FourthWall Media Launches Adapt to Cross-Platform Onboard Campaigns

  • Platform Combines Viewing and Advertising Data from Smart TVs and Set-Top Boxes Across All US Markets, and Now Includes Adapt™ to Onboard Linear, Addressable, and Digital Ad Campaigns.

  • FourthWall Media®, a leader in television analytics, has introduced Adapt™.

  • A platform tool to onboard linear, addressable, and digital advertising campaigns, and has added Automatic Content Recognition (ACR) data from Smart TVs to its cable set-top box viewing dataset into the Reveal analytics platform.


The combined dataset and the ability to ingest advertisers' campaigns from any source will extend audience-based analysis of content and advertising across a wide range of viewing platforms.

Adapt is FourthWall's method of onboarding advertising campaign data from any source, including linear as-run logs, addressable exposure files, and digital impression data, and analyzing the combined impressions to produce holistic campaign performance analytics. Reveal will now quickly and easily generate insights such as Tune-In Reporting, Optimal Frequency Analysis, and Attribution for any custom or syndicated audience segment. Adapt continues the development of FourthWall's vision of a comprehensive and independent media analytics platform.

The second-by-second set-top box data already within FourthWall's Reveal analytics platform is now combined with Nielsen (NYSE: NLSN) Smart TV viewership data, enabling marketers to have more holistic viewing data from both traditional and Smart TV devices covering all U.S. markets. By leveraging Nielsen's powerful and patented ACR technology from millions of Smart TVs, FourthWall is adding glass-level content and ad exposure data to its end-to-end analytics platform at scale.

Modern marketers demand a 360° view of their target customers' content and advertising viewing behavior to more effectively speak to their customers. Reveal's second-by-second cross-platform data gives marketers the opportunity to plan and measure their content and advertising performance with common audiences and metrics across multiple video distribution channels. Media planning and business outcome attribution using Reveal deliver the insights needed to yield greater efficiencies in this rapidly evolving business environment.

Adding ACR data from Smart TVs to FourthWall's established cable set-top box data gives us a unique opportunity to fuel Reveal with two carefully-curated datasets covering different viewing modes. Adding Smart TV viewership data to Reveal is exciting and delivers valuable cross-platform insights to our customers. In addition, we've extended our ability to import ad campaign data from linear as-run logs to now include addressable and digital exposure files. All the ingredients necessary in automating cross-platform media analytics are now brought together within Reveal.

- Dudar, Chief Product Officer of FourthWall Media


In today's complex media environment, marketers need a simple and clear view of where and how their customers interact with media. With Nielsen Smart TV data as one of the components that helps fuel FourthWall's Reveal platform, marketers will have more robust and actionable analytics to identify their audiences across viewing platforms in a seamless and efficient way.

- Matt Reid, SVP, Product Leadership, Nielsen


Reveal is a comprehensive analytics and planning platform for audience creation, optimized media planning, and outcome-based performance analytics. Reveal analytics can be generated nationally or within any local U.S. market.

About FourthWall Media

FourthWall Media® is an innovator in television analytics, fueled by the largest independent source of second-by-second cable television viewing data. The company's Reveal™ analytics platform delivers key insights to marketers, agencies, programmers, and other stakeholders across the television ecosystem. FourthWall Media provides end-to-end solutions from data collection and processing to audience discovery, optimized media planning, post analysis, and attribution.

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