Audience identity and interoperability have quickly become some of the most important conversations in advertising. Between Publicis Groupe’s acquisition of LiveRamp, ongoing discussions around Unified ID 2.0 (UID2), advancements in AI-driven audience modeling, and CIMM’s Identity Infrastructure 2.0 initiative, identity resolution has moved from a niche ad tech topic into a foundational layer of modern media infrastructure.
At the center of many of these conversations sits Connected TV (CTV).
For years, CTV was positioned as the evolution of traditional television advertising: combining the scale and storytelling power of TV with the targeting and measurement capabilities of digital media. In many ways, it delivered on that promise. Advertisers now have far more visibility into audiences, outcomes, attribution, and activation than they did in the traditional linear TV ecosystem. But streaming also introduced a new layer of fragmentation the industry is still trying to solve.
Much of modern CTV advertising still operates on probabilistic assumptions underneath the surface, even as the industry often markets audience targeting as deterministic and highly precise. That distinction matters more than many marketers realize.
The Unique Identity Challenges of CTV
Historically, programmatic advertising benefited from highly individualized digital experiences. Desktop browsers, mobile apps, cookies, and login-based environments helped create relatively direct relationships between users and digital identifiers. CTV operates differently.
In many streaming environments, targeting and measurement still rely heavily on household-level identifiers, IP addresses, device graphs, and probabilistic matching methodologies rather than direct one-to-one authenticated user relationships.
A connected television in a living room is rarely tied to a single person. Multiple individuals share devices. Co-viewing remains common. Devices move across networks. Different publishers maintain different identity frameworks with varying levels of authentication and signal quality.
The industry often markets CTV targeting with the same deterministic confidence as digital advertising, even though co-viewing alone guarantees that much of the ecosystem still operates probabilistically.
CTV targeting can still be highly effective. However, audience precision in streaming environments is often more modeled and inferred than many marketers assume. As a result, audience resolution in CTV becomes fragmented across publishers, DSPs, SSPs, OEMs, measurement providers, retail media networks, and clean room environments.
Addressable Does Not Always Mean Accurate
One of the biggest misconceptions in modern advertising is the assumption that “addressable” automatically means “accurate.” Audience targeting is only as strong as the underlying identity signals powering activation.
One of the clearest examples is language targeting. Many consumers have experienced streaming platforms repeatedly delivering ads in languages that clearly do not align with the actual viewer. For example, a non-Spanish speaker may be targeted by Spanish language ads if they are watching streaming TV in a predominantly Hispanic neighborhood.
In many cases, these mismatches stem from low-fidelity third-party audience assumptions, stale household signals, shared device behavior, or probabilistic models attempting to infer audience attributes from incomplete data.
Co-viewing creates another challenge that traditional digital advertising largely did not have to solve at the same scale.
For example, a connected television may identify a household as fitting within a targeted audience segment while the actual viewer in that moment could be entirely different. A parent watching television with their child may suddenly receive a highly targeted condom advertisement because the broader household or device graph matched a presumed audience profile. Technically, the targeting may have “worked” according to the underlying identity assumptions. In practice, the real-world viewing experience tells a different story.
These examples expose a broader industry issue: not all audience identity data carries the same level of precision, persistence, or reliability.
The industry often talks about CTV audience targeting as if it can solve fragmentation. In reality, many audience models are still attempting to infer identity relationships across incomplete and inconsistent signals. Advertisers should be careful not to confuse increasing sophistication with perfect precision.
The More Things Change, the More Some Fundamentals Stay the Same
Despite rapid advancements in AI, clean rooms, identity graphs, and privacy-safe targeting frameworks, many of the core principles of advertising remain highly relevant.
Understanding your audience still matters. Placement transparency still matters. Context still matters. Operational discipline still matters.
Traditional television environments historically provided far more transparency into where advertisements actually aired at the show level. In many streaming environments today, marketers operate through fragmented and abstracted supply paths where transparency varies depending on the platform, publisher, or buying methodology.
One of the risks in today’s ecosystem is that marketers place too much blind faith in activation platforms or audience segments without fully understanding how those audiences are constructed, validated, or maintained.
Identity resolution is not magic. It is infrastructure. And like any infrastructure, it depends heavily on execution quality, interoperability, governance, and operational consistency.
A recent AdExchanger article highlighted a case where a publisher incorrectly implemented UID2 signals, resulting in degraded audience matching and reduced signal quality across the bidstream. That example reinforces an important reality: many identity resolution failures are operational rather than theoretical.
Why Interoperability Is Becoming Strategic Infrastructure
As fragmentation across streaming grows, advertisers are looking for more consistency across planning, activation, measurement, attribution, and audience management.
Activating audiences is no longer the hard part. The real challenge is creating interoperable systems that function consistently across fragmented ecosystems. This is where companies like LiveRamp, unified ID frameworks like UID2, clean room providers, and broader identity infrastructure initiatives become strategically important.
The Publicis acquisition of LiveRamp reflects this shift. Identity infrastructure is no longer a supporting capability sitting quietly in the background of ad tech stacks. It is becoming core strategic infrastructure supporting data collaboration, AI enablement, audience insights, and cross-platform measurement.
One of the industry’s biggest challenges is not necessarily access to technology. It is connecting fragmented systems across multiple partners, publishers, platforms, and data environments quickly and consistently.
In many ways, identity infrastructure resembles physical infrastructure.
Highways may connect cities efficiently across long distances, but the final delivery still depends on navigating local roads, custom routes, traffic patterns, and operational coordination. The same dynamic increasingly exists across advertising technology ecosystems.
Many platforms can handle 80–90% of the workflow through standardized integrations and scalable infrastructure. The remaining “last mile” is often where implementation timelines slow down due to custom APIs, audience mappings, governance requirements, measurement alignment, data normalization, or publisher-specific workflows.
Edge-case integrations rarely fail because the technology itself is incapable. More often, fragmented organizations lack clear ownership, prioritization, coordination, or operational advocacy to move implementations forward consistently across brands, agencies, publishers, and platform partners.
Many interoperability problems are not purely technological. They are operational and organizational.
Even sophisticated platforms still rely on coordination across multiple teams managing fragmented workflows, competing priorities, and inconsistent data environments. Technology can improve interoperability. It cannot eliminate organizational complexity.
As identity frameworks become more interconnected, the ability to operationalize and orchestrate those integrations efficiently may become just as important as the underlying technology itself.
Contextual Targeting Is Becoming More Important Again
As the industry continues investing heavily in audience identity infrastructure, contextual targeting is quietly regaining importance.
This does not mean contextual targeting replaces audience targeting. The strongest strategies will likely combine both approaches. However, contextual alignment can often serve as an important validation layer when identity confidence weakens.
A highly relevant content environment may sometimes provide stronger audience alignment than a low-confidence third-party audience segment operating on weak household assumptions.
Traditional television has always understood the importance of contextual alignment. Live sports, premium entertainment, genre-based programming, and culturally relevant content carried strong audience expectations long before modern identity graphs existed.
Streaming is rediscovering some of those same principles through a modernized data and activation framework.
AI Will Improve Identity Modeling, But It Will Not Eliminate Fragmentation
AI is becoming an increasingly important part of the identity conversation as companies explore synthetic IDs, predictive audience modeling, graph enrichment, and probabilistic matching enhancements.
These technologies can improve how fragmented signals connect across devices, platforms, and datasets. But weak signals still produce weak outcomes. Poor signal quality, inconsistent integrations, fragmented publisher frameworks, or inaccurate audience assumptions can still create downstream inefficiencies regardless of how sophisticated the modeling layer becomes.
AI may improve audience modeling and interoperability, but it does not eliminate the need for operational rigor, organizational alignment, and high-quality identity infrastructure. In some cases, AI may even increase false confidence if marketers overestimate the precision of modeled audiences operating on weak underlying signals.
Best Practices for Navigating Audience Resolution in CTV
As advertisers continue navigating audience fragmentation across streaming, several best practices are becoming increasingly important:
- Prioritize authenticated first-party data wherever possible
- Focus on interoperability across DSPs, SSPs, publishers, clean rooms, and measurement systems
- Test identity frameworks before scaling aggressively
- Continuously audit and validate audience targeting workflows
- Invest in operational education around interoperability
- Balance audience targeting with contextual alignment
Final Thoughts
The advertising industry is entering a new phase where identity infrastructure is becoming foundational to how media is bought, measured, optimized, and valued.
Streaming created enormous new opportunities for targeting, measurement, and activation. It also exposed how fragmented audience resolution, interoperability, and operational workflows still are underneath the surface.
The industry has made meaningful progress toward more interoperable and privacy-safe advertising ecosystems. However, many of the core challenges around household resolution, frequency management, audience fidelity, organizational coordination, and operational orchestration remain works in progress.
CTV audience targeting is improving rapidly, but advertisers should be careful not to confuse increasing sophistication with perfect precision.
The future of CTV advertising may depend less on building perfect identity systems and more on improving the industry’s ability to operationalize imperfect systems together across increasingly fragmented media ecosystems.
At Three First Names & Associates, we bring a unique and forward-thinking approach to Connected TV by combining a deep expertise in TV advertising with a creative passion for the music industry. Founded by Nathan Scott, our consulting firm leverages nearly a decade of experience in media and advertising to guide brands and agencies through the transition from traditional TV to streaming.