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Key Takeaways
Election years compress the marketing calendar into a narrow, high-stakes window. Between Labor Day and Election Day, political coverage will inevitably dominate the news cycle, and every media plan running through that stretch will face closer scrutiny over where ads land and why. For many teams, that means treating news content as risky across the board and retreating from the category until the cycle cools.
But while this reaction is understandable, it’s a reflex that ultimately protects less than it costs. Leading advertisers are moving past the old binary of “brand safe versus unsafe” and toward a more nuanced evaluation of brand suitability—a more granular standard that assesses whether a specific environment aligns with where a brand actually wants to appear, rather than screening out entire categories at once. Advertisers that view news content as unsafe by default and avoid it altogether miss out on some of the most engaged, high-value audiences consuming digital media. Rather than avoiding such content entirely, taking a brand suitability approach lets advertisers manage election season risk without giving up the reach that news content offers.
The election season this fall carries risk and opportunity, often in the same placement. The advertisers who meet that moment with a granular, suitability-first approach, rather than a blanket retreat from news, will protect their brands while keeping the audiences that make protecting them worth the effort.
Advertisers’ concerns around news and brand safety aren’t unfounded, as serving ads near polarizing or offensive content can erode brand trust at best and incite consumer backlash at worst. However, those concerns can be misdirected. In fact, studies comparing brand engagement across hard news like crime and lighter categories like sports have found virtually no gap in brand purchase intent, indicating that blanket avoidance often screens out safe inventory along with the risky.
Treating all news as unsafe misreads the value of the category. It blocks brands off from engaged, high-value audiences while guarding against risks that don't apply to many kinds of news content and can be managed with granular brand suitability controls.
For example, avid news consumers tend to skew towards higher income, with nine in 10 CEOs and board directors calling themselves news junkies. News content also offers considerable reach: A quarter of Americans identify as news junkies, checking headlines around six times a day and reading about seven articles daily.
Considering all this, the advertisers best positioned to succeed are trading blanket avoidance for a more granular, strategic approach that acknowledges the opportunity presented by high-quality, balanced news content.
Rather than avoiding all news content, advertisers should aim for precision. To start, brands must assess how comfortable they are running ads around news during election season, as well as which types of news content they're willing to advertise around.
Jackie Huelbig McLaughlin, VP of Candidates and Causes at Basis, says some brands running always-on campaigns might dial their news exposure up or down as the cycle intensifies, tightening contextual targeting with stricter allow and block lists during the most charged stretches, or weighting spend toward environments like search or sports content on CTV.
Other brands will opt to lean further into news content, but with the same deliberate approach. "I think most advertisers' fear is around appearing near content that's too far on one side of the political spectrum or the other," says Huelbig McLaughlin. "Depending on your audience and your brand, a center-of-the-road publisher that you know will offer a fair perspective is often a safe way to reach those high-value audiences."
Advertisers should also consider how AI is both increasing brand risk in digital spaces and offering more evolved solutions for managing brand suitability. The spread of AI-generated content has fueled mis- and disinformation online, widening the brand safety risks advertisers must account for. A 2025 Basis survey found that 100% of marketers believe AI poses a brand safety and misinformation risk, with 88.6% calling that risk moderate to significant.
However, the same technology can also let advertisers capture more signals and make brand suitability calls more precisely. AI-led semantic approaches, for example, read each impression in context rather than screening on keywords alone, weighing real-time signals to cut waste more efficiently.
AI and machine learning can sharpen optimizations too, though Huelbig McLaughlin stresses that human oversight still matters. "In the sensitive stretch leading up to the midterms, it helps to maintain a human touch in optimizations, so the team can catch sensitive placements and adjust quickly as coverage shifts.”
The midterms will test how thoughtfully brands handle both brand suitability and news content exposure. The teams who strategize around balancing opportunity with risk, set guardrails that are granular rather than blunt, and maintain human oversight will protect their brands without surrendering the reach that makes news audiences worth pursuing.
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Looking for more insights around how political advertising will impact nonpolitical advertisers in the lead up to the midterms? How Political Advertising Will Impact the Media Landscape in 2026 provides a deep dive, plus expert recommendations for how nonpolitical advertisers may want to adjust their strategies.
What is the difference between brand safety and brand suitability?
Brand safety is a binary assessment of whether a certain advertising environment is safe or unsafe. Brand suitability is a more granular read of whether a specific environment fits where a brand wants to appear. Suitability moves past blanket category-level blocking and evaluates individual placements against a brand's audience, values, and risk tolerance. That precision is what lets advertisers stay present in valuable news environments during an election cycle instead of screening out an entire category at once.
Should advertisers avoid news content during an election year?
When advertisers avoid news content during an election year altogether, they risk missing out on engaged, high-value audiences. Studies comparing brand engagement across hard news and lighter categories have found virtually no gap in purchase intent, which means category-wide blocking screens out safe inventory along with the risky. News content also delivers engaged, high-value audiences that are difficult to reach elsewhere. A suitability-first approach allows advertisers to manage risk while maintaining reach.
Why are news audiences valuable to advertisers?
News audiences tend to be highly engaged and higher-income than the general population. Avid news consumers check headlines multiple times a day and read several articles daily, and a meaningful share of senior business decision-makers identify as heavy news readers. Cutting news entirely out of a media plan forfeits access to those audiences in exchange for guarding against risks that granular controls can manage.
How is AI affecting brand safety and suitability?
The spread of AI-generated content has fueled mis- and disinformation, exacerbating brand safety risks for advertisers. At the same time, AI is giving advertisers more granular brand suitability controls that they can use to address these and other risks.
How can advertisers prepare their media plans for the 2026 midterms?
Preparation starts with deciding how much news exposure a brand is comfortable with and which types of news content align with it. From there, teams can calibrate through granular allow and block lists, weight spend toward environments like search or sports content on CTV during the most charged stretches, and lean on center-of-the-road news publishers to reach high-value audiences with less risk. Human oversight remains essential across the pre-election window, so teams can catch sensitive placements and adjust as coverage shifts.
Key Takeaways:
AI’s role in media isn’t new. However, the rise of generative AI presents powerful new opportunities for advertisers to harness the technology to drive impact for their brand or clients.
According to Basis's 2025 report on AI and the future of marketing, almost all marketing and advertising professionals report using generative or agentic AI at work at least once a month, but only a third use it every day. This reflects the barriers limiting wider adoption, including insufficient data readiness as well as inadequate skills and training, unclear strategy, and concerns about reliability.
Overcoming these hurdles creates real separation from competitors, with media strategy emerging as the biggest unlock due to the speed and performance the technology enables. Successful adoption hinges on understanding where AI can provide the most value to media strategists. To that end, this article explores key use cases for AI in media strategy as well as recommendations for successful implementation.
Three use cases stand out when it comes to AI’s applications on the media strategy side:
AI has long been used in media via machine learning algorithms that analyze large datasets to optimize programmatic ad buying, predict audience behavior, and automate bidding strategies. Today’s AI tools create new opportunities to activate advanced data analysis—helping media strategists quickly structure and clean inputs like historical performance, first-party data, brand health studies, and MMM outputs, which often come in the form of massive unwieldy Excel sheets.
Similarly, AI tools can help media strategists quickly gather and synthesize information around audience, competition, and trends in the marketplace to ground themselves in the context of their business or their client’s business. These tasks, which have historically taken media strategists days and weeks to complete, can now be consolidated down with the help of AI.
AI can also serve as a powerful brainstorming partner, helping media strategists generate thought starters and new ideas that they wouldn’t have thought of on their own. This application resonates widely: Nearly 80% of marketers report using AI for brainstorming, making it the most common AI use case, according to Basis' 2025 AI report.
Which tools and platforms are advertisers using for data analysis, information synthesis, and brainstorming? ChatGPT appears to be far and away the most commonly used tool, with 88.6% of marketers reporting that either they or their organization use it. Gemini (45%) and Copilot (41%) round out the top three most-used platforms.
While AI’s applications in media strategy will continue to evolve, these three use cases—data analysis, information synthesis, and brainstorming—have already proven their value and should be part of every media strategist's toolkit.
Of course, to make the most of AI’s expanding media strategy capabilities, it takes more than just having the right tools: The way marketers and marketing teams harness AI in service of these use cases has an enormous impact on the technology’s effectiveness. To realize this potential, individual marketers need to refine their approach to AI, and leaders must establish the right infrastructure and practices across their teams.
Getting real value from AI in media strategy takes more than access to a general-purpose chatbot. The strategists who pull ahead treat AI as a collaborative partner, validating its outputs, grounding them in proprietary data, and shaping them through careful prompting. This four-step framework makes that approach repeatable:
One of the most critical considerations for advertisers to keep in mind when using AI to assist in any marketing function is the technology’s tendency to hallucinate. 35% of brand marketers cite reliability concerns, especially those around hallucinations, as the most significant hurdle for marketing AI implementation. One study found that close to half of marketers experience AI inaccuracies multiple times a week, and over 70% say they dedicate multiple hours per week to fact-checking as a result. Recent tests of six major LLMs found that ChatGPT tended to hallucinate the least and produced the highest percentage (59.7%) of fully correct answers, while Grok had the highest error rate (21.8%) and the lowest proportion of fully correct answers (39.6%). Because of this tendency to hallucinate, media strategists must validate all AI outputs to ensure accuracy, demonstrating how human expertise and critical thinking will continue to be indispensable to the media buying process.
Media strategists will also be essential to ensuring that any AI-assisted work produces differentiated, compelling recommendations rather than generic outputs. It’s easy to look at AI tools as a shortcut to which we can outsource work. But they’re most impactful as collaborative partners, blending AI’s computational power with human creativity. Since AI tools like Claude and Gemini train on similar data, their outputs tend toward the generic—and the last thing any marketer wants is a media plan that mirrors their competitor’s. Turning standard AI outputs into expert-level recommendations requires that strategists embed their brand knowledge, category perspective, competitive insight, and planning approach into the process. It’s also what ensures marketers can defend their recommendations and confidently explain the strategy behind them, rather than presenting AI outputs they don't fully understand.
Finally, the way media strategists go about prompting the AI tools they work with is a critical differentiator. This includes everything from incorporating guardrails around hallucinations to instructing tools on how they’re expected to “think” and communicate with the prompter. For example, LLMs are trained to be polite, so they’re rarely going to give negative feedback. This is something marketers must actively work around in their prompting as well as in their evaluation of AI outputs. Equally important is anchoring prompts in brand context—clear audiences, KPIs, and competitive dynamics—so outputs optimize for your goals, not generic playbooks. The closer the input reflects your real business nuances, the more differentiated the recommendations.
While human oversight, subject-matter expertise, and thoughtful prompting are critical, they’re not the sole drivers of distinctive AI-powered strategy. The use of proprietary, brand-specific data to augment the broader LLM datasets is foundational to unlocking AI's full strategic potential, enabling it to generate recommendations that reflect a brand's unique audiences, competitive positioning, and historical performance rather than broad, generalized patterns. Without proprietary data, even the most sophisticated AI tools will produce strategies that could belong to any brand in any category.
Leaders play a critical role in ensuring their media strategy teams are implementing AI effectively. This includes regularly discussing AI applications with their employees and actively encouraging its use. It also means establishing expectations around how teams leverage the technology and setting guardrails around utilization, so that employees don’t default to thinking of it as an “easy button” and risk losing the human skills that are so important to successful AI use.
Leaders can also empower their teams by working to operationalize AI. This might mean investing in differentiated or custom tools for media strategy purposes to complement their team’s use of tools like ChatGPT and Claude. Specialized AI tools can offer capabilities tailored to media planning workflows, proprietary data integrations, and industry-specific insights that general-purpose AI tools lack—all of which will help to further differentiate and strengthen AI outputs.
One key aspect of operationalizing AI effectively—and making use of custom AI tools—is data readiness. Because AI outputs are only as good as their inputs, advertisers need large volumes of high-quality data to fuel high-quality media channel recommendations, audience insights, budget allocation strategies, and any other tasks AI tools are used for. And, to make those outputs as differentiated and brand-specific as possible, organizations need ready access to clean, comprehensive data across channels and campaigns.
Currently, most organizations lack the data readiness to fuel their AI use in this way: Only a fifth of marketers call first-party data “foundational” to their organizations’ AI initiatives, and one-third report that first-party-data plays little or no role in their organizations’ AI initiatives. To truly make the most of AI, leaders must treat data infrastructure as a strategic priority, investing in systems that collect, organize, and make first-party data accessible for AI applications.
All in all, leaders who establish clear usage guidelines, invest in the right tools, and build robust data infrastructure will position their teams to extract maximum value from AI in media strategy.
The trajectory of AI in media strategy points toward increasing automation. However, humans will continue to play a key role in managing AI tools and ensuring their organizations are maximizing their AI outputs.
As AI models improve and prove their capabilities through consistent results, marketers may become more comfortable reducing human intervention. For now, however, the winning approach balances AI’s computational power with human strategic judgment. Media strategists who master this collaboration—validating outputs, injecting expertise, and continuously refining their AI tools—will lead the field as the technology matures.
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Looking for more insights on AI’s impact across marketing? We surveyed professionals at leading brands and agencies to uncover adoption patterns, performance gains, and roadblocks to implementation. Check out AI and the Future of Marketing for comprehensive findings on the forces driving—and hindering—AI integration in marketing.
How do media strategists use AI?
Media strategists use AI in three main ways: analyzing large datasets, synthesizing insights on audiences, competitors, and market trends, and brainstorming creative ideas. These use cases speed up work that once took days or weeks. Human strategists should still validate and refine every output to ensure accuracy.
How do you prevent AI hallucinations in media strategy?
Prevent hallucinations by building guardrails into your prompts (for example, instructing the tool to flag uncertainty and cite sources) and by fact-checking every output before acting on it. Human validation is essential, as AI can produce confident but false information.
Why does proprietary brand data matter for AI?
Proprietary, brand-specific data is what makes AI outputs distinctive rather than generic. It lets AI generate recommendations grounded in a brand's real audiences, competitive position, and historical performance instead of broad patterns any competitor could produce. Without it, even sophisticated AI tools generate strategies that could belong to any brand in any category.
Is generative AI replacing media strategists?
No. Generative AI accelerates data analysis, synthesis, and ideation, but human strategists remain essential for validating outputs, injecting brand and category expertise, and defending recommendations. The winning approach pairs AI's computational power with human strategic judgment.
What is the difference between generative AI and agentic AI?
Generative AI creates new content—text, images, or analysis—in response to prompts based on patterns in its training data. Agentic AI can plan and execute multi-step tasks autonomously to reach a goal, rather than only responding to individual prompts. Both are now used in media workflows.
Which AI tools do marketers use most for media strategy?
ChatGPT is by far the most widely used, with 88.6% of marketers reporting that they or their organization use it, according to Basis's 2025 report. Gemini (45%) and Copilot (41%) round out the top three most-used platforms.
What makes an effective AI prompt for media strategy?
Effective prompting combines anti-hallucination guardrails, proprietary brand and competitive context, instructions on how the AI should think and communicate, and explicit requests for critical feedback. Because LLMs are trained to be polite, strategists must actively prompt for negative or dissenting views.
How should leaders guide AI adoption on media teams?
Leaders should establish clear usage guidelines, actively encourage responsible use, invest in specialized or custom tools tailored to media workflows, and prioritize data infrastructure. Setting guardrails prevents teams from treating AI as an "easy button" and losing critical human skills. Data readiness is foundational, yet only a fifth of marketers call first-party data foundational to their AI initiatives.
How much do marketers currently use AI?
According to Basis's 2025 report, nearly all marketing and advertising professionals use generative or agentic AI at least once a month, but only a third use it every day. Barriers to daily use include insufficient data readiness, inadequate skills and training, unclear strategy, and reliability concerns.
Over the past several years, AI use in advertising has steadily increased. It’s now delivering measurable gains in speed and output, empowering teams to plan smarter, buy faster, sharpen targeting, and personalize creative at scale.
At the same time, the technology is transforming consumer behavior. On the other side of the (not so proverbial) screen, people are spending real, measurable, and fast-growing time inside AI environments—so much so that AI is starting to be talked about and measured as its own digital media channel.
Time spent with AI is still modest compared to other channels. US adults will spend an average of 16 minutes per day with generative AI platforms in 2026, representing just a sliver of the nearly 13.5 hours people spend with all media. But that modest share is climbing quickly. Generative AI is the fastest-growing channel, with daily time up 62.8% this year after more than tripling the year before. Among active users, the average already reaches 32 minutes per day and is projected to climb to 45 minutes by 2028.
For advertisers, that growing time spent in AI environments makes understanding AI as a channel all the more important. Consumers are asking questions, comparing options, and forming preferences within these AI spaces. To shape how their brands or clients are represented within them as they mature, teams can focus on organic visibility, track how paid placements evolve, and adapt their measurement approaches for zero-click environments.
Key Takeaways:
Generative AI represents only about 2.9% of the time US adults spend with digital media, and it currently ranks last among key digital activities. In other words, most people aren’t trading their TikTok scrolling or their streaming hours for a chatbot…yet.
How fast that use is growing, however, is what should put it on advertisers’ radar. Time spent tripled last year and is forecast to grow another 62.8% this year, a sign of a habit taking hold. CTV, which now commands three hours of daily viewing, followed a similar trajectory, starting as a smaller share of time spent before spiking and growing into a critical channel for advertisers.
Treating AI as a channel now, while the numbers are still small, gives advertisers room to learn the environment before competition for attention intensifies. Teams who wait for the time-spent figures to grow risk arriving after audience behavior has already settled.
Time spent with gen AI tools is distributed unevenly. Though ChatGPT has commanded substantial market share since its release just a few years ago, it recently slipped below 50% as other players like Anthropic’s Claude, Google’s Gemini, and Microsoft’s Copilot, among others, gained ground. That said, ChatGPT still accounts for over 1 billion monthly users worldwide, far outpacing any single competitor.
Where people use these tools tells an interesting story as well. After more than a decade of decline, desktop and laptop usage is rebounding, with some attributing the reversal largely to gen AI, which is more computer-centric than mobile-first. In fact, about 63% of gen AI time now happens on desktops and laptops. As advertisers consider how to connect with users in these spaces, defaulting to mobile-first assumptions likely will not fit how people are actually engaging.
For advertisers, these patterns help guide where effort should go first. Attention still concentrates on just a few platforms, so teams can concentrate their generative engine optimization (GEO) and early paid testing (where available) on the platforms where audiences already are—ChatGPT given its scale, then Claude, Gemini, and Copilot as usage spreads—rather than trying to cover everything at once. And because so much of that engagement happens on desktop, teams building creative and optimizing for organic reach should be thinking around longer, more considered sessions rather than the mobile-first, scrolling-centric behavior that defines so many other digital channels.
Knowing where audiences spend their AI time begs the question of what they’re doing with that time. And increasingly, shopping is a big part of that answer. Time spent in AI environments is progressively shaping real purchase decisions rather than staying purely informational.
Among shoppers who use AI, it now ranks as the second most influential source in the customer journey, trailing only search engines and outranking retailer sites and recommendations from friends and family. And research on the 2026 holiday shopping season finds that 40% of consumers are open to using the technology for shopping, with Gen Z leading adoption. Primary use cases include using it to find deals, generate ideas, and compare products.
However, there's a meaningful limit to how much shoppers are willing to use AI (at least for now). Consumers want AI to help them make shopping decisions, but they don't want AI tools to make those decisions for them. For advertisers, that clarifies where to focus. Because AI use clusters around discovery and evaluation, the highest-leverage work is making sure a brand is surfaced and accurately represented when those comparisons happen. To do so, teams need to prioritize the structured, factual content AI systems pull from (i.e., product pages, specifications, reviews, and comparison-friendly details) through GEO, rather than conversion-focused tactics that fit later, decision-stage moments.
Presence in AI environments comes with a caveat that other digital channels don't carry to the same degree: Consumers remain wary of the technology itself. Half of US adults say the growing use of AI in daily life makes them more concerned than excited, and only 10% say the opposite.
In particular, consumers are concerned about the accuracy of AI answers. Among the small share of adults who get news from AI chatbots, about half say they at least sometimes encounter information they believe is inaccurate. Those inaccuracies can negatively impact brands who appear in the same environments: A brand surfaced by an AI tool the user half-trusts inherits some of that doubt, which can damage the brand’s credibility.
That challenge is compounded by how little control brands have over how they appear in AI environments. In AI answers, an algorithm decides which sources to surface, synthesize, and recommend—so a brand can be summarized, compared to competitors, or left out entirely, all without any input from the brand itself.
The strategy that gives brands the most influence here, at least at present, is GEO. GEO involves shaping content so AI systems can find, understand, and accurately represent it. Because AI systems reward structured and factual material, clear product pages, specifications, and FAQs are both what AI systems surface most reliably and what holds up when a cautious user scrutinizes the result. Though GEO won’t give brands full control over how they’re portrayed in answer engines, it’s the best way they can shape what shows up for users.
Paid access to advertising in AI environments is still nascent. Google, Microsoft, and OpenAI have all begun offering or testing ads within their AI search and chat products, but availability is limited as is research on consumer sentiments towards ads in these spaces. Tracking how these placements work and where they’re headed is worthwhile now, as is early experimentation, even if buying in at scale isn’t yet realistic for most teams.
As such, the most immediate work in terms of building readiness is organic. Large language models decide which brands to cite and recommend, and optimizing for that inclusion through GEO is available to any team today. If brands fail to make such information easily available to LLMs, they likely won’t be included.
Measurement needs to evolve as well, which calls for a shift in what counts as success. Impressions alongside AI-generated summaries or within AI chatbots influence purchase decisions even when no one clicks, so click-through rate alone understates the value of showing up. Statistical modeling and brand lift studies help capture influence that last-click reporting misses.
Finally, the channels teams already control carry more weight while influence over AI environments is limited. Video, CTV, social, and display remain the places where advertisers fully control the message, anchoring brand presence as AI environments mature. Coordinating all of these channels gets harder as platforms multiply, each with its own formats and reporting. Managing that channel spread from a single, unified platform—where planning, performance, and optimization live together—keeps teams nimble as AI media environments mature and the rules change.
The habits forming inside AI tools right now will likely shape how audiences discover and choose brands for years. And forward-thinking advertisers are already building their understanding of it as a true media channel. People are spending fast-growing time in AI environments, and that time is starting to shape what they discover, consider, and buy.
As audiences continue to spend time, form preferences, and make real decisions with these tools, advertisers who work to influence how they show up in AI environments today will be better positioned than those still waiting for the ad inventory to mature or time spent to look more impressive on paper. Teams can focus on keeping track of available advertising offerings, earning organic visibility, adapting measurement to capture influence that clicks miss, and holding steady presence across the channels where they have more control.
Consumer habits inside these tools haven't hardened yet, so the work advertisers do now—learning how their brands appear in these environments and shaping that appearance where they can—is what will set them apart once they become more standard.
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Looking for more insights specifically on advertising in tools like ChatGPT, Copilot, and AI Overviews? Check out What Do Marketers Need to Know About Advertising in AI Environments?
Connected TV (CTV) advertising is the practice of buying and serving video ads on televisions connected to the internet, including smart TVs, streaming devices, and gaming consoles that run streaming apps. For agency and brand media teams, CTV pairs the reach of television with the targeting and measurement of digital, and it keeps growing as viewers trade cable for streaming.
Connected TV’s reach and influence are clear enough. Its terminology, on the other hand? That’s another matter. Marketers use “CTV,” “OTT,” and “streaming” as if they were interchangeable, and they aren’t—each describes a different part of how a video ad reaches a viewer. This guide walks through the fundamentals: what connected TV is, how it differs from OTT and linear TV, how a CTV ad actually reaches the screen, how inventory and targeting work, and how you measure and protect what you’re buying.
Connected TV advertising is video advertising delivered to a television that is connected to the internet. Instead of running on a fixed broadcast schedule, the ads are served through streaming apps and internet-connected devices, alongside on-demand and live content. The ads themselves look a lot like traditional TV commercials: full-screen video that typically plays before, during, or after what someone is streaming.
The defining feature is the delivery method. A traditional TV ad reaches a viewer because a broadcaster transmits it at a set time over cable, satellite, or antenna. A CTV ad reaches a viewer because content is streamed to an internet-connected television, and an ad is served into that stream.
A connected TV is any television made capable of streaming internet content. There are three common ways a television falls under the “CTV” label:
What unites all three is the outcome: a television screen displaying internet-delivered video. The path to that screen varies, but the result is the same category of inventory.
Over-the-top (OTT) describes video content delivered over the internet, rather than via traditional cable, satellite, or broadcast. It is a delivery method, and it is device-agnostic: OTT content can play on a television, a laptop, a tablet, or a phone.
Though sometimes used interchangeably, there is a difference between OTT and CTV. OTT describes how the content is delivered, over the internet, bypassing legacy distribution. CTV describes the type of screen it plays on, a television specifically. An OTT stream watched on a smart TV is CTV. The same OTT stream watched on a phone is still OTT, but it is not CTV, because the screen isn’t a television.
The cleanest way to hold the relationship in your head is that CTV is a subset of OTT. Think of squares and rectangles. Every square is a rectangle, but not every rectangle is a square. In the same way, every CTV impression is an OTT impression (it’s internet-delivered video) but not every OTT impression is CTV, because OTT also includes video served to phones, tablets, and computers.
“Streaming” is the everyday word for watching internet-delivered video either on demand or live. Streaming is what the viewer is doing. OTT is the delivery method that makes it possible. CTV is streaming that happens to be on a television. The three overlap heavily, which is why they get used interchangeably—but they aren’t synonyms, and scoping a campaign correctly depends on knowing which one you mean.
Linear TV is television delivered on a fixed schedule through cable, satellite, or over-the-air broadcast. You watch what’s airing when it airs, the same feed goes out to everyone in a market at once, and ads are inserted into that broadcast stream.
The dividing line between CTV and linear is the delivery method rather than the screen. Linear is schedule-delivered: A programmer decides what airs and when. CTV is internet-delivered: Content is streamed to an individual device on demand or as a live stream, and ads are served into that individual stream. Both can appear on the same television set. What separates them is whether the content arrives on a broadcaster’s schedule or over an internet connection. That difference is also why CTV campaigns can be targeted, optimized, and measured in ways linear generally can’t match.
| Term | Delivery Method | Device | Example Placement |
| CTV | Internet-delivered | Internet-connected television | A video ad in a streaming app on a smart TV |
| OTT | Internet-delivered | Any internet-connected device (TV, phone, tablet, computer) | A video ad in a streaming app on a phone, tablet, or TV |
| Linear | Schedule-delivered (cable, satellite, broadcast) | Traditional television | A commercial in a scheduled broadcast break |
| Streaming | Internet-delivered | Any internet-connected device | On-demand or live video watched over the internet |
A CTV ad travels from a content stream to a viewer’s screen in a handful of steps, most of them happening in real time as the person watches:
Those three placement positions are the basic slots a CTV ad can occupy. Pre-roll runs before the content begins, mid-roll runs during the content at a break in the stream, and post-roll runs after the content ends. Pause ads are a newer type of ad that can be placed when viewers pause whatever content they are streaming.
CTV ads run inside the streaming environments a viewer opens on a connected television. Typically that includes:
Together, these are the places CTV inventory lives. How much of it any given platform can reach—and the quality of that inventory—is a separate question, and one worth weighing when you evaluate how to choose a CTV advertising platform.
CTV inventory is accessed two broad ways: programmatically, through automated buying, or through direct deals with a publisher. Programmatic CTV ads are bought and served automatically, rather than through insertion orders negotiated deal by deal. Most programmatic buying falls into three models, which trade scale for control differently:
| Buying Model | What It Is | Best Suited For | Tradeoffs |
| Open Exchange | Inventory open to any advertiser across a wide pool of publishers and apps | Scale and cost efficiency | Less control over exactly where ads run |
| Private Marketplace (PMP) | Invitation-only inventory that select publishers offer | Curated, premium environments and tighter quality control | Can be more expensive than the open exchange |
| Programmatic Guaranteed | Reserved inventory at a set price and volume, bought programmatically | Guaranteed delivery in specific premium content | Less flexibility than auction buying, with commitment up front |
Most teams end up using a mix, leaning on the open exchange for reach and PMPs or programmatic guaranteed when a client needs premium or brand-safe placements. Because private marketplaces also give you control over which publishers you run with, they double as a quality lever as well as an access one.
That said, programmatic isn’t the only route. Agencies can also buy CTV ads directly from a publisher, negotiating terms with the streaming service or network and running the campaign through an insertion order, much the way traditional TV has always been bought. Direct deals offer maximum control and can open access to premium or exclusive inventory, though they take more manual setup and allow less in-flight flexibility than programmatic.
CTV targeting is typically built around a household rather than one individual user. Since a connected TV is usually a shared screen, campaigns tend to target and measure at the household level, using the devices that share a network as the unit.
Frequency capping is where household logic is especially important. Because several people may watch the same screen, caps set at the household level help keep a campaign from overexposing that household to the same spot, something device-only caps tend to miss.
Advertisers reach the right households a few different ways. Contextual targeting places ads based on what viewers are watching on-screen, including content type, content category (ex. news, sports, or entertainment), app, and broadcast type. First-party data lets advertisers activate their own audiences. And cross-device connections extend a CTV impression into follow-up messaging on phones, tablets, and desktops, which is what turns CTV from a standalone awareness play into part of a coordinated strategy.
AI has made contextual targeting far more precise on CTV. Rather than matching ads to broad category labels, AI-powered targeting can read what’s actually happening in the content and place ads against the right moments—for instance, running a lighthearted spot during an ad break in a comedy…rather than in the middle of a crime drama.
Frame-by-frame analysis uses computer vision to identify scenes, objects, and themes in the video itself. Metadata and language analysis reads captions, transcripts, and program information to infer subject and tone. Frame analysis catches visual cues that text alone would miss, while language analysis is lighter to run and often enough for category-level decisions. Used together, they help advertisers find relevant, brand-safe placements even where individual identity signals are limited.
CTV measurement focuses on two different things. Attribution connects an ad exposure to a later action, such as a site visit or a purchase, usually across devices in the same household. Incrementality goes a step further and asks whether the ad caused the outcome at all, typically by holding out a comparable group and measuring the difference.
Both depend on good data. Log-level data—impression-by-impression records rather than aggregated summaries—is what lets you connect a CTV exposure to a conversion that happens later on another screen. This is also why household and IP-based matching matters: Without it, a view on the living-room TV and a purchase on a laptop can look unrelated.
CTV budgets need protection from two things in particular: invalid traffic, meaning bots posing as viewers, and made-for-advertising (MFA) apps, which are low-quality apps built mainly to collect ad revenue rather than serve real audiences. Both quietly drain spend without delivering results.
Fraud protection for connected TV works in layers. Pre-bid filtering blocks known bad or low-quality inventory before a bid is ever placed. Post-bid verification then checks delivered impressions after the fact, flagging anything suspicious for exclusion in the future. Allowlists and blocklists give you standing control over where ads can and can’t run, and private marketplaces add another layer by limiting inventory to vetted publishers.
CTV creative is almost always video (save pause ads, a newer offering), and a few constraints shape it. Spots typically run :15, :30, or :60 seconds, and because the TV environment isn’t built for clicks, direct response usually relies on QR codes or a second-screen prompt rather than a clickable button. Front-loading the message helps, since these are full-screen formats where the first few seconds carry the most weight. It’s also worth confirming that creative meets each publisher’s format and quality requirements before launch, since specs vary across the ecosystem.
A shared vocabulary makes CTV reporting easier to compare across campaigns and platforms. These are some of the common measurement metrics used for CTV advertising:
CTV is one channel and works best as part of a holistic, omnichannel strategy. Viewers move across screens and formats throughout the day, and CTV is most effective as one coordinated component alongside search, social, display, and direct media, rather than as an isolated buy. That coordination is where the harder questions begin: which inventory to access, how to target, how to measure, and how CTV performance fits with everything else you’re running.
Those questions get easier to answer when CTV lives inside a single operating system rather than a separate tool. With Basis, for example, teams plan, activate, measure, and reconcile CTV alongside programmatic, search, social, and direct media in one place—every channel and every workflow connected, so growing CTV investment doesn’t mean adding another tool or another late night of manual reporting.
What is the difference between CTV and OTT advertising?
OTT describes how video is delivered: over the internet rather than through cable, broadcast, or satellite. CTV describes the device: a television connected to the internet. All CTV advertising is OTT, but OTT also includes ads on phones, tablets, and desktops, so not all OTT is CTV.
Is Netflix CTV or OTT? Is YouTube CTV or OTT?
Both are OTT services, meaning internet-delivered video content. Whether a given impression counts as CTV depends on the device. Watched through a streaming app on an internet-connected television, an OTT service is being accessed via a CTV device. Watched on a phone, the same service is still OTT but not CTV. The delivery is OTT; the screen decides whether “CTV” applies.
Is connected TV advertising the same as programmatic advertising?
No, though the two overlap. Programmatic is an automated way of buying ads, and most CTV inventory is bought programmatically through a DSP. But CTV can also be purchased through direct deals with publishers, so programmatic is a common method for CTV rather than a definition of it.
What’s the difference between a CTV platform and a DSP?
A DSP (demand-side platform) is buy-side software: the tool advertisers use to buy ad inventory programmatically. A CTV platform is the broader environment and tooling for operating in the channel, which may include buying but also spans the surrounding workflow. For how to weigh the two, see the guide to evaluating a CTV advertising platform.
How is CTV advertising different from linear TV advertising?
Linear TV airs on a fixed schedule and is bought against broad audience estimates. CTV is streamed on demand, addressable at the household level, and measurable impression by impression. That means CTV campaigns can be targeted, optimized, and measured in ways linear generally can’t match.
How is CTV advertising measured?
CTV is measured with completion and delivery metrics like VCR, CPCV, reach, and frequency, then connected to outcomes through attribution and incrementality. Because viewers often convert on a different device than the one they watched on, household-level and log-level data are what tie a CTV exposure to a later action.
Is CTV advertising fraud a real risk?
Yes. Like other digital channels, CTV carries fraud risk, including bots and spoofed apps that misrepresent inventory. It’s a channel characteristic worth understanding rather than a reason to avoid CTV. For how platforms should guard against it and what to look for, see the guide to choosing a CTV advertising platform.
What does connected TV advertising with Basis look like?
With Basis, CTV is one channel inside a single omnichannel advertising platform—an operating system for advertising that connects every channel and workflow. Teams plan, activate, measure, and reconcile CTV alongside programmatic, search, social, and direct media in one place. That connected setup lets agencies grow CTV investment without adding tools or piling on manual reporting.
Key Takeaways
For years, advertisers have viewed campaign measurement through the rearview mirror. Media planners spend much of their time looking back at campaign performance metrics month over month and year over year, searching for insights from past investments that can sharpen future efforts.
These legacy performance metrics play an important role in measurement: They provide discipline, accountability, and a directional view into what happened. But as media ecosystems become more fragmented and investment decisions grow more complex, reporting alone can only take us so far. Legacy signals are scattered across platforms and channels, each with its own reporting methodology, making it hard to see the combined effect of those investments or tie them confidently to business outcomes.
Modern modeling helps advertisers overcome these challenges. By connecting fragmented signals into a single view, it gives marketers a more holistic understanding of performance and a clearer path for planning future investments.
Modern modeling is an advanced method designed to help marketers make better investment decisions. Rather than focusing solely on reporting what happened, modern modeling evaluates future opportunities and guides budget allocation. It uses historical performance data to help marketers understand the drivers of outcomes, evaluate potential investment opportunities, and make decisions with greater confidence before dollars are committed.
At its core, modern modeling is about answering a simple question: What should we do next?
Key approaches include:
The biggest advantage of modern modeling is that it enables stronger holistic campaign decisioning. This future-forward framework allows teams to identify patterns that siloed reporting misses, empowering them to make better investment decisions with greater confidence.
With modern modeling, omnichannel campaigns spanning the open web and walled gardens can be weighed against metrics like brand health, sales growth, and profitability, giving marketing teams a more comprehensive view of how their investments work together and how they contribute to business outcomes. By using these techniques to forecast likely outcomes before dollars are committed, teams can evaluate tradeoffs, compare scenarios, and allocate budgets with a more complete understanding of what is likely to drive business results.
In the past, these types of modeling were cost-prohibitive, available only to large brands with large budgets. However, as the technology has matured, MMM, scenario planning, and statistical analysis have become accessible for teams of nearly any size.
AI is expanding the value of modern measurement by making advanced modeling more accessible, scalable, and actionable. Tasks that once required significant manual effort can increasingly be streamlined, enabling marketers to move from reporting performance to evaluating future opportunities and scenarios more quickly. Already, 69% of analytics teams are scaling AI within their advanced measurement workflows.
However, data quality and accessibility issues rank as a top challenge expected by advertisers when it comes to adopting advanced AI-powered measurement techniques. The more unified a team's data across platforms and channels, the more it stands to gain from AI-driven modeling. As organizations improve the accessibility and usability of their data, AI-powered modeling can provide a more complete view of potential outcomes, helping teams evaluate tradeoffs and invest with greater confidence.
At the same time, even the best dataset falls short if modeling doesn't feed real decisions. The organizations that benefit most will be those that let modeling continuously guide and sharpen their approach. As AI shapes the future of modeling, the goal stays the same: helping marketers make more informed, more confident choices about where to invest next.
Measurement will always involve looking back. But with modern modeling growing more accessible, the next era of measurement will focus more on strengthening decisioning for the future. As such, the advertisers adopting these advanced measurement approaches will be the ones setting the pace as the space continues to evolve.
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Looking for more insights around measurement and effectiveness? In Bringing Momentum to Measurement: What Marketers Are Missing in the Pursuit of Effectiveness, I dig into which measurement approaches are driving results in 2026, where teams get stuck, and how marketing teams can translate data they already have into strategies that move the needle.
Media quality has become one of advertising's biggest competitive advantages—but most brands are still measuring it the wrong way. In this episode of AdTech Unfiltered, Sara Maskivish, SVP of Market Enablement - Verification at Protected by Mediaocean, breaks down why the industry is moving beyond traditional brand safety and toward brand suitability.
She explains how AI is reshaping verification, why made-for-advertising sites remain a growing challenge, how contextual signals are redefining media quality, and where advertisers continue to leave money on the table. This conversation offers a practical look at where verification is headed next.
Laurie Lam, Chief Brand Officer at e.l.f. Beauty, joins AdTech Unfiltered to unpack what it takes to build one of the world's most culturally relevant brands.
From pioneering platforms before they're mainstream to forging unexpected partnerships and embracing community feedback in real time, Laurie shares how e.l.f. stays ahead. She also explores the evolving role of SEO in an AI-driven world, why bold experimentation beats playing it safe, and what marketers should do today to remain relevant as consumer behavior continues to shift.
YouTube has evolved from a simple video sharing platform into the most powerful digital advertising channel for political campaigns. With YouTube generating more than $11 billion in advertising revenue in Q2 2026 alone—up nearly 13% year over year, understanding how to leverage YouTube effectively is now essential for political advertisers.
YouTube is the largest and most scalable video advertising platform available to US political campaigns in 2026. It reaches more than 76% of registered US voters weekly, spans every demographic group, and now leads living-room viewing—making it a genuine broadcast alternative with digital targeting and measurement built in.
YouTube Political Advertising at a Glance:
When examining the landscape of social video platforms accepting political advertising in 2026, YouTube stands virtually alone at scale. While platforms like Netflix, Disney+, and Prime Video have opted out of political ads, YouTube continues to offer unparalleled reach across every demographic group.
The numbers tell a compelling story. The 2026 cycle is projected to reach $11.6 billion in total political ad spending—the most expensive election cycle on record—with connected TV (CTV) taking a growing share. YouTube leads within the CTV landscape: It recorded 13.8% of total US TV watch-time in May 2026, the largest share of television among all TV distributors for a third consecutive month. The platform's reach extends beyond traditional metrics. A single video from a top creator like Mr. Beast can generate viewership equivalent to an NBA Finals game or Monday Night Football broadcast.
Perhaps most significantly, TV screens have surpassed mobile and desktop as the primary consumption device for YouTube content in the United States. This shift transforms YouTube from a digital-only platform into a genuine broadcast alternative with superior targeting capabilities.
YouTube offers three core inventory types—Standard Auction, YouTube Select, and YouTube TV—each mapped to a different campaign objective and reservation requirement.
Standard auction inventory is YouTube's core, real-time-bid advertising supply available across all channels and content—the most flexible and scalable option for political campaigns.
YouTube Select is a curated set of premium lineups from top creators, sports, entertainment, and family content. It is best used when campaigns want brand-safe placement alongside YouTube's highest-quality inventory. YouTube Select provides access to premium content from top creators and brands. The platform organizes this inventory into curated lineups including top artists, popular creators, sports content, entertainment, and families. For political advertisers, the broadcast lineup is particularly valuable as it provides access to YouTube TV inventory.
This premium placement requires advance reservations, especially during high-demand periods. Working with a Google Premier Partner can unlock discounted rates not available through standard channels.
YouTube TV lets political advertisers run video as traditional commercials inside live TV and DVR environments, extending broadcast strategy with greater efficiency and preliminary audience targeting. During peak political season, particularly September through November when live sports dominate viewership, this inventory becomes especially competitive.
YouTube Inventory Types at a Glance:
| Inventory Type | Access Method | Reservation Required | Best Use Case |
|---|---|---|---|
| Standard Auction | Real-time bidding | No, never sells out | Flexible, scalable reach for any campaign, including quick-turn needs |
| YouTube Select | Reservation | Yes | Premium, brand-safe placement alongside top creators and content |
| YouTube TV | Auction or reservation | Required for guaranteed placement, especially during peak periods | Extending broadcast strategy into live TV and DVR environments |
Political campaigns can choose from seven YouTube ad formats, from skippable in-stream ads built for efficient reach to CTV pause ads built for the living room. Each is optimized for different strategic goals:
Skippable in-stream ads play before or during video content and can be skipped after five seconds, making them ideal for efficient, broad-reach awareness campaigns where you pay only for views that aren't skipped. The strategic advantage is clear: If a viewer skips your ad, you pay nothing. Those first five to 10 seconds before the skip option appears represent free impressions. This format is a strong fit for awareness campaigns where broad reach matters more than guaranteed completion.
Non-skippable in-stream ads guarantee full message delivery by preventing viewers from skipping, and are best used when a campaign's message must be seen in its entirety. When your message requires full delivery, non-skippable ads ensure viewers watch the entire spot. This category includes bumper ads (six seconds) and longer formats up to 60 seconds or more. While YouTube recommends standard lengths of six, 15, 30, and 60 seconds, the platform accommodates custom lengths like 38 or 48-second spots without requiring editing.
Video sequencing serves a series of ads in a set order to build a narrative over multiple exposures, and it is well suited to candidate introduction campaigns that develop a story over time. If a viewer engages with the first ad, they'll see the second, then the third. If they skip, the system can route them to alternative content based on regular targeting parameters.
In-feed ads appear in search results, on the YouTube homepage, and among recommended videos, delivering highly qualified views because voters must actively choose to click and watch. Users must actively click to watch, creating a triple qualification: they see the ad, choose to click, and then watch the content. While this can command premium pricing, it delivers highly qualified views from genuinely interested voters.
YouTube Shorts is YouTube's fast-growing vertical short-form video inventory, best used to reach mobile-first voters with vertical creative already running on other platforms. As YouTube's answer to TikTok and Instagram Reels, Shorts represents rapidly growing inventory. While horizontal video ads can run in Shorts, vertical creative performs significantly better and delivers a superior user experience. Campaigns already running vertical content on other platforms can easily extend that investment to YouTube Shorts.
YouTube audio ads reach listeners consuming podcast and music content across YouTube and YouTube Music, and offer a solution for campaigns without video assets. YouTube and YouTube Music have become major podcast players. Audio ads allow campaigns to reach listeners consuming podcast content, including popular shows from NPR and other major publishers.
CTV pause ads are image-based ads that appear when a viewer pauses YouTube content on a television screen, offering an added living room touchpoint sold on a CPM (cost per thousand impressions) basis. This newer format displays ads when users pause YouTube content on television screens. After a 10-second pause, the ad appears and remains visible until the viewer resumes playback.
YouTube advertising operates through two primary campaign structures, each optimized for different objectives.
Designed for persuasion, video reach campaigns excel at delivering candidate biography content and contrast ads highlighting differences with opponents. These campaigns are purchased on a dynamic CPM basis, with campaign managers optimizing bids to balance efficiency with quality audience exposure.
Video views campaigns are built for consideration, since you pay only when a voter completes the video. They are often the strongest fit for get-out-the-vote (GOTV) initiatives. The key difference lies in the buying model: Advertisers only pay when viewers complete the entire video (up to 30 seconds). For a three-minute video, cost is incurred at the 30-second mark. This approach maximizes impressions while ensuring payment only for engaged viewing.
Recommended ad lengths vary by campaign type:
| Campaign Type | Recommended Lengths | Best Use Case |
|---|---|---|
| Video Reach | 15s, 30s, 60s | Persuasion, candidate bios, contrast ads |
| Video Views | 15s, 30s | Consideration, GOTV, issue education |
YouTube's targeting options differ significantly from other programmatic platforms, with specific restrictions political advertisers must understand. YouTube does not support voter-file or third-party audience targeting, so political campaigns must rely on contextual signals like placements, topics, and keywords, plus limited demographics (age and gender) and geography down to the congressional district.
Demographics are limited to age and gender. Third-party audience segments and first-party data onboarding (including voter files commonly used in Connected TV campaigns) are not available on YouTube.
Geographic targeting supports zip codes, cities, designated market areas (DMAs), states, and countries. Importantly for down-ballot races, congressional district targeting remains available.
The majority of YouTube targeting relies on contextual signals:
For example, a Second Amendment-focused candidate might target channels discussing firearms and hunting. An environmentally-focused campaign, meanwhile, could target content about green energy and electric vehicles. Keyword targeting works similarly to search advertising, reaching voters actively seeking information on specific topics.
YouTube does not allow targeting based on:
These restrictions reflect Google's approach to maintaining trust and transparency in political advertising while complying with federal and state regulations.
Every political advertiser must complete Google's verification process before running ads, and all political ads appear publicly in Google's Ads Transparency Center.
Google defines election ads broadly. Any content promoting current or potential candidates, political parties at any level, or ballot measures, initiatives, and propositions falls under political advertising restrictions.
Before running political ads, accounts must complete Google's verification process. This requires a few pieces of information, including:
Working with a Google Premier Partner streamlines this process. These partners represent the top 3% of Google Partners, providing direct access to human support rather than automated bot reviews. This becomes critical for quick-turn campaign needs and troubleshooting disapproved ads.
All political ads appear in Google's Ads Transparency Center at adstransparency.google.com. This public database allows anyone to search for competitors, view their creative, see when ads ran, and access approximate spending levels. Smart campaigns use this resource for competitive intelligence and budget planning.
Political advertising costs on YouTube rose 20-50% during peak periods in 2024, driven by high demand and the crowded advertising environment. These premiums intensified during early Q4 when political spending overlapped with traditional brand advertising for the holiday season. Political advertisers should plan for these increases and reserve YouTube Select and YouTube TV inventory early because it can sell out.
YouTube Select and YouTube TV inventory require advance planning. In previous cycles, YouTube TV inventory has completely sold out regardless of budget, leaving late-moving campaigns without access. Securing premium placements well in advance of go-live dates is essential.
Standard YouTube auction inventory never sells out due to the platform's massive scale. While costs may fluctuate based on demand, campaigns can always access this inventory even for quick-turn needs.
Account verification should happen as early as possible, ideally before campaign launch. While expedited processing is sometimes available, building buffer time prevents delays when launching time-sensitive messaging or responding to campaign developments.
While less common in political advertising, two campaign types deserve mention for specific use cases:
These campaigns drive consideration across YouTube, Google Discover, and Gmail. They require conversion tracking implementation but can effectively build interest in candidates or initiatives when measurable website actions matter.
Performance Max, or "PMax," runs across all Google inventory, optimizing toward specific conversion goals like newsletter signups or volunteer registrations. This approach works when driving trackable actions matters more than broad awareness.
Both formats require website tracking tags and work best when clear conversion events can be defined and measured.
Success on YouTube requires understanding both the platform's capabilities and its constraints. Here are some tips for achieving success when advertising on YouTube this election season:
Complete account verification immediately. Reserve premium inventory for critical flight dates, especially during September through November when live sports and peak political activity converge.
Don't rely on a single ad type. Combine skippable ads for efficient reach with non-skippable formats for guaranteed message delivery. Layer in-feed ads to capture active searchers and shorts to reach mobile-first voters.
The same 30-second spot that works on broadcast may underperform on YouTube if it doesn't capture attention in the first five seconds. Test multiple creative approaches and let performance data guide budget allocation.
Without access to voter file targeting, contextual signals become crucial. Invest time identifying channels, topics, and keywords that align with your target voter's content consumption habits.
Use the Ads Transparency Center to track opponent spending and messaging. This intelligence informs budget decisions and creative strategy.
Build budgets assuming 20-50% cost increases during peak periods. This prevents mid-campaign budget shortfalls when competition intensifies.
YouTube's political advertising requirements, verification processes, and optimization strategies differ significantly from other platforms. Partner with teams holding Google Premier Partner status and specific political advertising experience.
YouTube represents the most scalable, targetable video advertising platform available to political campaigns in 2026. While restrictions on audience targeting require different strategic approaches than other digital channels, the platform's reach across every demographic group and its dominance in both mobile and living room viewing make it indispensable.
Success requires understanding the full toolkit: From skippable in-stream ads delivering efficient reach, to YouTube TV placements extending broadcast strategies, to contextual targeting replacing voter file approaches, to verification processes enabling compliant campaigns.
The campaigns that master these elements early, secure premium inventory in advance, and optimize creative for YouTube's unique environment will gain significant advantages in the crowded 2026 election cycle.
Whether you're managing a congressional campaign, a down-ballot race or a national initiative, Basis has the expertise and technology to help you win in 2026.
Basis provides political advertisers a unified, omnichannel platform to execute precise, targeted media strategies across YouTube, CTV, streaming audio, programmatic, and beyond. And our team of experienced political advertising specialists understands the verification requirements, timing pressures, and platform nuances that can make or break a campaign.
Explore Basis’s political advertising capabilities at basis.com/political-advertising-2026.
Can you target voter files on YouTube?
No. YouTube does not support first-party voter-file onboarding or third-party audience segments. Political campaigns reach voters through contextual signals instead (placements, topics, and keywords), plus limited demographics (age and gender) and geography down to the congressional district.
Which video platforms accept political ads in 2026?
YouTube remains the primary video platform accepting political advertising at scale in 2026. Netflix, Prime, and Disney+ (among others) have opted out of political ads, leaving YouTube as a dominant choice for political video reach.
How long does Google political ad verification take?
Verification timing varies, and expedited processing is sometimes available. Because timelines aren't guaranteed, campaigns should complete verification as early as possible, ideally well before launch, to avoid delays on time-sensitive messaging.
Do you pay for skipped YouTube ads?
It depends on the format. With skippable in-stream ads, if a viewer skips after five seconds, you pay nothing. Those first five to 10 seconds function as free impressions, which is why skippable formats are efficient for awareness campaigns. Video views campaigns work differently: You pay only when a viewer completes the video (up to 30 seconds), so a skip before completion costs nothing there too. The exception is any format bought on a CPM basis—like non-skippable ads or CTV pause ads—where you pay for the impression regardless of skips.
What information do you need to verify a political advertising account with Google?
Google requires a Federal Employer Identification Number (EIN) or Federal Election Commission (FEC) number, the candidate or organization name, representative contact information, and an email address with organizational details.
Does YouTube political ad inventory sell out?
Premium inventory can. YouTube Select and YouTube TV require advance reservations, and in past cycles YouTube TV inventory sold out regardless of budget. Standard auction inventory does not sell out due to the platform's scale, so it remains accessible even for quick-turn needs.
Key Takeaways
Advertisers’ ultimate goal is to drive effectiveness by identifying the optimal media mix for specific campaigns to drive key business outcomes. Equally important, though, is proving out that effectiveness: Connecting media investments to results through compelling, cohesive narratives that build stakeholder confidence and secure continued investment.
Yet media fragmentation creates significant barriers to achieving both goals. When performance signals are scattered across platforms and channels, each with its own reporting methodology, omnichannel campaign evaluation—and the ability to make real-time adjustments—is often painfully slow at best.
This matters enormously: In the pursuit of advertising effectiveness, omnichannel campaign evaluation is the holy grail, empowering teams to both drive results and demonstrate impact. As such, it’s essential for marketing teams to strategize around how to achieve holistic campaign assessment amidst fragmentation. Building a strong marketing measurement strategy—one that combines platform-level attribution with broader methods like marketing mix modeling and incrementality testing—is essential for marketing teams looking to navigate fragmentation successfully. The best approaches include systems and frameworks that allow teams to streamline performance signals across channels and evaluate effectiveness in both the short- and long-term.
Today’s consumers demand advertising experiences that seamlessly span the many digital spaces where they spend time. At the same time, diversifying media spend across channels delivers greater returns in revenue and brand performance.
But as marketers split their efforts across a growing number of platforms and channels, the resulting tech stack sprawl—over half of agency marketers’ stacks consist of eight or more tools, and 40% are using 10 or more—presents a variety of problems. In fact, 45% of agency leaders cite siloed or disconnected systems as a top challenge.
Data fragmentation from these disconnected systems underlies a host of marketing leaders’ biggest pain points. Most fundamentally, manually consolidating data from multiple sources is both time-consuming and error-prone, which prevents the agile and strategic decision making necessary to drive effectiveness. Consequently, only 21% of senior marketers report receiving actionable data in real time.
Fragmentation also curbs teams’ ability to demonstrate the impact of their work, which strains client partnerships for agencies and impacts budgets and stakeholder confidence for brands. Marketing leaders at brands identify connecting marketing activities to revenue outcomes as their most pressing challenge—a particularly urgent issue as CMOs face heightened pressure to prove ROI.
To succeed in this fragmented landscape, marketing teams must develop dedicated strategies for holistically measuring performance across both the short- and long-term.
The baseline for short-term cross-channel measurement is tracking performance on each individual platform and channel. Advertisers must examine each source of truth across their media mix and assess platform-level performance using attribution and KPIs like immediate sales, cost of acquisition, or ROAS.
Because of the lack of interoperability among walled gardens and the open web, advertisers typically won't know if a consumer saw the same ad on, say, both Facebook and Pinterest. While this can result in double-counting conversions, it doesn't hinder platform-level optimization, which remains essential for driving and demonstrating effectiveness.
The disconnection between walled gardens and the open web creates significant limitations, but marketing leaders can help their teams manage the complexity. The key is streamlining performance signals across channels to understand how they work together to drive short-term outcomes. Advertising platforms that automatically aggregate data from multiple sources across walled gardens and the open web—eliminating time-consuming manual work—provide teams with a comprehensive view and a significant competitive advantage.
However, to fully achieve holistic measurement, marketing teams must look beyond immediate, platform-level performance metrics and find ways to take a broader view of their investments.
To truly drive and demonstrate effectiveness, omnichannel campaign evaluation can’t end with short-term strategies. When assessing how successfully your advertising is driving business outcomes, it’s critical to take a long-term perspective.
Assessing long-term effectiveness holistically is all about finding ways to gather every available signal into one place and extract bigger learnings about their impact. To that end, integrated campaigns across the open web and walled gardens can be evaluated over a longer term against key metrics that can’t be properly attributed to a single source, such as brand health, sales growth, and profitability.
Marketing mix modeling (MMM) and experiments are two tools marketing teams can use to assess these bigger metrics.
Marketing mix modeling (MMM), also called media mix modeling, is a statistical approach that uses historical sales and marketing data to measure how each channel and tactic contributes to business outcomes over time. With MMM, advertisers can model how their investments across multiple platforms drive business outcomes over time. For instance, by analyzing historical data, MMM can predict that investing X amount of money across digital marketing contributed to Y% of overall sales, led by Facebook, Google, and programmatic tactics. Increasingly, MMM marketing approaches inform not just past performance reporting but also forward looking decisions about where to invest media spend for the strongest returns.
Experiments—also called incrementality testing—allow advertisers to test hypotheses about long-term effectiveness through controlled tests. In a geo lift study, advertisers can run an omnichannel campaign at different intensities across similar geographic markets and measure the differential impact on sales growth or brand awareness. Market studies can compare regions with varied media strategies to understand which combinations drive better long-term outcomes.
These modes of long-term measurement have often been underused by marketers, although they’re gaining steam as the industry grapples with measurement challenges: Between 2024 and 2025, the share of marketers using experiments to measure effectiveness doubled from 18% to 36%.
However, in 2024, only 2% of marketers were using a combination of MMM, experiments, and attribution to assess advertising effectiveness. The underutilization of this multi-pronged approach to long-term omnichannel campaign evaluation presents a major opportunity for advertisers seeking to both drive and demonstrate advertising effectiveness better than their competitors.
There's no getting around the fact that omnichannel campaign measurement remains a challenge for marketing teams of all sizes. However, precisely because of this challenge, teams who strategize around it more successfully than their peers stand to gain major competitive advantages.
Success depends on streamlining performance signals and implementing diverse strategies for both short-term and long-term measurement. Ultimately, the teams that dedicate the necessary resources to achieving omnichannel campaign evaluation amidst fragmentation will also be the most successful when it comes to driving and demonstrating effectiveness.
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Looking for more insights into how to navigate the biggest challenges and opportunities facing marketers? Check out Rewinding to Fast Forward: The 2026 Digital Advertising Trends Report for a breakdown of four key trends set to define the industry this year.