Advancing Addressability in CTV with AI-Driven Contextual and Behavioral Targeting

Thanks to the wealth of first-party audience data and rich metadata, such as genre and channel, connected TV (CTV) empowers advertisers to connect their messages to the right viewers at scale, without sacrificing performance. However, fragmentation and a lack of standardization often hinder consistent activation. This is where Magnite’s strategic application of artificial intelligence (AI) plays a crucial role. By normalizing content and audience signals, AI is helping unlock new possibilities for activating premium media with precision and efficiency, paving the way for smarter CTV targeting.

AI-Driven Tech for Improved CTV Outcomes

Unlike traditional digital channels, CTV doesn’t rely on cookies; it relies on device identifiers. Despite CTV’s unique nature, advertisers demand targeting solutions that are accurate and easy to activate at scale across the landscape. Publishers want to make their inventory easily discoverable and monetizable to optimize their yields. Artificial Intelligence is helping us achieve both.

At Magnite, we have long employed AI-powered techniques such as statistical models, natural language processing, and machine learning. For instance, applying large language models (LLMs) in highly practical and problem-solving ways, we’re presenting new opportunities for addressability and activation at scale across the premium CTV ecosystem.

Whether you’re a buyer seeking to activate genre-based campaigns without guessing what “drama” means on one platform versus another, or a seller trying to package your inventory in a way that highlights content value and relevance, Magnite provides multiple powerful paths forward for CTV addressability—including Contextual Signaling and Magnite Audiences in CTV.

Contextual Signaling: Smarter Context, Built for Scale

While contextual targeting has been rejuvenated as an effective, cookieless alternative, targeting streaming content contextually at scale is still a key challenge. Media owners send metadata like genre and channel in requests, but the lack of industry standards often makes this data inconsistent, fragmented, and difficult to act on.

Using Magnite’s AI capabilities, Contextual Signaling normalizes media owner content signals into a standardized taxonomy of 67 content types. Those signals are ingested through SpringServe and passed directly to ClearLine, ensuring that publishers can confidently share their metadata without it being exposed to third-party DSPs.

For buyers, this means they can seamlessly and accurately target (or exclude) content based on a consistent, AI-normalized taxonomy. Crucially, this offering allows for a more scalable workflow and saves resources since no manual mapping of content is needed anymore. This can then be activated at scale across SpringServe and ClearLine, Magnite’s unified curation and activation solution.

For media owners, this makes it easier to package and monetize their inventory through content-based deals, reducing reliance on exact string-matching or manual effort.

An internal test by Magnite compared its CTV Contextual Signaling (LLM-based) approach with traditional genre targeting via string matching. Using one day of U.S. SpringServe Programmatic ad request data, the LLM-produced taxonomy showed 2-9x increases in scale in comparison to individual genre signal targeting, demonstrating the potential scale achievable when using our LLM-driven feature.

Magnite Audiences: Publisher-Powered Behavioral Segments in CTV

Magnite Audiences are behavioral segments created from user activity across Magnite’s premium display publisher network that can then be activated on display and CTV. These segments can be activated within CTV environments without additional tags or custom integrations. These segments are available free of data fees, giving advertisers more budget to invest in the media buy, meaning better performance efficiency and increased ROI.

To create these segments, Magnite uses Natural Language Processing (NLP) and Machine Learning to classify on-page content. This enables us to easily analyze publisher web content and visit details, including content users consume, how often they visit, and the patterns that emerge from those journeys. Leveraging our AI to classify these audience segments, Magnite uses IAB’s standard taxonomy across dimensions like interest, demographics, and seasonal behaviors.

For buyers, this makes the workflow of activating known audience segments at the household level on large screens more efficient, scalable, and with privacy built in. Omnichannel alignment and the ability to activate the same audiences across CTV and display, via Magnite’s DV+, is a further win for advertisers.

For publishers, Magnite Audiences enhances inventory value by connecting their premium content to high-intent audiences in a scalable way, providing incremental value.

Intelligent and Scalable CTV Activation Made Easy

One of the biggest challenges in unlocking advanced targeting for CTV has been operational complexity. From onboarding data and building deals across fragmented platforms to navigating inconsistent content metadata, both advertisers and publishers have faced unnecessary friction. Activation often meant manual effort, added costs, or delayed speed-to-market.

With Contextual Signaling and Magnite Audiences fully integrated across SpringServe and ClearLine, buyers can deploy smarter, more strategic campaigns while publishers can better package and monetize their inventory – all within simplified workflows.

As we move into a new phase of CTV growth defined by AI-powered targeting and scalable, premium supply, Magnite is helping advertisers and publishers work more seamlessly together. Get in touch to see how you can bring smarter, signal-based targeting to your next CTV campaign.