How AI Can Supercharge Audience Targeting in CTV AdTech

Introduction

As Connected TV (CTV) continues to pull ad budgets away from linear and even digital formats, it’s becoming clear that the ecosystem has outgrown traditional targeting approaches. With the loss of third-party cookies and the limited availability of deterministic user identifiers, CTV advertisers are forced to rethink how audiences are defined and reached.

Fortunately, advancements in Artificial Intelligence are helping to fill this gap. In particular, LLM-powered semantic audience matching and content understanding, along with probabilistic behavioral modeling are emerging as powerful solutions for building and activating intelligent audience segments in CTV.

The Audience Targeting Gap in CTV

CTV platforms today face several key limitations:

• Identity resolution is inconsistent: There’s no universal ID across all CTV devices or apps.
  

• First-party data is fragmented: Each platform collects and stores its own usage data in unique ways.
  

• Behavioral signals are shallow or anonymized: Unlike web or mobile, CTV lacks rich, continuous browsing behavior.
  

The result? Targeting is often imprecise, reliant on household-level assumptions, and unable to personalize messaging beyond broad demographics or genre-based segmentation.

Using AI to Build Smarter Audiences in CTV

To address these challenges, AI offers a new framework—one that doesn’t rely on granular user data, but instead learns patterns, predicts preferences, and maps content and ad messages into shared semantic space. Here’s how this works in practice:

LLM-Powered Content Understanding

One of the most underutilized assets in CTV is the content itself. Shows, episodes, movie descriptions, and even subtitles provide rich metadata that can reveal viewer interests, tone preferences, and genre affinities.

Using Large Language Models (LLMs), we can process this metadata and extract contextual meaning—classifying content into categories (e.g., “family-friendly science fiction” or “true crime documentary with political themes”) without needing manual tagging.

This enables CTV platforms to build contextual user profiles based on what is being watched, rather than who is watching.

Behavioral Modeling from Device-Level Signals

While CTV doesn’t offer user logins or cookies, it does offer device- and household-level behavioral patterns. AI models can analyze:

• Viewing frequency and duration
  

• App and content engagement trends
  

• Co-viewing behavior over time

These patterns can be used to create probabilistic audience clusters—such as “late-night binge watchers of true crime,” “morning news viewers,” or “family co-watch households.”

Even anonymized or sparse data can become meaningful when interpreted through trained ML models.

Semantic Audience Matching

Here’s where things get truly powerful. Once content profiles and behavioral clusters are established, the next step is matching them to ad creatives in a way that’s meaningful—not just categorical.

Semantic audience matching uses hybrid search (semantic + lexical) to embed both the content context and the ad creative metadata into a shared vector space. This allows for:

• Matching an ad about eco-friendly products with nature documentaries
  

• Pairing a fast-paced trailer for an action film with viewers of high-energy reality TV
  

• Serving financial services ads during content centered around career growth or education

The key advantage? It works even when traditional user attributes are unavailable. AI doesn’t need to know who the viewer is—it just needs to know what the context is, and what content has previously signaled interest or relevance. Developing improved contextual targeting in parallel to evolving how to process user and device identities is crucial to both media companies and advertisers as they pursue a multi-pronged approach to monetizing their data. 

Real-World Applications

Semantic audience matching and AI-enhanced modeling in CTV can support:

• Better frequency capping and pacing, based on behavioral trends
  

• More relevant creative delivery, by aligning tone, theme, and timing
  

• Automated segment generation, updated in real time as viewing data evolves
  

• Cross-channel consistency, using the same models across web, mobile, and CTV platforms
  
  

Looking Ahead: Context and AI as Core Infrastructure

As the CTV advertising landscape grows more competitive—and more privacy-conscious—contextual AI tools will become essential infrastructure. Rather than rely on hardcoded segments or outdated identity graphs, platforms will increasingly lean on real-time AI pipelines that process content, match creative, and deliver insights based on meaning, not metadata.

These systems don’t just scale better—they’re also more adaptable, more privacy-compliant, and more aligned with the way content is actually consumed in modern CTV households.

If you’re exploring how to implement AI-powered targeting in CTV—especially with a focus on semantic understanding, automation, and privacy compliance—this is the right time for an AI-firest approach.