For all the talk about artificial intelligence complicating the business of advertising and media, there’s an argument to be made about how it simplifies the challenge of content discovery. For Gracenote, the path to easing consumers’ desire to find what they want to watch on TV involves allowing advertisers to upload brand briefs into large language models that identify appropriate targeting opportunities across television inventory based on contextual metadata.
“You can simply upload that brand brief into any LLM, including our own, to say, ‘Which types of content should I target?’ And we can find all the types of content,” Kanishk Prasad, VP of Product at Gracenote, told Beet.TV contributor David Kaplan. “If your brand ambassador is a celebrity, we can find every single piece of content that that celebrity has ever been in and make sure your advertising contains that.”
This capability stems from Gracenote’s foundation powering consumer TV experiences across operating systems, generating standardized metadata that advertising now can leverage for targeting precision.
Consumer experience data powers advertising
Gracenote has operated for decades providing metadata that enables viewers to find shows across Fire TV, Hulu, and other platforms, creating a foundation that advertisers can use to reach audiences.
“As from a consumer standpoint, Gracenote is the thing that is powering their experiences on television,” Prasad said. “Because we power that consumer experience for the vast majority of consumers watching TV, we believe that data can be immensely helpful for advertisers to reach the right viewers at the right time.”
The company is working toward CES conversations with partners and advertisers about utilizing contextual metadata that remains underutilized despite its availability across connected TV environments.
Standardization enables scale
Gracenote’s standardization prevents fragmentation issues where single shows appear in multiple versions across platforms, applying the same principle to advertising where marketers want consistent targeting across publishers.
“When you’re looking for The Simpsons on a TV operating system, you don’t want 10 different versions of the same episode. You want one version,” Prasad said. “Same thing applies from an advertiser standpoint where if you know that your consumers like to watch family friendly movies, you want to be able to see that across all the different publishers.”
This standardization provides scale and reach alongside accuracy and transparency because Gracenote powers the underlying consumer experience.
Metadata addresses signal loss
As audience-based advertising faces scaling challenges in television due to limited identity solutions, metadata provides privacy-safe alternatives by targeting content types viewers enjoy rather than tracking individuals.
“Audience-based advertising that exists is great, and I think it helps you reach the right people. But one of the core problems that we hear from advertisers is it’s hard to scale exact audience-based targeting,” Prasad said. “This is where metadata comes in, because if you can have an understanding of these are the types of shows that your consumer watches, these are the types of movies that a consumer watches, that type of metadata helps them really reach those consumers without necessarily needing to know exactly who they’re watching.”
Gracenote provides approximately 40 metadata attributes per content piece, covering mood, brand safety, violence, political content, and other characteristics that enable privacy-safe scaled targeting.
Meeting advertisers in existing workflows
While Gracenote offers direct platform access for segment building, the company’s primary approach involves working through advertisers’ existing technology partners. Advertisers can access Gracenote metadata either through hands-on-keyboard platform use or via DSP and SSP integrations that fit their current workflows.
The company’s traditional business solved content identification across operating systems, making Gracenote IDs ubiquitous. Current efforts focus on pushing data through advertising technology pipes where demand exists.
Scene-level optimization emerging
AI enables deeper content analysis at scene level, identifying background elements like trees or mountains that optimize performance for specific advertiser categories.
“You’re able to look at scene level understanding of the piece of content, so you know that the background is pink versus there’s trees in there or mountains, and then you start to create this world of how do you optimize against that,” Prasad said.
Measurement analysis can reveal that outdoor brands perform well when trees appear in backgrounds, enabling targeting of outdoor scenes within movies or TV shows for future campaigns.
“When there were trees in the background, an outdoor brand did really well. So now when you’re targeting them, you can find all of those outdoor scenes that are in a particular movie or a TV show and be able to target that,” Prasad said.
You’re watching “The Road to CES 2026: Planning and Buying CTV the Way Viewers Watch”, a Beet.TV Leadership Series, presented by Gracenote. For more videos from this series, please visit this page.
