IAS Launches Quality Attention
Quality Attention uses a variety of signals obtained as part of IAS's core products, including viewability, ad density, and user interaction.
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Integral Ad Science, a media measurement and optimisation platform provider, launched its Quality Attention post-bid measurement product.
Quality Attention combines IAS’s access to more than 280 billion daily digital interactions with attention research to create a powerful way for marketers to get a greater impact from their advertising campaigns.
Quality Attention uses a variety of signals obtained as part of IAS’s core products, including viewability, ad density, and user interaction, and weighs them into a single attention score and three sub-scores for visibility, situation, and interaction for decisive outcomes. These three key media signals can predict if an impression is likely to lead to attention and advertising outcomes.
IAS research found a 198 percent average lift in the conversion rate when comparing high-attention impressions with low-attention impressions. Leveraging these signals with IAS’s scoring system provides actionable insight on attention and opens up new opportunities for advertisers to optimise messaging executions, develop creative, and evaluate which programmatic partners are driving higher attention.
“Harnessing attention is pivotal to executing a successful advertising campaign. But not all attention products are created equal. IAS has created a research-backed model on attention based on outcomes and three key media signals, including visibility, situation, and interaction, that can predict if an impression is likely to lead to attention and results,” said Yannis Dosios, chief commercial officer of IAS, in a statement. “We have developed a comprehensive attention product based on data, research, and testing, designed to deliver meaningful results for marketers.”
IAS is also working with partners like Lumen to expand the industry standard for attention measurement by bringing cutting-edge eye-tracking technology and predictive attention models to market.
