IAS Announces Mobile In-App Support for First Attention Product to Unify Media Quality and Eye Tracking

Quality Attention™ Expands Coverage and Improves Accuracy of Correlation Between Attention Scores and Business Results

Integral Ad Science, a leading global media measurement and optimization platform, announced the expansion of its Quality Attention™ measurement product, adding support for mobile in-app environments. Quality Attention is the first measurement product to unify media quality and eye tracking with machine learning to deliver proven results.

This enhancement to Quality Attention continues IAS’s commitment to providing advertisers with expanded coverage across additional channels and formats. In addition to expanded environment support, the Quality Attention model has improved accuracy of the correlation between attention scores and outcomes. IAS’s attention model is designed to predict if an impression is more likely to lead to a business result including awareness, consideration, and conversion.

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According to eMarketer, apps are predicted to reach a dominant 82% share of the anticipated $200B in mobile ad spend this year. Advertisers now have access to an attention measurement product that will drive superior results across their mobile in-app campaigns and protect their growing investments in mobile. In the recent IAS report, Taking Action on Attention: Volume II, when comparing business results between low and high attention scores, higher attention impressions experienced success rates (e.g. conversions) that were twice as high as those with low attention.

“It’s essential for attention measurement to drive outcomes and campaign performance for advertisers,” said Khurrum Malik, CMO of Integral Ad Science. “The latest enhancements to our purpose-built Quality Attention offering are expected to provide advertisers with more granular signals and expanded coverage across the channels and formats that are most important to them.”

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Quality Attention provides global advertisers with:

• Expanded Coverage and Metrics: Measurement across mobile in-app environments and new metrics including the number of ads that paused, resumed, skipped and started a video ad, in addition to volume change and sub-metrics of volume change.
• An Advanced Machine Learning Model: A singular view of campaigns’ attention performance, trained based on a pool of data consisting of billions of impressions and millions of conversion events.
• Proven Performance and Brand Results: Up to a 130% lift in conversion rates when comparing high attention impressions to low attention impressions, with greater attention scores seeing 91% higher brand consideration and 166% higher purchase intent.
• Unification of Media Quality with Human Attention: 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.

With Quality Attention, advertisers can capture higher attention to drive campaign performance and unlock proven results. Quality Attention uses advanced machine learning technology, actionable data from Lumen Research’s eye-tracking technology, and a variety of signals obtained as part of IAS’s core technology, including viewability, ad situation, and user interaction, and weighs them into a single attention score.

“The latest enhancements to IAS’s Quality Attention offering is a step forward in creating a more accurate picture of attention for advertisers,” said Mike Follett, CEO at Lumen Research. “We were excited to combine our cutting-edge eye-tracking data with IAS’s attention model and now advertisers have access to even more granular information across the in-app environment.”

In January 2024, IAS announced the general availability of its Quality Attention™ measurement product – the first to unify media quality and eye tracking with machine learning. The offering provides transparent metrics to help global advertisers increase return on investment, drive brand consideration, and boost conversions.