobile in-app environments addresses a growing need in the digital advertising landscape, as apps are predicted to capture 82% of the estimated $200 billion in mobile ad spend this year, based on data from eMarketer cited in the announcement.

The technology behind Quality Attention™ combines several advanced components to provide a holistic view of ad performance. At its core, the product utilizes machine learning algorithms that process a vast array of data points. These include traditional media quality metrics such as viewability and ad placement, as well as user interaction signals. What sets Quality Attention™ apart is its integration of eye-tracking data from Lumen Research, which provides insights into actual human visual engagement with advertisements.

IAS's approach to measuring attention goes beyond simple visibility metrics. The company's machine learning model is trained on billions of impressions and millions of conversion events, allowing it to predict the likelihood of an impression leading to specific business outcomes. These outcomes span the marketing funnel, including awareness, consideration, and conversion. This predictive capability is particularly valuable for advertisers seeking to optimize their campaigns for tangible results rather than just impressions or clicks.

The expansion to mobile in-app environments brings several new metrics to the Quality Attention™ suite. Advertisers can now track the number of ads that are paused, resumed, skipped, or started within video ad formats. Additionally, the product provides data on volume changes, offering insights into how users interact with the audio components of advertisements. These granular metrics allow for a more nuanced understanding of user engagement in the mobile app ecosystem.

One of the most compelling aspects of the Quality Attention™ product is its demonstrated impact on advertising performance. According to IAS's recent report, "Taking Action on Attention: Volume II," impressions with high attention scores showed success rates (such as conversions) that were twice as high as those with low attention scores. Furthermore, the company reports that high attention impressions can lead to up to a 130% lift in conversion rates compared to low attention impressions. In terms of brand metrics, higher attention scores correlated with a 91% increase in brand consideration and a 166% boost in purchase intent.

The integration of eye-tracking data into the Quality Attention™ model is a key differentiator for IAS. By partnering with Lumen Research, IAS has access to one of the world's largest consumer attention biometric datasets. This collaboration allows for a more accurate representation of human attention patterns, which is then combined with IAS's proprietary media quality metrics to create a comprehensive attention score.

Khurrum Malik, Chief Marketing Officer of Integral Ad Science, emphasized the importance of attention measurement in driving tangible outcomes and campaign performance for advertisers. He stated that the enhancements to Quality Attention™ are expected to provide advertisers with more detailed signals and expanded coverage across crucial channels and formats.

The concept of attention measurement in digital advertising is not without its challenges. Critics argue that attention metrics may not always correlate directly with business outcomes and that the definition of "attention" can vary across different contexts and platforms. IAS's approach of combining multiple data sources and using machine learning to create predictive models attempts to address these concerns by providing a more nuanced and contextual understanding of attention.

The mobile in-app environment presents unique challenges and opportunities for attention measurement. Apps often provide a more immersive and focused user experience compared to web browsing, potentially leading to higher attention levels. Conversely, the smaller screens of mobile devices and the often fast-paced nature of app usage can make it more challenging to capture and maintain user attention. IAS's expansion of Quality Attention™ to this environment suggests that the company has developed methods to accurately measure attention in these unique conditions.

As the digital advertising ecosystem continues to evolve, tools like Quality Attention™ may play an increasingly important role in helping advertisers navigate the complex landscape of user engagement and campaign effectiveness. By providing a unified view of media quality and human attention across multiple platforms, including the crucial mobile in-app environment, IAS is positioning itself as a key player in the future of digital advertising measurement.

The expansion of Quality Attention™ to mobile in-app environments represents a significant step forward in the field of digital advertising measurement. By combining advanced machine learning techniques with real-world eye-tracking data and traditional media quality metrics, IAS is offering advertisers a powerful tool to optimize their campaigns and drive better business results. As the digital advertising industry continues to grapple with issues of transparency, effectiveness, and user privacy, attention-based metrics may provide a valuable alternative to traditional measurement approaches.

Key facts about IAS's Quality Attention™ expansion

Announced on July 31, 2024

Extends support to mobile in-app environments

Addresses the 82% share of mobile ad spend predicted to go to apps in 2024

Utilizes machine learning trained on billions of impressions and millions of conversion events

Incorporates eye-tracking data from Lumen Research

Provides new metrics for video ads including pause, resume, skip, and start rates

Reports up to 130% lift in conversion rates for high attention impressions

Shows 91% higher brand consideration and 166% higher purchase intent for high attention scores

Expands coverage to align with advertisers' needs across channels and formats

Aims to provide a more accurate picture of attention for global advertisers