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Mid-flight, Meadows at Mystic Lake’s CTV campaign was delivering at just 17.4% of target. With budget on the line and the flight window closing, the team needed a fast answer.
The issue: Brand protection costs were consuming campaign budget at $1.60 CPM, restricting delivery and preventing the campaign from serving at full capacity across tactics.
Using Basis, the team switched to Protected by Mediaocean. Brand protection costs dropped from $1.60 to $0.10 CPM, a 94% reduction, and the impact on delivery was immediate.
Within one week, the campaign reversed its course. It went from 17.4% pacing to ahead of target, serving across all tactics at full spend.
To manage the recovery, the team added budget across tactics, then pulled daily budgets back once full delivery was confirmed, staying on target without overspending.
During the same period, the team activated a new content targeting tactic through Basis, delivering an $18.94 CPM—the lowest of the campaign.
“When we identified brand protection as the issue, we made the switch to Protected by Mediaocean and didn’t look back. The campaign recovered faster than expected, and we talked away with a new brand protection set we can use across all campaigns.” - Senior Integrated Media Specialist
Evaluating whether or not your brand should in-house its media buying often involves a critical realization: In-housing media buying is not a single decision. Rather, it is a series of them, with multiple valid answers depending on your specific brand and context. Which channels do you bring in-house first? What stays with the agency? Who operates the platform? Who holds the contract? And what happens to your data if any of those answers change?
This guide covers what in-house advertising actually involves, exploring the three operating models—fully outsourced, hybrid, and fully in-housed—that brands choose among, and the practical questions of data ownership, transparency, capability, and agency dynamics that should drive the decision.
In-house advertising is the practice of a brand taking direct ownership of its media buying operations—channels, platforms, data, and reporting—that agencies have traditionally handled on its behalf. Rather than briefing an agency and receiving results, a fully in-housed brand operates the buying platform itself and keeps the resulting data inside its own ecosystem.
Programmatic is where in-house media buying gets the most attention, and for good reason: It is often the largest line item in a brand's digital budget, the most operationally complex channel to manage through a third party, and the one where fee structures and supply-path economics are hardest to see into. It is also where a brand's most valuable assets—its audience segments, rate intelligence, and performance history—are most likely to live inside a platform the agency controls, which makes the ownership question particularly acute.
Though programmatic garners significant attention when it comes to in-house advertising, the same ownership and transparency questions apply to other digital channels as well. What unites brands evaluating some level of in-house media buying is a desire for more control over spend visibility, optimization speed, and first-party data, three things that get harder to guarantee when a third party sits between the brand and its media execution, whatever the channel.
In-house advertising exists on a spectrum. Some brands in-house everything; many in-house one or two channels and keep agency support for the rest; others stay fully outsourced but insist on owning the underlying technology contract and data.
The right operating model is the one that matches your media spend, your in-house talent, and how much operational overhead you are willing to carry, which is why three brands of different sizes can make three different, equally correct choices.
The brand briefs an agency, which then plans, buys, and reports on its behalf. This model demands the least internal capability and offers the most immediate access to specialized talent and cross-client scale. The cost is distance: The brand often sees results rather than the levers behind them, and fee structures and the gap between what the brand pays and what reaches the publisher are not always fully visible.
The brand handles some channels or functions internally and retains agency support for others, often keeping paid search or social in-house while leaning on the agency for complex programmatic, or owning the platform and data while the agency provides the activation muscle. For most brands, this is a pragmatic destination. It captures transparency where it matters most while preserving agency expertise where the internal team is still ramping. Brands can split media operations between in-house teams and agency partners when the underlying platform supports shared access and clean handoffs. Basis supports this shared setup through Unify, giving in-house advertising teams and agency partners access to the same campaigns, data, and reporting so work can move between them easily.
The brand runs planning, buying, optimization, and reporting end to end. This model delivers maximum transparency and typically complete data ownership, but it also concentrates the operational burden (including hiring, training, technology, and process design) entirely on the brand. It rewards organizations with sufficient spend, existing media operations talent, and executive patience, and can be challenging for those that underestimate any of the three.
The table below summarizes how the three models compare across the factors brands weigh most. Data ownership is deliberately absent. It is the one dimension that tracks the platform contract rather than the operating model—whether a brand in-houses its media, fully outsources, or runs a hybrid.
| Dimension | Fully Outsourced | Hybrid | Fully In-Housed |
| Transparency | Can be limited into fees and supply path | High where brand operates directly | Full cost and supply visibility |
| Internal capability needed | Minimal | Moderate and growing | Substantial |
| Optimization speed | Bound by agency cycles | Fast on owned channels | Fastest; no external approval layer |
| Best fit for | Lower spend, lean teams | Most brands building capability | High spend, mature media teams |
A sound in-house advertising decision starts with four questions. The first—Do you own your media data, and can you see what is happening with your spend?—is one brands often underweight. The rest center on internal capability, cost, and the agency relationship, and are the practical considerations that determine if, how, and when to move.
This is the question many brands discover too late, and it has two halves: custody and visibility.
Custody typically depends on who holds the platform contract, not who runs the campaigns. When an agency manages buying on a platform it controls, the brand's campaign history, audience segments, rate intelligence, and performance benchmarks live inside the agency's environment—and can walk out the door when the relationship ends. That is a big reason agency transitions are so disruptive and expensive, and a dimension brands often overlook going in. The fix is contractual, and whether the right contract is available to you depends on the platform you build on.
Visibility is the other half. When buying runs through an agency on a platform the brand cannot see into, the gap between what the brand pays and what reaches the publisher—sometimes referred to as a “tech tax”—is not always disclosed, and neither are the data and platform fees layered on top. Real transparency means seeing performance, budgets, and spend across every channel in real time, rather than waiting on a monthly report assembled by someone else. That visibility is what lets a marketing leader defend the budget internally, catch waste early, and optimize on evidence rather than on an agency's summary.
Both halves are achievable without going fully in-house. The deciding factor is whether the platform surfaces the underlying data and spend to the brand, regardless of who operates the campaigns. Solutions like Unify by Basis are designed to allow brands to own their media data and technology, treating that ownership and line of sight as the default rather than a concession.
Capability is the dimension that most often decides whether in-house media buying improves performance or quietly degrades it. Brands that bring buying in-house without the right people and processes can end up worse off than they were with their agency. A functioning in-house operation generally needs several key roles, which smaller teams may choose to consolidate across fewer senior hires early on:
Technology is the other half of capability, and the two have to scale together. A skilled buyer limited by manual processes is as much a bottleneck as a powerful platform with no one to run it. The practical move for most brands is to consolidate buying and reporting into a single workflow through an omnichannel advertising platform, rather than stitching together point tools. A unified platform lowers the technical bar for a new in-house team and shortens the ramp.
The cost of in-house advertising depends on which model you choose and how fast you are able to move. A fully in-housed media buying operation carries fixed costs, including salaries for a buyer, planner, ad ops lead, and analyst, plus the platform fees and data subscriptions that come with running media directly. For brands with smaller budgets, these costs can be prohibitive and are part of the reason hybrid models are so common: They let a brand absorb the fixed costs gradually as internal capability grows, rather than front-loading them on day one.
The less visible cost is often the transition itself. Ramp time, parallel operations while the agency hands off, and recruiting and competing for experienced hires are real and worth budgeting for. As such, brands that plan a transition with overlap tend to come out cleaner than those that try to flip a switch.
Brand-agency relationships are under strain: In the 2026 Basis Advertising Agency Report, 54% of agency professionals said client tensions have increased over the past two years. That strain is often part of what puts evaluating in-house media buying on the table, and part of why the move is so often framed as “firing” the agency. The more durable framing, however, is renegotiating the division of labor. The strongest brand-agency relationships are built on trust and continuity, and a brand that owns its platform and data can restructure the relationship—shifting some channels in-house, keeping the agency on others—without the steep cost of a full review and re-onboarding.
That said, owning your platform and data is not simply a hedge against a messy agency breakup. Brands with strong agency relationships benefit too, as increased visibility can improve collaboration.
The platforms best suited to in-house and hybrid media buying models share one defining trait: The brand holds the technology contract and the data, with direct visibility into spend and performance. That combination, ownership plus transparency, is what lets a brand run its own media, see what every dollar actually buys, and shift work between internal teams and agencies as its needs change.
Unify by Basis is a solution specifically for brand-side involvement and increased brand-agency collaboration. It allows brands to retain control of their media stack and data, unify performance across channels, and partner more effectively with agencies. Powered by the Basis platform, its capabilities include:
The practical effect is that a brand does not have to choose its operating model once and live with it. It can sit anywhere on the outsourced-to-in-housed spectrum, and move along it over time, while keeping ownership of the asset that matters most: its data. The result is faster optimization, full spend visibility, and audience and performance data that compounds inside the brand rather than walking out with the next agency change.
Bringing advertising in-house is a significant decision, but it does not have to be an overwhelming one, and it does not have to be all-or-nothing. Start with the questions that matter most—for example, “Do you own your data, and can you see your spend?”—then weigh the practical considerations of cost, capability, and the agency relationship against them. For most brands, the answer is some form of hybrid rather than a full leap, and the right model is the one that keeps the brand in control no matter who runs the campaigns.
The technology you choose shapes how well any of these models works, because the platform is what determines whether your data stays yours.
Basis keeps brands in control across programmatic, direct, search, social, and CTV. Unify, the Basis solution for brand-side control and brand-agency collaboration, combines the automation, transparency, and flexible services that make in-house and hybrid media buying models manageable from day one—so brands can move faster on optimization, defend budget with real-time spend visibility, and protect the audience and performance data that compounds over time. Contact us to talk through the model that fits your brand.
What is in-house advertising?
In-house advertising is when a brand takes direct ownership of the media buying that agencies have traditionally handled, including the demand-side platform, audience data, campaign workflow, and reporting. Instead of briefing an agency and receiving results, an in-housed brand operates the buying platform itself and keeps the resulting data inside its own ecosystem. In practice, brands adopt this on a spectrum, from moving a single channel in-house to running the entire operation internally.
What is programmatic in-housing?
Programmatic in-housing is when a brand takes direct control specifically of its programmatic media buying, typically including the demand-side platform, the audience targeting, the real-time bidding, and the performance data those campaigns generate. It is often a starting point for in-housing because programmatic tends to be the largest digital spend category, the most operationally complex, and the channel where fee and supply-path transparency really matter.
What are the benefits of in-house advertising?
The primary benefits are speed (optimization without agency approval cycles), transparency (full visibility into fees, data costs, and what actually reaches the publisher), and control over first-party data (audience segments and performance history stay inside the brand's ecosystem). In-house advertising teams also build institutional knowledge of the brand's audiences and what works over time.
How do brands bring media buying in-house?
Brands bring media buying in-house by assessing readiness across spend, talent, data strategy and executive support; securing access to an omnichannel media buying platform (ideally one that keeps data and the contract with the brand); hiring or developing core roles; and phasing the transition channel by channel rather than all at once. Many brands use a flexible services model during the transition, drawing on outside expertise while the internal team builds capacity.
Can brands split media operations between in-house media buying teams and agency partners?
Yes. A hybrid model runs some channels in-house while the agency handles others. It works when the platform supports shared access, role-based permissions, and clean handoffs so both sides operate in one system. Basis supports this shared brand-and-agency setup through Unify.
Which advertising platforms let brands keep their data when switching agencies?
Platforms that offer solutions designed for brand-side control keep the technology contract and first-party data with the brand rather than the agency, so campaign history, audience segments, rate intelligence, and performance benchmarks stay portable through an agency change. Unify by Basis is one such solution: A brand's first-party data always belongs to it and can be moved or shared when switching partners.
Which platforms support programmatic in-housing?
The platforms best suited to programmatic in-housing keep the technology contract and the data with the brand, give the brand direct visibility into spend and performance, and support all-channel activation from one interface. Unify by Basis specifically supports this, supporting fully in-housed, hybrid, and outsourced models on the same underlying platform.
What advertising platforms are designed for brand-side media teams?
Platforms built for brand-side teams prioritize data ownership and portability, cross-channel transparency, all-channel activation from one interface, and flexible expert services a brand can scale up or down. Unify by Basis is a solution that allows brands to retain control of their media stack and data, unify performance across channels, and partner more effectively with agencies, supporting fully in-housed, hybrid, and outsourced models on the same underlying platform.
In 2026, consumer trust is anything but a given. In fact, for many audience segments, distrust is the norm.
A recent Edelman Trust Barometer framed today’s climate as a global “Crisis of Grievance,” the result of events over the past 25 years—from the Iraq War to the 2008 financial crisis to the COVID-19 pandemic—that have chipped away at trust in leaders and institutions. Evidence of this crisis includes an unprecedented decline in employees’ trust in their employers to do what’s right, record-high levels of concern that leaders lie to the public, and four in 10 global respondents reporting that they view hostile activism (including threats or even violence) as a viable means for driving change. That ratio rose to 1 in 2 among people between the ages of 18 and 34.
Among major institutions, brands still hold a relatively advantageous position—ranking as more trusted than government, NGO, and media entities—but this climate of growing distrust is impacting them as well. The rise of AI usage among brands has opened up fresh concerns: A stark 79% of Americans don’t trust businesses to leverage AI responsibly. AI has also amplified consumers’ data privacy concerns, with fewer than half (48%) of US consumers saying the benefits of online services outweigh the privacy risks.
Even more, consumers have grown increasingly willing to voice their displeasure by “voting with their wallets.” Recent years have seen a variety of high-profile consumer boycotts—on companies ranging from Bud Light and Cracker Barrel to Target to Apple and Netflix—driven by consumers unwilling to support brands whose actions conflicted with their values. Nearly a third of Americans have boycotted a business, and 45% say they research a company's values or stance before buying at least some of the time—a figure that climbs to 59% among Gen Z. To top it all off, US consumer sentiment dropped to a record low in May, adding economic anxiety to an already fragile trust environment.
Brands today cannot take consumer trust for granted. As leaders strategize around how to earn and maintain that trust, authenticity, consistency, data privacy, and brand safety must remain top of mind.
Over the past decade or so, the rise of conscious consumerism has led to brands increasingly taking social and environmental stands. But consumers and stakeholders demand authenticity and expect a coherent alignment between brands’ words and their actions: Those who try to talk the talk without walking the walk will quickly garner backlash and lose consumer trust—and dollars—as a result (see: “greenwashing”; “rainbow washing”; “woke-washing” ).
But a key aspect of effective authenticity that’s less discussed (though equally important) is consistency. “Brands need to be loud and proud about what they stand for,” says Molly Marshall, Client Strategy and Insights Partner at Basis. “But they also need to do that consistently. When brands try to please everyone or shift their values according to the cultural or political climate, that’s when they receive backlash.”
Bud Light offers one of the most notorious recent examples of how inconsistency can generate backlash from multiple directions at once. In 2023, the brand partnered with influencer Dylan Mulvaney on a promotional campaign. The campaign received backlash from conservatives, with critics accusing Bud Light of “going woke” for featuring a transgender woman in its campaign. That response drew its own backlash, with calls for a "buycott" encouraging people to buy Bud Light in support of the campaign. Anheuser-Busch released a statement that further fanned dissent on both sides by neither standing by its partnership with Mulvaney nor directly addressing the controversy at all. Added together, the financial and brand damage was sustained and significant: Bud Light sales and purchase incidence were roughly 28% lower than the same period in prior years in the three months following the boycott, a decline that persisted for close to eight months.
Another cautionary tale around consistency (or lack thereof) has been playing out in the industry in recent years in relation to Target. In 2023, the brand received blowback for the Pride month-themed merchandise it featured, with conservative consumers and social media creators encouraging a boycott. Target, which at that point had celebrated Pride month with Pride-themed merchandise for over a decade, then released a statement that it would remove some of the items from that year’s Pride collection in response to the backlash. This, in turn, garnered even more negative reactions: The company received bomb threats accusing it of betraying LGBTQIA+ people, and a coalition of 15 state attorneys general came together to encourage the brand to stand by the LGBTQIA+ community. Panelists discussing LGBTQ brand advocacy at SXSW the following year agreed that Target’s decision to walk back its stance in the face of backlash ultimately made things worse for the brand. Still, the controversy continues in 2026: The State Board of Administration of Florida has an ongoing class-action lawsuit against Target alleging that the brand misled shareholders about the risks associated with its 2023 Pride Month campaign, resulting in billions of dollars in investor losses.
A similar controversy began in early 2025, when Target—known at the time for its strong support of diversity, equity, and inclusion (DEI) initiatives and Black-owned businesses after the 2020 George Floyd protests—announced plans to scale back several DEI programs. This move aligned with a broader trend among major brands and came as the Trump Administration took steps to end government DEI programs. The retail giant received swift blowback from consumers, including boycotts that appeared to compound Target’s existing financial and operational struggles, with Target executives admitting in May 2025 that it had contributed to a decrease in sales. The company’s CEO stepped down in August of that year as the company reported its third consecutive quarter of declining sales—a slump that continued through 2025 and is only now beginning to lift.
“When consumers see a brand like Target—which had previously committed to DEI—pull those commitments back, they’re going to wonder if they can trust them to authentically act out their brand values or if they’re just going to react based on what’s happening politically,” says Kate Diehl, Group VP of Integrated Client Solutions at Basis.
For brands, the combination of today’s crisis of trust with the rise of conscious consumerism and a polarized political climate means that taking a stand on social and political issues comes with real risk. Brands should only take a stand when they can back it up with authentic action and are prepared to weather criticism. And, when pushback comes, the best response is often to stay the course and maintain their initial stance to demonstrate consistency.
That said, for many brands, the best move may be to simply not take a position on such polarizing issues. US adults are split on whether or not businesses should take a public stance on current events, with 51% saying they should and 49% saying they shouldn’t. For brands whose products or services aren’t related to social or political issues, that split may be reason enough not to engage.
Ethical data use is also central to building consumer trust in 2026. Data privacy concerns have increased dramatically in recent years, with the share of US consumers worried about data privacy and security increasing by 10 percentage points between 2024 and 2025, from 60% to 70%. Advertisers recognize how these concerns impact trust, with 46% naming transparent communication about data practices as the best way to grow customer confidence. “Data privacy is considered table stakes by consumers at this point,” says Marshall. “Still, brands and advertisers are struggling to implement it consistently.”
In addition to consumer concern, signal loss has also pushed advertisers towards more privacy-first approaches in recent years. “Even beyond building trust with consumers by respecting their data privacy, advertisers need to be able to rely on privacy-friendly solutions like first-party data to successfully target and measure their campaigns as signals drop off,” says Diehl. At the same time, first-party data comes with an ethical responsibility for advertisers—to gather, organize, store, and leverage that data in ways that preserve consumers’ privacy.
The rise of AI has further amplified privacy considerations. With 95% of digital advertisers reporting that they use generative or agentic AI in their work at least once a month, marketing teams must take even more care to protect customer data.
Some AI-driven tools, particularly those used in collecting and analyzing data, present serious data privacy risks. Agentic AI raises the stakes even further, as AI agents can access data across systems and act on it autonomously, meaning a privacy misstep can compound before a human ever reviews it. As advertisers increasingly adopt these tools, brands risk garnering significant distrust if they don’t take the proper precautions.
To adapt AI responsibly, advertisers must establish clear data governance protocols and place guardrails around what data feeds AI tools. AI partner selection is equally important: Marketers should audit AI vendors to ensure their commitment to data privacy, prioritizing solutions that offer transparency and demonstrate the reasoning behind their outputs rather than operating as a black box. Brands that treat privacy as a core component of their AI strategy will be best positioned to earn and maintain consumer trust.
Finally, advertisers looking to build more trust with consumers should work to prioritize brand safety across their campaigns, with recent industry developments making this focus all the more critical.
Advertisers have felt increasing concern around brand safety for years now—indeed, close to half of media experts name brand suitability as their top priority when it comes to media quality. The rise of generative and agentic AI has only amplified these concerns, with 100% of marketers agreeing in a recent survey that AI presents a brand safety and misinformation risk, and 88.6% describing that risk as moderate to significant. The proliferation of MFAs and AI-generated content online has made rigorous supply path optimization (SPO) even more critical for programmatic advertisers—without it, ad spend will continue to flow toward low-quality, AI-generated inventory that both compromises brand safety and inflates impression counts while delivering little real value.
Social media carries the highest brand risk of all digital media channels, according to just over half of advertisers. In fact, two-thirds of global marketing and advertising decision makers feel concerned about the suitability of ads placed on social platforms. Recent years have given advertisers plenty of reasons for that unease. In one high-profile 2025 incident, Meta apologized after Instagram users reported seeing extreme violence in their Reels feeds, including videos of people being murdered.
“This is an example of a brand safety concern that’s really hard for brands and agencies to get ahead of,” says Marshall. “I do think it brings up larger questions around what platforms are safe and effective for advertisers, and what consumers expect from brands who run on those platforms.” Even more, content moderation rollbacks at social media platforms like Facebook, Instagram, and X have aggravated the riskiness of social media environments.
Beyond social media, brand safety made headlines just last year as a result of an Adalytics report that found that multiple adtech companies have placed ads for major brands on websites hosting CSAM. (Note: The report cites Basis as an adtech vendor who did not serve ads on any of the sites in question.) This extraordinary brand safety crisis underscores how critical it is for brands to have robust and multi-layered systems to ensure their advertising content is only shown in safe and suitable environments—to safeguard consumer trust as well as to avoid ethical catastrophes such as these.
Of course, there’s a case to be made that consumers are now savvy enough to know that brands aren’t choosing to serve ads next to disturbing content, hate speech, or misinformation, particularly on social media. Still, 82% say it’s important to them that the content around online ads is appropriate, and three-quarters say they would feel less favorable towards brands that serve ads on sites that contain misinformation. Considering this majority opinion as well as the broader culture of consumer distrust, brands who prioritize brand safety likely stand to gain a competitive advantage over their peers who take a laxer approach.
Leading advertisers are responding by investing in brand safety tools that go beyond surface-level filtering. For example, solutions such as Protected by MediaOcean are using semantic intelligence to evaluate content in context rather than relying on keyword blocking alone, and can incorporate real-time signals to stop waste more efficiently. And as ad fraud and brand safety concerns rise in the realm of CTV, vendors like Peer39 offer content-level contextual segments that give advertisers more control over where their CTV ads are served. Teams who stay at the forefront of these technological evolutions stand to gain concrete performance advantages over competitors.
In the face of deepening consumer distrust, heightened social tensions, and growing scrutiny around corporate behavior, earning and maintaining trust from target audiences will be a defining priority for today’s most successful brands. And authenticity, consistency, data privacy, and brand safety will be foundational elements of their strategies.
For marketing and advertising leaders, now is the time to double down on trust as a core metric of success. Failing to do so may carry financial consequences in a world where consumers are spending more intentionally and brand loyalty is increasingly difficult to earn and maintain.
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Looking for more insights on how AI is changing advertising? Our AI and the Future of Marketing report explores how marketers are using the technology, navigating its risks, how it’s reshaping advertising jobs and teams, and more.
Marketers are measuring more than ever, but confidence in campaign decisions is still lagging. What’s the disconnect?
Join Basis Effectiveness Lead Lauren Johnson and VP of Media Innovations + Technology Noor Naseer for a candid look at what separates real campaign effectiveness from measurement for measurement’s sake. They’ll unpack what’s moving the needle in measurement in 2026, where most teams get stuck, and how to turn the data you already have into clear next steps.
You’ll walk away with:
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.