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Ronk Communications used Basis to expand Colorado State University Global's programmatic presence across display, audio, and CTV, uncovering new audiences to reach and increasing spend to 63% YoY while maintaining a $6.18 eCPA.
Ronk Communications is a media buying agency for high-performing digital, social and traditional ad campaigns across events, venues, government and higher education. Their client, Colorado State University Global (CSU Global), is the nation's first fully accredited, 100% online state university, dedicated to providing top-ranked, affordable Bachelor’s and Master’s degree programs with the flexibility students need to succeed
Colorado State University Global engaged Ronk Communications to drive prospective students into its enrollment funnel through paid media. A 2024 test delivered strong results to justify a larger 2025 investment, with a goal of scaling spend while finding new audience signals.
With a 63% budget increase and more markets to crack, Ronk Communications needed tighter campaign control, smarter optimization, and a partner that could support an evolving strategy in real time.
Ronk Communications used Basis to expand CSU Global's targeting beyond Denver into California and Colorado Springs, layering in new audience strategies, PMPs, and SmartBid optimization throughout the flight.
The results:
"Ronk Communications is armed to service a major advertiser like Colorado State University Global and exceed their expectations with a multichannel, high-performing ad campaign thanks to our trusted partnership with Basis. We are supported every step of the way whether it comes to high-quality inventory, brand protection, targeted audiences, pixels, reports, data providers or vendor collaboration. Basis is a part of your team, making you compete with any sized ad agency." - Ronk Communications
The programmatic advertising trends shaping 2026 mark a shift from volume-driven growth to a more disciplined, accountable phase of strategy. Rising scrutiny around media quality, shifting consumer discovery patterns, and the deepening role of AI are pushing advertising leaders to rethink how programmatic delivers true value that moves beyond scale.
Key Takeaways:
These trends underscore a broader shift in programmatic advertising, and success in 2026 will depend less on maximizing volume and more on managing complexity with intention, structure, and accountability.
Programmatic Advertising in 2026, By the Numbers:
As global ad spending is set to surpass $1 trillion for the first time, programmatic advertising continues to play a central role.
In 2025, programmatic digital display ad spending in the US grew 16.6%, surpassing $187 billion and accounting for nearly 95% of all digital display ad spend. That momentum shows no signs of slowing. In 2026, US programmatic display spending is expected to exceed $220 billion, representing year-over-year growth of 17.4%. Globally, programmatic is projected to account for roughly 90% of display ad budgets and nearly all incremental growth in display for the foreseeable future. With major events like the Winter Olympics, FIFA World Cup, and key midterm elections fueling the media landscape in 2026, the scale and stakes of advertisers’ programmatic investments will be especially high.
This growth is occurring against a backdrop of both challenge and opportunity. Privacy expectations continue to rise, data signals remain inconsistent, and long-standing assumptions about addressability and measurement are being tested.
While Google reversed its plans to fully deprecate third-party cookies in Chrome, the decision did little to resolve the broader challenges associated with signal loss. For many advertisers, declining data quality and shrinking addressable audiences remain daily realities, regardless of browser policy changes. Yet these same pressures are driving innovation in inventory curation, first-party data strategies, and privacy-conscious targeting approaches that promise more effective, sustainable advertising.
Artificial intelligence is further reshaping the programmatic ecosystem. AI is now embedded across the advertising workflow—from campaign planning and optimization to creative development and analytics—but its most alarming impact may be the flood of AI-generated content entering the programmatic supply chain. The rapid proliferation of generative AI is raising the stakes for brand safety and media quality, as low-quality synthetic content becomes harder to distinguish from legitimate inventory. While AI is also powering many of the tools to combat these challenges, the race between synthetic content creation and detection will be a defining feature of programmatic quality in 2026.
Meanwhile, consumer attention continues to splinter across channels and formats. Retail media networks are expanding quickly, short-form video is commanding a growing share of budgets, and ad-supported streaming is evolving with new formats and placements. At the same time, discovery itself is changing, as zero-click search experiences and AI-generated summaries reduce traditional paths to traffic and engagement (not to mention attribution).
Taken together, these forces have made programmatic advertising all the more powerful…and all the more demanding. The year ahead will test how well advertisers can manage AI-driven risks and opportunities, consolidate data foundations, adapt to new discovery patterns, and execute across evolving video and commerce environments.
Simply put, AI is reshaping the programmatic landscape.
While the technology has improved efficiency, the rapid spread of AI-generated content has increased the volume of low-quality inventory that can slip into campaigns, making it harder to separate genuine human interest from surface-level impressions.
AI-generated sites and content farms can deliver high impression counts and engagement signals that look legitimate in reporting but don’t translate to brand recall or purchase intent. This dynamic has elevated brand safety from a reputational concern to a performance issue. With 54% of advertisers believing generative AI has contributed to a decline in overall media quality, teams must rethink how they evaluate inventory and interpret campaign results to ensure optimization is grounded in authentic engagement.
Advertising leaders are responding by layering smarter controls into their buying strategies. Pre-bid protections, contextual intelligence, curated supply, and ongoing delivery analysis can help advertisers identify patterns associated with low-value or synthetic content before spend accumulates. These approaches reduce waste and ensure optimization decisions are based on reliable signals rather than volume that masks low-quality environments.
In 2026, advertisers must find ways to reliably and consistently apply these guardrails systemically across their programmatic strategies, leveraging AI for routine monitoring while reserving human expertise for strategic oversight and decision-making.
As consumer concerns around data privacy remain high and signal loss continues to reshape addressability, first-party data has become central to programmatic strategy.
In 2025, 40% of US marketers relied on first-party data as their primary privacy-centric targeting approach. Meanwhile, the usefulness of third-party cookies has continued to decline, this despite Google’s U-turn on deprecation in Chrome. As audiences move fluidly across platforms and formats, the signals cookies provide are increasingly partial, limiting their effectiveness as a foundation for long-term planning.
Yet as powerful a tool as first-party can be, data alone is not enough. Without strong organization and consolidation, even high-quality data struggles to deliver impact, particularly as AI becomes more deeply embedded in planning, activation, and optimization.
Fragmented data remains a major barrier for marketers: Fewer than one in five industry professionals say their first-party data is extensive and well-structured, while 34% describe it as limited or disconnected. Much of this stems from tech stack sprawl, with more than half of agency professionals reported to use eight or more tools to manage campaigns and 40% juggling 10 or more. These gaps make it harder to respect consumer choice, apply consistent governance, and generate reliable insights, while simultaneously undermining AI performance by limiting data-powered optimizations and increasing the likelihood of flawed recommendations driven by incomplete or inconsistent inputs.
Without data consolidation, these challenges compound. Unifying data across systems enables privacy-conscious targeting, clearer measurement, and more responsible use of AI—allowing advertisers to do more with less signal while maintaining trust and performance.
With the emergence of zero-click search environments, advertisers must also adapt to how consumers discover and evaluate brands.
As AI-generated summaries, AI Overviews, and AI agents increasingly resolve queries directly within a search or chatbot interface, fewer users are clicking through to websites. Recent research shows that only about 8% of users click links from Google’s AI summaries, signaling a meaningful shift in how discovery happens. More broadly, a growing share of searches conclude within the results pages themselves, with many users finding the information they need without clicking through to another destination.
This evolution challenges long-standing assumptions about search performance and attribution. When answers are delivered without a click, traditional KPIs like CTR and last-touch conversions become less reliable indicators of impact.
In this new context, brand exposure, contextual relevance, and repeated presence across channels increasingly shape consideration throughout the customer journey, with audiences visiting websites later and later in the process…if they visit at all. In parallel, brands must also account for how they appear within AI-driven search results and large language model (LLM) outputs, where summaries, recommendations, and cited sources can shape perception without any direct interaction. Visibility now extends beyond links and placements to include how—and whether—a brand is represented in these emerging information environments.
For programmatic advertisers, this shift elevates the role of display, video, and contextual placements in the awareness and consideration process. These channels help establish familiarity and preference in moments when consumers are forming opinions, even if they never leave the search or AI interface. Measurement frameworks and media strategies must evolve to reflect a world where visibility still drives value—just not always traffic—and where influence is distributed across a broader, more decentralized ecosystem.
As measurement and attribution models evolve to account for zero-click influence, programmatic budgets continue flowing toward environments that connect media to transaction data.
Commerce media has been one of the fastest-growing areas within programmatic advertising, reshaping how brands connect media exposure to purchase behavior. In 2025, retail media programmatic display spending grew more than twice as fast as total programmatic display, reflecting advertisers’ appetite for environments tied closely to transaction data and retail signals. WPP’s end-of-year forecast highlighted just how quickly the category has scaled, projecting that commerce media would surpass television in total spend by the end of 2025.
As the category matures, however, the focus is shifting from rapid expansion to execution and integration. Growth may not continue at the same pace, and leaders are increasingly grappling with inconsistency across retail media networks, misaligned measurement frameworks, and rising operational demands. At the same time, early signs of agent-driven commerce are beginning to influence how value is created within these environments. Industry forecasts suggest that by 2028, a meaningful share of digital storefront interactions could be handled by automated “machine customers.” Early examples are already visible in agent-driven shopping experiences, where AI assistants compare products, surface promotions, and complete purchases on a shopper’s behalf—raising the stakes for clean product feeds and accurate pricing data. As these systems play a larger role in product discovery, comparison, and purchase decisions, the quality and consistency of product data, pricing signals, and promotions are becoming just as important as media placement itself.
Today, commerce media demands integration rather than investment alone. Advertisers that treat commerce media as a core component of their programmatic strategies—supported by disciplined measurement, strong data foundations, and intelligent automation—will be better positioned as retail media evolves from a breakout growth channel into a more machine-mediated, long-term pillar of the media mix.
Programmatic video is increasingly concentrating around short-form, mobile-first formats. As audiences turn to quick, scroll-based video for entertainment, inspiration, and product research, ad budgets are following in kind.
In 2025, social video accounted for 53.7% of programmatic video ad spending, reflecting how budgets have followed audience behavior. Much of that demand sits with platforms built expressly for the format—TikTok, Instagram Reels, and YouTube Shorts—where vertical, scroll-based video shapes both entertainment and product discovery. Engagement patterns reinforce this shift, with a majority of consumers interacting with short-form video multiple times per day and using it as a source for finding new products as well as recommendations.
This dynamic is especially pronounced among younger audiences: Gen Z—whose spending power is projected to reach $12 trillion by 2030—engages with short-form video at a higher frequency than older cohorts and relies on it as a primary source of entertainment, inspiration, and discovery. As a result, short-form video ads are influencing consideration earlier and compressing the path from exposure to action faster than traditional video formats.
In 2026, the challenge for advertisers will be less about whether to invest in short-form video and more about how deliberately they do so. Creative must be built for the format and paired with strong brand safety guardrails and deliberate KPIs that account for how quickly short-form video can drive movement from awareness to action. As short-form video continues to absorb both attention and budgets, advertisers that integrate it thoughtfully within broader omnichannel strategies will be better positioned to convert fleeting moments into durable impact.
Subscription fatigue is driving viewers toward ad-supported streaming—and turning CTV into a laboratory for format innovation. The experimentation is pushing CTV beyond standard pre-roll and mid-roll placements toward formats designed to complement the viewing experience rather than interrupt it. Pause ads, interactive units, content hubs, and contextual sponsorships are gaining traction as advertisers look for ways to capture attention without increasing ad load.
Audience behavior is reinforcing this shift, with viewers increasingly multitasking while streaming, thereby creating demand for formats that invite engagement rather than passive exposure. Interactive CTV ads are emerging as one response: More than 40% of US marketers already use interactive features across social and CTV, and over half expect interactive elements to account for at least a quarter of their ads. Early performance signals suggest these formats can deliver meaningful lift, including higher unaided recall and stronger brand affinity, when aligned with content and context.
As CTV inventory continues to fragment across platforms and environments, new formats have in turn created new operational challenges. The absence of universal CTV standards has created significant variability in how ads are served, measured, and experienced, increasing the need for structure and visibility as experimentation accelerates. Programmatic activation—supported by a unified, omnichannel platform—provides a framework for testing emerging formats with guardrails, allowing advertisers to compare performance, manage frequency, and maintain consistency across placements. In 2026, advertisers that pair creative experimentation with programmatic discipline will be better positioned as CTV shifts from a reach-first channel to a more interactive, performance-aware component of the media mix.
Across all these channels and formats, a common thread emerges: the need for greater control over where ads appear and how budgets are deployed. Programmatic curation pairs the scale of the open exchange with vetted, curated supply paths, giving advertisers more control over inventory quality, transparency, and working spend.
Programmatic advertising has long been synonymous with scale. Yet as programmatic investment continues to climb, so does scrutiny around media quality and working spend. Leaders are increasingly expected to defend not just performance, but also the transparency and quality of the media supply chain. In 2025, inefficiencies and waste in programmatic spend were estimated to total about $26.8 billion globally, underscoring the gap between dollars invested and working media outcomes.
Curated inventory packages give advertisers more visibility into where ads appear and how supply paths function, helping reduce inefficiencies and improve confidence in campaign outcomes. Curation has gained traction as marketers seek stronger alignment between media quality and performance, with 41% citing curated deals as a path to higher ROI. DSPs and SSPs are responding by expanding tools that support flexible deal structures and clearer supply chain insight.
The open exchange remains an important source of reach, but in 2026, it is increasingly paired with curated strategies that bring added control. Together, they enable advertisers to pursue growth without compromising trust.
The trends shaping programmatic advertising in 2026 reflect a steady recalibration (rather than a dramatic reset). As budgets continue to grow and scrutiny intensifies, advertisers are moving away from volume-driven approaches and toward strategies that prioritize quality, accountability, and adaptability. The shift is visible across the programmatic ecosystem—from consolidated data foundations and stronger media quality guardrails to evolving commerce media, changing discovery and video environments, and curated supply paths.
What connects these shifts is the rising importance of integration. Disconnected channels, formats, and signals require systems and operating models that support consistent decision-making across planning, activation, and measurement. As automation becomes more embedded in programmatic workflows, human oversight will become less focused on manual execution and more centered on governance, interpretation, and long-term value creation.
This year, advertisers that pair thoughtful experimentation with clear guardrails, maintain transparency as strategies evolve, and combine automation with human expertise will be best positioned to evaluate performance, defend investment decisions, and sustain growth amidst ongoing change. Programmatic success in 2026 will be, in large part, measured by how well advertisers manage complexity, turning fragmented tools, signals, and channels into a unified system that supports accountable, AI-ready media execution.
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Looking for more insights into the major innovations and opportunities to watch this year? Our 2026 Trends Report: Rewinding to Fast Forward provides real perspective on the key trends that are poised to shape the year ahead.
Key Takeaways:
Few technologies have reshaped marketing as quickly as AI.
In the less than four years since ChatGPT’s public debut, AI has become increasingly embedded in many marketers’ workflows, influencing everything from media buying to creative development. Now, its role is expanding.
Agentic AI—autonomous systems that both generate outputs and execute decisions, often with less human review at each step—is moving deeper into advertising workflows. In fact, 77.7% of agency leaders plan to increase their AI investment over the next 12 months and 90.7% of industry professionals believe that AI will radically transform the industry within the next three to five years. Yet amidst this enthusiasm, teams also need to consider whether they have the data infrastructure needed to support this rapid pace of adoption.
When AI runs on fragmented or low-quality inputs, results are unreliable. Insights get blurred, personalization misses the mark, and recommendations fall flat. Such missteps have a direct cost, quickly compounding into wasted spend and weakened consumer trust. The organizations that thrive in the AI era will be those that invest in clean, unified, privacy-compliant first-party data—the foundation AI needs to deliver accurate, differentiated value.
From faster analysis to smarter targeting to more relevant creative and beyond, the potential for AI in marketing is significant. But the reality of implementing AI effectively is that its accuracy hinges on the quality of the data it consumes.
When data is inaccurate, siloed, or inaccessible, the risk of error increases significantly. Poor data muddies results and raises the odds of hallucinations, where AI generates outputs that appear credible but are instead fabricated. Because the technology mimics the information it’s trained on, gaps or inaccuracies in the data increase the likelihood of such mistakes. This is a significant problem, with a recent study finding that nearly half of marketers encounter AI inaccuracies several times a week.
Those hallucinations can look like real insights: an optimization tool shifting spend toward audiences built on incomplete signals, a personalization engine delivering irrelevant product recommendations with full confidence, or a dashboard surfacing “top-performing” keywords that don’t exist. These are the kinds of costly missteps weak data foundations can produce. Even small inaccuracies can snowball, feeding back into models and negatively shaping future decisions. And as AI moves from assisting to acting, the stakes grow even higher: An agent executing decisions autonomously removes the human checkpoint that might otherwise catch these errors before they compound into a much larger issue.
Agentic AI is top-of-mind for US ad buyers. Two-thirds say agentic AI ad buying and execution is an increased focus this year, and 84% cite media planning and buying recommendations as a current or likely use case. Yet, despite this interest, many are also hesitant: 40% of buyers report that understanding agentic ad buying and campaign execution is one of their greatest concerns or challenges at present.
Data is often a meaningful part of that hesitation. Understanding how an AI agent functions means understanding the data it acts on. Generative AI and agentic AI can both produce errors that are difficult to catch—such as biased outputs, fabricated insights, or recommendations built on incomplete signals—but the workflows around them are inherently different. Generative AI typically sits inside a process with regular human review, whereas agentic AI often makes multiple sequential decisions before a human is involved. When an agent acts on flawed data, that single error can compound across each decision—a budget shift, a paused campaign, or a reallocated bid—all before anyone notices. Which is why data readiness, rather than enthusiasm, is what separates teams that benefit from agentic AI from those it puts at risk.
For all the focus on AI (whether generative or agentic), most organizations are still lagging behind when it comes to data readiness. A patchwork of state-level privacy regulations and increasing signal loss have already made first-party data critical, and AI adoption further raises the stakes.
Clean, consented, and unified data is what ultimately powers accurate insights and effective personalization. Yet few organizations are currently treating it that way: Just 21.4% of industry professionals say first-party data is foundational to their AI efforts, while more than a third admit it plays little or no role.
That gap has proven to be a significant impediment to agentic AI adoption, with eight in ten companies currently citing data limitations as a barrier to scaling agentic AI. And this challenge only grows as advertisers add more tools to their tech stacks, as each new source adds yet another potential point of fragmentation.
Even when teams recognize the importance of first-party data, the data they have often isn’t ready to deliver. Roughly 34% of industry professionals say their first-party data is limited and fragmented, and less than one in five describe it as extensive and well-structured.
Some of the key challenges that hinder marketers’ ability to effectively leverage data are data accuracy and quality, as well as scale and volume of data. While most teams have access to large amounts of data, fragmentation prevents them from forming a reliable single view of the customer. For example, a travel company might struggle to connect loyalty profiles with search behavior and purchase history. Despite having plenty of data, it’s difficult to use and more prone to errors because it’s siloed across platforms and systems. AI trained on only one slice of that picture could misread intent, recommending irrelevant offers or overvaluing the wrong audiences. Multiply that problem across numerous systems and campaigns, and marketers lose both efficiency and accuracy to data problems.
AI delivers its strongest results when it runs on a solid data foundation. Advertisers that prioritize clean, consolidated, and accessible data systems see stronger AI-powered targeting, personalization, and optimizations. For agencies, reliable data ecosystems fuel creative and strategic outputs that capture the nuances of their clients’ audiences.
This groundwork starts with first-party data itself: ensuring it is collected with consent, stored securely, and structured in ways that make it easy to analyze and share across teams. Data hygiene practices, such as regularly auditing for accuracy, de-duplicating records, and unifying customer identifiers, are also essential for maintaining quality at scale. Leaders that embed these practices into ongoing workflows, rather than treating them as one-off clean-up projects, see compounding benefits over time.
Equally important is making data actionable. Tools that unify disparate data sources and streamline reporting consolidate scattered signals into one source of truth, giving AI tools consistent inputs and reducing the errors that come from siloed systems. This also creates a shared foundation across marketing, sales, and finance, making it easier to align strategy and measure impact.
The stakes climb higher with agentic AI. Some platforms now build a unified data foundation directly into the system, so that agentic capabilities draw on consolidated, accurate inputs from the start. Even then, the inputs are only as good as the underlying data. Those foundations matter more, not less, as execution becomes more autonomous.
AI has quickly become embedded in marketing workflows, but its value depends largely on the data that fuels it. Too often, that foundation is fragmented or incomplete.
Organizations that invest in systems to ensure clean, compliant, and unified first-party data will be positioned to capture AI’s full value. The payoffs are significant: stronger ROI, personalization that resonates, and long-term differentiation. In the years ahead, as AI shifts from assisting to acting, those who have built the strongest data foundations are likely to come out ahead.
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Looking for even more insights into the state of AI in marketing? We surveyed marketing and advertising professionals from leading agencies and brands for our third annual AI and the Future of Marketing report. It’s filled with insights to help industry leaders evaluate how to use AI responsibly, strategically, and with urgency.
Media buying has never run on a single tool, and for most teams, that fragmentation is a daily operational reality. Campaigns span display, video, connected TV, audio, and more—each with its own levers, its own data, and its own reporting format to reconcile at the end of the week. As programmatic has matured and channel options have multiplied, the DSP at the center of that programmatic stack has become one of the most consequential platform decisions an advertising team can make. Choose well, and you consolidate complexity into a unified, optimizable workflow. Choose poorly, and you add another silo to an already fragmented stack.
Choosing the right omnichannel DSP for your campaigns comes down to evaluating inventory breadth across channels, cross-channel reporting and attribution capabilities, cost structure transparency, optimization logic, and integration with AI-driven planning workflows. The best DSP for your team is one that consolidates fragmented media buying into a single platform, giving you unified control over campaign activation, measurement, and optimization across display, video, connected TV, audio, DOOH, and more. This guide covers the criteria, pitfalls, and comparison framework agency teams need to make the call.
An omnichannel DSP is a demand-side platform that enables advertisers to plan, buy, and optimize programmatic media across multiple channels—such as display, video, native, connected TV, audio, and mobile—from a single interface. Rather than managing separate tools or logins for each channel, an omnichannel DSP centralizes the entire media buying workflow so that audience targeting, budget allocation, and performance reporting happen in one place.
A single-channel or specialized DSP, by contrast, focuses on one specific media type or environment. A CTV-only DSP, for example, may offer deep inventory access within streaming environments but lacks the ability to coordinate messaging across display, audio, or mobile simultaneously. Similarly, a mobile-focused DSP may excel at in-app targeting but leaves you managing separate platforms for every other channel in your media plan.
The core difference is operational scope. An omnichannel DSP treats your campaign as a unified effort across touchpoints, while a specialized DSP treats each channel as a silo. For teams running cross-channel campaigns, this distinction directly impacts efficiency, data consistency, and the ability to optimize holistically rather than channel by channel.
If you're newer to programmatic advertising and want a foundational overview of how demand-side platforms work before diving into the omnichannel comparison, this DSP explainer covers the basics.
Consolidating media buying into a single omnichannel DSP matters because it eliminates the fragmentation that slows down campaign execution, muddies reporting, and inflates operational costs. The scale of the problem is significant—and accelerating. More than one-third of full-service and media agencies are now managing 10 or more tools across their adtech stack, more than twice as many as in 2024, according to Basis' 2026 Advertising Agency Report. And only 10% of marketers say their ad tech stacks are fully connected across channels like CTV, social, display, and retail media. When your team runs campaigns across three or four separate platforms, every additional tool introduces its own data taxonomy, reporting cadence, and optimization logic, making it nearly impossible to get a clean, unified view of performance.
The direction of buyer investment reinforces the urgency. The IAB's 2026 Outlook Study found that two-thirds of buyers are now focused on agentic AI for ad buying and campaign execution, with five of the top six buyer priorities tied to AI. Agentic AI doesn't work on top of fragmented systems—it needs connected data and unified workflows to make autonomous decisions worth trusting. The agencies preparing for that shift are the ones consolidating their stacks now, not later.
A consolidated approach means your audience data flows across channels without manual reconciliation. You can see how a connected TV impression influenced a display click or how an audio ad contributed to a conversion—all within the same reporting environment. This cross-channel visibility is what allows media buyers to make smarter allocation decisions in real time rather than waiting for post-campaign analysis to reveal what worked.
There's also a practical efficiency argument. Managing fewer vendor relationships, fewer invoices, and fewer platform-specific training requirements frees up time that campaign managers can redirect toward strategy and optimization. For agencies managing multiple clients, this consolidation compounds, reducing overhead across every account.
Omnichannel DSPs matter because today's consumers don't move through neat, single-channel journeys, and the programmatic campaigns that reach them effectively can't be planned as if they do. A typical consumer might encounter a brand through a CTV ad during their evening stream, see a related display ad the next morning, and hear a programmatic audio spot during their commute. Each of those touchpoints is part of one buyer journey, but in a fragmented programmatic stack, they're activated, optimized, and reported on as if they were separate campaigns.
That disconnect is where strategic value gets lost. When programmatic channels operate as silos, agencies lose the ability to manage frequency across the full programmatic plan, leading to overexposure on some channels and underexposure on others. Budget allocation becomes a guessing game because performance data isn't comparable across DSPs. And full-funnel measurement within programmatic—the ability to attribute conversions back to upper-funnel awareness touchpoints across CTV, display, audio, and more—becomes nearly impossible without manual data stitching.
Omnichannel DSPs solve these problems by treating the programmatic campaign as the unit of measurement, and not merely the channel. As a result, audience targeting flows across channels, rather than being rebuilt in each one. Frequency caps apply to the consumer, not the platform. And performance reporting reflects the full programmatic journey, which means optimization decisions can shift budget toward the channels and tactics actually driving outcomes, and not just the ones that look strongest in isolation.
This shift carries strategic weight beyond any single campaign. Agencies operating on omnichannel DSPs build cleaner, more unified historical programmatic performance data over time. That data becomes a strategic asset: the foundation for AI-driven planning, the evidence base for client recommendations, and the proof points that justify budget increases. Agencies and brands still running programmatic across siloed, channel-specific DSPs accumulate fragmented data that's harder to learn from and harder to act on.
That said, programmatic is only one part of the modern media mix. Even the most capable omnichannel DSP leaves search, social, and direct buys running in separate tools—and the same logic that argues for unifying programmatic channels argues for unifying programmatic with the rest of the workflow. But we'll talk more about that in just a bit. (For a deeper look at how omnichannel advertising platforms fit into broader agency strategy, this overview of omnichannel advertising platforms covers the category in more depth.)
The best DSP for cross-channel campaigns is one that scores well across five core evaluation criteria: inventory breadth, cross-channel measurement, cost transparency, optimization capabilities, and channel coverage depth. Each of these areas directly impacts whether a platform can serve as your team's single source of truth for omnichannel media buying.
Inventory breadth and quality: Evaluate how many supply-side platforms (SSPs) and exchanges the DSP integrates with, and whether it provides access to premium inventory across the channels you care about. A platform with limited supply partnerships will constrain your reach and force you back into channel-specific tools for certain buys.
Cross-channel reporting and attribution: The DSP should offer unified reporting that connects impressions, clicks, and conversions across channels in a single dashboard. Look for platforms that support multi-touch attribution models rather than last-click-only measurement, so you can understand the full path to conversion.
Cost structure transparency: Understand how the DSP charges—whether through a percentage of media spend, a flat platform fee, or a hybrid model. Hidden fees, opaque auction mechanics, or bundled data costs can erode your effective CPMs and make it difficult to compare true cost efficiency across platforms.
Optimization logic: Assess whether the platform offers algorithmic optimization that works across channels, not just within them. The best DSP will automatically shift budget toward the highest-performing channel-audience combinations based on your campaign goals, whether that's reach, engagement, or conversions.
Channel coverage depth: True omnichannel capability means more than checking boxes. With US CTV ad spend projected to reach $37.95 billion in 2026 at nearly 15% year-over-year growth per eMarketer, a DSP that treats emerging channels as afterthoughts will limit your ability to reach audiences where they're increasingly spending time. Evaluate whether the DSP offers robust activation across high-priority channels like connected TV and audio, not just display and video.
To see how these criteria map to a specific platform's capabilities, explore Basis DSP and compare its feature set against the framework above.
AI-driven planning workflows are fundamentally changing how teams evaluate omnichannel DSPs because they compress the time between campaign brief and activation from days to minutes. This capability—where AI converts a campaign brief into a complete, ready-to-activate omnichannel media plan—is emerging as a key differentiator that no third-party review site or analyst report currently covers in depth.
Traditionally, building a cross-channel media plan required manual research into audience segments, channel mix recommendations, budget allocations, and bid strategies. A media planner might spend hours or days assembling these components before a single impression is served. AI-native DSP features automate this process by taking in campaign objectives, audience parameters, and budget constraints, then generating a recommended plan that spans channels and tactics.
This matters for DSP selection because it shifts the evaluation conversation from "Which platform has the most features?" to "Which platform makes my team faster and smarter?" An omnichannel DSP with agentic AI capabilities—such as Basis' Compass, which converts campaign briefs into optimized omnichannel media plans—can go beyond merely executing buys by accelerating the strategic planning process that precedes them.
And that execution gap is real. Mediaocean's 2026 Advertising Outlook Report found that 41% of marketers cite difficulty connecting AI insights across systems as one of the top barriers to scaling AI effectively, second only to data quality and access. AI in the planning layer is far more valuable when it sits inside a platform that can also activate, optimize, and report against the resulting plan. The question to add to vendor assessments is whether the DSP integrates AI into the planning layer or only the optimization layer. As crucial as AI-powered optimization is to campaign performance, a platform that only uses machine learning for bid adjustments after a campaign launches is solving a different problem than one that uses AI to architect the entire campaign from the start.
An omnichannel DSP solves part of the fragmentation problem...but only when it comes to programmatic. Most agency teams still run paid search through one interface, paid social through another, direct buys through a third, and reconcile billing in a fourth. Each handoff between systems is a point where data gets lost, plans get duplicated, and margin gets quietly eaten by manual work.
A truly omnichannel advertising platform consolidates the full digital media workflow—programmatic, site-direct, search, social, and CTV—into a single system. Planning, activation, optimization, reporting, and billing share one data layer, which means a media planner, a buyer, and a finance lead all work from the same source of truth.
This matters for three operational reasons:
This is also where the broader market is heading: 39% of marketers are prioritizing cross-platform orchestration this year, signaling a shift away from siloed point solutions toward more connected infrastructures.
For agencies and brands evaluating an omnichannel DSP as a standalone purchase, it's worth considering whether the addition of another point solution could wind up recreating the same fragmentation problem one layer down—and whether a unified advertising platform would solve it once.
Truly omnichannel advertising platforms answer that question by closing the gap structurally rather than asking agencies to bridge it with workflow. Basis, for instance, integrates its DSP with search, social, direct, and CTV, layers in agentic AI media planning through Compass, and connects financial workflows through its partnership with Mediaocean.
Simply put, an omnichannel advertising platform is better than single-channel tools because it unifies planning, activation, optimization, reporting, and billing across every digital advertising channel—programmatic, search, social, direct, and CTV—within one system, while single-channel tools handle only one piece of that workflow in isolation. The difference shows up in five operational areas:
Data and reporting: An omnichannel platform produces a single, unified view of campaign performance across channels. Single-channel tools produce channel-specific reports that have to be manually reconciled, which delays optimization decisions and introduces measurement inconsistencies.
Cross-channel optimization: An omnichannel platform can shift budget, frequency, and audience targeting across channels in response to live performance data. Single-channel tools optimize within their own channel only, which means cross-channel budget allocation decisions happen offline, in spreadsheets, after the fact.
Workflow continuity: Planning, buying, reporting, and billing live in one environment on an omnichannel platform. With single-channel tools, every workflow stage requires a separate login, a separate data export, and a separate reconciliation step—each one a point where errors are introduced.
AI and automation readiness: AI-driven planning and agentic optimization depend on connected, unified data. An omnichannel platform feeds AI tools with the full picture of campaign performance. Single-channel tools produce fragmented datasets that limit what AI can actually do, even when each tool offers its own AI features.
Team coordination: Media planners, buyers, and finance teams work from the same data on an omnichannel platform. With single-channel tools, every team works from its own version of the truth, which slows decisions and creates version-control problems on media plans and budgets.
The perceived trade-off is depth vs. breadth—essentially, that a single-channel tool will outperform on its specific channel because that's all it does. But in practice, that trade-off is overstated. Modern omnichannel platforms can offer the same channel-level depth as single-channel tools while also delivering the cross-channel data, optimization, and workflow advantages above. The best omnichannel advertising platforms provide granular streaming inventory access, full programmatic capabilities, paid search and social functionality, and direct buying without forcing users to choose between specialization and consolidation.
The most common pitfall when choosing a demand-side platform is evaluating it based on feature lists alone rather than testing how those features perform in your actual workflow. A DSP can claim omnichannel support across dozens of channels, but if the user experience for activating campaigns on those channels is clunky, siloed, or requires workarounds, the feature list becomes meaningless. After all, inefficient processes (44.1%) and siloed systems (40.4%) top the list of obstacles agencies face today—and the wrong DSP (in the wrong environment) can add to both.
Pitfall: Choosing based on brand name alone: The largest DSPs in the market command significant share, but size doesn't guarantee fit. It's critical to match the platform to your team's actual operational reality.
Pitfall: Ignoring the onboarding and support model: Switching DSPs is a high-friction decision. If the vendor doesn't offer structured onboarding, dedicated account support, and training resources, your team will spend months underperforming on the new platform. Ask about implementation timelines, support SLAs, and whether you'll have access to platform specialists, not just a help center.
Pitfall: Overlooking data portability and integration: Your DSP doesn't operate in isolation. It needs to integrate cleanly with your other media buying workflows—such as search, social, and site-direct—as well as your DMP, CDP, analytics stack, and creative management tools. If the platform locks you into proprietary data formats or doesn't support standard API integrations, you'll create new silos even as you try to eliminate old ones.
Pitfall: Conflating optimization with transparency: Some DSPs offer strong algorithmic optimization but provide limited visibility into how decisions are made. If you can't see why the platform shifted budget from one channel to another, you lose the ability to learn from your campaigns and refine your strategy over time. Look for a platform that does both.
For practitioner-level perspective on how experienced programmatic professionals navigate these selection mistakes, this conversation on thriving as a programmatic consultant offers insights from experts who've evaluated and switched platforms multiple times.
Use this checklist as a structured framework when your team is shortlisting and comparing omnichannel DSP vendors. It translates the evaluation criteria covered above into a practical scoring tool you can bring into vendor meetings and RFP processes.
| Evaluation Criteria | Questions to Ask | What to Look For |
|---|---|---|
| Channel coverage | Which channels can I activate from a single platform? | Support for display, video, native, CTV, audio, mobile, and DOOH without requiring separate tools |
| Inventory access | How many SSPs and exchanges does the DSP integrate with? | Broad supply partnerships with premium and open exchange inventory across all supported channels |
| Cross-channel reporting | Can I see unified performance data across channels in one dashboard? | Single reporting environment with multi-touch attribution, not channel-by-channel exports |
| Cost transparency | How is pricing structured, and are there hidden fees? | Clear fee model (percentage of spend, flat fee, or hybrid) with no opaque markups on data or inventory |
| Optimization logic | Does the platform optimize across channels or only within them? | Cross-channel algorithmic optimization that reallocates budget based on holistic campaign goals |
| AI planning capabilities | Does the DSP use AI at the planning stage, not just the execution stage? | Agentic AI features that generate media plans from campaign briefs, reducing manual planning time |
| Data integration | Does the platform integrate with my existing tech stack? | Open APIs, standard data formats, and native integrations with major DMPs, CDPs, and analytics tools |
| Platform breadth | Does the DSP live inside a broader platform that also handles search, social, and direct? | Unified workflow across programmatic, search, social, and direct buying, with planning and billing connected |
| Onboarding and support | What does the implementation process look like? | Structured onboarding timeline, dedicated account support, and ongoing training resources |
| Scalability | Can the platform grow with my team's needs? | Flexible seat licensing, multi-client management for agencies, and enterprise-grade infrastructure |
This checklist is designed to be used alongside competitive research. For a complementary view of how the leading agency platforms compare on media buying capabilities, this comparison of the top advertising agency platforms provides additional context for your evaluation.
Choosing the right omnichannel DSP is ultimately about aligning platform capabilities with your team's operational needs, campaign goals, and growth trajectory. The evaluation isn't just technical—it's strategic. The platform you select will shape how your team plans, activates, measures, and optimizes media across every channel.
Start by mapping your current pain points. If fragmented reporting is your biggest challenge, prioritize cross-channel measurement capabilities. If manual planning is consuming too much of your team's time, weight AI-driven planning workflows more heavily. If you're an agency managing multiple clients, scalability and multi-account management should move to the top of your checklist.
Then pressure-test your shortlist against the criteria and pitfalls outlined in this guide. Request demos that walk through your actual use cases. Ask for references from teams with similar campaign structures and channel mixes. And evaluate the vendor's roadmap: A DSP that's investing in AI-native planning, expanded channel coverage, and transparent reporting is one that's building for where the industry is heading, and not just where it's been.
The most important question to ask, though, is the one most evaluations skip: Does this DSP live inside a platform that also handles the rest of your digital media workflow? An omnichannel DSP that solves programmatic fragmentation but leaves search, social, and direct in separate tools has only moved the problem. Teams that select for full-platform unification—not just DSP-level omnichannel—are the ones positioned to operate faster, measure more accurately, and build the connected data assets that AI and agentic systems will increasingly depend on.
What is an omnichannel DSP and how does it work?
An omnichannel DSP is a demand-side platform that allows advertisers to buy and manage programmatic media across multiple channels—including display, video, native, connected TV, audio, and mobile—from a single interface. It works by connecting to multiple supply-side platforms and ad exchanges, enabling real-time bidding on inventory across channels while centralizing audience targeting, budget management, and performance reporting in one place.
How is an omnichannel DSP different from a single-channel or specialized DSP?
An omnichannel DSP supports campaign activation across multiple media channels within one platform, while a single-channel or specialized DSP focuses on one specific environment, such as CTV or mobile. The key difference is that an omnichannel DSP enables unified audience targeting, cross-channel optimization, and consolidated reporting, whereas specialized DSPs require separate tools and workflows for each channel, creating data silos and operational fragmentation.
What channels does an omnichannel DSP support?
A true omnichannel DSP supports display, video, native, connected TV, audio, mobile, and digital out-of-home. The specific channel coverage varies by platform, so it's important to verify that the DSP offers robust activation capabilities—not just nominal support—for the channels that matter most to your media plan.
What should I look for when choosing the best DSP for omnichannel campaigns?
When choosing the best DSP for omnichannel campaigns, evaluate five core areas: inventory breadth across channels, cross-channel reporting and attribution capabilities, cost structure transparency, algorithmic optimization that works across channels rather than within them, and AI-driven planning features that accelerate campaign activation. Additionally, assess the platform's integration with your existing tech stack and the quality of its onboarding and support model.
How does an omnichannel DSP improve cross-channel measurement and attribution?
An omnichannel DSP improves cross-channel measurement by consolidating performance data from all channels into a single reporting environment. This enables multi-touch attribution models that show how impressions across display, CTV, audio, and other channels collectively contribute to conversions, rather than measuring each channel in isolation with last-click-only models. Unified measurement gives teams the visibility needed to make smarter budget allocation decisions in real time.
Why does an omnichannel DSP need to live inside a unified advertising platform?
An omnichannel DSP addresses programmatic fragmentation, but most agency teams also run search, social, and direct buys through separate tools. When the DSP lives inside a broader platform that handles all of these channels plus planning, reporting, and billing, the agency operates from a single data layer rather than reconciling outputs from multiple systems. This unification is what enables true cross-channel pacing, frequency management, and the connected historical data that AI and agentic systems need to optimize outcomes.
What makes an omnichannel advertising platform better than single-channel tools?
An omnichannel advertising platform unifies planning, activation, optimization, reporting, and billing across programmatic, search, social, direct, and CTV in one system, while single-channel tools handle only one piece of that workflow in isolation. The platform model delivers unified reporting, cross-channel optimization, connected data for AI workflows, and shared visibility across media planning, buying, and finance teams. Single-channel tools may offer deeper functionality within their specific channel, but they require manual reconciliation across the rest of the workflow.
What are the main benefits of running campaigns on an omnichannel DSP?
The main benefits of running campaigns on an omnichannel DSP include:
Is an omnichannel DSP suitable for both brand awareness and performance campaigns?
Yes, an omnichannel DSP is suitable for both brand awareness and performance campaigns. Upper-funnel channels like connected TV and audio drive reach and awareness, while display, native, and mobile support mid- and lower-funnel performance goals like clicks and conversions. Running both campaign types within a single DSP allows teams to measure the full-funnel impact of their media spend and optimize across objectives rather than treating awareness and performance as separate efforts.
AI-powered strategic media planning tools are software platforms that analyze client briefs and automatically generate structured media plan drafts, including channel recommendations, budget allocations, and strategic guidance.
These tools address one of the biggest challenges facing agencies today: inefficient planning processes that drain time, create inconsistency across teams, and prevent planners from focusing on strategic work. As AI adoption accelerates across the industry, understanding how these tools work and who benefits most from them matters for teams looking to stay competitive.
Key Takeaways
An AI-powered strategic media planning tool is software that analyzes client briefs and generates a structured media plan draft, including channel recommendations, budget allocations, and tactical suggestions.
This technology works by interpreting natural language from client briefs and matching campaign goals with industry benchmarks and channel-specific insights. Rather than starting from a blank page, planners receive a structured plan they can refine and customize based on client needs as well as their own strategic and creative judgment.
For example, a tool like Compass by Basis reads a client brief, identifies campaign objectives and target audiences, and generates a complete omnichannel media strategy—including prioritized audience segments, competitive context, channel-by-channel budget allocations with visual breakdowns, and campaign flighting—all within a conversational interface inside the platform where campaigns get activated. Planners can refine the strategy through follow-up prompts before building it into client deliverables or moving into activation.
Though powered by many of the same technologies, these tools differ from more general AI assistants or chatbots because they're purpose-built for media planning workflows. They understand advertising terminology, channel dynamics, and how to structure plans that translate directly into campaign execution. Compass, for instance, is built on Basis’ proprietary IMPACT omnichannel framework—a methodology used across thousands of successful media campaigns—rather than relying on generic AI reasoning alone.
According to the IAB State of Data 2025 report:
And, data from Basis’ 2026 Advertising Agency report finds:
This gap highlights where AI adoption has lagged most: operational planning, not creative execution.
Agencies who begin to implement AI toward operational inefficiencies now can gain a strategic edge, freeing up their teams to focus on strategy rather than manual tasks.
AI planning tools follow a structured process to convert briefs into actionable media strategies:
1. Brief Upload and Extraction
The planner uploads a client brief—whether a structured planning document or a simple prompt—and the tool extracts and summarizes key information. This includes campaign objectives, target audiences, budget parameters, KPIs, geographic focus, and timing constraints. In Compass, this extraction step is visible in the interface, so planners can confirm the tool understood the brief correctly before strategy generation begins.
2. Audience Strategy and Prioritization
The tool builds prioritized audience segments based on the brief, going beyond basic demographics. Each segment includes targeting rationale, recommended channels for reaching that audience, and messaging direction. This creates a strategic foundation where audience strategy, channel selection, and messaging are connected from the start.
3. Strategic Framework and Competitive Context
The tool generates a broader strategic framework that includes competitive context, key challenges, and a recommended approach, rather than just a channel list. This strategic layer guides the channel and budget recommendations that follow, grounding them in campaign-specific logic rather than general best practices. The framework also documents the channels the tool evaluated but chose not to recommend, with the reasoning behind each decision. This gives planners a defensible rationale to share with clients, showing that the recommended mix reflects deliberate trade-offs rather than default choices.
4. Channel Mix and Budget Allocation
The tool recommends a channel mix with specific budget allocations, including dollar amounts and percentage breakdowns with rationale for each channel. Visual outputs like budget allocation charts make it easy to see how spend is distributed and share recommendations with stakeholders. The tool can also generate multiple budget scenarios at different investment levels, each with its own channel allocation and rationale. When a client adjusts the budget or asks to see options, planners have ready-made tiers to work from instead of rebuilding the plan each time.
5. Campaign Plan and Flighting
AI maps out campaign flighting with budget allocation by phase, accounting for seasonal moments, tentpole events, and how different channels should ramp up or down throughout the flight. Channel-specific timing guidance ensures the plan reflects real-world campaign dynamics, not just even budget distribution.
6. Measurement and KPI Framework
The tool builds a measurement framework that maps KPIs to each channel’s role in the funnel, complete with relevant benchmarks. Planners get primary and secondary metrics for each channel, along with the business outcome each metric ladders up to. Having these benchmarks built in saves planners from researching performance standards across channels and gives clients a clear view of how success will be measured from the start.
7. Refinement and Strategy Delivery
The strategy generates within a conversational interface where planners can ask follow-up questions, request deeper analysis on specific sections, or adjust recommendations through natural prompts. The result is a complete, structured strategy that planners can refine and use to build client-ready deliverables. Because Compass lives inside the Basis platform, the strategy and eventual campaign activation share the same system, reducing the manual handoffs and reformatting that typically separate planning from execution.
When orchestrated by agentic AI media planning tools, this entire process can happen in minutes. What traditionally required multiple planning sessions, spreadsheet modeling, and cross-referencing past campaigns now generates automatically, giving agency talent more time to focus on strategic refinement and client-specific nuances.
AI media planning tools deliver several concrete benefits that address some of agencies’ most pressing operational challenges:
The time savings from AI planning tools come from automating specific tasks that eat up planners' days.
Take benchmark research. Manually researching industry benchmarks for CPMs, CTRs, and conversion rates across different channels takes significant time. AI tools have this data built in and automatically apply relevant benchmarks based on campaign parameters.
Budget modeling works similarly. Testing different budget scenarios manually requires rebuilding spreadsheets for each variation. AI tools can generate multiple budget allocation models instantly, letting planners compare approaches without manual calculation work.
Channel analysis is another time sink. Evaluating which channels make sense for a specific audience and campaign goal requires cross-referencing multiple data sources. AI planning tools synthesize this analysis automatically, presenting channel recommendations with supporting rationale.
Then there’s plan documentation—formatting decks, documenting strategic rationale, and creating presentation-ready outputs. AI-powered planning tools produce formatted plans that planners can review and refine rather than building from scratch.
With so many manual tasks wrapped up in drafting media plans, time savings derived from using AI-powered planning tools can add up fast. For instance, teams can create media plans 50% faster when using Compass by Basis, and that time savings can then shift to strategic consultation, client communication, or campaign optimization.
Consistency in media planning creates several advantages for agencies:
Standardized Strategic Approach: AI tools encode best practices into their planning logic. Every plan starts from the same strategic foundation: proven frameworks for audience targeting, channel selection, and budget allocation. This doesn't mean every plan looks identical, but it ensures no planner misses critical strategic considerations.
Quality Baseline for Junior Planners: Junior team members can often struggle without senior guidance. AI tools give them access to senior-level strategic thinking, helping them develop better plans while learning. The tool serves as a training resource that improves plan quality across experience levels.
Reduced Errors: Manual planning risks introducing errors such as calculation mistakes, overlooked channels, and misallocated budgets. AI tools eliminate these mechanical errors, catching issues before plans reach clients. This improves client trust and reduces the costly back-and-forth of fixing mistakes.
Scalable Quality Control: As agencies grow, maintaining consistent plan quality becomes harder. AI tools scale that quality automatically—the hundredth plan generated gets the same strategic rigor as the first.
These consistency benefits matter even more when you consider the tech stack complexity most agencies face. More than one-third (36.8%) of agencies now juggle 10+ tools in their tech stack—up dramatically from 17.3% in 2024—and managing that many disconnected systems can create inconsistency. When AI planning tools integrate into unified platforms where planning connects directly to activation, consistency extends beyond plan creation into execution. The fewer handoffs between systems, the fewer opportunities for plans to get lost in translation.
One concern about AI tools is the "black box" problem, i.e., a lack of visibility or understanding around how the AI reaches its recommendations. But well-designed AI planning tools address this through transparency features, providing rationale into their reasoning as well as ample opportunities for human interaction, iteration, and oversight.
IAB research finds that 51% of brands worry they don't have enough transparency about how agency partners use AI. Transparent AI tools that clearly show their work help address this concern: Agencies can demonstrate their value and provide visibility into their strategic process.
The key difference between manual and AI-powered media planning is how planner time is allocated: manual planning prioritizes mechanics, while AI planning prioritizes strategy.
| Aspect | Manual Media Planning | AI-Powered Media Planning |
| Speed of Drafting Initial Plan | Hours to days per campaign | Minutes per campaign |
| Benchmark Research | Manual lookup across multiple sources | Automatic application of relevant benchmarks |
| Consistency | Varies by planner experience and approach | Standardized strategic framework across all plans |
| Budget Modeling | Manual spreadsheet work for each scenario | Instant generation of multiple allocation models |
| Junior Planner Support | Depends on senior availability for guidance | Built-in access to senior-level strategic thinking |
| Measurement Setup | Research benchmarks and build KPI framework manually | KPI framework with channel-level benchmarks generated automatically |
| Error Rate | Higher risk of calculation and oversight errors | Reduced mechanical errors |
| Time Allocation | More time on mechanics, less on strategy | More time on strategy, less on mechanics |
| Scalability | Requires adding planners to handle more volume | Same team handles increased planning volume |
| Knowledge Transfer | Lost when team members leave | Captured in the tool |
| Planning-to-Activation Handoff | Manual export, reformatting, and rebuilding in activation platform | Strategy built inside the same platform where campaigns get activated |
Using AI-powered media planning tools doesn’t mean replacing planners. Rather, it allows planners more time to scale their work effectively and efficiently, while simultaneously providing them with more time to focus on the deep, strategic work best completed by humans. Manual planning forces planners to focus on mechanical tasks. AI planning shifts that time to strategic consultation, creative collaboration, and client relationship building.
This shift matters because 54.0% of agencies report more strained client relationships compared to two years ago. When planners spend less time on administrative work, they have more capacity for the client-facing strategic work that strengthens relationships.
AI-powered media planning tools are best suited for agencies and brands managing planning complexity, scale, or constrained resources.
Agencies managing multiple clients across various industries handle significant planning volume. AI tools help these agencies scale planning operations without proportionally scaling headcount, improving profitability while maintaining quality. They're particularly valuable when agencies need to pitch new business quickly or accommodate compressed timelines.
Organizations adding junior planners benefit from AI tools that give newer team members strategic scaffolding. Instead of requiring constant senior oversight, junior planners can produce quality work more independently while learning planning fundamentals.
Any agency where inefficient processes or disconnected systems create operational friction will benefit from AI planning tools. Given that 48.9% of agency leaders cite inefficient processes as their top challenge, this includes a significant portion of the industry.
AI planning tools deliver the most value when integrated into platforms where planning connects directly to activation. When the same system that generates the plan also executes it, data flows seamlessly—no manual transfers, no disconnected spreadsheets, no reconciliation work. This integration addresses the silos/disconnected systems problem that 40.4% of agencies identify as a major challenge.
The ideal scenario combines AI-powered planning with all-channel activation capabilities and AI that extends across the entire media buying process, from brief to activation to optimization. When these capabilities exist within a single platform rather than requiring multiple point solutions, agencies avoid the tech stack bloat that creates new inefficiencies (or accentuates existing ones).
Agencies looking to adopt AI-powered media planning tools should follow a structured approach:
Document how much time planning currently takes and where bottlenecks exist. Identify which parts of the planning process consume the most time and which would benefit most from automation. This assessment creates a baseline for measuring improvement.
Don't add another disconnected tool to an already complex tech stack. Look for AI planning capabilities that integrate with existing systems or exist within unified platforms. The planning tool should connect seamlessly to wherever campaigns get activated—whether that's programmatic buying, publisher-direct placements, or search and social platforms (or ideally, a platform that combines all of these in one).
Test AI planning on a small set of campaigns before rolling out across all clients. Choose campaigns that represent typical planning challenges, such as finding the right mix of channels, moderating complexity, and meeting realistic timelines. This pilot phase helps teams learn the tool and build confidence before scaling.
Invest in training so planners understand what the tool can do and how to refine its outputs effectively. Focus on explaining the logic behind recommendations so planners can make informed decisions about when to accept, modify, or override AI suggestions.
Create clear workflows for how AI-generated plans get reviewed and approved. Define who validates outputs, what criteria determine plan quality, and how feedback gets incorporated to improve future plans. This process maintains quality control while scaling efficiency.
Track metrics that matter: planning time per campaign, error rates, client feedback on plan quality, and planner satisfaction. These measurements justify the investment and identify areas for continued optimization.
Once the pilot proves successful, expand AI planning to additional teams and client accounts. Gradual rollout allows for learning and refinement without disrupting operations.
The investment priority is clear in the data: 77.7% of agency leaders plan to increase AI investment in the next 12 months, with automation tools tied for the second priority at 44.7%. Agencies moving quickly on AI planning implementation gain competitive advantage while others wait.
Basis Compass is purpose-built to solve the operational challenges agencies cite most: inefficient processes and disconnected systems. Here’s what sets it apart:
Compass gives agency employees back the time they need to do the strategic and creative work that clients are seeking, while automating the spreadsheet juggling that drains valuable hours from every week.
The shift to AI-powered media planning represents an opportunity to amplify human judgment, while reducing the manual tasks that slow teams down. These tools handle the mechanical work that drains time and creates inconsistency, giving planners capacity to focus on strategy, creativity, and client relationships. For agencies facing increasing complexity, tighter timelines, and pressure to do more with the same resources, AI planning tools offer a practical path forward.
The agencies that integrate these capabilities thoughtfully, particularly within unified platforms that connect planning directly to activation, will differentiate themselves through both efficiency and quality. They'll respond to briefs faster, produce more consistent work, and give planners more time for the strategic thinking that clients value most.
Wheelhouse DMG leveraged Basis’ tech and programmatic expertise to increase ad spend by 41% and deliver privacy-friendly performance for a leading healthcare brand.
Wheelhouse DMG is a performance marketing agency built for highly regulated industries, with deep expertise in medical device and healthcare.
Wheelhouse DMG was engaged by an industry-leading healthcare tech client to promote a specialized medical device to a highly specific US healthcare audience of emergency medicine physicians, physicians and doctors' offices, medical doctors, and hospitals and medical offices.
To do so, they turned to Basis.
Prior to the campaign’s launch, Basis’ Programmatic Strategy team audited the 2024 setup and provided recommendations for scaling success in 2025.
This January–November 2025 programmatic campaign with the goals of driving:
Through the partnership and combined efforts between Wheelhouse DMG and Basis, programmatic is consistently one of their top performing channels.
The Results:
Key Takeaways:
It seems to happen every year, like clockwork: There’s a damning new report on ad fraud, MFA site proliferation, or brand safety failures, and suddenly everyone wants to talk about supply path optimization (SPO). But then, of course, the fervor dies down, the urgency fades away, and SPO moves from “front of mind” to “on the backburner.”
But the truth about SPO is that failing to sustain it comes with a price tag in the form of wasted spend, degraded inventory, and performance left on the table.
Understanding these stakes requires clarity around what SPO seeks to accomplish. At its core, SPO is about answering a deceptively simple question: What’s the best path for an ad dollar to travel between a brand and its target audience, and how much value gets lost along the way? The answer has significant implications for efficiency, quality, and business outcomes.
The benefits of getting SPO right are striking: In Q4 2025, advertisers implementing rigorous quality governance directed 56.7% of their programmatic spend into impressions that were viewable, measurable, and fraud- and MFA-free, while lower-performing advertisers directed just 37.5%.
That performance gap adds up. Across the industry, an estimated $21.6 billion in programmatic spend is lost to supply chain inefficiency every year. For advertisers serious about recapturing that value, SPO must be treated as a non-negotiable strategic priority rather than a periodic rallying cry.
Programmatic advertising involves more intermediaries than most advertisers account for. Before an impression reaches a publisher's ad server, it may pass through a chain of buying platforms, selling platforms, exchanges, and resellers, each of which takes a cut, slows delivery, and adds another potential point where things can go wrong.
Reducing the number of hops in that chain means that more of each dollar functions as working media rather than disappearing into intermediary fees. Because each additional intermediary introduces another opportunity for arbitrage and low-quality inventory to slip through, shorter paths also reduce opportunities for fraud or invalid traffic.
In recent years, several converging forces have come together to make SPO increasingly consequential:
The payoff for managing that complexity well is measurable. Even as overall programmatic market performance softened in Q4 2025, advertisers who optimized their supply chains around viewable, fraud-free, and MFA-free impressions—rather than CPM alone—still made quarter-over-quarter gains. Even more, those advertisers saw cost per conversion drop by nearly 40%, even as their CPMs went up.
That data makes the case for SPO clear. Disciplined supply path management drives stronger ROAS, lower cost per conversion, and higher quality inventory. As such, brands that treat SPO as a standing priority will continue to pull ahead of competitors who only prioritize it periodically.
While advertisers agree that the industry needs more transparency, how teams frame SPO internally shapes the effectiveness of their efforts. Teams that treat SPO primarily as a cost-cutting exercise often end up optimizing for the lowest CPM rather than the best outcome. That approach tends to push spend toward lower-quality inventory, undermine publisher relationships, and produce results that look efficient on paper but underperform in practice.
The most successful advertisers flip that logic. They evaluate each supply path by what it delivers—verified inventory, real audience reach, measurable conversions—and route spend accordingly, even when the cleaner path costs more per impression.
Research from the 614 Group identified eight questions advertisers can use to evaluate and compare platforms on their SPO capabilities, giving buyers a concrete way to make more informed investment decisions. Together, they form a practical test of whether a platform is meaningfully equipped to support SPO as a core business discipline.
Before committing to, or continuing with, an advertising platform, teams should pressure-test its SPO capabilities against these questions:
A platform's ability to answer these questions clearly is a direct signal of its SPO maturity. Vague or incomplete answers usually mean the visibility isn’t there.
In general, advertisers should prioritize platforms that offer neutral buy-side transparency, or the ability to see clearly across the supply chain without the platform's own conflicts coloring the picture. Strong platforms will offer visibility across multiple SSPs, publisher-level reporting, deal-type comparisons, and brand safety controls built into the buying process rather than layered on afterward. Platforms without a stake in the sell side are better positioned to give buyers an objective view of the full supply chain—including retail media networks, where conflicts of interest are especially common and supply path norms are still being established.
Ultimately, advertisers who approach SPO with the right framing, the right questions, and the right partners will be best positioned to use it as a real business advantage.
SPO will keep resurfacing in industry conversations. Each new report on ad fraud, MFA inventory, and brand safety will bring another wave of urgency—and another round of advertisers scrambling to respond. Those who only move when the headlines do will continue to lose out on the value rigorous SPO can provide.
The advertisers pulling ahead are asking harder questions, demanding more from their platforms, and managing their supply paths with the same rigor they bring to audience targeting or creative performance. That rigor is what turns SPO from a recurring industry fire drill into a sustained competitive advantage.
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Agencies today manage campaigns across an average of eight or more separate tools for planning, buying, reporting, and billing. Each tool creates its own data silo, its own login, its own reporting format, and its own set of manual handoffs that slow campaigns down and introduce error. That fragmentation is a growing competitive liability at a time when global ad spend is projected to surpass $1 trillion and agency teams are under pressure to do more with fewer resources.
An AI advertising platform is specialized software that uses machine learning, predictive analytics, and automation to plan, execute, and optimize digital ad campaigns with minimal manual intervention. Unlike basic automation tools that follow static rules, a true AI advertising platform continuously learns from campaign data—adjusting bids, reallocating budgets, refining audience targeting, and testing creative in real time. The defining characteristic is adaptive intelligence: the system improves over time without requiring a human to manually update its logic.
The category has matured. What separates the leading platforms in 2026 is not whether they use AI, but how deeply AI is integrated across the full campaign lifecycle, and whether that integration helps agencies consolidate fragmented workflows or simply adds another tool to the stack.
The core difference between an AI advertising platform and a traditional demand-side platform (DSP) is the degree of autonomous decision-making. A traditional DSP executes programmatic media buys based on rules and parameters set by a human operator. An AI advertising platform layers predictive models and real-time optimization on top of that execution, making campaign adjustments that would be impossible for a human to perform at the same speed or scale.
Instead of waiting for a buyer to analyze yesterday's data and adjust bids manually, an AI platform processes live signals—shifting spend toward higher-performing placements, pausing underperforming creative, and expanding into audience segments the model identifies as high-probability converters.
| Capability | Traditional DSP | AI Advertising Platform |
|---|---|---|
| Bid optimization | Rule-based, manually adjusted | Real-time, model-driven, self-adjusting |
| Audience targeting | Predefined segments set by buyer | Dynamic segmentation with predictive modeling |
| Creative management | Manual A/B testing | Automated multivariate testing and generation |
| Budget allocation | Set at campaign launch, periodically reviewed | Continuously reallocated based on live performance |
| Cross-channel coordination | Typically siloed by channel | Unified optimization across channels |
| Reporting | Retrospective dashboards | Predictive insights with recommended actions |
For agency teams running campaigns through a legacy DSP, the need now is to evaluate whether a platform can integrate AI into existing workflows without creating disruption—and whether it can extend that intelligence beyond a single channel.
The most reliable way to evaluate AI advertising platforms is to assess them across five dimensions: automation depth, real-time optimization, cross-channel integration, creative testing, and provable ROAS impact.
Automation depth refers to how much of the campaign workflow the platform handles without manual input. Can it autonomously launch campaigns, adjust targeting, and reallocate budgets? Or does it surface recommendations that a human still needs to act on? 77.7% of agency leaders plan to increase their AI investment in the next 12 months—but the gap between investing in AI and operationalizing it remains wide. Platforms that automate end-to-end workflows, and not just individual tasks, close that gap fastest.
Real-time bid optimization is the engine behind campaign efficiency. Platforms that adjust bids in milliseconds based on live auction data, audience behavior, and conversion probability consistently outperform those relying on hourly or daily batch updates. When evaluating vendors, ask how frequently their models retrain and how granular their bid adjustments are.
Cross-channel integration determines whether you can manage programmatic, search, social, CTV, and direct buys from a single platform. While 86% of marketers say cross-channel orchestration is important, only 10% report having fully unified ad tech systems in place. That gap—between the ambition for unified media buying and the reality of fragmented tools—is where platform selection has the greatest impact.
Creative testing capabilities have become a key differentiator. Platforms that generate creative variations and automatically test them against live audiences accelerate the optimization cycle significantly. Look for platforms that go beyond A/B testing to run multivariate experiments at scale.
Provable ROAS impact is the ultimate measure. Any platform can claim improved performance, but few can provide transparent attribution, clear before-and-after benchmarks, and reporting that you can confidently present to clients. The IAB's AI Transparency and Disclosure Framework, released in January 2026, underscores the growing industry expectation that AI-driven decisions should be explainable—not opaque.
The leading AI advertising platforms span a range of approaches, from full-stack omnichannel solutions to specialized programmatic execution engines. The right choice depends on your agency's operational needs, client portfolio, and the degree of workflow consolidation you need.
| Platform | Primary Strength | AI Capabilities | Channel Coverage | Strongest For |
|---|---|---|---|---|
| Basis | Omnichannel unification | Agentic AI planning (Compass), AI-driven optimization (SmartBid) | Programmatic, search, social, direct, CTV | Agencies needing planning-through-billing in one platform |
| The Trade Desk | Programmatic execution | Kokai AI (deep learning bid optimization) | Programmatic (display, video, CTV, audio, DOOH) | Agencies running large-scale programmatic with full transparency |
| DV360 | Google ecosystem integration | Google AI/ML bidding, audience modeling | Programmatic, YouTube (exclusive), display, video, CTV | Agencies prioritizing YouTube inventory and Google stack integration |
| Amazon DSP | Commerce and shopper data | Purchase-based audience targeting, full-funnel automation | Programmatic, Prime Video, Twitch, Fire TV | Agencies with retail, CPG, and e-commerce clients |
| Mediaocean | Financial infrastructure | AI-driven ad serving (Innovid), orchestration | Planning, billing, reconciliation, ad serving | Large agencies needing financial workflow and ad operations at scale |
| StackAdapt | Accessible multi-channel programmatic | AI-powered optimization, contextual targeting | Programmatic (display, native, CTV, DOOH, audio, in-game) | Mid-sized agencies prioritizing ease of use and pricing transparency |
Basis is an AI-powered advertising platform built specifically for how agencies operate. It consolidates campaign planning, programmatic buying, paid social, search, direct deals, reporting, and billing into a single platform—eliminating the fragmentation that drives up cost and manual effort across agency teams.
What distinguishes Basis from other platforms in this comparison is that it addresses the full campaign lifecycle, not just a single buying channel. Most platforms on this list are programmatic execution engines; Basis connects programmatic with search, social, and direct buys in one interface, with planning through billing unified end to end. Compass, the platform's agentic AI media planning tool, takes a campaign brief and produces a complete, ready-to-activate omnichannel media plan—the first independent platform to connect brief-to-activation across major channels spanning the open web and walled gardens. SmartBid, Basis's AI-driven bidding engine, continuously optimizes bids across programmatic campaigns in real time—adjusting to live auction signals, audience behavior, and conversion probability to improve performance throughout the campaign flight. Agencies that use SmartBid have reported up to 5x improvement in advertising performance.
Basis also partners with Mediaocean on financial workflows, connecting media planning data with downstream billing and reconciliation systems—making it compatible with agencies already using Mediaocean for back-office operations.
Strongest for: Agencies managing complex, multi-channel campaigns that need planning, buying, reporting, and billing unified in one platform.
The Trade Desk is widely regarded as one of the most technically advanced independent DSPs on the market. Its Kokai platform integrates deep learning across every stage of the programmatic buying process, processing millions of ad impression opportunities per second to optimize bid decisions in real time.
Key differentiators include Unified ID 2.0, an open-source identity framework for post-cookie targeting, and access to a massive third-party data marketplace. The Trade Desk has strong CTV positioning, and is a preferred DSP for many premium streaming services.
The Trade Desk is programmatic-only. Agencies using the platform still need separate tools for paid search, paid social, and direct buys, plus additional platforms for billing and reconciliation. User reviews consistently note the platform's complexity, particularly with the Kokai interface, and tech fees can accumulate quickly.
Strongest for: Agencies running large-scale programmatic campaigns that prioritize bidding transparency, open-internet inventory, and advanced identity solutions.
DV360 is Google's enterprise DSP, part of the broader Google Marketing Platform. Its primary competitive advantage is deep integration with Google-owned properties—most notably exclusive access to YouTube inventory, the Google Display Network, and seamless interoperability with Campaign Manager 360 and Google Analytics 4.
The platform connects to over 70 ad exchanges and supports programmatic buying across display, video, CTV, audio, and DOOH. Recent developments include biddable access to NBCUniversal's live sports CTV inventory and expanded premium streaming partnerships. Google's AI and machine learning power the platform's bidding and audience modeling capabilities.
DV360 does not handle paid social, direct media buys, billing, or financial reconciliation. It is a programmatic activation and measurement tool within Google's ecosystem—not a full agency operational platform. Agencies prioritizing platform independence may find the Google-ecosystem dependency limiting.
Strongest for: Agencies that need exclusive YouTube programmatic access and deep Google stack integration for large-scale campaigns.
Amazon DSP is Amazon's demand-side platform for programmatic display, video, and audio advertising on and off Amazon. The core differentiator is exclusive access to Amazon's first-party shopping and streaming data—a reported 300 million+ active customer accounts globally—which powers audience targeting based on actual purchase behavior rather than inferred intent.
The platform provides access to premium inventory including Prime Video, Twitch, Thursday Night Football, and Fire TV, alongside thousands of third-party publishers. Amazon Marketing Cloud offers clean-room analytics for deeper measurement and attribution. Amazon recommends a $10,000 campaign minimum for some self-service formats to generate sufficient data for optimization, and managed-service campaigns require a $50,000 monthly minimum.
Amazon DSP operates as a walled garden: data generated within Amazon's ecosystem stays within it, limiting portability and cross-platform measurement. The platform's strongest value is for retail, CPG, and e-commerce advertisers. Agencies with diverse client portfolios spanning non-commerce verticals will find the core data advantage less relevant. Amazon DSP does not handle search (outside Amazon's own sponsored ads), paid social, media planning workflows, billing, or financial reconciliation.
Strongest for: Agencies with retail, CPG, and e-commerce clients who need purchase-based audience targeting and premium streaming inventory.
Mediaocean is one of the advertising industry's foundational financial and workflow platforms, processing over $200 billion in annualized ad spend across more than 100,000 users globally. Its product suite includes Prisma (the industry-standard system of record for media management and finance), Innovid (ad serving and measurement), Flashtalking (dynamic creative optimization), and Protected (brand safety and ad verification).
Mediaocean's own 2026 Advertising Outlook Report acknowledged the orchestration problem directly: only 10% of marketers say their ad tech stacks are fully connected across channels, with 42% citing data quality issues and 41% citing difficulty connecting AI insights across systems as barriers to scaling AI effectively.
Mediaocean's strength is financial infrastructure and ad serving—not campaign activation, optimization, or performance buying. The product portfolio is assembled through acquisitions rather than built as a natively unified system, which can create integration gaps. For agencies that use Mediaocean for billing and finance, Basis is a good fit to serve as the execution engine that sits in front of it. For agencies that do not need holding-company-scale financial infrastructure, Basis can serve as the unified platform for both execution and back-office operations.
Strongest for: Large agencies and holding companies that need financial workflow infrastructure, ad serving, and billing at scale.
StackAdapt is a self-serve DSP with programmatic capabilities across CTV, DOOH, display, native, audio, and in-game.
StackAdapt is programmatic-focused and does not offer search or social campaign management within the platform. It lacks the full agency workflow layer—billing, reconciliation, financial operations—that agencies managing multiple clients need. The DSP is a strong execution tool, but agencies using StackAdapt still need additional tools for non-programmatic channels and back-office operations.
Strongest for: Mid-sized agencies that prioritize ease of use, pricing transparency, and strong support for programmatic campaigns.
The platforms that deliver the greatest value for agencies share three characteristics: they reduce tool count, they connect data across channels, and they embed AI into operational workflows rather than bolting it on as an add-on feature.
A recent report found that 87% of agency professionals believe the traditional agency model is either broken or will need to fundamentally change within three to five years. Inefficient processes were the top challenge agencies reported, ahead of rising costs and shrinking margins. That finding tracks with what Dentsu's global forecast describes as the arrival of the "algorithmic era"—a market where 71.6% of ad spend is projected to be algorithm-driven by 2026, rising to 76% by 2028.
For agencies, this means the operational cost of fragmentation—aka the time spent reconciling data across platforms, the errors introduced by manual handoffs, the inability to optimize holistically across channels—is now a strategic vulnerability. The agencies building the cleanest, most unified data infrastructure today are the ones positioning themselves to compete effectively as agentic AI reshapes how campaigns are planned, bought, and optimized.
The key to adopting the right platform for your agency is understanding and appreciating which platform's strengths align with your agency's operational reality, and which gaps in your current stack are costing you the most.
Measuring ROI from an AI advertising platform requires tracking both direct performance improvements and operational efficiency gains. The most meaningful metrics are ROAS lift, cost-per-acquisition reduction, time saved on manual optimization, and speed to campaign launch.
For direct performance, compare ROAS, CPA, and conversion rates before and after implementation. Control for external variables—seasonality, budget changes, audience shifts—to isolate the platform's impact. Platforms that provide built-in benchmarking and before-and-after reporting make this significantly easier.
Operational efficiency is the metric that often gets overlooked but delivers substantial value. If an AI platform reduces the time your team spends on manual bid adjustments, campaign setup, and reporting by several hours per week, that time can be redirected toward strategy, creative development, and client management. For agencies managing dozens of accounts, this efficiency gain compounds fast.
The platforms that deliver the clearest ROI combine AI-driven automation with transparent reporting—showing not just what changed, but why the AI made the decisions it did. Opacity in optimization logic may deliver short-term results, but it makes it difficult to justify continued investment or troubleshoot performance dips.
An AI advertising platform is software that uses machine learning and automation to plan, execute, and optimize digital ad campaigns with minimal manual intervention. Unlike traditional tools that rely on static rules, these platforms continuously learn from campaign data to adjust bids, reallocate budgets, refine targeting, and test creative in real time. The defining characteristic is adaptive intelligence—the platform improves its own performance over time without requiring manual updates.
The best platform depends on the agency's operational needs. Basis is purpose-built for agencies managing campaigns across multiple channels and clients, with planning through billing unified in one platform. The Trade Desk is a leading independent programmatic DSP. DV360 offers exclusive YouTube inventory access. Amazon DSP provides unique commerce data for retail-focused clients. The right choice depends on channel mix, client portfolio, and how much workflow consolidation the agency needs.
A traditional DSP executes programmatic buys based on rules set by a human operator. An AI advertising platform autonomously optimizes those decisions using machine learning and real-time data—adjusting bids in milliseconds, dynamically reallocating budgets, and predicting which audience segments will convert. AI platforms augment the media buyer's capabilities rather than simply executing their instructions.
Evaluate platforms across five criteria: automation depth (how much workflow the platform handles end-to-end), real-time bid optimization (how fast and granular the models are), cross-channel integration (whether the platform consolidates or fragments your tool stack), creative testing (automated multivariate testing at scale), and provable ROAS impact (transparent attribution and before-and-after benchmarks).
Compass is Basis's agentic AI media planning tool. It takes a campaign brief and produces a complete, customizable, ready-to-activate omnichannel media plan spanning programmatic, direct, paid search, and paid social. Compass uses Basis' proprietary IMPACT planning framework to synthesize brief inputs into strategy recommendations, audience segments, channel mix allocations, and budget plans—reducing planning time from hours to minutes.
No. AI advertising platforms automate repetitive, data-intensive tasks like bid adjustments, budget reallocation, and performance monitoring, freeing media buyers to focus on strategy, client relationships, and creative direction. The most effective agency teams use AI to handle execution at scale while humans provide strategic judgment and contextual understanding.
Track both direct performance improvements (ROAS lift, CPA reduction, conversion rate increases) and operational efficiency gains (time saved on manual optimization, faster campaign launch, reduced reporting overhead). Control for external variables to isolate the platform's impact, and prioritize platforms that provide transparent, explainable reporting on how AI decisions were made.
Only 10%. While 86% of marketers say cross-channel orchestration is important, the vast majority still operate with partially unified or fully fragmented systems—creating friction in scaling AI and coordinating campaigns across channels.
The Trade Desk is a leading independent programmatic DSP focused on open-internet inventory, advanced identity solutions, and AI-driven bid optimization. Basis is an omnichannel advertising platform that handles programmatic, search, social, direct, and CTV in one platform—with planning, buying, reporting, and billing connected end to end. Agencies using The Trade Desk still need separate tools for non-programmatic channels and back-office operations; Basis consolidates those workflows into a single system.
Connected TV ad spending in the US is projected to reach $37.95 billion in 2026. Programmatic CTV will account for more than 93% of that. As budgets grow, the connected TV platform that an agency selects can have significant downstream effects on everything from day-to-day efficiency, to campaign performance, to client confidence.
Agencies evaluating CTV platforms face several structural challenges: The channel is highly fragmented across dozens of streaming apps and publishers. Ad fraud is growing, with 57% of marketers who advertise on CTV now worrying that a significant portion of their spend is wasted due to fraud. And attribution remains difficult—especially when CTV drives awareness, but conversions occur later on different devices, often outside traditional click-based measurement frameworks.
When evaluating CTV platforms, agencies should look for premium inventory access within trusted streaming environments, AI-powered contextual targeting, multi-layered fraud prevention, comprehensive measurement that proves business impact, unified workflow integration, and strategic partnership that extends beyond a transactional vendor relationship.
Key Takeaways
When it comes to CTV inventory, quality matters more than raw reach alone. Agencies should evaluate platforms on premium publisher partnerships, household reach benchmarks, and controls that limit exposure to low-quality inventory.
A reliable CTV platform should maintain direct relationships with major streaming providers such as Hulu, ESPN, Roku, Disney+, and Amazon Fire TV. These relationships signal that the platform has passed publisher vetting and can access premium, ad-supported inventory. Additionally, access to supply-side platforms like FreeWheel provides programmatic access to broadcast and cable content through CTV devices.
Additionally, platforms that consolidate CTV inventory access within a broader omnichannel buying interface reduce the need for agencies to manage separate tools for each channel.
CTV platforms should also offer both open exchange inventory for scale and private marketplace (PMP) deals for quality control. Access to such inventorygives agencies flexibility to curate inventory lists per client, and it helps avoid made-for-advertising apps that lead to substantial wasted ad spending.
And, of course, programmatic guaranteed deals offer another option, combining traditional TV reach with programmatic targeting precision and more flexibility than upfront commitments.
A competitive CTV platform should provide at least 1,000+ targeting parameters, including device-specific targeting, demographic segmentation, behavioral data, geographic precision, and content category targeting.
Platforms should offer granular content-level reporting beyond app names. For instance, for sports inventory, agencies need the specific sport, teams, and location rather than generic "Sports" category. This enables tactical optimization and demonstrates brand-suitable placements to clients.
Agencies should also look for platforms that support the latest targeting capabilities. Take CTV contextual targeting: Historically, metadata was limited to broad categories like "Sports." But AI can now identify specific topics within shows, visual scenes, sentiment, and contextual relevance. This matters for performance—consumers pay nearly 4x more attention to contextually relevant CTV ads. AI-powered contextual ads delivered 300% higher aided brand recall and 2x unaided brand recall versus demographic targeting. CTV targeting solutions like IRIS.TV analyze content frame-by-frame to create contextual segments impossible through manual categorization. And platforms like Basis integrate IRIS.TV directly into their buying workflow, letting agencies activate contextual CTV segments without toggling between separate tools.
Finally, a strong CTV advertising platform will support first-party data activation and cross-device targeting. With privacy regulations tightening, contextual targeting based on content category, broadcast type, and device offers privacy-compliant alternatives to individual tracking.
CTV ad fraud is on the rise. In Q3 2025, 18% of programmatic CTV traffic in the US was invalid. In other words, nearly one in five “viewers” might have actually been a bot binge-watching your ads. The right platform prevents fraud through multiple protection layers:
Competitive CTV advertising platforms should track at least 80+ metrics across performance dimensions including video completion rate (VCR), reach and frequency, impressions delivered, cost per completed view (CPCV), and tactical performance breakdowns by app, device, and content category.
CTV ads consistently deliver high engagement. Completion rates approach 98%, with attention rates exceeding 50%, outperforming many other digital video formats. Because CTV inventory is inherently full-screen and viewable, agencies can focus measurement efforts on deeper performance indicators like completion rate by content category, frequency distribution, and cost per completed view.
Beyond baseline metrics, agencies need outcome-oriented measurement, including incrementality studies, brand lift analysis, and sentiment tracking. QR codes and cross-device signals help connect CTV exposure to downstream actions on mobile and desktop, filling common attribution gaps.
Platforms should also provide real-time performance dashboards, not just end-of-campaign reports. Automated reporting reduces manual work compiling data from multiple sources. Platforms that generate cross-channel reports from a single interface save agencies the most time here.
Last-click attribution models undervalue CTV because viewers typically see an ad on one screen and convert later on another device. Advanced platforms solve this through log-level data and IP-to-impression matching, connecting CTV exposure at household level to subsequent conversions.
This approach links CTV impressions to household IP addresses. When conversions occur later on other devices within the same household, those actions can be attributed back to CTV exposure. This requires detailed impression logs and timestamped conversion data, not modeled estimates alone.
The strongest approaches integrate CTV data with client CRM systems, tracking the full journey from exposure through conversions. Platforms should support multiple attribution models—such as linear, time-decay, and position-based—and not just last-click attribution.
Additionally, incrementality studies can measure what conversions wouldn't have happened without CTV exposure using holdout groups. This proves actual impact rather than correlation.
Then, to bring everything together visually, real-time dashboard access enables mid-campaign optimization. For instance, if attribution shows CTV driving strong assisted conversions in specific markets, then agencies can shift budgets toward those geos immediately.
Strong CTV results come from pairing platform capability with partnership: the technical infrastructure to close attribution gaps, combined with the research and specialist support that turn measurement into proven business impact.
CloudControlMedia, a performance-based digital marketing agency specializing in higher education, needed to prove CTV could drive conversions and close attribution gaps. Their clients had historically relied on lower-funnel tactics, making upper-funnel CTV investment a harder internal sell.
CloudControlMedia partnered with Basis to launch campaigns for Abilene Christian University. Basis provided research and proposal support to pitch brand awareness campaigns confidently. Log-level data enabled IP-to-impression matching that tied CTV exposure to ACU's CRM data, closing the attribution loop. Basis acted as an extension of the CloudControlMedia team, connecting them to subject matter experts.
Results included:
CloudControlMedia cited Basis as a responsive research partner that extended their internal capabilities. Without log-level data and CRM integration, these conversion lifts would have remained invisible, limiting CTV investment despite measurable enrollment impact.
This partnership illustrates what agencies should evaluate beyond platform features: whether the vendor provides research support, pitch-ready materials, and access to channel specialists who accelerate time to value.
CTV fragmentation often forces agencies to manually compile data across multiple tools, turning media planners into spreadsheet archaeologists and increasing reporting time and operational fatigue.
Unified, all-channel activation platforms eliminate inefficiencies by managing CTV alongside other channels in a single interface. Doing so provides a wide range of benefits, including unified reporting, streamlined workflows, automated reconciliation, cross-channel optimization, and centralized asset management via shared document storage capabilities.
Competitive platforms should provide a wide breadth of API integrations spanning ad servers (ex. Google Campaign Manager), billing facilitation, search and social platforms (ex. Google Ads, Meta, LinkedIn, TikTok, Snapchat, Reddit, Pinterest), data partners (ex. LiveRamp), inventory sources (ex. DIRECTV, Hulu, ESPN), and verification vendors (ex. DoubleVerify, Peer39, Comscore, Protected by Mediaocean).
When evaluating platforms, agencies should ask how many of their existing tools (ad servers, billing systems, search and social platforms, verification vendors) connect natively. These integrations create automated data flows rather than manual uploads. The fewer manual data transfers required, the lower the operational burden on the team.
Beyond unification, agencies need white-label reporting, transparent fee structures, team collaboration tools, multi-client management, and granular permissioning to support internal teams and client transparency.
For advertisers who are looking for precision, CTV's advantage over linear TV is the ability to optimize in-flight. Platforms should be able to facilitate A/B testing across creative versions, video lengths (:15 seconds, :30 seconds, :60 seconds), and interactive elements. And the best CTV platforms can automatically shift budget toward higher-performing variants as results emerge, rather than waiting for post-campaign analysis.
And you know how frustrating it is when you see the same ad every…single…commercial…break? Blame it on the platform. Look for one that offers granular frequency capping, which prevents ad fatigue. Meanwhile, settings like "no more than two impressions per user per day" balance reach and repetition.
Platforms should be able to use AI to automatically optimize bids based on performance against KPIs, bidding more aggressively on high-performing placements and reducing bids on underperforming segments. Agencies should ask whether a platform’s AI optimization extends beyond CTV to other channels within the same interface, since siloed optimization limits cross-channel budget decisions.
CTV advertising platforms should come with access to ample premium inventory. And when standard inventory doesn't meet needs, agencies should look for platforms that provide custom private marketplace deals directly within the buying interface.
Additional capabilities worth seeking out in a CTV advertising platform include flexible dayparting, real-time geographic budget shifts, high-definition video support up to 4K, interactive overlays and QR codes, and performance-based pacing that accelerates spending when campaigns exceed targets.
Agencies should expect dedicated CTV experts who understand channel-specific nuances like brand safety in streaming environments, creative best practices for large screens, measurement approaches for cross-device journeys, and inventory quality distinctions.
Strong partners provide market research, client pitch support, strategic recommendations, and access to subject matter experts. This turns the platform into a planning partner, not just a buying tool.
Support should include prompt responses for critical issues, designated account contacts, availability during agency working hours, and proactive monitoring. Platforms should also offer comprehensive onboarding, regular training, certification programs, and educational resources.
And partners should conduct regular business reviews, in which they’ll cover performance trends, new features, optimization recommendations, and opportunities to expand successful tactics across clients.
BasisTV+ brings premium CTV inventory, advanced targeting, multi-layered fraud protection, and cross-channel measurement into a single agency-facing workflow, built to scale CTV investment without increasing operational drag. Here’s what that looks like in practice:
Use this framework to assess CTV advertising platforms:
The platform you choose shapes your agency's ability to scale CTV investment without drowning in manual reporting or watching client budgets evaporate to bot traffic. Agencies selecting platforms with premium inventory, advanced targeting, robust fraud prevention, comprehensive measurement, unified workflow management, and strategic partnership will operate more efficiently and prove CTV’s impact to clients with confidence.