Technology
Basis Platform
Advertising Operating System
Communication Portal
Intelligent Collaboration
All Channel Activation
Activate Media on Every Channel
Consulting
Resources
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.
__
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.
—
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.
—
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.
Throughout programmatic advertising history, brands have often entrusted its execution to agencies and trading desks. But today, in a world of high consumer expectations and constantly shifting market dynamics, many brands are searching for an alternative to the traditional brand-agency model as they look to gain greater transparency into their media buys, more holistic control over their data, and greater assurance of compliance with privacy regulations.
The result: programmatic in-housing.
Programmatic in-housing is the practice of a brand bringing some or all of its programmatic media buying, data management, and ad operations in-house rather than outsourcing them to an agency or trading desk.
Numerous heavy hitters have already brought elements of their programmatic operations in-house, including Marriott, Colgate-Palmolive, Procter & Gamble, Coca-Cola, Bayer, Wayfair, American Express, Unilever, Anheuser-Busch, Netflix, Target, Deutsche Telekom, and Ally Financial. Now, as those groundbreaking programs mature and their outcomes come into view, more brand advertising leaders are peering over with interest and beginning to question whether they, too, should take some—or total—control of their own programmatic business.
With many different levels of programmatic in-housing available, as well as a variety of potential benefits and challenges, leaders must carefully consider which, if any, in-housing option is best for their brand and team. Whether opting for full control, a hybrid approach, or fully outsourcing, choosing the right in-housing model can significantly impact both campaign performance and operational efficiency.
Key Takeaways:
The first adopters of in-house programmatic media buying were mostly digital natives like Netflix and Target: brands boasting rich, voluminous first-party data that gave them a head start on the process. Over time, though, the types of companies in-housing have become more diversified. And, they have done so with varying degrees of success.
In 2025, headlines about brands “un-housing” their internal agencies suggested the model might be losing momentum, but the practitioners closest to the work tell a more nuanced story. In a 2026 ANA survey of In-House Excellence Awards judges, only 7% saw in-housing declining, while 35% said marketers are in-housing more than ever and 58% still considered it a viable alternative to external agencies.
Those who struggled often did so because they underestimated the logistical hurdles involved and became too fixated on the stereotype that in-housing is an all-or-nothing play. But this idea of agency versus in-house programmatic as a dichotomy is outdated, as it doesn’t suit the needs of modern brands.
As marketing leaders learn more about the intricacies behind this digital transformation, a whole range of in-house programmatic manifestations are emerging, with brands and agencies breaking new ground and creating new operational frameworks as their relationships evolve. Indeed, in-housing programmatic is far from a death knell for agencies—brands still lean heavily on their external partners, and agency investments still command more than 20% of total marketing budgets, according to Gartner’s 2025 CMO Spend Survey—though that share is slowly eroding as more CMOs move to trim agency spending. Understanding market logistics and maximizing technology-driven optimization opportunities requires as much critical insight as possible, and agencies remain ideally positioned to provide that guidance.
When it comes to programmatic in-housing, one thing is clear: Where we were five years ago looks very different from today, and where we are today will look very different five years from now.
While it is difficult to neatly classify the numerous variations of in-housing—especially considering all the factors involved—here are three of the most common arrangements:
| Model | What It Is | Best For | Key Tradeoff |
| All-In on In-Housing | Full adtech stack, media strategy, ad ops, and data management sit inside the brand | Brands wanting maximum control and transparency | Highest commitment of time, talent, and resources |
| Hybrid | Brand shares programmatic execution with agency or on-demand partners | Brands wanting control plus outside expertise | Requires clear division of roles and governance |
| Fully Outsourcing | Agency or trading desk runs programmatic on the brand's behalf | Brands without internal resources | Least transparency and control over data |
This is the quintessential in-housing set-up, where an adtech stack sits within a brand organization in tandem with media strategy, ad operations, data management, and campaign stewardship. An in-house operation that looks like this is still relatively uncommon, given the major commitment of time, resources, and internal talent marketing organizations need to launch, maintain, and refine it.
Despite the complexities involved, the popularity of programmatic in-housing is rising, and brands are building relationships with the technology platforms and the talent they need for in-housing to succeed. According to the ANA’s 2023 “Continued Rise of the In-House Agency” study (a survey they conduct every five years) , 66% of brands say they have contracts with technology providers like DSPs or verification partners, and two-thirds also say they have “hands on keyboards” doing the actual work.
For brands going down this route, the classic approach involves forming an internal agency-style trading desk and equipping them with a demand-side platform (DSP). Going all-in can be a monumental task, but with the right preparation, planning, and implementation, the returns are significant.
Today, established in-house units are taking programmatic on directly: Paper manufacturer Georgia-Pacific and UK retailer Boots, for example, now run their own programmatic trading teams, gaining a clearer view of their media investment and closer control over how budgets are spent. Pharmaceuticals giant Bayer, which first began developing their in-housing operations back in 2017, reportedly cut its programmatic buying costs by over $10 million in just the first six weeks. That figure grabbed headlines, but for most brands the more durable payoff is ownership and control versus a fast cost cut. The company has also pointed to a number of additional in-housing related benefits, including ownership of their tech stacks, data, and dashboards.
Bayer’s success presents a microcosm of the upsides that come from in-housing programmatic. Transparency is a major one: With data privacy at the front and center of public consciousness, it is paramount that brands know exactly what their advertising is doing.
Many agencies still fall short on providing real visibility into the digital ad buying process. This lack of transparency, combined with increased control and potential cost savings, drives many brand leaders to pursue in-housing. Platforms like Basis are designed to address exactly this need, centralizing transparency tooling so brands can see precisely where every dollar goes. Indeed, gaining access to unfiltered campaign performance data empowers brands to do a host of valuable things, including:
Cost is no longer the headline reason brands bring programmatic in-house: In 2026, an ANA survey of in-house practitioners found just 9% now name cost savings as the primary role of internal teams, down from 30% in 2023. The efficiencies are still real, though.
Brands often see cost benefits over time as they save on agency fees, including fixed rates, platform costs, and media expenses. Internal programmatic teams can concentrate solely on maximizing profitability for their brand. In contrast, agency partners balance this goal with their own revenue needs and margin requirements. Going all-in on in-housing removes these additional layers, potentially streamlining the path to increased profitability.
Then there is the matter of cost efficiencies through better campaign execution. The switch in-house ultimately enables brands to invest in—and nurture—talent within their marketing organization. While outsourced staffing solutions can provide some great results, they may not be able to optimize the consumer journey with the same granularity as an in-house staffer who knows the brand through and through.
Now, there may be many upsides to in-housing, but brand leaders should not underestimate the organizational, technological, and cultural challenges involved in this digital transformation.
For starters, programmatic in-housing takes time. Ideally, the transition should be a well-researched, deliberate journey with calculated investment in the right resources and tech over a period of many months, if not years. From the outset, those leading the change need to ensure they involve a wide range of internal stakeholders from all corners of their organization, including finance, legal, product, and IT. All these teams will have either a direct or indirect influence on the program’s success, so it is important to listen to their input on the process and get their buy-in before proceeding. What are the security implications, for example, from an IT perspective? How much budget is available for the required tools? How are privacy regulations going to be respected? What are the data challenges from a CRM standpoint? A multitude of questions must be answered, so the more support earned from across the organization, the better.
In sum, programmatic in-housing is not something you can just do on a whim, and it is critical to set the expectation internally that transformational results are unlikely to arrive immediately. Brands accustomed to focusing on short-to-medium term initiatives and goals must learn to adapt to protracted, longer-term processes and a new way of working if they want to successfully in-house programmatic.
Of course, most marketing leaders are thinking hard about how to wrangle more out of their advertising dollars, and many are concluding that they need outside help and guidance to do so successfully. At the same time, many media agencies are adopting new structures—reorganizing in a way that makes it easier to offer on-demand services and step into a more advisory role for clients that seek to in-house some of their programmatic budgets. It is a development that makes sense for both parties: Agencies have extensive experience in what works and what doesn’t in the programmatic sphere—not to mention the tools necessary for executing on programmatic media buys—and clients need to tap into those resources as they bring some of that operational activity inside their own walls.
This, in essence, is what the hybrid in-housing model is all about: An in-house marketing team tees up the overarching strategy before consulting with an agency on how to deliver it in the market. Sometimes, this will lead to the agency assuming the day-to-day responsibilities of implementing the plan and pulling the levers, and other times it will lead to the advertiser doing the work with their own platform under the agency’s direction.
The latter scenario is the one that more brands are pursuing, as it ultimately merges many of the best aspects of both in-housing and outsourcing. With hybrid in-housing, brands can:
An Association of National Advertisers study frames the hybrid in-housing trend in numerical terms, finding that, in 2023, only 32% of marketers had in-house programmatic capabilities, and just 17% of the remaining respondents were considering adding those capabilities within the next year. More recent reporting suggests hybrid is now more common than going fully in-house: Roughly 13% of U.S. ANA members handle programmatic entirely in-house, while about twice that share, 26%, use a hybrid model. This highlights just how important agencies remain to brands. Programmatic advertising can be a complex venture, and there are still many marketing organizations that struggle to identify the right systems to help them better engage their audiences. The hybrid option alleviates that pressure and empowers brands to take whatever baby steps they want.
Option number three: fully outsourcing, a route that many brands still prefer despite the myriad benefits that in-housing offers. It represents a great option for those who don’t have the resources to build an internal programmatic team and all the costs that come with it—think upfront licensing fees, salaries, training, etc.
From a brand perspective, the main drawbacks to contracting out all aspects of programmatic advertising are the lack of control they will enjoy over their consumer data and the limited transparency they will get into media performance. To address exactly that, a growing number of brands are choosing to own their core technology stack and data outright—even when they still rely on outside partners for day-to-day execution. Keeping the stack and data in-house gives them full visibility into how campaigns perform and where every dollar goes, along with unfiltered access to their own first-party data. And because those insights live with the brand, the learnings stay put rather than walking out the door when an agency relationship changes.
One key area where agencies retain an upper hand in the in-housing/outsourcing calculation is talent. Managing and optimizing programmatic media is a complex art, and advertisers considering independence from agencies will need several staff additions to ensure they are doing things right—a programmatic manager, a data analyst, and a DSP operator, to name but a few.
The problem facing brand-side HR departments today is actually filling these positions. Experienced programmatic specialists remain scarce and expensive even as broader marketing headcount tightens, job titles are evolving (particularly as AI continues to reshape digital advertising), and, to make matters tougher, the available talent often take opportunities at agencies over brands because they can offer better career progression, opportunities to diversify, and ongoing learning. Within the four walls of a brand, those things can be difficult to come by, considering there is only one program to service and (most likely) fewer opportunities to influence overall strategy. One brand that built its own in-house programmatic team pointed to “recruiting, developing and retaining specialized talent that’s high in demand” as one of the biggest hurdles in building that internal team.
No player in the programmatic space can expect to be successful without the right people doing the bidding, so this talent shortage is a pressing concern for brands, often leaving them with no choice but to look to external partners.
From platforms to people to processes and everything in between, with so many factors to weigh when considering in-housing programmatic ad buying, where does one start? Here are a few considerations for marketing leaders looking into in-housing, whether they’re at the RFP stage or just the “I was thinking about…” step:
1. Evaluate “why” this is important to your organization: Is your organization looking to cut costs? Hit a business objective? Gain more operational control? Identifying your in-housing “why” will allow you to understand your needs, wants, limitations, and what you’re trying to change. It also creates space for a feasibility check and an objective session to help move from problem to solution.
2. Understand the landscape: More than a decade ago, in-housing came into vogue thanks to tactics like RTB. However, during the height of the COVID-19 pandemic, long-term contracts and full-time employees became more of a burden as businesses just tried to survive. If you do consider in-housing, it’s important to consider it in context of the larger marketing and economic landscapes to ensure that now is the right time to take the first steps.
3. Review your readiness: When it comes to technology, are you comfortable vetting new options to streamline how you use your existing tech stack? In terms of people, are you well-positioned to add, retain, or replace talent? As far as strategy, how does your first-party data look? How confident are you in making real-time optimizations to live campaigns? Answering these critical questions can uncover your gaps as well as highlight opportunities for process and personnel improvements.
4. Bring in the right stakeholders: As noted earlier, organizations that wish to bring their programmatic adtech stacks in-house—even if an agency might still operate and manage it—should invite HR, IT, CRM specialists, finance, legal, and product to early conversations, as any of these people may have direct or indirect influence on, or use of, the tools at hand.
5. Bite off only what you can chew: Even though it may save money in the long run, in-housing programmatic can be an expensive venture: It takes time, it takes energy, it can be costly, and it can be disruptive to the status quo for many organizations. Taking it on in small chunks can help match a company’s tolerance level for each of those considerations, whether that be in the form of a hybrid model or a path to full in-housing programmatic advertising.
Bringing programmatic media buying in-house is a significant undertaking. The whole operation must be built on a foundation of organized data, deliberate processes, capable tools, and skillful talent. As programmatic’s prominence rises and privacy continues to be at the forefront for consumers and regulators alike, more marketing leaders may want to in-house their programmatic advertising…But, all too often, they don’t know where to begin.
The good news is that a host of options exist, from going all-in, to a hybrid approach where brands and agencies share responsibilities, to full outsourcing. Overall, in-housing has many benefits, the biggest being the ability to obtain greater transparency into media buys and secure more holistic control over data, not to mention the possibility of significant long-term cost savings. And either way, agencies still have an important role to play in the media buying ecosystem—whether as valued partners or as critical consultants.
—
If you’re starting to think about making changes to your organization’s programmatic advertising strategy and are looking for some guidance, our Programmatic Readiness Quiz can help! In just three minutes, you’ll discover whether outsourcing, hybrid, or in-house programmatic ad buying is the best path for achieving your team's goals.
What is programmatic in-housing, and is it right for my company?
Programmatic in-housing is the practice of a brand bringing some or all of its programmatic media buying, data management, and ad operations in-house rather than outsourcing them entirely to an agency or trading desk. Whether it's right for your company depends on your goals, resources, and appetite for control. Brands that want greater transparency into their media buys, tighter ownership of their data, and the ability to optimize campaigns in real time tend to benefit most, while brands without the internal talent or time to invest may prefer a hybrid or agency-led model.
How do brands bring media buying in-house?
Most brands bring media buying in-house gradually rather than all at once. The typical path involves standing up an internal trading team, licensing the adtech they need such as a demand-side platform, and building processes for data management, campaign execution, and reporting. Many start with a hybrid setup that shares execution with an agency or on-demand partner, then take on more functions over time as their internal capabilities mature.
What are the benefits of in-housing programmatic advertising?
The leading benefits are transparency and control. In-housing gives brands a clear view into where every media dollar goes, direct ownership of their first-party data and tech stack, and the ability to adjust campaigns in the moment. It also lets internal teams focus solely on the brand's performance rather than balancing an agency's margin needs, which can improve efficiency over time. While cost savings can materialize, most brands now view ownership, agility, and data control as the more durable payoffs.
Can brands split media operations between in-house teams and agency partners?
Yes. This hybrid approach is one of the most common in-housing models. Brands keep certain functions, such as strategy, data ownership, or specific channels, in-house while relying on agency or on-demand partners for added capacity and specialized expertise. The key to success is a clear division of roles and strong governance so both sides know who owns which decisions. Hybrid models let brands gain control and transparency without taking on the full operational burden at once.
Which advertising platforms let brands keep their data when switching agencies?
Platforms that offer solutions built for brand ownership let advertisers retain their first-party data, campaign history, and audience assets independent of any single agency relationship. This means that when a brand changes agency partners, its data and configurations stay with the brand rather than leaving with the outgoing agency.
What advertising platforms are designed for brand-side media teams?
Brand-side media teams need platforms that combine transparency, ease of use, and full-funnel execution in one place. The most suitable platforms unify programmatic buying, data management, and reporting so an internal team can run campaigns end to end without stitching together multiple point solutions.
Key takeaways:
It might not feel like it, but the end of the year is just around the corner. One more glorious month of summer, and then school’s back in session, fall sports seasons are kicking off, and consumers are starting to shop for the holidays. (That’s right, holiday shopping starts in September now!)
As advertisers begin to plan their holiday campaigns, accounting for consumer sentiment and behavior is critical. And while each audience segment has its own characteristics and motivations, one theme will unite most gift givers: economic uncertainty.
2026 has been a trying year for US consumers. January marked a two-year low in confidence in the economy, and consumer sentiment dropped to a record low in May, driven by cost-of-living concerns. Ongoing geopolitical conflicts have also led to dramatic fluctuation in oil prices, which have weighed on the broader economy and household budgets. While total holiday retail spending is still expected to grow year over year, much of the forecast 4% increase likely reflects higher prices rather than more purchasing.
As such, holiday shopping this season will be defined by strategic, considered spending. And with purchases expected to stretch across the final four months of the year, advertisers should be prepared to stay present and competitive throughout the season. That means understanding how economic anxiety is changing consumer behavior, then rethinking how to plan, pace, and coordinate campaigns in response.
Economic and geopolitical conditions are forefront in consumers’ minds in 2026. Almost half (45%) of US consumers say that political and geopolitical change has shifted their outlook on the holidays. This will lead many consumers to spend with more discretion: Among US consumers who plan to spend less on holiday gifts, wanting to save due to concerns about the economy ranks as the leading reason.
That discretion will show up in how consumers spend, more than in how much they spend. More than half plan to spend the same as last year, but a growing share are approaching the season differently. Roughly a quarter of gift buyers plan to hunt for more deals (26%) or prioritize a smaller number of carefully chosen gifts (28%). Some shoppers are also widening their consideration sets and slowing down their decision-making: 30% say they compare more retailers than they did two or three years ago, and 27% report taking more time to research products before they buy. These shifts, while not universal, point to a holiday shopper who is more deliberate about where their money goes and more willing to shop around before committing.
In addition to how consumers shop, economic pressures will also impact when they shop. A quarter of US consumers plan to spread out their holiday shopping due to current events, and 68% say they shop early to avoid delays. That behavior traces back to the pandemic, which reshaped how consumers approach the season—extending it from a short sprint into a months-long window. What started as a workaround for shipping delays has since hardened into habit. Economic pressure is now reinforcing that habit, as shoppers who spread purchases across several months can pace their spending, watch for markdowns, and avoid the concentrated hit of a single December splurge. As a result, the holiday shopping season will span from September through December.
Taken together, these behaviors point to a holiday season defined by shoppers spending carefully, comparing widely, and starting early.
The most successful holiday campaigns in 2026 will be run like marathons rather than sprints, paced for delivering impact throughout the entire final third of the year. The longer, more deliberate shopping season will reward advertisers who prioritize value, maintain presence throughout, and coordinate campaigns across channels to build visibility and consideration.
Prioritizing value means surfacing deals and shipping perks early in the season. Shoppers will take their time with purchasing decisions—researching, weighing options, and validating choices before they buy—and value-focused messaging that shows up early and often will place brands in the consideration set from the moment consumers start crafting their shopping lists.
Reaching these discerning shoppers also demands more precision from advertisers. The season's length complicates planning, as there's less of a concentrated time frame within which to spend. This is especially true considering the diminished importance of Black Friday and Cyber Monday: Over half of US consumers now say that shopping during these events isn’t critical, since brands offer deals throughout the season.
Considering this, matching the right message to the right person at the right time and on the right platform during these months requires agile, data-driven planning. With shopping spread across four months rather than clustered around discrete events, building a strategy that reaches the right audiences across every channel and moment becomes far more complex.
New tools are helping advertisers craft these strategies more easily. For example, AI-powered omnichannel planning tools can quickly create holistic media strategies across walled gardens and the open web, aligning channels, budget, and objectives into one plan. These capabilities offer significant returns, with research showing that synergy between advertising channels (a major challenge for today’s advertisers) multiplies brand impact—in fact, one study found that 43% of brand impact comes from this synergy.
Of course, the work isn't done once the plan is set. To maintain and improve effectiveness, advertisers should learn from their campaigns as they go, applying new learnings week over week. This can be done manually, but given the level of complexity, all-channel optimization is well-suited to automation. Marketing teams who use AI-powered optimization to let each week of the campaign sharpen the next will be best positioned to reach shoppers with relevant messaging throughout the holiday season.
Ultimately, in a season this long and this deliberate, advertisers who plan early, coordinate widely, and optimize continuously will have a major advantage.
This year’s holiday season will reward patience and precision. Consumers are spending deliberately, comparing widely, and starting earlier, which means the old playbook of concentrating budget around a single peak weekend no longer fits how people shop. Advertisers who lead with value, craft holistic, omnichannel plans, and refine their approach as the season unfolds will stay present in the moments that matter, from the first September search to the last December purchase.
—
Want the full picture of what's driving holiday shoppers this year? Our 2026 Winter Holidays Shopping Trends Report breaks down the consumer behaviors shaping the season, along with what they mean for advertisers’ campaigns.