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Key Takeaways
Advertisers’ ultimate goal is to drive effectiveness by identifying the optimal media mix for specific campaigns to drive key business outcomes. Equally important, though, is proving out that effectiveness: Connecting media investments to results through compelling, cohesive narratives that build stakeholder confidence and secure continued investment.
Yet media fragmentation creates significant barriers to achieving both goals. When performance signals are scattered across platforms and channels, each with its own reporting methodology, omnichannel campaign evaluation—and the ability to make real-time adjustments—is often painfully slow at best.
This matters enormously: In the pursuit of advertising effectiveness, omnichannel campaign evaluation is the holy grail, empowering teams to both drive results and demonstrate impact. As such, it’s essential for marketing teams to strategize around how to achieve holistic campaign assessment amidst fragmentation. Building a strong marketing measurement strategy—one that combines platform-level attribution with broader methods like marketing mix modeling and incrementality testing—is essential for marketing teams looking to navigate fragmentation successfully. The best approaches include systems and frameworks that allow teams to streamline performance signals across channels and evaluate effectiveness in both the short- and long-term.
Today’s consumers demand advertising experiences that seamlessly span the many digital spaces where they spend time. At the same time, diversifying media spend across channels delivers greater returns in revenue and brand performance.
But as marketers split their efforts across a growing number of platforms and channels, the resulting tech stack sprawl—over half of agency marketers’ stacks consist of eight or more tools, and 40% are using 10 or more—presents a variety of problems. In fact, 45% of agency leaders cite siloed or disconnected systems as a top challenge.
Data fragmentation from these disconnected systems underlies a host of marketing leaders’ biggest pain points. Most fundamentally, manually consolidating data from multiple sources is both time-consuming and error-prone, which prevents the agile and strategic decision making necessary to drive effectiveness. Consequently, only 21% of senior marketers report receiving actionable data in real time.
Fragmentation also curbs teams’ ability to demonstrate the impact of their work, which strains client partnerships for agencies and impacts budgets and stakeholder confidence for brands. Marketing leaders at brands identify connecting marketing activities to revenue outcomes as their most pressing challenge—a particularly urgent issue as CMOs face heightened pressure to prove ROI.
To succeed in this fragmented landscape, marketing teams must develop dedicated strategies for holistically measuring performance across both the short- and long-term.
The baseline for short-term cross-channel measurement is tracking performance on each individual platform and channel. Advertisers must examine each source of truth across their media mix and assess platform-level performance using attribution and KPIs like immediate sales, cost of acquisition, or ROAS.
Because of the lack of interoperability among walled gardens and the open web, advertisers typically won't know if a consumer saw the same ad on, say, both Facebook and Pinterest. While this can result in double-counting conversions, it doesn't hinder platform-level optimization, which remains essential for driving and demonstrating effectiveness.
The disconnection between walled gardens and the open web creates significant limitations, but marketing leaders can help their teams manage the complexity. The key is streamlining performance signals across channels to understand how they work together to drive short-term outcomes. Advertising platforms that automatically aggregate data from multiple sources across walled gardens and the open web—eliminating time-consuming manual work—provide teams with a comprehensive view and a significant competitive advantage.
However, to fully achieve holistic measurement, marketing teams must look beyond immediate, platform-level performance metrics and find ways to take a broader view of their investments.
To truly drive and demonstrate effectiveness, omnichannel campaign evaluation can’t end with short-term strategies. When assessing how successfully your advertising is driving business outcomes, it’s critical to take a long-term perspective.
Assessing long-term effectiveness holistically is all about finding ways to gather every available signal into one place and extract bigger learnings about their impact. To that end, integrated campaigns across the open web and walled gardens can be evaluated over a longer term against key metrics that can’t be properly attributed to a single source, such as brand health, sales growth, and profitability.
Marketing mix modeling (MMM) and experiments are two tools marketing teams can use to assess these bigger metrics.
Marketing mix modeling (MMM), also called media mix modeling, is a statistical approach that uses historical sales and marketing data to measure how each channel and tactic contributes to business outcomes over time. With MMM, advertisers can model how their investments across multiple platforms drive business outcomes over time. For instance, by analyzing historical data, MMM can predict that investing X amount of money across digital marketing contributed to Y% of overall sales, led by Facebook, Google, and programmatic tactics. Increasingly, MMM marketing approaches inform not just past performance reporting but also forward looking decisions about where to invest media spend for the strongest returns.
Experiments—also called incrementality testing—meanwhile, allow advertisers to test hypotheses about long-term effectiveness through controlled tests. In a geo lift study, advertisers can run an omnichannel campaign at different intensities across similar geographic markets and measure the differential impact on sales growth or brand awareness. Market studies can compare regions with varied media strategies to understand which combinations drive better long-term outcomes.
These modes of long-term measurement have often been underused by marketers, although they’re gaining steam as the industry grapples with measurement challenges: Between 2024 and 2025, the share of marketers using experiments to measure effectiveness doubled from 18% to 36%.
However, in 2024, only 2% of marketers were using a combination of MMM, experiments, and attribution to assess advertising effectiveness. The underutilization of this multi-pronged approach to long-term omnichannel campaign evaluation presents a major opportunity for advertisers seeking to both drive and demonstrate advertising effectiveness better than their competitors.
There's no getting around the fact that omnichannel campaign measurement remains a challenge for marketing teams of all sizes. However, precisely because of this challenge, teams who strategize around it more successfully than their peers stand to gain major competitive advantages.
Success depends on streamlining performance signals and implementing diverse strategies for both short-term and long-term measurement. Ultimately, the teams that dedicate the necessary resources to achieving omnichannel campaign evaluation amidst fragmentation will also be the most successful when it comes to driving and demonstrating effectiveness.
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Looking for more insights into how to navigate the biggest challenges and opportunities facing marketers? Check out Rewinding to Fast Forward: The 2026 Digital Advertising Trends Report for a breakdown of four key trends set to define the industry this year.
Key Takeaways:
The best tools for automating media operations are systems that unify campaign planning, activation, optimization, reporting, and reconciliation across programmatic, direct, search, social, and CTV. But while these systems are optimal, many advertisers in 2026 are operating from a far different place, piecing together a stack of single-purpose point solutions.
According to Basis’ 2026 Advertising Agency Report, more than one-third of full-service and media agencies (36.8%) now manage ten or more tools to run their clients’ campaigns—more than double the share that did so in 2024. It’s no surprise, then, that inefficient processes (44.1%) and siloed, disconnected systems (40.4%) top the list of operational challenges that agencies encounter.
Today’s advertisers face a market full of vendors promising automation, and a buying environment where the line between genuine operational lift and AI-flavored marketing copy is increasingly blurry. This guide breaks down what advertising automation can actually deliver in 2026, where marketing promises run ahead of platform reality, and how media teams should evaluate platforms for the next stage of automated advertising: the autonomous era.
Advertising automation lives on a spectrum, and knowing where a platform sits on that spectrum is the first filter for any serious evaluation.
At one end: rule-based automations, such as “if-then” logic that pauses a campaign at a spend cap or fires a pacing alert when delivery falls behind. At the other end: autonomous advertising, wherein AI systems plan, execute, and optimize campaigns end-to-end with humans in an oversight role rather than an execution role.
Most of what the industry markets as “automation” today sits in the middle of that spectrum. Rule-based and semi-automated workflows deliver reliable, measurable time savings—and for many teams, they are an impactful capability a platform can offer.
The teams getting the most out of automation today are the ones evaluating across all four tiers. They also scrutinize every 'autonomous' claim, checking where a platform truly lands on this spectrum rather than where its marketing says it does.
A practical way to categorize where a platform’s automation actually operates:
| Automation Tier | What it does | Maturity in 2026 |
| Rule-based automation | Predefined if-then triggers and actions (e.g., pause at a spend cap, fire off a pacing alert) | Reliable, generally transparent, widely available |
| Semi-automated workflows | Templates, bulk operations, and guided processes that cut manual steps but still need human initiation | Common today |
| Algorithmic optimization | Machine learning adjusts bids, budgets, and targeting within human-set parameters | Maturing, in production |
| Autonomous advertising | AI plans, executes, and optimizes independently, with humans in oversight rather than execution | Emerging, evaluate on a platform-by-platform basis for real capability |
In 2026, four categories of advertising automation are delivering genuine, measurable time savings for media teams right now: automated planning, automated performance, automated measurement, and automated billing. Each of these maps to a stage of the campaign lifecycle where manual work has historically consumed the most hours.
Automated planning: AI-driven media planning translates briefs into omnichannel strategies in minutes, pulls in past performance to sharpen recommendations, and exports presentation-ready plans without the manual build—all within the same platform where campaigns are eventually activated. The 2026 Advertising Agency Report found that only 29.1% of agencies currently use AI for media planning and 22.1% for media buying strategy—among the lowest adoption rates of any AI use case, despite both being among the highest-leverage. AI advertising platforms that handle planning natively, like Compass by Basis, allow teams to create media plans 50% faster.
Automated performance: This is where many teams already feel the impact: algorithmic bid optimization, real-time budget reallocation, and continuous mid-flight optimization that's difficult for human teams to replicate at scale. For instance, Basis’ SmartBid AI optimization tool drives a 36% decrease in cost per acquisition and 35% increase in CTR for clients deploying it across their programmatic campaigns. This is also a tier where 'AI-powered' claims can be easy to test. Rather than asking whether a platform optimizes bids and budgets (since nearly all of them do), ask how much it moved CPA and CTR and how consistently.
Automated measurement: Pulling performance data from a fragmented stack, normalizing metrics across channels, and assembling client-ready reports is one of the most time-intensive jobs in media operations. Unified dashboards that aggregate programmatic, direct, search, social, and CTV data into a single view eliminate hours of weekly spreadsheet work with live reporting teams can actually trust, and they connect into the tools agencies already run on—such as Looker, Datorama, TapClicks, and Ninjacat—rather than forcing a rebuild.
Automated billing: Reconciliation and invoicing remain among the least glamorous, most time-consuming, and most error-prone work in media operations. Spend has to be matched, contracts have to tie out, and invoices can't go out until the numbers agree. Platforms that offer automated billing, like Basis, treat this as a closed loop from first media plan to final invoice, keeping planning, activation, reporting, and reconciliation in one system rather than handing data between disconnected tools and finance teams. With Basis, this results in a 15% average reduction in time to collect. Many 'automation' pitches skip this stage entirely, so it's worth asking a vendor how much of the plan-to-payment cycle actually runs without manual intervention.
Not every automation claim holds up under operational scrutiny (especially when AI is involved), and the gap between marketing and infrastructure is where many evaluations break down. That said, the right kind of skepticism is not anti-AI. Rather, it's a demand for specifics: What the AI actually does, where it operates in the workflow, what data is powering it, and what it measurably changes.
Separating real automation from marketing comes down to recognizing a few recurrent patterns:
“AI-powered” without specifics: The "AI" label has become so broadly applied that it has lost meaning without further elaboration. A platform using a rules engine to automate a workflow is not the same as one using machine learning to optimize in real time, yet both often market themselves as AI-driven. When evaluating platforms, ask what specific model powers each capability, what data it trains on, how often it retrains, and how its recommendations get validated.
Black-box optimization: Some platforms surface AI recommendations that the buyer cannot inspect, override, or audit. That works fine in a demo. It is a serious problem in a campaign where questions like “Why did we spend $10,000 on this placement?” need an answer the team can actually defend. Look for platforms that expose the logic, show the inputs, and let humans intervene at any step.
Optimization biased toward the platform’s own inventory: Walled-garden AI tools—particularly the ones bundled with single-channel ad products—often optimize toward their own inventory by default. When evaluating an omnichannel platform, the question to ask is whether a platform's optimization logic is built to favor inventory it controls or built to favor the marketer's outcome. Some platforms own inventory and bias toward it. Some own inventory and choose not to. Some have no inventory stake at all. Basis, for instance, owns Basis DSP but does not optimize toward it; media decisions in Compass and SmartBid are made against marketer outcomes, not internal inventory preference. That distinction becomes even more important as autonomous systems take on a larger share of budget allocation decisions.
Instant, zero-effort onboarding: Vendors sometimes imply that automation works out of the box with no configuration, training, or workflow adjustment. In practice, meaningful automation requires setup: defining rules, mapping workflows, integrating reliable data sources, and training teams.
Teams often evaluate automation platforms across several key criteria that map to real operational lift: workflow coverage, integration depth, maturity-tier honesty, and human-in-the-loop control:
These criteria typically sort platforms into four broad groups. Where a platform lands shapes both its automation ceiling and whether its optimization works toward the marketer’s outcome or its own inventory:
| Platform type | Best for | Automation ceiling | Risk of inventory bias |
| Unified omnichannel platforms | Unified planning through reconciliation across all channels | Algorithmic optimization across channels, with a foundation built toward autonomous workflows | Depends on platform |
| Legacy DSPs with AI add-ons | Programmatic buying with bolted-on optimization | Algorithmic optimization | Depends on vendor |
| Walled-garden / single-channel tools | Deep automation within one channel | Channel-specific optimization and automation | High (often optimizes toward own inventory) |
| Point solutions | A single workflow (e.g., planning, reporting, or reconciliation) | Task-level automation | Depends on vendor |
Autonomous advertising runs on infrastructure, data, and interconnectivity. The teams ready for the next several years of AI evolution are the ones with the operational layer already in place: a connected platform that automates the campaign lifecycle, AI applied transparently against unified data, and human judgment looped in intentionally.
That is the foundation Basis is built around. Compass for AI-powered planning. SmartBid for performance optimization. Unified dashboards and automated billing reconciliation closing the loop from plan to payment. All inside a single omnichannel platform—connecting programmatic, search, social, site direct, and CTV—with the auditability and oversight media teams need to actually trust the AI underneath it.
What is advertising automation, and how does it work?
Advertising automation is the use of technology—including rule-based logic, algorithmic optimization, and AI-driven decisioning—to streamline tasks across the paid media campaign lifecycle: planning, trafficking, bid optimization, cross-channel reporting, and financial reconciliation. It reduces operational hours, minimizes errors, and frees talent up to focus on strategy.
How is advertising automation different from marketing automation software?
Advertising automation focuses on paid media operations across programmatic, search, social, direct, and CTV. Marketing automation software manages email workflows, lead nurturing, and CRM sequences. The two categories solve different problems and serve different teams within an organization.
Which platforms automate campaign planning and activation?
Platforms that automate planning and activation natively, rather than offering planning as a standalone module, are the ones delivering the most lift today. Look for AI-driven media planning that translates briefs into omnichannel strategies, exports presentation-ready plans, and activates campaigns with one click against live line items. Compass by Basis is one example, and teams that use Compass build media plans 50% faster.
Can advertising automation work across programmatic, search, social, direct, and CTV in one platform?
Yes. The key distinction is whether the platform offers true workflow integration—where data flows automatically between planning, activation, and reporting—or simply provides multi-channel access through separate modules. A unified omnichannel platform like Basis eliminates the manual data transfers and context-switching that fragment cross-channel campaign management.
What advertising tools reduce time spent on campaign reconciliation?
Reconciliation tools that match delivered spend against contracted terms, flag discrepancies automatically, and push actuals into ERP systems eliminate hours of spreadsheet work and reduce billing errors. Platforms like Basis handle reconciliation natively rather than requiring exports to a separate finance system.
What is autonomous advertising, and which vendors lead the category?
Autonomous advertising is AI-driven planning, activation, optimization, and reconciliation across the full campaign lifecycle, with humans in an oversight role. The leaders in the category are omnichannel platforms with AI built natively into the campaign lifecycle, unbiased regardless of inventory ownership, and transparent in how their optimization logic works.
How do I measure whether advertising automation is delivering real ROI?
Track operational metrics before and after implementation: hours spent per week on campaign setup, reporting, and reconciliation; error rates in trafficking and reconciliation; time from campaign brief to activation; campaigns or accounts each team member can manage; reduction in billing discrepancies. Compare those savings against the total cost of the platform.
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 advertising 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.
For agencies buying CTV at scale, BasisTV+ is built to bring clarity and efficiency. It reaches 93% of US smart TV households and unifies CTV with programmatic, search, social, and direct media in a single interface, allowing teams to grow CTV investment without adding tools, manual reporting, or operational drag.
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 advertising 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 ad platforms should also offer both open exchange inventory for scale and private marketplace (PMP) deals for quality control. Access to such inventory gives 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 advertising 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:
The CTV platforms with the most detailed reporting combine real-time dashboards, log-level data, and trackable metrics in a single interface alongside all other digital channels. 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.
The strongest platforms measure CTV alongside programmatic, search, social, and display in a single interface, then connect exposure to conversions across devices using log-level data and IP-to-impression matching. That unified view is what makes cross-channel measurement possible: Rather than evaluating CTV in isolation, agencies see how it works with every other channel to drive outcomes.
This is important because last-click attribution models undervalue CTV, since viewers typically see an ad on one screen and convert later on another device. Advanced platforms solve this by connecting CTV exposure at the household level to subsequent conversions (instead of crediting only the final click) using log-level data and IP-to-impression matching.
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.
What is the best platform for agencies buying CTV at scale?
The best platform for buying CTV at scale combines premium inventory reach with unified workflow management, so agencies can grow investment without adding tools or manual work. BasisTV+ reaches 93% of US smart TV households and manages CTV alongside programmatic, search, social, and direct media in one interface. That consolidation lets agencies scale CTV efficiently while proving its impact to clients.
Which CTV advertising platforms offer the most detailed reporting and analytics?
The most detailed platforms pair real-time dashboards and log-level data with 80+ trackable metrics and white-label, cross-channel reporting. BasisTV+ delivers this with automated reporting, eliminating the manual work of compiling data from multiple sources. This gives agencies the granularity to optimize mid-campaign and report with confidence.
Which trusted CTV platforms integrate with programmatic buying tools?
Trusted CTV platforms integrate natively with programmatic exchanges, DSP functionality, ad servers, billing systems, data partners, and verification vendors. BasisTV+ offers 170+ API integrations, including trusted partners such as DoubleVerify, Peer39, Comscore, Protected by Mediaocean, LiveRamp, and FreeWheel. These native connections create automated data flows instead of manual uploads.
Which platforms offer cross-channel measurement that includes CTV?
Platforms that measure CTV alongside programmatic, search, social, and display in a single interface offer true cross-channel measurement. BasisTV+ connects CTV exposure to conversions across devices using log-level data and IP-to-impression matching, rather than crediting only the last click. This shows how CTV works with every other channel to drive outcomes.
How do I choose the best connected TV advertising platform this year?
Evaluate platforms across key criteria: inventory quality, targeting depth, brand safety, measurement, integration, and support. Prioritize premium inventory access, AI-powered contextual targeting, multi-layered fraud prevention, cross-device attribution, unified workflow integration, and strategic partnership beyond a transactional vendor relationship. The platform you choose shapes your agency's ability to scale CTV without drowning in manual reporting or losing budget to bot traffic.
What CTV advertising platforms offer cross-device retargeting?
Platforms that support cross-device retargeting use household-level identity resolution to re-engage CTV-exposed viewers on their mobile and desktop devices. This turns CTV from a standalone awareness tactic into a connected, full-funnel strategy. BasisTV+ supports cross-device targeting directly within the buying workflow, so agencies can extend CTV exposure into retargeting without a separate tool.
Choosing the right media buying platform is one of the most consequential operational decisions an advertising agency can make. The wrong choice fragments your workflow, inflates overhead, and limits your ability to scale. But the right one can centralize planning, execution, reporting, and billing, so your team spends less time managing tools and more time delivering results.
The stakes are real. According to Basis' 2026 Advertising Agency Report, 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. Inefficient processes and siloed systems are the top operational challenges agencies face. The platform you choose either adds to that burden or helps eliminate it.
Below is a comparative overview of five leading platforms shaping how agencies buy media today.
| Platform | Core Strength | Best For |
|---|---|---|
| Basis | Unified end-to-end automation across programmatic, social, search, and direct | Agencies seeking full workflow consolidation |
| The Trade Desk | Enterprise-grade programmatic scale and transparency | Large-budget, tech-savvy programmatic teams |
| Google Marketing Platform | Deep attribution and Google ecosystem integration | Google-centric measurement and analytics |
| StackAdapt | Self-serve programmatic with strong usability and multi-channel reach | Mid-sized agencies prioritizing ease of use and flexibility |
| Amazon DSP | Purchase-intent targeting via proprietary shopping data | E-commerce-focused clients with high spend |
A media buying platform is specialized software that enables agencies to plan, activate, and measure digital ad campaigns across multiple channels, centralizing workflow, data, and financial processes within a single system.
A demand-side platform (DSP), meanwhile, is a software system that enables buyers to purchase digital ad inventory in real time across multiple exchanges, using automated bidding and data-driven targeting.
The best platforms do more than execute buys. They connect every stage of the campaign lifecycle—from initial planning and audience targeting through activation, optimization, reporting, and financial reconciliation—in one environment. That end-to-end connectivity is what separates a true agency operating platform from a point solution that handles only one part of the workflow.
For agencies evaluating their options, it's important to go beyond merely counting up the number of features that a platform provides, and to thoughtfully consider which platform eliminates the most friction across your full operation. The platforms below represent the leading options in the market today, evaluated across channel coverage, workflow depth, pricing accessibility, and fit for agency use cases.
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 tool fragmentation that drives up operational cost and increases manual effort across agency teams.
Here's how a typical agency campaign flows through Basis:
Basis' partnership with Mediaocean extends its financial workflow capabilities, connecting media planning data with downstream billing and reconciliation systems—reducing the manual handoffs that typically slow campaign closes. For agencies that use Mediaocean for billing and finance, Basis functions as the execution engine that sits in front of it.
The platform's AI optimization capabilities have demonstrated measurable performance gains, with some agencies reporting up to a 5x improvement in advertising performance. That combination of operational efficiency and performance outcomes is what distinguishes Basis from point solutions that address only part of the campaign lifecycle.
Basis also has a leading independent DSP that is built into the platform, extensive partnerships with data and inventory providers, and an array of integrations with leading publishers and platforms including ad servers (Google Campaign Manager), billing systems (Advantage and Freewheel), and search and social APIs (Google Ads, Bing, Meta, LinkedIn, TikTok, Snapchat, Reddit, Pinterest).
Lastly, Basis has an award-winning customer service team that is known to partner closely with users to ensure their success across onboarding, education, and campaign execution.
Basis is strongest for: Full-service and media agencies that need one platform to handle every stage of the campaign lifecycle, from planning through billing.
The Trade Desk has built a strong reputation for enterprise-grade programmatic buying. Its bidding capabilities, supply-path transparency, and access to connected TV inventory make it a credible choice for sophisticated, large-scale programmatic programs.
That sophistication comes with real requirements. The platform carries a steep learning curve, and significant monthly minimums make it best suited to agencies with dedicated programmatic expertise and clients with substantial media budgets. The Trade Desk is also a programmatic-only platform. It does not handle paid search, paid social, or direct media buys, which means agencies still need separate tools for non-programmatic channels and a separate system for billing and reconciliation.
Basis vs. The Trade Desk — a quick comparison:
| Capability | Basis | The Trade Desk |
|---|---|---|
| Programmatic buying | ✓ | ✓ |
| Direct media buys | ✓ | ✗ |
| Paid social integration | ✓ | ✗ |
| Paid search integration | ✓ | ✗ |
| Billing & reconciliation | ✓ | ✗ |
| Monthly minimum | Lower threshold | ~$10K+ |
| Technical complexity | Moderate | High |
| CTV access | ✓ | ✓ |
The Trade Desk is strongest for: Large enterprise agencies running high-volume programmatic programs with dedicated ad tech resources and clients whose media mix is weighted toward programmatic channels.
Google Marketing Platform (GMP)—which includes Campaign Manager 360 and Display & Video 360—offers detailed attribution, analytics, and tight integration with Google's ad ecosystem. For advertisers running Google-heavy campaigns, its measurement capabilities are hard to match.
Attribution in digital advertising is the process of crediting conversions or business outcomes to specific touchpoints across a campaign, enabling accurate measurement of performance and ROI. GMP's attribution tools are among the most mature in the market, particularly for campaigns running across Google Ads, YouTube, and the Google Display Network.
But the tradeoffs are significant. GMP is not available as a self-serve product, and access requires a Google Marketing Platform contract, with practical minimum spend thresholds around $50,000 or more per month. Setup is complex, technical requirements are substantial, and the platform's utility diminishes quickly outside of Google-owned inventory. Direct deals and non-Google media channels are not its strength. For agencies whose clients require omnichannel reach beyond Google's ecosystem, GMP addresses only a portion of the buying workflow.
Some agencies use DV360 as a standalone programmatic buying tool rather than as part of the full Google Marketing Platform suite. Even in that configuration, DV360 addresses only the programmatic activation layer. Agencies running it alongside separate tools for paid search, paid social, and direct buys are still managing fragmented data pipelines, manual reporting aggregation, and disconnected billing processes. The programmatic capability is real, but it comes at the expense of operational consolidation.
Google Marketing Platform is strongest for: Performance advertisers managing Google-heavy campaigns that require granular attribution, particularly teams with in-house analytics expertise already operating within the Google stack.
StackAdapt is a self-serve programmatic DSP with a reputation for usability, onboarding support, and pricing flexibility. StackAdapt has earned recognition for making programmatic buying accessible across CTV, display, video, native, audio, DOOH, and in-game advertising.
StackAdapt's DSP is known for its low minimum spend requirements and its strong customer support infrastructure. Recent additions include integrated email marketing and a data hub for first-party data activation, signaling an expansion toward the intersection of adtech and martech. That said, StackAdapt remains a programmatic execution platform. It does not offer search or social campaign management, and it lacks the agency workflow layer—billing, reconciliation, financial operations—that agencies managing multiple clients at scale require.
StackAdapt is strongest for: Mid-sized agencies prioritizing programmatic execution, ease of use, and flexible pricing, particularly those whose client base is concentrated on the open web.
Amazon DSP gives agencies access to something few platforms can replicate: targeting built on Amazon's proprietary shopping data. Purchase-intent signals derived from Amazon's retail ecosystem—a reported 300 million+ active customer accounts globally—offer uniquely powerful audience targeting for e-commerce-focused clients, based on actual purchase behavior rather than inferred intent.
Amazon DSP provides access to premium inventory both on and off Amazon properties, including Prime Video, Twitch, Thursday Night Football, and Fire TV. The tradeoff is cost and scope. Self-service access carries no hard minimum spend requirement, giving agencies flexibility to right-size budgets based on each client's objectives. Amazon recommends a $10,000 campaign minimum for some self-service formats to generate sufficient data for optimization. Managed service, run by Amazon's team, requires a minimum client commitment of $50,000 USD per month. Agencies with clients outside those verticals—or whose media mix extends beyond Amazon's ecosystem—will find limited applicability.
Amazon DSP does not handle paid search, paid social, direct buys, media planning workflows, billing, or financial reconciliation. For agencies managing diverse client portfolios, it addresses one channel within the buying workflow, not the full operational picture.
Amazon DSP is strongest for: Agencies with retail and e-commerce clients that have the budget to access Amazon's data advantage and closed inventory ecosystem.
Evaluating a platform requires more than comparing feature checklists. The right tool depends on how your agency operates, what your clients need, and where you plan to grow.
Must-have capabilities to assess:
Understanding the trade-offs:
Platforms like The Trade Desk offer significant programmatic depth, but come with higher operational costs, steeper technical requirements, and channel coverage gaps that require additional tools to fill. Unified platforms like Basis are built for broader channel coverage and end-to-end workflow automation without requiring a dedicated engineering team to operate them, or a separate tool stack to complete the workflow.
The right platform scales with your agency, and not just with your media spend.
Most agencies manage programmatic and direct buys as separate workflows—different platforms, different data pipelines, different billing processes. That fragmentation adds overhead at every stage.
Consolidating both buy types within a single platform streamlines the entire operation:
| Stage | Fragmented Approach | Unified Platform |
|---|---|---|
| Planning | Separate tools per channel | One plan, all channels |
| Media Buying | Multiple systems, manual entry | Single interface for all placements |
| Reporting | Manual aggregation across sources | One dashboard, unified data |
| Billing | Separate invoices and reconciliation | Centralized financial workflow |
Basis was built specifically for this consolidation. Agencies use it to manage programmatic inventory, direct site buys, paid social, and search campaigns from a single interface, with planning, buying, reporting, and billing all connected. That eliminates the data handoffs and manual reconciliation that consume significant agency resources when operating across multiple tools.
Cross-channel advertising is the practice of running coordinated ad campaigns across multiple digital channels to maximize reach, efficiency, and data-driven performance. As client rosters grow and campaign complexity increases, the ability to scale without multiplying operational overhead becomes a strategic advantage.
A scalable platform should accommodate:
When evaluating scalability, agencies should estimate realistic monthly spend thresholds for their client base and assess whether a platform's minimums and pricing model align with their growth trajectory. A platform that works well at $500K in monthly media spend may not be the right fit at $5M, and vice versa.
Manual reporting and billing are among the highest-friction activities in agency operations—they're time-consuming, error-prone, and difficult to scale. The operational burden is significant: according to Basis' 2026 Advertising Agency Report, inefficient processes and siloed systems are the top two challenges facing agencies today, with more than one-third of agencies now managing 10 or more tools across their adtech stack. Platforms that automate these workflows create measurable, compounding operational gains.
Here is how an automated reporting and billing workflow typically functions in a platform like Basis:
The benefits compound over time. Reducing manual errors lowers the risk of billing disputes. Faster reconciliation accelerates cash flow. Centralized data gives account teams cleaner insight into campaign performance without waiting on reporting pulls, and gives agency leadership the unified visibility they need to make faster, more confident decisions.
What is a media buying platform for advertising agencies? A media buying platform is specialized software that enables agencies to plan, activate, and measure digital ad campaigns across multiple channels—centralizing workflow, data, and financial processes within a single system. The best agency platforms handle everything from campaign planning and programmatic buying to reporting, billing, and financial reconciliation.
What is a demand-side platform and how does it support media buying? A demand-side platform (DSP) is software that enables buyers to purchase digital ad inventory in real time across multiple exchanges, using automated bidding and data-driven targeting. DSPs sit at the core of most programmatic media buying operations. Some platforms, like Basis, combine DSP capabilities with broader agency workflow tools—including search, social, direct buying, CTV, and billing—in a single interface.
What is the difference between Basis and The Trade Desk? The Trade Desk is a programmatic-only DSP focused on large-scale, enterprise programmatic buying. Basis is a unified agency platform that handles programmatic, paid social, paid search, and direct media buys—along with planning, reporting, and billing—in a single system. Agencies using The Trade Desk still need additional tools for non-programmatic channels and back-office operations; Basis consolidates those workflows into one platform.
Can one platform manage both programmatic and direct media buys? Yes. Several modern agency platforms, including Basis, enable end-to-end management of both programmatic and direct media buys within a single interface, simplifying workflow and consolidating reporting across deal types. This eliminates the manual data handoffs and reconciliation overhead that come with managing separate systems for each buy type.
What features should agencies prioritize when choosing a media buying platform? Agencies should prioritize centralized media planning, unified cross-channel reporting, AI-driven optimization, billing and reconciliation automation, and server-side tracking for privacy compliance. Beyond feature coverage, evaluate minimum spend thresholds, technical complexity, and whether the platform handles your full channel mix, or only part of it.
How do advertising agency platforms handle billing and reconciliation? Leading platforms automate the billing process by logging impression delivery and performance data against insertion orders, comparing delivered results against contracted terms, and flowing reconciled data into invoicing workflows. Platforms built for agency operations, like Basis, handle this end to end, from campaign execution through financial close, within a single system.
What budget considerations should agencies have when choosing a platform? Agencies should account for minimum monthly spend requirements, platform fees, setup costs, and long-term scalability. Some enterprise platforms carry significant monthly minimums; Amazon DSP managed service requires a minimum client commitment of $50,000 USD per month. Factor in the total cost of operation—including staffing, training, and the tools you'll still need to run alongside the platform—not just licensing fees.
How does AI improve campaign performance on agency advertising platforms? AI-driven optimization improves bidding efficiency and placement quality throughout a campaign flight, adjusting in real time based on performance signals. On platforms like Basis, AI is also applied to media planning—for instance, Compass, Basis' agentic AI planning tool, takes a media brief and produces a fully optimized, ready-to-activate omnichannel media plan. Some agencies have reported up to a 5x improvement in advertising performance using Basis' AI optimization capabilities.
What is the best advertising platform for mid-sized agencies? Mid-sized agencies benefit most from platforms that offer broad channel coverage, flexible pricing, and operational efficiency without requiring a dedicated engineering team. Basis and StackAdapt both serve this market well—Basis for agencies that need full workflow consolidation from planning through billing, StackAdapt for agencies prioritizing programmatic execution with strong usability and no minimum spend requirements.
Ad fraud is no longer a back-office concern. It’s a line item in every media director’s risk calculus, and it’s growing faster than the budgets used to fight it. Global advertisers lost an estimated $63 billion to invalid traffic in 2025, with roughly 8.5% of all paid digital traffic flagged as invalid—bots, automated scrapers, malicious competitor clicks, and synthetic engagement that drains budgets and corrupts the optimization signals AI-driven campaigns depend on.
That figure is on a steep upward curve. Global ad fraud losses are expected to reach $172 billion by 2028 as bot networks adopt generative AI and agentic automation. For agencies managing multi-million-dollar client portfolios, fraud protection has grown from a checkbox feature into a critical vendor-selection criterion. The DSP you choose either defends ad spend against this growing threat or quietly funnels a share of every campaign budget into the fraud economy.
This guide compares six leading demand-side platforms on the dimensions that matter most for brand safety and ad fraud protection: certification posture, IVT filtering methodology, verification partner integrations, and how each platform handles emerging fraud vectors like signal loss and agentic bot traffic.
| Platform | Core fraud protection approach | Best for |
| Basis | AI-powered inventory cleansing combined with human monitoring; integrated verification across Comscore, DoubleVerify, Peer39, and Protected by Mediaocean | Agencies and brands that want unified ad fraud and brand safety controls integrated across programmatic, social, search, and direct |
| The Trade Desk | Pre-bid filtering with IAS, DoubleVerify, and HUMAN Security; supply path optimization through OpenPath | Enterprise programmatic teams with dedicated ad ops resources |
| DV360 | Google-owned verification, Active View viewability, and third-party integration with major verification vendors | Performance teams operating primarily within the Google ecosystem |
| Amazon DSP | Proprietary fraud detection on Amazon-owned inventory; third-party verification on off-Amazon supply | Retail and e-commerce advertisers buying primarily within Amazon’s ecosystem |
| StackAdapt | Pre-bid and post-bid filtering through DoubleVerify, IAS, and Peer39 integrations | Mid-sized agencies running programmatic on the open web |
| Viant | Household ID-based verification, AI Lattice Brain anomaly detection, third-party verification partners | CTV-heavy buyers prioritizing identity-based measurement |
Ad fraud protection is the set of technologies, certifications, and operational controls that detect and block invalid traffic, fraudulent inventory, and brand-unsafe placements before, during, and after a campaign runs. It spans pre-bid filtering, post-bid analysis, supply path optimization, and third-party verification, and it determines how much of an advertiser’s media spend actually reaches real human audiences.
The financial stakes have changed the conversation. When fraud losses sat at 5% to 8% of media spend, many advertisers still treated brand safety as a compliance step. At today’s projected loss rates, fraud exposure can rival the margin on a mid-sized account. Industry research from Fraudlogix, based on analysis of 105.7 billion impressions, found a global invalid traffic rate of 20.64%— in other words, roughly one in five impressions across the open programmatic ecosystem showed signals consistent with fraudulent or non-human activity.
Agencies that run campaigns on a DSP without robust fraud controls are paying a fraud tax measured against client revenue, reporting decks that omit IVT filtering data are overstating performance, and contract renewal conversations now include increasingly pointed client scrutiny on how each platform protects budget against fraud exposure.
Ad fraud is the deliberate practice of generating fake impressions, clicks, or conversions to extract payment from advertisers without delivering real human engagement. The Media Rating Council (MRC) defines its detection scope using two categories of invalid traffic:
Within those categories, the fraud techniques most relevant to programmatic buyers include:
Domain spoofing: Fraudulent supply sources misrepresent the inventory they’re selling, claiming impressions from a premium publisher when ads actually serve on a low-quality or fraudulent site. The ads.txt and sellers.json standards from IAB Tech Lab were created specifically to verify which intermediaries are authorized to sell a given publisher’s inventory.
Ad stacking: Multiple ads are layered on top of each other within a single placement, with only the top ad visible. Advertisers pay for impressions that no human ever sees.
Pixel stuffing: Ads are served into a 1x1 pixel space—technically loaded and counted as impressions, but invisible to users.
Click farms and bot networks: Coordinated networks generate clicks and engagement signals at scale, often from real devices manipulated through malware or paid human operators.
Made-for-advertising (MFA) sites: Low-quality websites built primarily to harvest ad revenue rather than serve users. These sites often pass basic fraud filters while delivering near-zero campaign value.
The newest fraud vector is agentic AI bot traffic: Autonomous systems that mimic human browsing patterns, including scrolling, hesitation, and form interaction. These bots are designed specifically to defeat traditional pattern-based detection, and they’re already showing up in CTV and mobile environments, where verification infrastructure is less mature. To cite just one example, DoubleVerify detected 140% more CTV fraud schemes in Q1 2026 than Q1 2025, identifying more than 50 distinct bot attacks and variants in 2025 alone.
Programmatic fraud protection operates at three points in the campaign lifecycle, and you can measure a DSP’s strength by how it handles all three.
Pre-bid filtering: Before a bid is placed, the DSP screens the inventory request against blocklists, IVT signal libraries, ads.txt/sellers.json verification, and supply path metadata. Inventory that fails the screen is filtered out, and no bid is placed. Pre-bid filtering is widely considered the most effective fraud defense because it prevents wasted spend at the source.
In-flight monitoring: During campaign delivery, the platform continuously analyzes impression-level signals—device fingerprints, behavioral patterns, supply path consistency, viewability data—and dynamically adjusts buying behavior. Suspicious supply sources are throttled or suspended, and campaign budgets are reallocated to verified inventory.
Post-bid analysis and reporting: After delivery, the platform reconciles served impressions against fraud verification data, identifies invalid traffic that slipped through pre-bid filters, and generates reporting that quantifies the IVT rate. Strong post-bid analysis enables agencies to recoup wasted spend through make-good negotiations and to refine future supply path decisions.
Each layer requires both proprietary technology and third-party verification. No DSP can credibly verify its own fraud filtering rates without independent measurement, which is why integration with established verification vendors should be a baseline requirement when considering any programmatic platform.
The decline of third-party cookies and traditional identifiers has reshaped the fraud landscape in ways many media teams underestimate. As deterministic signals erode, fraud detection systems have less data to work with—and fraudsters have more room to operate.
Traditional fraud detection relies heavily on cross-site behavioral signals, device graphs, and identity-based pattern recognition to distinguish human users from sophisticated bots. When those signals weaken, detection accuracy weakens with them, and bots that previously failed cross-site consistency checks now operate in environments where those checks no longer apply.
The shift to alternative identity frameworks has introduced its own exposure. Some publisher-side IDs and probabilistic graphs are easier to spoof than legacy device IDs, particularly when verification infrastructure hasn’t caught up. Platforms that have invested in privacy-resilient measurement, contextual targeting infrastructure, and AI-based behavioral detection will hold up better as signal loss accelerates, but DSPs that depend heavily on legacy identity signals are exposed on two fronts, as addressability continues to shrink and fraud detection degrades simultaneously.
Evaluating a DSP for fraud protection means going beyond marketing claims and asking for documentation of three things: certifications, verification partnerships, and operational controls.
MRC accreditation: The Media Rating Council audits and accredits measurement methodologies, including impression counting, viewability, and invalid traffic filtration. A DSP or verification vendor with current MRC accreditation has been independently audited against published standards. Note that accreditation is product-specific—for example, a vendor may be accredited for desktop display IVT filtration, but not for CTV—so be sure to ask which specific measurements are accredited, and which are not.
IAB Tech Lab standards: Ads.txt and sellers.json are the industry’s authoritative supply chain transparency standards. Ads.txt files allow publishers to specify which sellers are authorized to represent their inventory. Sellers.json lets buyers verify the identity of every intermediary in the supply path. DSPs that crawl and enforce these standards as part of pre-bid filtering can identify and block unauthorized resellers and many forms of domain spoofing before a bid is placed.
SOC 2 Compliance: SOC 2 reports cover a platform’s security, availability, processing integrity, confidentiality, and privacy controls. While SOC 2 doesn’t directly measure fraud filtering effectiveness, it’s a baseline indicator of operational maturity, and is particularly relevant for compliance teams evaluating data handling, access controls, and incident response.
Independent verification vendors—such as DoubleVerify, Integral Ad Science (IAS), HUMAN Security, Peer39, Comscore, and Protected by Mediaocean—provide the third-party measurement that turns a DSP’s fraud claims into auditable data. The strongest DSPs offer:
Beyond certifications and partner logos, the operational details that separate strong fraud defense from weak fraud defense come down to a handful of practical questions worth asking every vendor:
That last item matters more than most agencies realize. Some platforms charge separately for pre-bid fraud blocking, which means the baseline product includes meaningfully less protection than the platform’s marketing suggests.
The platforms below represent the leading options agencies and brands evaluate when fraud protection is a primary buying criterion. Each entry covers the platform’s fraud detection methodology, brand safety controls, certification posture, and verification partnerships.
Basis is an AI-powered advertising platform built for how agencies operate, consolidating campaign planning, programmatic, social, search, direct deals, reporting, and billing into a single platform. That consolidation matters for fraud protection because brand safety controls and verification data flow through the same workflow as campaign activation—rather than living in separate dashboards that require manual reconciliation.
Fraud detection methodology: Basis combines AI-powered automated inventory cleansing with human monitoring to filter fraudulent and questionable traffic before a bid is placed. The platform’s pre-bid blocking incorporates IAB/ABC Spiders and Bots User Agent Lists, Pixalate data sets, ads.txt crawling, and proprietary fraud signal data. Suspicious inventory is filtered at the bid request layer, and ongoing monitoring continues throughout campaign delivery.
Brand safety controls: Basis applies pre-bid brand protection layers and filters ahead of campaign launches, with post-bid analysis and block-list application as a second layer of defense. Advertisers can configure controls at the advertiser, campaign, and placement level.
Verification partnerships: Basis integrates with Comscore, DoubleVerify, Peer39, and Protected by Mediaocean for third-party brand safety and verification. The Protected by Mediaocean integration brings AI-driven media quality, brand safety, and attention signals directly into Basis’ campaign activation workflows, enabling real-time verification inside the same interface used to plan, buy, and optimize campaigns. The integration eliminates the operational gap that exists when verification data lives outside the activation environment, as media quality controls become part of the buying decision in flight (rather than a post-campaign audit.)
Compliance posture: Basis is SOC 2 compliant, with documented controls across security, availability, and confidentiality. The platform’s commitment to supply chain transparency includes active enforcement of ads.txt and sellers.json standards.
Basis is strongest for: Agencies and brands that want fraud protection and brand safety controls integrated into a unified workflow that spans programmatic, social, search, and direct, with verification data and campaign management in the same platform.
The Trade Desk is one of the most technically capable programmatic DSPs on the market, with broad CTV inventory access and a long-standing focus on supply path transparency. Its fraud protection posture reflects that enterprise orientation.
Fraud detection methodology: The Trade Desk applies pre-bid filtering across its supply, with proprietary detection augmented by integrations with major verification vendors. The platform’s OpenPath initiative emphasizes direct publisher integrations and supply path simplification as a structural defense against fraudulent intermediaries.
Brand safety controls: Pre-bid brand suitability segments are available through IAS, DoubleVerify, and HUMAN Security. Custom blocklists and category-level controls are configurable at the campaign level.
Verification partnerships: Integrations with IAS, DoubleVerify, and HUMAN Security cover pre-bid filtering and post-bid measurement.
Limitations to weigh: The Trade Desk is a programmatic-only platform. Fraud protection within The Trade Desk only covers the programmatic portion of an agency’s media mix—search, social, and direct buys run through separate systems with separate brand safety controls. The Kokai interface and the platform’s overall complexity require dedicated ad ops resources to configure and manage fraud settings effectively, and some advanced features trigger additional fees that can compound across campaigns.
The Trade Desk is strongest for: Large enterprise agencies running high-volume programmatic programs with dedicated ad ops resources and clients whose media mix is weighted toward programmatic channels.
DV360 is Google’s enterprise programmatic platform, part of the Google Marketing Platform. Its fraud and brand safety posture benefits from Google’s scale and infrastructure—and inherits the constraints of operating inside the Google ecosystem.
Fraud detection methodology: Google’s proprietary invalid traffic filtration operates across DV360 inventory, with Active View viewability measurement integrated natively. Fraudulent and non-human traffic is filtered pre-bid and reconciled post-bid through Google’s own measurement systems.
Brand safety controls: DV360 offers content category targeting and exclusion, keyword blocklists, and inventory-level filtering. Integration with IAS, Scope3, DoubleVerify, and HUMAN Security is confirmed for pre-bid fraud filtration and brand safety verification.
Limitations to weigh: DV360 prioritizes Google-owned environments, and verification flexibility on YouTube and other Google properties is more constrained than on the open programmatic ecosystem. Access requires a Google Marketing Platform contract with practical spend thresholds that exclude smaller agencies. DV360 is also not a full agency workflow platform—paid social, direct buys, and billing run on separate systems.
DV360 is strongest for: Performance advertisers managing Google-heavy campaigns who need YouTube inventory access and have the team to manage the platform’s technical complexity.
Amazon DSP offers exclusive access to Amazon’s shopping and streaming data, with fraud protection structured around that closed ecosystem.
Fraud detection methodology: Inventory served on Amazon-owned properties—such as Prime Video, Twitch, and Fire TV—benefits from Amazon’s first-party fraud detection across logged-in user environments. Logged-in identity reduces certain fraud vectors significantly. Off-Amazon programmatic inventory bought through Amazon DSP uses third-party verification and pre-bid filtering through standard verification vendors.
Brand safety controls: Pre-bid brand suitability targeting is available, with controls configurable at the campaign level. Inventory-level filtering and exclusion lists are supported.
Verification partnerships: Amazon DSP supports integration with major third-party verification vendors for off-Amazon inventory measurement.
Limitations to weigh: Amazon DSP operates as a walled garden. Data generated within Amazon’s ecosystem stays within it, which limits cross-platform measurement and audit options. The platform is built for commerce verticals, and agencies with clients outside retail and CPG may find the platform’s core data advantage less applicable. Self-service carries flexibility, while managed service requires a $50,000 monthly minimum.
Amazon DSP is strongest for: Retail and e-commerce advertisers buying primarily within Amazon’s ecosystem who benefit from logged-in user verification and Amazon’s commerce data.
StackAdapt is a self-serve programmatic DSP with a footprint across CTV, DOOH, display, native, audio, and in-game.
Fraud detection methodology: StackAdapt applies pre-bid filtering through integrations with leading verification vendors, supplemented by proprietary supply path controls.
Brand safety controls: Pre-bid and post-bid brand safety segments are available through DoubleVerify and IAS integrations. Inventory and category-level exclusions are configurable. Unlike many DSPs that operate proprietary IVT detection layers, StackAdapt relies primarily on third-party verification vendors for fraud filtering, which can limit detection depth on emerging fraud patterns not yet covered by integrated partners.
Limitations to weigh: StackAdapt is programmatic-only—no search or social campaign management—so brand safety controls cover one portion of an agency’s media mix. The platform also lacks the agency workflow layer (billing, reconciliation, financial operations) needed to manage fraud exposure as part of a unified operational picture across all client buys.
StackAdapt is strongest for: Mid-sized agencies prioritizing programmatic execution with select third-party verification on the open web.
Viant is a CTV-focused programmatic DSP with a deterministic identity infrastructure built around its Household ID system. The platform’s fraud protection approach is tied closely to its identity-based measurement model.
Fraud detection methodology: Viant’s AI Lattice Brain analyzes campaign data for anomalies and fraudulent traffic patterns, with verification supplemented by DoubleVerify and IAS integrations across CTV and display. Its Household ID system links household-level identity to connected devices within that database, which may help strengthen verification signal density in CTV environments.
Brand safety controls: Pre-bid brand safety filtering and verification integrations are available across CTV, display, and other supported channels.
Limitations to weigh: Viant is programmatic-only, with no search, social, or direct buying capabilities. Additionally, the platform’s autonomous “Outcomes” product reduces trader control over optimization decisions, which can be a concern for agencies that want hands-on visibility into how fraud signals influence buying behavior. Viant’s smaller scale relative to larger DSPs may also raise platform-stability questions for agencies underwriting long-term enterprise commitments.
Viant is strongest for: CTV-focused advertisers prioritizing identity-based measurement and AI-driven optimization within the connected TV ecosystem.
Three patterns separate DSPs with mature fraud protection from those with marketing claims:
That last point is where the broader operational picture intersects with fraud defense. Agencies running fragmented stacks across multiple DSPs, separate social and search tools, and disconnected verification dashboards lose visibility at the seams. Fraud signals that surface in one system may never reach the team making buying decisions in another. The agencies with the cleanest fraud protection postures are typically the ones operating from unified platforms, not the ones stacking up the most verification vendor logos.
For agencies and brands evaluating DSPs on fraud protection, a structured audit framework helps separate marketing claims from operational reality. The following questions are worth asking every vendor under consideration before contract renewal:
The answers separate platforms that have built fraud protection into their operational fabric from platforms that bolt verification onto baseline product capabilities. For agencies with multi-million-dollar client portfolios, the difference is measured directly against client trust.
Basis is built for unified fraud protection across every channel an agency runs, with verification data and campaign management in the same platform.
What is ad fraud protection and how does it work in programmatic advertising? Ad fraud protection is the set of technologies and operational controls that detect and block invalid traffic, fraudulent inventory, and brand-unsafe placements across the campaign lifecycle. In programmatic environments, protection operates pre-bid (filtering bid requests against fraud signal libraries before bidding), in-flight (continuous monitoring during delivery), and post-bid (reconciling served impressions against verification data after delivery). The strongest protection combines proprietary platform detection with third-party verification from vendors like DoubleVerify, IAS, Peer39, and Protected by Mediaocean.
Which DSPs offer the strongest ad fraud protection and brand safety features in 2026? The leading DSPs for fraud protection in 2026 include Basis and The Trade Desk, as well as DV360, Amazon DSP, StackAdapt, and Viant. Basis differentiates by integrating fraud protection and brand safety controls across programmatic, social, search, and direct from a single platform—including verification partnerships with Comscore, DoubleVerify, Peer39, and Protected by Mediaocean. The Trade Desk, DV360, and Viant focus on programmatic; Amazon DSP focuses on its closed commerce ecosystem; StackAdapt focuses on self-serve programmatic on the open web.
What is the difference between ad fraud protection and brand safety? Ad fraud protection focuses on filtering invalid traffic, fraudulent inventory, and non-human engagement to ensure ads reach real audiences. Brand safety focuses on controlling the content environment ads appear in, keeping campaigns away from inappropriate, controversial or off-brand contexts. The two functions overlap operationally (most verification vendors offer both) but address different risks: fraud protection defends spend, while brand safety defends reputation.
How do MRC accreditation and IAB Tech Lab standards protect against ad fraud? MRC accreditation independently audits a platform’s measurement methodologies—including impression counting, viewability, and invalid traffic filtration—against published standards. A DSP or verification vendor with current MRC accreditation has had its specific measurements independently validated. IAB Tech Lab standards like ads.txt and sellers.json create supply chain transparency by letting publishers declare authorized sellers and letting buyers verify every intermediary in the supply path. Together, MRC accreditation and IAB Tech Lab enforcement provide independent validation that a platform’s fraud filtering does what it claims to do.
What types of invalid traffic does ad fraud protection software detect and block? Ad fraud protection detects two categories of invalid traffic. General Invalid Traffic (GIVT) includes known bots, spiders, data center traffic, and pre-listed non-human user agents identifiable through routine filtration. Sophisticated Invalid Traffic (SIVT) includes hijacked devices, falsified location signals, manipulated measurement, and bots designed to mimic human behavior—including the newest generation of agentic AI bots that simulate scrolling, hesitation, and form interaction. Detecting SIVT requires advanced behavioral analytics, machine learning, and human review.
How can advertisers measure whether their DSP’s ad fraud protection is actually working? Effective measurement requires independent third-party verification rather than relying on the DSP’s self-reported data. Verification vendors—DoubleVerify, IAS, HUMAN, Peer39, Protected by Mediaocean—measure IVT rates, viewability, and brand safety performance independently of the DSP, providing an audit layer advertisers can use to validate platform claims. Strong fraud protection postures publish IVT rates, support pre-bid integration with multiple verification vendors, and surface verification data inside the same interface used for campaign management.
How does third-party cookie deprecation increase ad fraud exposure? Cookie deprecation weakens the deterministic signals fraud detection systems rely on to distinguish human users from sophisticated bots—for example, cross-site behavioral patterns, device graphs, and identity-based pattern recognition. As those signals erode, detection accuracy degrades. Bots that previously failed cross-site consistency checks now operate in environments where those checks don’t apply, and CTV and mobile in-app environments show higher IVT rates as a result. Platforms that have invested in privacy-resilient measurement and AI-based behavioral detection are better positioned to maintain detection accuracy as signal loss accelerates.
Can a DSP’s built-in fraud detection replace dedicated verification tools? No DSP can credibly verify its own fraud filtering rates without independent measurement. Built-in fraud detection is the first line of defense, but third-party verification from vendors like DoubleVerify, IAS, HUMAN, Peer39, and Protected by Mediaocean provides the independent audit layer that lets advertisers validate platform claims. The strongest fraud protection postures combine robust DSP-native detection with deeply integrated third-party verification, not just one or the other.
What questions should agencies ask DSP vendors about fraud protection before contract renewal? The questions that matter most include: Which specific measurements have current MRC accreditation? Which verification vendors are integrated pre-bid versus post-bid? Are pre-bid blocking controls included in base pricing or do they trigger additional fees? Does the platform actively crawl and enforce ads.txt and sellers.json? Are blocklists configurable at the advertiser and campaign levels? Is the platform SOC 2 compliant? These questions separate platforms with operational fraud protection from platforms with marketing claims.
On this episode of AdTech Unfiltered, host Noor Naseer sits down with Frank-Lamont Wade, Director of Performance Marketing at the University of Phoenix, to discuss what it takes to run a high-performing, accountable performance marketing operation.
Frank shares insights on balancing lead quality with volume, navigating long customer acquisition timelines, refining measurement and attribution models, and building collaborative testing cultures across media channels. The conversation also explores the critical thinking skills modern performance marketers need to thrive in an increasingly complex advertising environment. Frank draws on years of experience in higher education, but the strategies and insights he shares apply across industries and verticals.
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.