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IAS Selected to Provide Transparency to Netflix’s Advertising Platform

Integral Ad Science, a global leader in digital media quality, today (October 14th, 2022) announced it has been selected by Netflix as a partner to provide transparency into advertising performance on the upcoming Netflix ad-supported tier. Using IAS’s viewability and invalid traffic (IVT) verification, brands and agencies will gain insights into campaign reach and engagement to drive outcomes and shape marketing strategies.   

“We are excited to partner with Netflix as they introduce their much-anticipated ad-supported tier that will dramatically increase the global supply of CTV advertising inventory,” said Lisa Utzschneider, CEO, IAS. “IAS provides marketers with the tools necessary to monitor the quality of their media buys as they expand their CTV inventory. We look forward to offering essential coverage to brands and the ability to purchase ads on the Netflix platform with confidence.” 

IAS verification on Netflix will enable advertisers to maximise engagement through insights and make every impression count. The offering provides: 

 

  • Independent, third-party viewability and invalid traffic reporting for Netflix inventory. 
  • Trend identification to understand campaign performance and optimise for peak engagement. 
  • Consistent verification across media buys to understand performance. 

 

“IAS will be one of our verification partners for the Basic with Ads plan - our new ad-supported tier,” said Jeremi Gorman, president of worldwide advertising at Netflix. “We’re excited to bring IAS’ industry-leading verification suite of tools that will give advertisers confidence in the performance and placement of their ads.”

IAS Netflix verification will be available Q1 2023. 

 

Integral Ad Science

Integral Ad Science (IAS) is a global leader in digital media quality. IAS makes every impression count, ensuring that ads are viewable by real people, in safe and suitable environments, activating contextual targeting, and driving supply path optimi...
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