LAS VEGAS — Targeting ads to specific scenes within TV content sounds like the ultimate precision play. The challenge is that precision without scale doesn’t spend budgets, and most scene types don’t appear frequently enough to matter.
“Someone’s in a shower scene, going to sell them soap. How many shower scenes are there? There’s not that many,” Bill Michels, chief product officer of Gracenote, told Beet.TV Editorial Director Lisa Granatstein at CES. “We need things that work at scale and we also need standardized taxonomies against them.”
Gracenote is focusing on within-scene targeting that operates through standardized approaches allowing buyers to spend budgets at scale rather than limiting purchases to experimental trial levels.
Metadata powers matching
Artificial intelligence helps match brand intent with content. Buyers use conversational interfaces like ChatGPT and Gemini to pursue goals ranging from broad reach to specific audience targeting to precise content placement.
“What we see for the AI and experiences it is providing, it’s helping match the brand or the buyer’s intent with the actual content itself,” Michels said.
This enables optimization in real-time and pre-campaign reporting by coupling data with desired brand outcomes.
TMS ID enables transactions
Gracenote’s TMS ID serves as the canonical reference for content identification across major CTV platforms, enabling systems to transact and unlock data. The unique identifiers map TV programs and movies with rich metadata that augments targeting quality.
“The ability for different systems to transact on that and unlock the data associated with it becomes a critical piece,” Michels said.
CTV buying historically relied on IP addresses or app-level data, but increasing scale now enables specific content targeting that helps brands identify optimal message placement.
Program-level visibility returns
Connected TV is migrating back toward linear television’s content-based buying approach, though focusing on content type, genre, series, and live status attributes rather than specific shows.
“If we go back to the linear days, that’s how ads were always bought. It was just based on the content. I bought The Cosby Show, you bought Monday Night Football,” Michels said. “We’re seeing more attributes come in to help articulate and get more specific about that buy.”
Brands need reach and storytelling at scale, requiring standard taxonomies and sufficient inventory for meaningful spending rather than small trials.
Contextual as optimization signal
Contextual relevance is evolving in two impactful areas: serving as a performance optimization signal and achieving scale for meaningful campaign spend.
“Traditionally ads, especially in walled gardens, are optimized in real time on purchases, app installs. It’s tied to user data,” Michels said. “I expect we’ll see on CTV that the contextual data is the signal. What is working, what is not, what is giving this campaign the performance I need.”
Brands previously couldn’t allocate sufficient CTV budgets to contextual targeting due to limited ID scale, but standardized IDs now enable revisiting these approaches with meaningful campaign amounts.
ID availability expanding
The number of IDs enabling content understanding will increase significantly beyond app-level or channel data that historically limited CTV targeting and blocking capabilities.
“We’ve really not had that in CTV,” Michels said. “We’ve had app level, maybe we’ve had a channel. But we’re really going to start to see the availability of IDs to help target and sort of block against where you want your spend to go.”
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.
