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Every programmatic impression travels through a chain of intermediaries before it reaches a person, and most advertisers can't see what happens along the way. That blind spot carries a measurable cost. Only 43.3% of programmatic ad spend reached a quality impression—viewable, measurable, fraud-free, and clear of made-for-advertising inventory—in Q1 2026. For the lower-performing half of advertisers in the ANA's benchmark, the figure drops to 32.1%, meaning more than two-thirds of every dollar was wasted.
That gap isn't random. It separates advertisers who actively manage supply quality, measurement coverage, and inventory curation from those who don't. Transparency is what makes that management possible. Yet most advertisers still can't see exactly where their money goes once a bid is placed, which intermediaries extracted fees along the way, or whether their ads ran in environments that match their brand standards.
This guide covers what programmatic transparency means in 2026, how independent DSPs and walled garden platforms compare on the dimensions that matter most, what a layered brand safety and suitability approach looks like in practice, and how to evaluate any platform's fraud protection claims with appropriate skepticism.
Key Takeaways
A programmatic advertising platform is transparent when buyers can see and verify the full path their money takes from a bid to a publisher's page: which intermediaries handled the impression, what each one charged, and what the publisher actually received. That visibility is called supply path transparency, the ability to trace every step an ad impression takes from publisher to DSP and verify each intermediary's legitimacy.
Programmatic advertising, of course, is the automated buying and selling of digital ad inventory through real-time bidding across display, video, audio, native, CTV, and DOOH. A typical transaction routes every impression through multiple intermediaries, each extracting fees and introducing a potential point of failure along the way. The quality of that supply chain determines the quality of the campaigns it supports.
IAB Tech Lab standards (ads.txt, app-ads.txt, and sellers.json) establish the baseline, letting publishers declare which sellers are authorized to represent their inventory and letting buyers verify every intermediary in the supply chain. Transparency goes beyond compliance with those standards. A transparent programmatic platform gives buyers four things:
Independent DSPs and walled gardens serve different purposes, and the transparency profile of each reflects that. Most advertisers run both.
| Evaluation Criteria | Independent DSPs (ex. Basis) | Walled Garden DSPs (ex. DV360, Amazon DSP) |
| Supply-path visibility | Publisher-level pricing, hop counts, deal IDs, multi-SSP reporting | Limited; platform controls most visibility into inventory sourcing |
| Domain-level reporting | Standard | Often aggregated or restricted to platform-defined metrics |
| Data ownership | First-party data exportable and portable across buys | Data generally retained within the platform |
| Cross-platform measurement | Native integrations with third-party verification and cross-channel measurement | Measurement primarily constrained to platform ecosystem |
| Third-party verification support | Native integrations with multiple vendors; open-systems reporting | Supports select vendors, often with platform-mediated reporting |
| Buy-side neutrality | No media ownership; no inventory bias | Own and sell media; inherent revenue-vs.-transparency conflict |
| PMP access | Broad open-web PMP inventory and DSP-agnostic deal access | PMP access may be limited to platform relationships |
| Fraud protection scope | Covers programmatic and open-web channels natively | Strong within platform; fragmented across other channels |
| Log-level data access | Impression-level log data available in leading independent DSPs | Limited or inconsistent; walled gardens generally restrict LLD access or provide it selectively |
| Minimum spend | Varies; accessible at agency and enterprise scales | Some managed services require high minimums (ex. Amazon DSP managed service: $50K/month) |
\Each model solves a different problem. Walled gardens deliver strong in-platform reach and performance for search, social, and commerce outcomes. Independent DSPs provide transparent, hands-on control over data, optimization, and cross-channel execution, along with supply path efficiency and access to high-quality open-web inventory where visibility, accountability, and brand safety are critical. For agencies and brands running campaigns across audio, CTV, display, DOOH, native, and video, a platform like Basis—which unifies those channels in a single workflow with consistent brand safety enforcement—can close the gap that fragmented stacks leave open.
Brand safety is the practice of ensuring ads are delivered in environments that align with an advertiser's standards for content, quality, and legitimacy across all channels. It combines content adjacency controls, supply-path validation, inventory curation, and pre- and post-bid verification to protect brand reputation and keep media investments in trusted environments.
No single control is sufficient. The platforms with the strongest brand safety and suitability offerings layer six mechanisms:
A practical note on trade-offs: tighter brand safety controls reduce available inventory and can increase CPMs. Higher-quality placements carry higher costs, and well-calibrated controls account for that trade-off.
Ad fraud is any deliberate activity that prevents proper delivery of ads to real human audiences, including bot traffic, domain spoofing, click injection, and ad stacking. Fraud protection encompasses the tools and processes used to identify and block these threats across the buying lifecycle, with a strong emphasis on pre-bid controls and supply-path validation to prevent invalid impressions before a bid is placed, alongside post-bid detection and remediation.
The most common fraud techniques in programmatic environments include:
Fraud protection operates at three points across the buying lifecycle:
A private marketplace (PMP) is an invite-only programmatic auction where select advertisers access premium publisher inventory through pre-negotiated deal terms. PMPs give advertisers a more direct, controlled path to premium inventory, reducing the supply hops, fraud exposure, and brand safety variability that come with open-exchange buying.
PMPs don't eliminate fraud entirely, and layered verification remains necessary regardless of buying method. But they reduce the attack surface significantly, and spending trends reflect that shift. PMP spending grew nearly 13% in 2025 against roughly 3% for the open exchange, per eMarketer—a gap that reflects advertisers' growing prioritization of inventory quality, brand safety, and supply chain accountability over bid-price savings.
When evaluating a platform's PMP offering, the size of the pre-negotiated deal library matters, but so does how it's organized. Platforms that maintain curated deal groups—by vertical, channel, content category, or audience type—save media buyers the time of evaluating individual deals from scratch. Basis maintains 2,000+ pre-negotiated deals organized in a browsable library that buyers can activate within the same workflow used for open exchange buying. Troubleshooting tools that surface setup issues before campaigns launch catch problems before they cost impressions.
Programmatic guaranteed (PG) deals extend this logic further. PG combines the predictability of insertion-order-based buys—fixed pricing, guaranteed impressions, direct publisher relationships—with the flexibility and automation of programmatic media. Inventory control is complete: the buyer always knows exactly where ads are appearing, and all PG buys consolidate into the DSP invoice rather than generating separate publisher invoices. Platforms with established PG relationships—Basis' partners include Equativ, Tubi, Beachfront, Google, Connatix, Magnite, OpenX, and FreeWheel—can accelerate deal setup considerably compared to negotiating publisher relationships from scratch.
Supply path optimization (SPO) is the strategic process of selecting the most efficient, transparent, and high-performing route for digital advertising transactions to flow from advertiser to publisher. Its goal is to find the best path to the target audience while maximizing value and minimizing waste. The demand for that visibility is broad-based: 88.3% of agency professionals say digital advertising needs more transparency, per Basis' 2026 Advertising Agency Report.
SPO has historically been framed as a cost-cutting exercise: fewer hops, lower CPMs. The 614 Group's study pushes back on that framing directly. When SPO is treated as a race to the bottom, it harms publishers, degrades inventory quality, and ultimately undermines advertiser outcomes. The more durable frame is optimization toward outcomes—using supply path visibility to improve ROAS, inventory quality, and brand safety at the same time, not just to shave a few basis points off CPMs.
The ANA Q1 2026 Benchmark puts numbers to what that difference looks like. The higher-performing cohort converted 54% of its spend into quality impressions, while the lower-performing cohort converted just 32.1%. Headline CPM tells only part of the story. Once waste is accounted for, the higher cohort's TrueCPM (the effective cost per quality impression) was $7.46. The lower cohort's TrueCPM was $19.04, or 2.6 times more for the same quality impression. The Benchmark found that a $1.95 difference in headline CPM becomes an $11.58 TrueCPM gap once non-measurable and non-viewable spend is stripped out. Tighter supply curation and better measurement coverage drive that difference, not better-negotiated rates. The higher cohort also runs a far more concentrated supply footprint: 32,998 unique domains and apps versus 67,049 for the lower half. More domains mean more exposure to hard-to-measure, hard-to-verify inventory, and more waste.
That reframe has practical implications for platform selection. The 614 Group study found that most buyers don't want more data; they want insights they can act on. Platforms that surface supply path intelligence as actionable reporting, rather than as data exports that require engineering to interpret, are the ones that make SPO a repeatable operational practice rather than a periodic project.
To identify where a platform stands on supply path transparency, the 614 Group's SPO research synthesized feedback from senior marketers and agency leaders into eight questions every buyer should ask their DSP.
Three tensions show up consistently when agencies and brands evaluate programmatic platforms on brand safety and transparency.
Transparency vs. ease of execution: Independent DSPs provide supply-path visibility and cross-channel control, and the best ones are built to minimize the operational overhead that complexity can create. Basis is designed to consolidate omnichannel campaign management, reporting, and brand safety controls in a single workflow, reducing the expertise barrier without sacrificing transparency. Walled gardens simplify execution within their own ecosystems but limit cross-platform visibility and data portability in ways that compound over time.
Safety vs. scale: PMPs offer strong open-web control through pre-approved publisher relationships but constrain available impressions compared to the open exchange. That trade is deliberate: PMPs exchange raw scale for quality and control. The optimal allocation depends on campaign objectives, not a fixed formula.
Cost vs. control: Higher brand safety, verification, and managed service support increase costs through higher CPMs, platform fees, or minimum spend requirements. Consolidating channels and controls within a unified platform can reduce the total cost of that control by eliminating tool fragmentation and operational overhead.
A portfolio approach works best. Walled gardens for intent-driven search, social, and commerce outcomes. Independent DSPs and curated PMPs for open-web reach, supply-path transparency, and brand safety mandates. The two models are complementary, not competitive.
Applying these practices rarely requires switching platforms, but it does require active management.
What is the difference between brand safety and fraud protection?
Brand safety ensures ads appear in appropriate, high-quality environments by controlling content adjacency and publisher context. Fraud protection prevents invalid or deceptive traffic, including bots, domain spoofing, and click injection. The two are distinct but complementary, and strong platforms address both through integrated controls rather than treating them as separate programs.
Which programmatic advertising platforms provide the most transparency?
Independent DSPs provide the most supply-path transparency, including domain-level reporting, publisher-level pricing, and cross-platform auditability. Walled garden platforms like DV360 and Amazon DSP offer strong in-platform measurement but limit visibility into supply chain economics and restrict data portability. Basis is an example of an independent omnichannel platform with a built-in DSP that provides publisher-level pricing, deal-type comparison reporting, and multi-SSP visibility without sell-side conflicts.
Which DSPs have the best brand safety and fraud protection?
The DSPs with the strongest brand safety and fraud protection combine pre-bid filtering, post-bid verification, native integrations with multiple verification vendors, and human monitoring. Basis integrates with DoubleVerify, Comscore, Peer39, and Protected by Mediaocean for third-party verification, with pre-bid brand safety layers and a dedicated RTB Operations team for ongoing inventory quality monitoring.
How do advertising platforms prevent ad fraud?
Fraud protection operates at three points: pre-bid filtering (screening inventory before a bid is placed), in-flight monitoring (continuous analysis during delivery), and post-bid analysis (reconciling impressions against verification data). The strongest platforms, including Basis, combine proprietary detection with independent third-party verification, ads.txt and sellers.json enforcement, and human review for sophisticated fraud patterns that automated systems miss.
Are private marketplace deals safer than open auction inventory?
Yes. PMPs offer more control, direct publisher relationships, and higher-quality inventory than the open exchange. But they are not completely immune to fraud, so layered pre-bid and post-bid verification is still recommended regardless of buying method.
Which programmatic platforms offer the best pre-negotiated private marketplace deals?
Platforms that maintain large curated PMP libraries with deal-group organization by vertical, channel, or content category give media buyers the fastest path to brand-safe premium inventory. Basis maintains 2,000+ pre-negotiated PMP deals, including programmatic guaranteed relationships with publishers across CTV, display, audio, and video. Deal management, troubleshooting, and activation happen within the same workflow as open exchange buying.
How do independent DSPs compare to walled garden platforms for transparency?
Independent DSPs provide greater supply-path visibility, cross-platform auditability, and first-party data portability. Walled garden platforms offer strong in-platform targeting and measurement but constrain visibility and data ownership to their ecosystems. The TAG TrustNet LLD Register illustrates this gap: Basis provides full log-level data with all required data fields; Amazon Advertising provides no LLD support; major social walled gardens—Meta, TikTok, X—are listed as unknown. The two models work best in combination: walled gardens for intent-driven in-platform outcomes, independent DSPs for open-web reach with supply-path transparency.
What is supply path optimization, and how does it relate to brand safety?
Supply path optimization (SPO) is the practice of evaluating and refining the routes through which inventory is purchased to prioritize high-quality, transparent, and efficient supply. Cleaner supply paths—fewer hops, more curated deals, tighter domain footprints—directly reduce non-measurable and non-viewable inventory, which is where most programmatic waste occurs.
Do we still need third-party verification if we buy through a PMP or walled garden?
Yes. Layered verification is industry best practice regardless of buying method. PMPs reduce fraud risk through vetted inventory, but they don't eliminate it. Walled gardens measure within their own ecosystems, which creates both coverage gaps and an inherent conflict of interest. Independent verification from accredited vendors provides the audit layer that makes fraud protection claims auditable.
When does PMP buying make more sense than open auction buying?
When brand safety, viewability, inventory quality, and supply-path transparency matter more than maximum scale or the lowest CPMs. PMPs are the appropriate primary channel for campaigns with explicit brand safety mandates, for advertisers in sensitive categories, and for any program where inventory context is as important as audience targeting.
Key Takeaways:
Brand safety and suitability look very different today than they did just a few years ago, and most advertisers’ strategies haven’t kept up with the new pace.
Online spaces increasingly characterized by harmful and polarizing content, the proliferation of AI-generated media, reduced platform moderation, and the growing complexity of digital advertising have combined to raise the brand risk profile for advertisers.
Closing that gap requires a mindset shift from leaders. Instead of treating brand safety as a box to check once campaigns are live, brand safety and suitability must be approached as a strategic consideration built into media planning from the start.
The brand safety and suitability environment has evolved considerably in recent years. The open web has grown more volatile, with offensive language, controversial content, and hate speech on the rise. Just between 2024 and 2025, the share of offensive content online rose by 72%. Considering that 64% of global consumers say the genre of content surrounding an ad influences how they perceive it, the increasing hostility of online spaces creates significant content adjacency issues for advertisers.
The emergence of generative AI and subsequent proliferation of AI-generated content online has exacerbated such concerns. A 2025 Basis study found that a full 100% of marketers and advertisers agree that AI presents a brand safety and misinformation risk, and 53% of media experts in the US cite advertisements’ proximity to gen AI content as a top media challenge this year.
Social media has grown particularly contentious, especially as major social platforms have rolled back their content moderation policies in recent years. Close to two-thirds of marketers running campaigns on social feel concerned about the brand suitability of those ad placements.
April Weeks, Chief Media Officer at Basis, says the combination of these and other factors has raised the stakes for brand safety and suitability. “The risk has increased,” says Weeks, “and to adapt, advertisers must treat brand safety and suitability as brand-specific governance issues that are integrated into the media plan.”
Beyond content adjacency issues, wasted spend is a major concern when it comes to programmatic investments.
The ANA’s latest Programmatic Transparency Benchmark found a considerable gap in how effectively advertisers convert their spend into working media. Higher-performing advertisers directed 54% of their programmatic investments toward impressions that were measurable, viewable, and free of invalid traffic and made-for-advertising (MFA) content. Lower-performing advertisers converted just 32.1%—in other words, more than two-thirds of their spend was wasted.
The platforms marketing teams use for programmatic advertising have a considerable impact on how effectively they’re able to direct their spend. For example, platforms that prioritize supply path optimization (SPO)—offering supply chain visibility, neutral buy-side transparency, and brand safety controls built into the buying process—help advertisers convert more spend into quality placements.
“The best DSPs clean up the supply chain before an advertiser even bids—vetting publishers, filtering out bots, and removing invalid traffic up front,” notes Lindsey Freed, SVP of Media Investment at Basis.
Successfully addressing brand suitability, brand safety, and programmatic waste in today’s media environment requires marketing leaders to think about these issues differently than they have in the past.
“Historically, brand safety meant not showing up next to negative content,” says Dan Wilson, GVP of Integrated Client Solutions at Basis. “Today, it's about safeguarding your brand's integrity: considering where your ads are placed, the quality of the surrounding content, and what's suitable for your brand, audience, message, and moment in the customer journey.”
Legacy brand safety approaches were characterized by post-campaign verification, a reliance on platforms to manage risk, and blunt controls like broad keyword blocks or genre-level content blocking. As the complexity of the digital media environment has grown, Weeks says that advertisers must take on more responsibility, taking the time to craft nuanced brand safety and suitability strategies that are engrained into the planning process.
Leading advertisers are now incorporating pre-bid tools alongside post-bid verification, adding solutions to block MFAs and other low-quality websites, and accounting for channel- and platform-specific risks. Social listening, for example, has become essential given the polarization of content on social platforms.
Content adjacency approaches are also becoming more nuanced. The most successful advertisers are moving away from binary “safe vs. unsafe” thinking, and towards more granular, context-specific approaches. Rather than applying a blanket block on all news content, for instance, advertisers can use inclusion lists of trusted publishers paired with contextual targeting to ensure ads appear alongside news the brand is comfortable with, and within trusted editorial environments. “It’s about approaching it from a lens that isn’t black and white,” says Wilson.
Technology is evolving to support advertisers in these more granular approaches. For example, newer solutions can go beyond keyword matching, using contextual and semantic analysis to assess whether content is actually suitable and incorporating real-time signals to reduce waste.
Ultimately, success depends on leaders shifting their mindset, considering brand safety and suitability in the planning phase, and addressing them through a nuanced, multi-pronged approach.
Crafting a brand safety strategy suited to the complexity of today's media landscape takes real investment. Auditing legacy approaches, building channel-specific controls, and evolving workflows and tech stacks all take time. For leaders willing to invest that time, however, the potential returns are significant.
"The opportunity amongst the complexity is there," says Wilson. "The question is, will advertisers take the time to find it?"
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For a deeper look at how supply path optimization supports stronger brand safety outcomes, check out The Case for Supply Path Optimization as Strategic Priority.
For most agencies, the DSP is where media plans turn into live campaigns, which makes it one of the more consequential platform decisions a team can make. Choose well, and planning, buying, and reporting move faster across every account. Choose poorly, and the platform adds friction to campaigns it was supposed to accelerate.
The best DSP for agencies depends on your agency's size, client volume, and channel mix. Platforms like Basis, The Trade Desk, DV360, Amazon DSP, Viant, and Simpli.fi each serve different agency profiles, from independent boutiques managing a handful of accounts to mid-market teams running programmatic, search, social, and direct buys across dozens of verticals. The right demand side platform reduces operational complexity, improves reporting transparency, and scales with your client roster rather than against it.
That decision carries more weight every year. Programmatic now accounts for roughly 91% of US digital display ad spend, so the platform an agency uses to access it shapes a growing share of the work. At the same time, tool sprawl is climbing: Basis' 2026 Advertising Agency Report found that more than one-third of full-service and media agencies now manage 10 or more adtech tools, more than double the share two years ago, with inefficient processes (44.1%) and siloed systems (40.4%) ranking as their top operational challenges. The DSP you choose either adds to that burden or helps remove it.
This guide compares leading demand side platforms by agency type, walks through the evaluation criteria that matter most, and helps you build a case for the right investment.
The right DSP for your agency is the one that matches your team's workflow, your clients' channel requirements, and your growth trajectory. There is no universal "best" platform. A platform that excels for a holding-company network may create friction for an independent shop, and vice versa.
Start by identifying where you fall across three dimensions:
Once you map your agency against these dimensions, the field narrows quickly. A boutique with five clients and a display-heavy media mix has different requirements than a mid-market shop running full-funnel campaigns across 30 accounts. For a deeper look at the criteria that should guide your evaluation, this guide to choosing the right omnichannel DSP covers the decision in more detail.
Agencies should evaluate DSPs across six core dimensions: inventory breadth, multi-client account management, campaign optimization, reporting transparency, onboarding support, and pricing model clarity.
Inventory breadth determines where your ads can run. The strongest programmatic platforms for agencies offer access to premium display, video, native, audio, CTV, and digital out-of-home inventory through direct publisher integrations, private marketplaces, and the open exchange. This matters more as budgets shift to streaming: US CTV ad spend is on pace to reach about $38 billion in 2026, up nearly 15% year over year, so inventory access increasingly means streaming reach. Brand safety and ad fraud protection are also part of this evaluation. (For a detailed look at how leading DSPs handle both, see this comparison of DSPs for ad fraud protection and brand safety.)
Multi-client account management is where many DSPs fall short for agencies. You need hierarchical account structures that let you manage budgets, audiences, and creative assets at the client level without cross-contamination. Platforms designed for single-advertiser use often require workarounds that slow your team down.
Campaign optimization should go beyond basic bid adjustments. Look for algorithmic optimization across KPIs, automated budget pacing, and the ability to shift spend across tactics in real time. With AI now used at more than 99% of agencies, the question is no longer whether a platform applies AI but how much routine optimization it removes from your team's plate.
Reporting transparency matters for both internal decisions and client communication. Your DSP should provide granular, exportable reporting with clear visibility into costs, margins, and performance by tactic, channel, and audience segment. If you need to rebuild reports outside the platform, that is a red flag.
Onboarding support is especially critical for agencies switching platforms or adopting programmatic for the first time. Structured onboarding, dedicated success managers, and ongoing training reduce time-to-value and protect campaign performance during the transition.
Pricing model clarity separates platforms you can trust from those that obscure costs. Understand whether a DSP charges on a CPM basis, a percentage of spend, a flat SaaS fee, or some combination. Hidden fees can erode your margins and make it harder to forecast profitability for your clients.
If you want a refresher on how these platforms work, this DSP fundamentals guide covers the essentials.
The leading demand side platforms popular with agencies in 2026 each have distinct strengths. The right fit depends on your agency's profile, so the comparison below is organized by use case rather than a single ranking.
| Platform | Core strength | Best for |
| Basis | Unified planning, buying, optimization, reporting, and billing across programmatic, search, social, and direct | Agencies that want to consolidate their full workflow in one platform, plus expansive programmatic inventory, supply path transparency, and award-winning service |
| The Trade Desk | Enterprise-grade programmatic scale | Large, tech-savvy programmatic teams with substantial budgets |
| DV360 | Deep Google ecosystem integration and YouTube access | Google-centric campaigns that lean on GA4 and CM360 |
| Amazon DSP | Purchase-intent targeting built on Amazon shopping data | Commerce and retail clients with high spend |
| Viant | CTV reach and people-based, cookieless identity | Programmatic teams prioritizing streaming and AI-driven execution |
| Simpli.fi | Localized and multi-location programmatic at scale | Agencies with franchise, local, or multi-location clients |
| Platform | Programmatic | Paid search | Paid social | Direct buys | Billing & reconciliation | CTV | Entry point |
| Basis | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | Lower threshold |
| The Trade Desk | ✓ | ✗ | ✗ | ✗ | ✗ | ✓ | ~$10K+/mo |
| DV360 | ✓ | Limited¹ | ✗ | ✗ | ✗ | ✓ | GMP contract (~$50K+/mo) |
| Amazon DSP | ✓ | ✗² | ✗ | ✗ | ✗ | ✓ | $10K rec. self-serve / $50K managed |
| Viant | ✓ | ✗ | ✗ | ✗ | ✗ | ✓ | Self-serve & managed |
| Simpli.fi | ✓ | ✗ | Partial³ | ✗ | Separate⁴ | ✓ | Self-serve & managed |
¹ DV360's search functionality is limited; agencies typically run paid search through Google Ads separately. ² Amazon's search ads run through its own sponsored-ads console, separate from Amazon DSP. ³ Simpli.fi reaches social inventory programmatically but is not a paid-social campaign-management tool. ⁴ Simpli.fi offers agency workflow and billing through its separate Advantage and Core Media product line, not a natively unified platform.
The best DSPs for agencies in 2026 include Basis, The Trade Desk, DV360, Amazon DSP, Viant, and Simpli.fi, and the right choice depends less on raw programmatic horsepower than on how much of the agency workflow a platform can absorb. A team running programmatic across a handful of large accounts, for instance, has different needs than a shop reconciling dozens of clients across channels. With that in mind, the sections below will evaluate each platform on channel coverage, workflow depth, and the agency profile it fits best.
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 manual effort across agency teams.
Here is 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. 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 has produced measurable gains, with some agencies reporting up to a 5x improvement in advertising performance. For agencies managing high client volume across verticals, that combination of consolidation and performance is what separates Basis from point solutions that address only one stage of the campaign lifecycle.
Basis is strongest for: Full-service and media agencies that need one platform to handle every stage of the campaign lifecycle—from planning to activating to optimizing to reporting to billing—across both the open web and walled gardens.
The Trade Desk has built a strong reputation for enterprise-grade programmatic buying. Its bidding capabilities, access to a large third-party data marketplace, and strong connected TV inventory make it a credible choice for sophisticated, large-scale programmatic programs.
That sophistication, however, comes with a steep learning curve, and its formidable monthly minimums make it best suited to agencies with dedicated programmatic expertise and clients with substantial budgets. The Trade Desk is also programmatic-only. It does not handle paid search, paid social, or direct buys, so agencies still need separate tools for non-programmatic channels and a separate system for billing and reconciliation.
The Trade Desk is strongest for: Large agencies running high-volume programmatic programs with dedicated ad tech resources and clients whose media mix is weighted toward programmatic.
DV360 (Google Display & Video 360), part of the broader Google Marketing Platform, offers programmatic buying with detailed attribution and tight integration across Google's ecosystem, including exclusive YouTube inventory, the Google Display Network, Campaign Manager 360, and Google Analytics 4. For advertisers running Google-heavy campaigns, that interoperability is hard to match.
The tradeoffs, however, are significant. DV360 is not available as a self-serve product: Access requires a Google Marketing Platform contract with practical minimum spend thresholds, and its utility diminishes outside Google-owned environments. Search functionality is limited, and the platform does not handle paid social, direct buys, billing, or financial reconciliation. For agencies whose clients need omnichannel reach beyond Google, DV360 addresses only part of the buying workflow.
DV360 is strongest for: Agencies running Google-heavy, attribution-focused campaigns, particularly teams with in-house analytics expertise already operating within the Google stack.
Amazon DSP gives agencies access to something few platforms can replicate: targeting built on Amazon's proprietary shopping, browsing, and streaming data. Purchase-intent signals derived from Amazon's retail ecosystem offer uniquely powerful audience targeting based on actual purchase behavior rather than inferred intent, alongside premium inventory across Prime Video, Twitch, Thursday Night Football, and Fire TV.
The tradeoff is cost and scope. Self-service access carries no hard minimum, though Amazon recommends roughly a $10,000 campaign budget for some formats to generate enough data for optimization, while managed service requires a minimum commitment of around $50,000 per month. The platform's core advantage is strongest for retail, CPG, and e-commerce clients; agencies serving other verticals will find it less relevant. Amazon DSP also operates as a walled garden and does not handle paid search, paid social, direct buys, media planning workflows, billing, or reconciliation.
Amazon DSP is strongest for: Agencies with commerce-focused clients that can put Amazon's shopper data and premium streaming inventory to work.
Viant is an AI-powered, CTV-focused programmatic DSP whose central differentiator is its Household ID, a deterministic, people-based identity solution built for cookieless, cross-device targeting and measurement. The platform pairs solid connected TV and video inventory with autonomous campaign features, including a product that handles setup, optimization, and management with limited manual intervention.
For agencies, the constraints mirror other programmatic-only platforms. Viant does not offer search, social, or direct buying, and it carries no agency workflow, billing, or financial operations layer. Its interface and advanced features can present a learning curve, and the autonomous approach, while innovative, reduces hands-on trader control, which some teams prefer to keep.
Viant is strongest for: Programmatic teams that prioritize CTV reach and people-based identity and are comfortable leaning on automated execution.
Simpli.fi is a programmatic platform with a clear specialty in localized and hyperlocal advertising, including geo-fencing, geo-conversion tracking, and multi-location campaign management at scale across CTV, display, video, native, and audio.
However, users flag a complex interface and a learning curve for new teams. Additionally, Simpli.fi's strength is concentrated in local and multi-location use cases, so agencies with national or non-local clients may find its core value proposition less applicable.
Simpli.fi is strongest for: Smaller agencies serving local, multi-location clients that need hyperlocal targeting at scale.
The strongest platforms automate routine optimization and surface reporting that scales across clients, so teams spend their time on strategy rather than manual adjustments.
Campaign optimization in modern DSPs goes well beyond setting a bid and walking away. Leading platforms use machine learning to adjust bids in real time, shift budget across tactics and channels as results come in, and pace spend to avoid over- or under-delivery, all aimed at the outcomes clients care about. Reporting is where that quality becomes visible to clients: The best platforms offer dashboards configurable per client, scheduled report delivery, transparent cost breakdowns that separate media from platform fees, and cross-channel views that show how programmatic, search, social, and direct buys perform together.
The deeper divide is structural. Most of the platforms above handle one slice of the workflow well. A programmatic DSP executes programmatic buys, but with most demand side platforms, agencies still need to run separate tools for search, social, direct deals, and the back-office work of billing and reconciliation. Each added tool is another login, another data silo, and another manual handoff, which is why tool sprawl tracks so closely with the inefficiency and siloed-system challenges agencies report. The platforms that stand apart are the ones that reduce the number of systems an agency has to operate, not add to it. That unified model is where Basis, in particular, is built to compete—and it is increasingly what cross-channel, AI-driven optimization depends on, since connected data is the input those systems need to work.
Independent DSPs operate without ties to a specific holding company or media conglomerate, giving agencies more flexibility in how they buy media and where they allocate spend. Holding-company-affiliated platforms may offer preferential pricing or bundled services, but they can also limit access to competitive inventory or lock teams into a single ecosystem.
For independent and boutique agencies, this distinction matters. If your agency is not part of a major network, you need a DSP that offers transparent pricing, open marketplace access, and support that does not assume you have a 50-person ad ops team. The best independent DSPs provide the same caliber of technology, inventory access, and optimization available to large networks, without enterprise-level minimums.
Third-party agencies evaluating DSPs should pay close attention to contract terms. Some platforms require long-term commitments or minimum monthly spend that can be prohibitive for smaller shops; others offer flexible models that scale with the business. The broader question is how the DSP fits the full tech stack alongside your ad server, data tools, and campaign management. For a wider view, see how top advertising agency platforms for media buying compare.
Building a business case for a new DSP requires framing the investment in terms leadership cares about: operational efficiency, margin improvement, and client retention.
Basis unifies programmatic, direct, search, social, and connected TV buying in a single platform, giving agencies one place to plan, execute, optimize, and report across every digital channel.
For agencies managing high client volume, Basis provides the multi-client account structures, automated reporting, and cross-channel visibility that reduce operational complexity. For independent agencies competing with larger networks, it offers enterprise-grade technology paired with dedicated onboarding, training through AdTech Academy, and ongoing support from a dedicated Success Manager.
Explore Basis DSP to see how it fits your agency's needs.
What is the best DSP for agencies? The best DSP for agencies depends on client volume, channel mix, and team experience. Agencies that want planning, programmatic, search, social, direct buys, and billing in one place tend to favor a unified platform like Basis, while teams focused purely on programmatic scale may prefer The Trade Desk or DV360. Map your requirements first, then match them to the platform that removes the most friction across your full operation.
What is a DSP and how do agencies use it for programmatic advertising? A DSP, or demand side platform, is software that lets advertisers and agencies buy digital ad inventory programmatically through automated, real-time auctions. Agencies use DSPs to plan, execute, and optimize campaigns across display, video, native, CTV, and audio from a single interface. The DSP automates bidding, applies audience targeting, and reports on performance, letting agencies manage media buying at scale across multiple clients.
Which leading demand side platforms are popular with agencies in 2026? Leading demand side platforms used by agencies in 2026 include Basis, The Trade Desk, DV360, Amazon DSP, Viant, and Simpli.fi. Each serves a different profile: Basis unifies programmatic, search, social, CTV and direct buys with planning, billing; The Trade Desk and Viant focus on programmatic scale and CTV; DV360 integrates with the Google ecosystem; Amazon DSP brings shopper-data targeting; and Simpli.fi specializes in localized, multi-location campaigns.
What is the best DSP for independent agencies? The best DSP for an independent agency offers transparent pricing, open marketplace access, flexible contract terms, and strong onboarding support without enterprise-level minimums. Independent shops should prioritize platforms that deliver the same technology and inventory access as large networks while providing hands-on support, since they rarely have large in-house ad ops teams.
Which DSP is best for agencies managing high client volume across verticals? Agencies managing high client volume across verticals need hierarchical multi-client account structures, per-client reporting, and broad channel coverage in one system. A unified platform that handles programmatic, search, social, and direct buys, along with billing and reconciliation, reduces the manual handoffs and data silos that multiply as account counts grow.
What DSPs offer the best customer support and onboarding? Agencies adopting or switching platforms should prioritize structured onboarding, dedicated success managers, and ongoing training, all of which reduce time-to-value and protect performance during a transition. Basis pairs its platform with a services organization that provides consulting, onboarding, and training through AdTech Academy, along with a dedicated Success Manager for ongoing support.
What is the difference between a managed-service DSP and a self-serve DSP for agencies? A self-serve DSP gives your team direct control over setup, optimization, and reporting, which suits agencies with experienced programmatic traders. A managed-service DSP provides hands-on support from the platform's team, handling some or all execution on your behalf. Many platforms offer both, letting agencies choose the level of support that matches their team's capabilities and workload.
Can small or independent agencies access the same DSP technology as large agency networks? Yes. Many leading DSPs offer independent agencies the same technology, inventory access, and optimization they provide to large networks. The key is to evaluate pricing minimums, contract flexibility, and onboarding support, since some platforms require enterprise-level spend commitments while others offer flexible models built for independent teams.
Where can I compare trusted DSP platforms for digital campaigns? You can compare DSPs using the at-a-glance and channel-coverage tables in this guide, which evaluate Basis, The Trade Desk, DV360, Amazon DSP, Viant, and Simpli.fi across channel support, billing, CTV access, and entry point. Score each platform against your own client volume, channel mix, and team experience to identify the strongest fit.
How much does it cost to run a DSP campaign through an agency? DSP campaign costs vary by pricing model, media spend, and the channels you activate. Common structures include a percentage of media spend, CPM-based fees, or a flat SaaS subscription, and some platforms combine them. Beyond platform cost, factor in creative production, data fees for audience targeting, and any managed-service charges. Request a transparent fee breakdown from each DSP you evaluate so you can forecast client margins accurately.
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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—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.