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Key Takeaways
Advertisers’ ultimate goal is to drive effectiveness by identifying the optimal media mix for specific campaigns to drive key business outcomes. Equally important, though, is proving out that effectiveness: Connecting media investments to results through compelling, cohesive narratives that build stakeholder confidence and secure continued investment.
Yet media fragmentation creates significant barriers to achieving both goals. When performance signals are scattered across platforms and channels, each with its own reporting methodology, omnichannel campaign evaluation—and the ability to make real-time adjustments—is often painfully slow at best.
This matters enormously: In the pursuit of advertising effectiveness, omnichannel campaign evaluation is the holy grail, empowering teams to both drive results and demonstrate impact. As such, it’s essential for marketing teams to strategize around how to achieve holistic campaign assessment amidst fragmentation. Building a strong marketing measurement strategy—one that combines platform-level attribution with broader methods like marketing mix modeling and incrementality testing—is essential for marketing teams looking to navigate fragmentation successfully. The best approaches include systems and frameworks that allow teams to streamline performance signals across channels and evaluate effectiveness in both the short- and long-term.
Today’s consumers demand advertising experiences that seamlessly span the many digital spaces where they spend time. At the same time, diversifying media spend across channels delivers greater returns in revenue and brand performance.
But as marketers split their efforts across a growing number of platforms and channels, the resulting tech stack sprawl—over half of agency marketers’ stacks consist of eight or more tools, and 40% are using 10 or more—presents a variety of problems. In fact, 45% of agency leaders cite siloed or disconnected systems as a top challenge.
Data fragmentation from these disconnected systems underlies a host of marketing leaders’ biggest pain points. Most fundamentally, manually consolidating data from multiple sources is both time-consuming and error-prone, which prevents the agile and strategic decision making necessary to drive effectiveness. Consequently, only 21% of senior marketers report receiving actionable data in real time.
Fragmentation also curbs teams’ ability to demonstrate the impact of their work, which strains client partnerships for agencies and impacts budgets and stakeholder confidence for brands. Marketing leaders at brands identify connecting marketing activities to revenue outcomes as their most pressing challenge—a particularly urgent issue as CMOs face heightened pressure to prove ROI.
To succeed in this fragmented landscape, marketing teams must develop dedicated strategies for holistically measuring performance across both the short- and long-term.
The baseline for short-term cross-channel measurement is tracking performance on each individual platform and channel. Advertisers must examine each source of truth across their media mix and assess platform-level performance using attribution and KPIs like immediate sales, cost of acquisition, or ROAS.
Because of the lack of interoperability among walled gardens and the open web, advertisers typically won't know if a consumer saw the same ad on, say, both Facebook and Pinterest. While this can result in double-counting conversions, it doesn't hinder platform-level optimization, which remains essential for driving and demonstrating effectiveness.
The disconnection between walled gardens and the open web creates significant limitations, but marketing leaders can help their teams manage the complexity. The key is streamlining performance signals across channels to understand how they work together to drive short-term outcomes. Advertising platforms that automatically aggregate data from multiple sources across walled gardens and the open web—eliminating time-consuming manual work—provide teams with a comprehensive view and a significant competitive advantage.
However, to fully achieve holistic measurement, marketing teams must look beyond immediate, platform-level performance metrics and find ways to take a broader view of their investments.
To truly drive and demonstrate effectiveness, omnichannel campaign evaluation can’t end with short-term strategies. When assessing how successfully your advertising is driving business outcomes, it’s critical to take a long-term perspective.
Assessing long-term effectiveness holistically is all about finding ways to gather every available signal into one place and extract bigger learnings about their impact. To that end, integrated campaigns across the open web and walled gardens can be evaluated over a longer term against key metrics that can’t be properly attributed to a single source, such as brand health, sales growth, and profitability.
Marketing mix modeling (MMM) and experiments are two tools marketing teams can use to assess these bigger metrics.
Marketing mix modeling (MMM), also called media mix modeling, is a statistical approach that uses historical sales and marketing data to measure how each channel and tactic contributes to business outcomes over time. With MMM, advertisers can model how their investments across multiple platforms drive business outcomes over time. For instance, by analyzing historical data, MMM can predict that investing X amount of money across digital marketing contributed to Y% of overall sales, led by Facebook, Google, and programmatic tactics. Increasingly, MMM marketing approaches inform not just past performance reporting but also forward looking decisions about where to invest media spend for the strongest returns.
Experiments—also called incrementality testing—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.
Throughout programmatic advertising history, brands have often entrusted its execution to agencies and trading desks. But today, in a world of high consumer expectations and constantly shifting market dynamics, many brands are searching for an alternative to the traditional brand-agency model as they look to gain greater transparency into their media buys, more holistic control over their data, and greater assurance of compliance with privacy regulations.
The result: programmatic in-housing.
Programmatic in-housing is the practice of a brand bringing some or all of its programmatic media buying, data management, and ad operations in-house rather than outsourcing them to an agency or trading desk.
Numerous heavy hitters have already brought elements of their programmatic operations in-house, including Marriott, Colgate-Palmolive, Procter & Gamble, Coca-Cola, Bayer, Wayfair, American Express, Unilever, Anheuser-Busch, Netflix, Target, Deutsche Telekom, and Ally Financial. Now, as those groundbreaking programs mature and their outcomes come into view, more brand advertising leaders are peering over with interest and beginning to question whether they, too, should take some—or total—control of their own programmatic business.
With many different levels of programmatic in-housing available, as well as a variety of potential benefits and challenges, leaders must carefully consider which, if any, in-housing option is best for their brand and team. Whether opting for full control, a hybrid approach, or fully outsourcing, choosing the right in-housing model can significantly impact both campaign performance and operational efficiency.
Key Takeaways:
The first adopters of in-house programmatic media buying were mostly digital natives like Netflix and Target: brands boasting rich, voluminous first-party data that gave them a head start on the process. Over time, though, the types of companies in-housing have become more diversified. And, they have done so with varying degrees of success.
In 2025, headlines about brands “un-housing” their internal agencies suggested the model might be losing momentum, but the practitioners closest to the work tell a more nuanced story. In a 2026 ANA survey of In-House Excellence Awards judges, only 7% saw in-housing declining, while 35% said marketers are in-housing more than ever and 58% still considered it a viable alternative to external agencies.
Those who struggled often did so because they underestimated the logistical hurdles involved and became too fixated on the stereotype that in-housing is an all-or-nothing play. But this idea of agency versus in-house programmatic as a dichotomy is outdated, as it doesn’t suit the needs of modern brands.
As marketing leaders learn more about the intricacies behind this digital transformation, a whole range of in-house programmatic manifestations are emerging, with brands and agencies breaking new ground and creating new operational frameworks as their relationships evolve. Indeed, in-housing programmatic is far from a death knell for agencies—brands still lean heavily on their external partners, and agency investments still command more than 20% of total marketing budgets, according to Gartner’s 2025 CMO Spend Survey—though that share is slowly eroding as more CMOs move to trim agency spending. Understanding market logistics and maximizing technology-driven optimization opportunities requires as much critical insight as possible, and agencies remain ideally positioned to provide that guidance.
When it comes to programmatic in-housing, one thing is clear: Where we were five years ago looks very different from today, and where we are today will look very different five years from now.
While it is difficult to neatly classify the numerous variations of in-housing—especially considering all the factors involved—here are three of the most common arrangements:
| Model | What It Is | Best For | Key Tradeoff |
| All-In on In-Housing | Full adtech stack, media strategy, ad ops, and data management sit inside the brand | Brands wanting maximum control and transparency | Highest commitment of time, talent, and resources |
| Hybrid | Brand shares programmatic execution with agency or on-demand partners | Brands wanting control plus outside expertise | Requires clear division of roles and governance |
| Fully Outsourcing | Agency or trading desk runs programmatic on the brand's behalf | Brands without internal resources | Least transparency and control over data |
This is the quintessential in-housing set-up, where an adtech stack sits within a brand organization in tandem with media strategy, ad operations, data management, and campaign stewardship. An in-house operation that looks like this is still relatively uncommon, given the major commitment of time, resources, and internal talent marketing organizations need to launch, maintain, and refine it.
Despite the complexities involved, the popularity of programmatic in-housing is rising, and brands are building relationships with the technology platforms and the talent they need for in-housing to succeed. According to the ANA’s 2023 “Continued Rise of the In-House Agency” study (a survey they conduct every five years) , 66% of brands say they have contracts with technology providers like DSPs or verification partners, and two-thirds also say they have “hands on keyboards” doing the actual work.
For brands going down this route, the classic approach involves forming an internal agency-style trading desk and equipping them with a demand-side platform (DSP). Going all-in can be a monumental task, but with the right preparation, planning, and implementation, the returns are significant.
Today, established in-house units are taking programmatic on directly: Paper manufacturer Georgia-Pacific and UK retailer Boots, for example, now run their own programmatic trading teams, gaining a clearer view of their media investment and closer control over how budgets are spent. Pharmaceuticals giant Bayer, which first began developing their in-housing operations back in 2017, reportedly cut its programmatic buying costs by over $10 million in just the first six weeks. That figure grabbed headlines, but for most brands the more durable payoff is ownership and control versus a fast cost cut. The company has also pointed to a number of additional in-housing related benefits, including ownership of their tech stacks, data, and dashboards.
Bayer’s success presents a microcosm of the upsides that come from in-housing programmatic. Transparency is a major one: With data privacy at the front and center of public consciousness, it is paramount that brands know exactly what their advertising is doing.
Many agencies still fall short on providing real visibility into the digital ad buying process. This lack of transparency, combined with increased control and potential cost savings, drives many brand leaders to pursue in-housing. Platforms like Basis are designed to address exactly this need, centralizing transparency tooling so brands can see precisely where every dollar goes. Indeed, gaining access to unfiltered campaign performance data empowers brands to do a host of valuable things, including:
Cost is no longer the headline reason brands bring programmatic in-house: In 2026, an ANA survey of in-house practitioners found just 9% now name cost savings as the primary role of internal teams, down from 30% in 2023. The efficiencies are still real, though.
Brands often see cost benefits over time as they save on agency fees, including fixed rates, platform costs, and media expenses. Internal programmatic teams can concentrate solely on maximizing profitability for their brand. In contrast, agency partners balance this goal with their own revenue needs and margin requirements. Going all-in on in-housing removes these additional layers, potentially streamlining the path to increased profitability.
Then there is the matter of cost efficiencies through better campaign execution. The switch in-house ultimately enables brands to invest in—and nurture—talent within their marketing organization. While outsourced staffing solutions can provide some great results, they may not be able to optimize the consumer journey with the same granularity as an in-house staffer who knows the brand through and through.
Now, there may be many upsides to in-housing, but brand leaders should not underestimate the organizational, technological, and cultural challenges involved in this digital transformation.
For starters, programmatic in-housing takes time. Ideally, the transition should be a well-researched, deliberate journey with calculated investment in the right resources and tech over a period of many months, if not years. From the outset, those leading the change need to ensure they involve a wide range of internal stakeholders from all corners of their organization, including finance, legal, product, and IT. All these teams will have either a direct or indirect influence on the program’s success, so it is important to listen to their input on the process and get their buy-in before proceeding. What are the security implications, for example, from an IT perspective? How much budget is available for the required tools? How are privacy regulations going to be respected? What are the data challenges from a CRM standpoint? A multitude of questions must be answered, so the more support earned from across the organization, the better.
In sum, programmatic in-housing is not something you can just do on a whim, and it is critical to set the expectation internally that transformational results are unlikely to arrive immediately. Brands accustomed to focusing on short-to-medium term initiatives and goals must learn to adapt to protracted, longer-term processes and a new way of working if they want to successfully in-house programmatic.
Of course, most marketing leaders are thinking hard about how to wrangle more out of their advertising dollars, and many are concluding that they need outside help and guidance to do so successfully. At the same time, many media agencies are adopting new structures—reorganizing in a way that makes it easier to offer on-demand services and step into a more advisory role for clients that seek to in-house some of their programmatic budgets. It is a development that makes sense for both parties: Agencies have extensive experience in what works and what doesn’t in the programmatic sphere—not to mention the tools necessary for executing on programmatic media buys—and clients need to tap into those resources as they bring some of that operational activity inside their own walls.
This, in essence, is what the hybrid in-housing model is all about: An in-house marketing team tees up the overarching strategy before consulting with an agency on how to deliver it in the market. Sometimes, this will lead to the agency assuming the day-to-day responsibilities of implementing the plan and pulling the levers, and other times it will lead to the advertiser doing the work with their own platform under the agency’s direction.
The latter scenario is the one that more brands are pursuing, as it ultimately merges many of the best aspects of both in-housing and outsourcing. With hybrid in-housing, brands can:
An Association of National Advertisers study frames the hybrid in-housing trend in numerical terms, finding that, in 2023, only 32% of marketers had in-house programmatic capabilities, and just 17% of the remaining respondents were considering adding those capabilities within the next year. More recent reporting suggests hybrid is now more common than going fully in-house: Roughly 13% of U.S. ANA members handle programmatic entirely in-house, while about twice that share, 26%, use a hybrid model. This highlights just how important agencies remain to brands. Programmatic advertising can be a complex venture, and there are still many marketing organizations that struggle to identify the right systems to help them better engage their audiences. The hybrid option alleviates that pressure and empowers brands to take whatever baby steps they want.
Option number three: fully outsourcing, a route that many brands still prefer despite the myriad benefits that in-housing offers. It represents a great option for those who don’t have the resources to build an internal programmatic team and all the costs that come with it—think upfront licensing fees, salaries, training, etc.
From a brand perspective, the main drawbacks to contracting out all aspects of programmatic advertising are the lack of control they will enjoy over their consumer data and the limited transparency they will get into media performance. To address exactly that, a growing number of brands are choosing to own their core technology stack and data outright—even when they still rely on outside partners for day-to-day execution. Keeping the stack and data in-house gives them full visibility into how campaigns perform and where every dollar goes, along with unfiltered access to their own first-party data. And because those insights live with the brand, the learnings stay put rather than walking out the door when an agency relationship changes.
One key area where agencies retain an upper hand in the in-housing/outsourcing calculation is talent. Managing and optimizing programmatic media is a complex art, and advertisers considering independence from agencies will need several staff additions to ensure they are doing things right—a programmatic manager, a data analyst, and a DSP operator, to name but a few.
The problem facing brand-side HR departments today is actually filling these positions. Experienced programmatic specialists remain scarce and expensive even as broader marketing headcount tightens, job titles are evolving (particularly as AI continues to reshape digital advertising), and, to make matters tougher, the available talent often take opportunities at agencies over brands because they can offer better career progression, opportunities to diversify, and ongoing learning. Within the four walls of a brand, those things can be difficult to come by, considering there is only one program to service and (most likely) fewer opportunities to influence overall strategy. One brand that built its own in-house programmatic team pointed to “recruiting, developing and retaining specialized talent that’s high in demand” as one of the biggest hurdles in building that internal team.
No player in the programmatic space can expect to be successful without the right people doing the bidding, so this talent shortage is a pressing concern for brands, often leaving them with no choice but to look to external partners.
From platforms to people to processes and everything in between, with so many factors to weigh when considering in-housing programmatic ad buying, where does one start? Here are a few considerations for marketing leaders looking into in-housing, whether they’re at the RFP stage or just the “I was thinking about…” step:
1. Evaluate “why” this is important to your organization: Is your organization looking to cut costs? Hit a business objective? Gain more operational control? Identifying your in-housing “why” will allow you to understand your needs, wants, limitations, and what you’re trying to change. It also creates space for a feasibility check and an objective session to help move from problem to solution.
2. Understand the landscape: More than a decade ago, in-housing came into vogue thanks to tactics like RTB. However, during the height of the COVID-19 pandemic, long-term contracts and full-time employees became more of a burden as businesses just tried to survive. If you do consider in-housing, it’s important to consider it in context of the larger marketing and economic landscapes to ensure that now is the right time to take the first steps.
3. Review your readiness: When it comes to technology, are you comfortable vetting new options to streamline how you use your existing tech stack? In terms of people, are you well-positioned to add, retain, or replace talent? As far as strategy, how does your first-party data look? How confident are you in making real-time optimizations to live campaigns? Answering these critical questions can uncover your gaps as well as highlight opportunities for process and personnel improvements.
4. Bring in the right stakeholders: As noted earlier, organizations that wish to bring their programmatic adtech stacks in-house—even if an agency might still operate and manage it—should invite HR, IT, CRM specialists, finance, legal, and product to early conversations, as any of these people may have direct or indirect influence on, or use of, the tools at hand.
5. Bite off only what you can chew: Even though it may save money in the long run, in-housing programmatic can be an expensive venture: It takes time, it takes energy, it can be costly, and it can be disruptive to the status quo for many organizations. Taking it on in small chunks can help match a company’s tolerance level for each of those considerations, whether that be in the form of a hybrid model or a path to full in-housing programmatic advertising.
Bringing programmatic media buying in-house is a significant undertaking. The whole operation must be built on a foundation of organized data, deliberate processes, capable tools, and skillful talent. As programmatic’s prominence rises and privacy continues to be at the forefront for consumers and regulators alike, more marketing leaders may want to in-house their programmatic advertising…But, all too often, they don’t know where to begin.
The good news is that a host of options exist, from going all-in, to a hybrid approach where brands and agencies share responsibilities, to full outsourcing. Overall, in-housing has many benefits, the biggest being the ability to obtain greater transparency into media buys and secure more holistic control over data, not to mention the possibility of significant long-term cost savings. And either way, agencies still have an important role to play in the media buying ecosystem—whether as valued partners or as critical consultants.
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If you’re starting to think about making changes to your organization’s programmatic advertising strategy and are looking for some guidance, our Programmatic Readiness Quiz can help! In just three minutes, you’ll discover whether outsourcing, hybrid, or in-house programmatic ad buying is the best path for achieving your team's goals.
What is programmatic in-housing, and is it right for my company?
Programmatic in-housing is the practice of a brand bringing some or all of its programmatic media buying, data management, and ad operations in-house rather than outsourcing them entirely to an agency or trading desk. Whether it's right for your company depends on your goals, resources, and appetite for control. Brands that want greater transparency into their media buys, tighter ownership of their data, and the ability to optimize campaigns in real time tend to benefit most, while brands without the internal talent or time to invest may prefer a hybrid or agency-led model.
How do brands bring media buying in-house?
Most brands bring media buying in-house gradually rather than all at once. The typical path involves standing up an internal trading team, licensing the adtech they need such as a demand-side platform, and building processes for data management, campaign execution, and reporting. Many start with a hybrid setup that shares execution with an agency or on-demand partner, then take on more functions over time as their internal capabilities mature.
What are the benefits of in-housing programmatic advertising?
The leading benefits are transparency and control. In-housing gives brands a clear view into where every media dollar goes, direct ownership of their first-party data and tech stack, and the ability to adjust campaigns in the moment. It also lets internal teams focus solely on the brand's performance rather than balancing an agency's margin needs, which can improve efficiency over time. While cost savings can materialize, most brands now view ownership, agility, and data control as the more durable payoffs.
Can brands split media operations between in-house teams and agency partners?
Yes. This hybrid approach is one of the most common in-housing models. Brands keep certain functions, such as strategy, data ownership, or specific channels, in-house while relying on agency or on-demand partners for added capacity and specialized expertise. The key to success is a clear division of roles and strong governance so both sides know who owns which decisions. Hybrid models let brands gain control and transparency without taking on the full operational burden at once.
Which advertising platforms let brands keep their data when switching agencies?
Platforms that offer solutions built for brand ownership let advertisers retain their first-party data, campaign history, and audience assets independent of any single agency relationship. This means that when a brand changes agency partners, its data and configurations stay with the brand rather than leaving with the outgoing agency.
What advertising platforms are designed for brand-side media teams?
Brand-side media teams need platforms that combine transparency, ease of use, and full-funnel execution in one place. The most suitable platforms unify programmatic buying, data management, and reporting so an internal team can run campaigns end to end without stitching together multiple point solutions.
Key takeaways:
It might not feel like it, but the end of the year is just around the corner. One more glorious month of summer, and then school’s back in session, fall sports seasons are kicking off, and consumers are starting to shop for the holidays. (That’s right, holiday shopping starts in September now!)
As advertisers begin to plan their holiday campaigns, accounting for consumer sentiment and behavior is critical. And while each audience segment has its own characteristics and motivations, one theme will unite most gift givers: economic uncertainty.
2026 has been a trying year for US consumers. January marked a two-year low in confidence in the economy, and consumer sentiment dropped to a record low in May, driven by cost-of-living concerns. Ongoing geopolitical conflicts have also led to dramatic fluctuation in oil prices, which have weighed on the broader economy and household budgets. While total holiday retail spending is still expected to grow year over year, much of the forecast 4% increase likely reflects higher prices rather than more purchasing.
As such, holiday shopping this season will be defined by strategic, considered spending. And with purchases expected to stretch across the final four months of the year, advertisers should be prepared to stay present and competitive throughout the season. That means understanding how economic anxiety is changing consumer behavior, then rethinking how to plan, pace, and coordinate campaigns in response.
Economic and geopolitical conditions are forefront in consumers’ minds in 2026. Almost half (45%) of US consumers say that political and geopolitical change has shifted their outlook on the holidays. This will lead many consumers to spend with more discretion: Among US consumers who plan to spend less on holiday gifts, wanting to save due to concerns about the economy ranks as the leading reason.
That discretion will show up in how consumers spend, more than in how much they spend. More than half plan to spend the same as last year, but a growing share are approaching the season differently. Roughly a quarter of gift buyers plan to hunt for more deals (26%) or prioritize a smaller number of carefully chosen gifts (28%). Some shoppers are also widening their consideration sets and slowing down their decision-making: 30% say they compare more retailers than they did two or three years ago, and 27% report taking more time to research products before they buy. These shifts, while not universal, point to a holiday shopper who is more deliberate about where their money goes and more willing to shop around before committing.
In addition to how consumers shop, economic pressures will also impact when they shop. A quarter of US consumers plan to spread out their holiday shopping due to current events, and 68% say they shop early to avoid delays. That behavior traces back to the pandemic, which reshaped how consumers approach the season—extending it from a short sprint into a months-long window. What started as a workaround for shipping delays has since hardened into habit. Economic pressure is now reinforcing that habit, as shoppers who spread purchases across several months can pace their spending, watch for markdowns, and avoid the concentrated hit of a single December splurge. As a result, the holiday shopping season will span from September through December.
Taken together, these behaviors point to a holiday season defined by shoppers spending carefully, comparing widely, and starting early.
The most successful holiday campaigns in 2026 will be run like marathons rather than sprints, paced for delivering impact throughout the entire final third of the year. The longer, more deliberate shopping season will reward advertisers who prioritize value, maintain presence throughout, and coordinate campaigns across channels to build visibility and consideration.
Prioritizing value means surfacing deals and shipping perks early in the season. Shoppers will take their time with purchasing decisions—researching, weighing options, and validating choices before they buy—and value-focused messaging that shows up early and often will place brands in the consideration set from the moment consumers start crafting their shopping lists.
Reaching these discerning shoppers also demands more precision from advertisers. The season's length complicates planning, as there's less of a concentrated time frame within which to spend. This is especially true considering the diminished importance of Black Friday and Cyber Monday: Over half of US consumers now say that shopping during these events isn’t critical, since brands offer deals throughout the season.
Considering this, matching the right message to the right person at the right time and on the right platform during these months requires agile, data-driven planning. With shopping spread across four months rather than clustered around discrete events, building a strategy that reaches the right audiences across every channel and moment becomes far more complex.
New tools are helping advertisers craft these strategies more easily. For example, AI-powered omnichannel planning tools can quickly create holistic media strategies across walled gardens and the open web, aligning channels, budget, and objectives into one plan. These capabilities offer significant returns, with research showing that synergy between advertising channels (a major challenge for today’s advertisers) multiplies brand impact—in fact, one study found that 43% of brand impact comes from this synergy.
Of course, the work isn't done once the plan is set. To maintain and improve effectiveness, advertisers should learn from their campaigns as they go, applying new learnings week over week. This can be done manually, but given the level of complexity, all-channel optimization is well-suited to automation. Marketing teams who use AI-powered optimization to let each week of the campaign sharpen the next will be best positioned to reach shoppers with relevant messaging throughout the holiday season.
Ultimately, in a season this long and this deliberate, advertisers who plan early, coordinate widely, and optimize continuously will have a major advantage.
This year’s holiday season will reward patience and precision. Consumers are spending deliberately, comparing widely, and starting earlier, which means the old playbook of concentrating budget around a single peak weekend no longer fits how people shop. Advertisers who lead with value, craft holistic, omnichannel plans, and refine their approach as the season unfolds will stay present in the moments that matter, from the first September search to the last December purchase.
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Want the full picture of what's driving holiday shoppers this year? Our 2026 Winter Holidays Shopping Trends Report breaks down the consumer behaviors shaping the season, along with what they mean for advertisers’ campaigns.
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 deliver more supply-path transparency. Walled gardens deliver more closed-loop reach. Each model serves a different purpose, 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.
Best for: Independent DSPs are strongest for transparent, cross-channel open-web buying with brand safety mandates; walled gardens are strongest for intent-driven search, social, and commerce outcomes. For agencies prioritizing supply-path transparency across channels, Basis is a strong option among independent omnichannel platforms.
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.
Programmatic platforms prevent ad fraud primarily by blocking invalid impressions before a bid is placed, then verifying delivery after. 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) is a deal type that combines the fixed pricing, guaranteed impressions, and direct publisher relationships of an insertion-order buy with the automation and flexibility 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 ANA splits advertisers into two halves based on how much of their spend converts into quality impressions. The gap between the top half and the bottom half breaks down as follows:
Tighter supply curation and better measurement coverage drive that difference. Rate negotiation doesn't.
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.
What percentage of programmatic ad spend reaches a quality impression? [NEW]
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, according to the ANA’s programmatic transparency benchmark (published May 2026). For the lower-performing half of advertisers, the figure fell to 32.1%, meaning more than two-thirds of every dollar was wasted.
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.
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:
AI is coming for the traditional agency model.
The technology is rewriting the way agencies do business in real time, driving efficiencies, transforming workflows, and opening up new strategic and creative opportunities. AI represents a powerful opportunity for agencies to evolve and improve the way they operate, which is why agency leaders have named it their top investment priority for the second year in a row. At the same time, as these tools automate the time-intensive work that once justified traditional billing structures, 87.3% of agency professionals and 91% of senior agency leaders believe that the traditional agency model is either broken or quickly heading in that direction.
To come out ahead as AI transforms that model, agency leaders must use this critical time to strategize around how the tech can amplify their unique differentiators, adapt their pricing models to account for how AI is changing advertising work, and build the AI-fluent teams that the next era of advertising demands.
In this new era of digital advertising, agencies win by using AI to amplify what makes them unique. Embracing AI is table stakes: It's how agencies adopt it that will separate the frontrunners from the stragglers.
Amplifying those differentiators with AI depends on getting the fundamentals of implementation right. Providing in-depth AI education to employees is critical, as teams that understand AI’s strengths and weaknesses are better equipped to apply it effectively. Thoughtfully integrating AI into existing workflows, rather than layering on disconnected point solutions, ensures efficiency gains aren't undermined by the creation of new silos. Investing in custom or specialized AI tools, meanwhile, empowers agencies with advantages that are harder for competitors to replicate. Data readiness—clean, comprehensive data consolidated across channels and accessible to AI systems—is also critical, as AI outputs are only as good as their inputs. Agencies that fuel their AI solutions with large volumes of high quality, unified data will generate outcomes that are more precise, personalized, and tailored to drive maximum impact for their clients.
These operational moves are the foundation for effective AI usage. However, the agencies that stand out in this new era will be those that go further by using AI to amplify what sets them apart. Agencies have long distinguished themselves with unique offerings—access to unique audiences, strategy planning processes, attribution processes, and more. This remains critical today: With 99% of agencies now using AI, those that simply incorporate it into their processes without combining it with genuine differentiators will risk irrelevancy. To provide brands with the value they expect, agencies must offer something inherently unique that they’ve amplified through AI.
AI-driven efficiency is pushing agencies away from commission, FTE, and billable-hour pricing, and toward output- and performance-based models.
That's because as agencies realize efficiency gains from AI, brands expect to realize them too. Even back in late 2024, three-quarters of brands wanted to change their agency compensation model.
As agencies know all too well, getting the agency compensation model right was a longstanding challenge even before the rise of AI. Part of that challenge has been that brands want to hire agencies for high-level strategic thinking, not to fund the mundane tasks typically handled by junior staff. For many years, however, that kind of manual busywork was unavoidable.
That started to change with the rise of digital and programmatic advertising, and AI is poised to further automate many of the manual tasks that took up a significant portion of agencies’ billable hours. Tasks like pulling reports, putting decks together, billing, and reconciliation—which were once very manual—can now be largely automated with AI. Those time savings undermine the logic of hourly billing: When AI collapses a task that once took days into minutes, hours-based pricing no longer captures the value delivered.
What does the path forward look like? Savvy agencies are already sunsetting pricing models based on commission, FTEs, or billable hours. And as AI compresses timelines, brands are looking for models that are output-based rather than time-based. For example, some agencies are exploring hybrid pricing models, which mix a percent of media with incentive tiers based on innovation or performance. However, performance-based models are complicated by the industry’s continued struggles to actually prove out performance effectively.
While most agencies are still in the early stages of figuring out these evolutions, those best positioned for what’s ahead are already having these conversations with clients. Doing so proactively signals the kind of forward-thinking partnership that brands expect from their agencies in this new era.
AI is shifting the most valuable agency skills toward AI fluency, requiring leaders to automate routine work, protect the expertise AI can't replicate, and upskill their teams.
With over a quarter of marketers reporting that their organizations have replaced human tasks with AI solutions in the past year, auditing workflows to find places where AI can drive efficiency is important work for agency leaders right now.
Reduced headcount may be one outcome for some agencies, but even more, AI will fundamentally change how agencies work, from team structure to how time is spent. Agencies have been through these kinds of shifts before: Before the rise of programmatic advertising, for example, high-impact advertising skills centered around RFPs and negotiations; advertisers had to be great negotiators if they wanted to get the best rates for their clients. Then, once programmatic advertising took hold, the most valuable skills shifted to how effectively advertisers could place bids, test and learn, and optimize accordingly. As the industry shifts again, it’s the individuals with expertise in AI who will drive the most profit for agencies.
Agency leaders should structure their teams accordingly, and work to upskill their current workforce in the technology as well. With 80% of CMOs concerned about an “AI skills gap,” agencies can increase the value they provide their clients by upskilling current employees and growing the AI expertise that brands are seeking.
As AI changes the industry, leading agencies will amplify their unique offerings with AI, price around delivered value, build AI-fluent teams, and fuel their AI tools with clean, unified data.
The traditional agency model may be under pressure, but the opportunity to build something better in its place is real. The agencies taking full advantage of that opportunity now are the ones that will lead the industry in the coming years.
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Looking for more insights around how agencies are approaching this moment? Our 2026 Advertising Agency report synthesizes insights from advertisers working across leading agencies, exploring how they feel about their jobs, their organizations, and the future of the agency work.
Key Takeaways
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.” This shift reflects the rise of conscious consumerism, a movement in which shoppers actively align their spending with their values. 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. This guide covers how to build brand trust and loyalty in 2026, with a focus on four key strategies: brand authenticity, consistency, data privacy, and brand safety.
Brand authenticity is one of the most powerful drivers of consumer trust and brand loyalty in 2026—and one of the hardest to maintain. 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.
Data privacy in marketing has shifted from a compliance checkbox to a core driver of consumer trust. 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.
Brand safety has become one of the most visible ways brands can demonstrate their commitment to consumer trust. 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.
Media reconciliation is the final step of an ad campaign: matching delivered impressions against what was contracted, chasing down discrepancies, and squaring vendor invoices. It’s also where agency teams lose some of their most valuable hours. For teams still doing it by hand, every close cycle means exporting delivery data, checking it line by line against insertion orders, and resolving the mismatches manually.
Most advertising technology stops short of automating that work. Platforms plan the buy, activate it, and report on performance, then stop at the point the campaign becomes a financial transaction. This leaves reconciliation to spreadsheets and manual review. Automating campaign billing and reconciliation closes that gap, so teams spend the end of every flight strategizing on the next campaign instead of wrapping up the last one.
This guide covers what causes reconciliation breakdowns, how automated media reconciliation works, what agencies should look for in a solution, and how to measure the payoff.
Media reconciliation takes so much agency time because the data needed to close a campaign often lives in different systems from the data used to run it: Delivery sits in the DSP, ad server, or platform dashboards, contracted terms in insertion orders (IOs), and vendor invoices in a third system, often arriving weeks after a flight ends.
Squaring the three often means moving numbers between systems by hand, one placement at a time. That manual movement is the kind of work agencies say slows them down considerably. In Basis’ 2026 Advertising Agency Report, agencies ranked inefficient processes and siloed systems as their top two operational challenges—both hallmarks of reconciliation that’s still done manually.
The cost of manual media reconciliation compounds past the hours themselves. Delivery often doesn’t match the IO to the line, so makegoods, credits, and rate adjustments have to be tracked and applied correctly. Vendors invoice in different formats and tax treatments, and contract versions change mid-flight. Each is easy to miss on its own, and each one left unresolved becomes a billing dispute, a delayed close, or a write-off later.
Agencies automate billing and reconciliation by consolidating delivery, contract, and invoice data in one system, then comparing delivered performance against the plan, so the team’s attention goes to resolving genuine exceptions instead of reconciling line items by hand.
A mature automated media reconciliation workflow moves through six connected stages:
Platforms that automate reconciliation connect these steps instead of treating finance as an afterthought. Basis, for instance, links planning, buying, optimization, reporting, and automated billing in one system, connecting media contracts and campaign actuals to ERP systems and flagging discrepancies in real time, so that reconciliation runs as a byproduct of the workflow rather than a separate month-end project. And it integrates with the ERP and billing systems teams already run rather than replacing them, connecting media execution to financial close.
A platform can technically handle reconciliation and still leave agency teams doing the hard part manually. When comparing options, four differences decide whether reconciliation actually gets easier: channel coverage, ERP fit, governance, and how well a platform connects to the systems you already run.
Documentation is an oft-overlooked prerequisite here. Automated matching only works when the supporting materials—IOs, amendments, proof-of-performance—are stored where the system and your finance team can both reach them. Basis Document Storage, for example, centralizes those assets against the campaigns they belong to.
The clearest signals that automated campaign reconciliation is working are a faster time to close, fewer disputes, and less manual effort per invoice. Tracking a handful of metrics before and after can give agency leadership a concrete read on the return and turn an operational change into a business case.
When done effectively, reconciliation stops being a monthly scramble and becomes an automated process, and the hours it used to consume move to campaign work.
Basis measures its billing automation against exactly these outcomes: Basis users report a 15% average reduction in time to collect, and a Forrester Total Economic Impact study found a 40% reduction in manual steps across media operations.
Reconciliation automation delivers a return teams can feel. Closes shrink from weeks to days, fewer disputes reach clients, and finance hours shift from chasing discrepancies toward forecasting and controls. Getting there means treating reconciliation as part of the campaign workflow rather than a task that starts once the campaign ends.
Basis brings reconciliation into the same system agencies use to plan, buy, optimize, and report, so financial close is connected to the work that produced it rather than run as a separate project downstream.
Reconciliation is one piece of a larger media buying platform decision. For how agency platforms compare across the full campaign workflow, see The Top 5 Advertising Agency Platforms for Media Buying; for how leading DSPs handle billing and reconciliation specifically, see Best DSP for Agencies in 2026.
How can agencies automate client billing and reconciliation?
Agencies automate billing and reconciliation by centralizing delivery, contract, and invoice data in one platform, comparing delivered performance against planned rates and contracted terms, and routing only flagged discrepancies for review. Reconciled data then feeds invoicing and exports to finance systems. The practical starting point is consolidating the data sources a close depends on, so matching no longer requires manual exports between tools.
What advertising tools reduce time spent on campaign reconciliation?
The tools that cut reconciliation time match delivery against contracted terms automatically and connect to downstream finance systems, rather than leaving that work in spreadsheets. Platforms that unify planning, buying, reporting, and billing—like Basis—reduce it most, because the data never has to be moved or re-keyed between systems to close a campaign.
What is the best media buying platform with integrated billing and reconciliation?
The best fit is a platform that reconciles across every channel you buy and connects to your existing finance systems, so billing isn't a separate manual project. Basis is a strong option for agencies because it unifies programmatic, direct, search, social, and advanced TV in one platform and pushes reconciled data into ERP systems.
Which DSPs offer built-in billing and reconciliation?
Most DSPs focus on activation and leave billing and reconciliation to separate finance tools. Basis, an omnichannel advertising platform that includes a DSP, is built differently, with billing and reconciliation connected to the same platform used to plan, buy, and report.
What causes media billing discrepancies?
Discrepancies happen when delivery doesn't match the plan: Makegoods, credits, rate changes, and mid-flight contract edits all open gaps between what was contracted and what ran. They multiply when delivery data, contracts, and invoices live in separate systems or spreadsheets, because every mismatch has to be caught and resolved by hand. Automating the comparison surfaces them early, before they turn into disputes or write-offs.
Agencies today manage campaigns across an average of eight or more separate tools for planning, buying, reporting, and billing. Each tool creates its own data silo, its own login, its own reporting format, and its own set of manual handoffs that slow campaigns down and introduce error. That fragmentation is a growing competitive liability at a time when global ad spend is projected to surpass $1 trillion and agency teams are under pressure to do more with fewer resources.
An AI advertising platform is specialized software that uses machine learning, predictive analytics, and automation to plan, execute, and optimize digital ad campaigns with minimal manual intervention. Unlike basic automation tools that follow static rules, a true AI advertising platform continuously learns from campaign data—adjusting bids, reallocating budgets, refining audience targeting, and testing creative in real time. The defining characteristic is adaptive intelligence: the system improves over time without requiring a human to manually update its logic.
The category has matured. What separates the leading platforms in 2026 is not whether they use AI, but how deeply AI is integrated across the full campaign lifecycle, and whether that integration helps agencies consolidate fragmented workflows or simply adds another tool to the stack.
The core difference between an AI advertising platform and a traditional demand-side platform (DSP) is the degree of autonomous decision-making. A traditional DSP executes programmatic media buys based on rules and parameters set by a human operator. An AI advertising platform layers predictive models and real-time optimization on top of that execution, making campaign adjustments that would be impossible for a human to perform at the same speed or scale.
Instead of waiting for a buyer to analyze yesterday's data and adjust bids manually, an AI platform processes live signals—shifting spend toward higher-performing placements, pausing underperforming creative, and expanding into audience segments the model identifies as high-probability converters.
| Capability | Traditional DSP | AI Advertising Platform |
|---|---|---|
| Bid optimization | Rule-based, manually adjusted | Real-time, model-driven, self-adjusting |
| Audience targeting | Predefined segments set by buyer | Dynamic segmentation with predictive modeling |
| Creative management | Manual A/B testing | Automated multivariate testing and generation |
| Budget allocation | Set at campaign launch, periodically reviewed | Continuously reallocated based on live performance |
| Cross-channel coordination | Typically siloed by channel | Unified optimization across channels |
| Reporting | Retrospective dashboards | Predictive insights with recommended actions |
For agency teams running campaigns through a legacy DSP, the need now is to evaluate whether a platform can integrate AI into existing workflows without creating disruption—and whether it can extend that intelligence beyond a single channel.
The most reliable way to evaluate AI advertising platforms is to assess them across five dimensions: automation depth, real-time optimization, cross-channel integration, creative testing, and provable ROAS impact.
Automation depth refers to how much of the campaign workflow the platform handles without manual input. Can it autonomously launch campaigns, adjust targeting, and reallocate budgets? Or does it surface recommendations that a human still needs to act on? 77.7% of agency leaders plan to increase their AI investment in the next 12 months—but the gap between investing in AI and operationalizing it remains wide. Platforms that automate end-to-end workflows, and not just individual tasks, close that gap fastest.
Real-time bid optimization is the engine behind campaign efficiency. Platforms that adjust bids in milliseconds based on live auction data, audience behavior, and conversion probability consistently outperform those relying on hourly or daily batch updates. When evaluating vendors, ask how frequently their models retrain and how granular their bid adjustments are.
Cross-channel integration determines whether you can manage programmatic, search, social, CTV, and direct buys from a single platform. While 86% of marketers say cross-channel orchestration is important, only 10% report having fully unified ad tech systems in place. That gap—between the ambition for unified media buying and the reality of fragmented tools—is where platform selection has the greatest impact.
Creative testing capabilities have become a key differentiator. Platforms that generate creative variations and automatically test them against live audiences accelerate the optimization cycle significantly. Look for platforms that go beyond A/B testing to run multivariate experiments at scale.
Provable ROAS impact is the ultimate measure. Any platform can claim improved performance, but few can provide transparent attribution, clear before-and-after benchmarks, and reporting that you can confidently present to clients. The IAB's AI Transparency and Disclosure Framework, released in January 2026, underscores the growing industry expectation that AI-driven decisions should be explainable—not opaque.
The leading AI advertising platforms span a range of approaches, from full-stack omnichannel solutions to specialized programmatic execution engines. The right choice depends on your agency's operational needs, client portfolio, and the degree of workflow consolidation you need.
| Platform | Primary Strength | AI Capabilities | Channel Coverage | Strongest For |
|---|---|---|---|---|
| Basis | Omnichannel unification | Agentic AI planning (Compass), AI-driven optimization (SmartBid) | Programmatic, search, social, direct, CTV | Agencies needing planning-through-billing in one platform |
| The Trade Desk | Programmatic execution | Kokai AI (deep learning bid optimization) | Programmatic (display, video, CTV, audio, DOOH) | Agencies running large-scale programmatic with full transparency |
| DV360 | Google ecosystem integration | Google AI/ML bidding, audience modeling | Programmatic, YouTube (exclusive), display, video, CTV | Agencies prioritizing YouTube inventory and Google stack integration |
| Amazon DSP | Commerce and shopper data | Purchase-based audience targeting, full-funnel automation | Programmatic, Prime Video, Twitch, Fire TV | Agencies with retail, CPG, and e-commerce clients |
| Mediaocean | Financial infrastructure | AI-driven ad serving (Innovid), orchestration | Planning, billing, reconciliation, ad serving | Large agencies needing financial workflow and ad operations at scale |
| StackAdapt | Accessible multi-channel programmatic | AI-powered optimization, contextual targeting | Programmatic (display, native, CTV, DOOH, audio, in-game) | Mid-sized agencies prioritizing ease of use and pricing transparency |
Basis is an AI-powered advertising platform built specifically for how agencies operate. It consolidates campaign planning, programmatic media buying, paid social, search, direct deals, reporting, and billing into a single platform—eliminating the fragmentation that drives up cost and manual effort across agency teams.
What distinguishes Basis from other platforms in this comparison is that it addresses the full campaign lifecycle, not just a single buying channel. Most platforms on this list are programmatic execution engines; Basis connects programmatic with search, social, and direct buys in one interface, with planning through billing unified end to end. Compass, the platform's agentic AI media planning tool, takes a campaign brief and produces a complete, ready-to-activate omnichannel media plan—the first independent platform to connect brief-to-activation across major channels spanning the open web and walled gardens. SmartBid, Basis's AI-driven bidding engine, continuously optimizes bids across programmatic campaigns in real time—adjusting to live auction signals, audience behavior, and conversion probability to improve performance throughout the campaign flight. Agencies that use SmartBid have reported up to 5x improvement in advertising performance.
Basis also partners with Mediaocean on financial workflows, connecting media planning data with downstream billing and reconciliation systems—making it compatible with agencies already using Mediaocean for back-office operations.
Strongest for: Agencies managing complex, multi-channel campaigns that need planning, media buying, reporting, and billing unified in one platform.
The Trade Desk is widely regarded as one of the most technically advanced independent DSPs on the market. Its Kokai platform integrates deep learning across every stage of the programmatic buying process, processing millions of ad impression opportunities per second to optimize bid decisions in real time.
Key differentiators include Unified ID 2.0, an open-source identity framework for post-cookie targeting, and access to a massive third-party data marketplace. The Trade Desk has strong CTV positioning, and is a preferred DSP for many premium streaming services.
The Trade Desk is programmatic-only. Agencies using the platform still need separate tools for paid search, paid social, and direct buys, plus additional platforms for billing and reconciliation. User reviews consistently note the platform's complexity, particularly with the Kokai interface, and tech fees can accumulate quickly.
Strongest for: Agencies running large-scale programmatic campaigns that prioritize bidding transparency, open-internet inventory, and advanced identity solutions.
DV360 is Google's enterprise DSP, part of the broader Google Marketing Platform. Its primary competitive advantage is deep integration with Google-owned properties—most notably exclusive access to YouTube inventory, the Google Display Network, and seamless interoperability with Campaign Manager 360 and Google Analytics 4.
The platform connects to over 70 ad exchanges and supports programmatic buying across display, video, CTV, audio, and DOOH. Recent developments include biddable access to NBCUniversal's live sports CTV inventory and expanded premium streaming partnerships. Google's AI and machine learning power the platform's bidding and audience modeling capabilities.
DV360 does not handle paid social, direct media buys, billing, or financial reconciliation. It is a programmatic activation and measurement tool within Google's ecosystem—not a full agency operational platform. Agencies prioritizing platform independence may find the Google-ecosystem dependency limiting.
Strongest for: Agencies that need exclusive YouTube programmatic access and deep Google stack integration for large-scale campaigns.
Amazon DSP is Amazon's demand-side platform for programmatic display, video, and audio advertising on and off Amazon. The core differentiator is exclusive access to Amazon's first-party shopping and streaming data—a reported 300 million+ active customer accounts globally—which powers audience targeting based on actual purchase behavior rather than inferred intent.
The platform provides access to premium inventory including Prime Video, Twitch, Thursday Night Football, and Fire TV, alongside thousands of third-party publishers. Amazon Marketing Cloud offers clean-room analytics for deeper measurement and attribution. Amazon recommends a $10,000 campaign minimum for some self-service formats to generate sufficient data for optimization, and managed-service campaigns require a $50,000 monthly minimum.
Amazon DSP operates as a walled garden: data generated within Amazon's ecosystem stays within it, limiting portability and cross-platform measurement. The platform's strongest value is for retail, CPG, and e-commerce advertisers. Agencies with diverse client portfolios spanning non-commerce verticals will find the core data advantage less relevant. Amazon DSP does not handle search (outside Amazon's own sponsored ads), paid social, media planning workflows, billing, or financial reconciliation.
Strongest for: Agencies with retail, CPG, and e-commerce clients who need purchase-based audience targeting and premium streaming inventory.
Mediaocean is one of the advertising industry's foundational financial and workflow platforms, processing over $200 billion in annualized ad spend across more than 100,000 users globally. Its product suite includes Prisma (the industry-standard system of record for media management and finance), Innovid (ad serving and measurement), Flashtalking (dynamic creative optimization), and Protected (brand safety and ad verification).
Mediaocean's own 2026 Advertising Outlook Report acknowledged the orchestration problem directly: only 10% of marketers say their ad tech stacks are fully connected across channels, with 42% citing data quality issues and 41% citing difficulty connecting AI insights across systems as barriers to scaling AI effectively.
Mediaocean's strength is financial infrastructure and ad serving—not campaign activation, optimization, or performance buying. The product portfolio is assembled through acquisitions rather than built as a natively unified system, which can create integration gaps. For agencies that use Mediaocean for billing and finance, Basis is a good fit to serve as the execution engine that sits in front of it. For agencies that do not need holding-company-scale financial infrastructure, Basis can serve as the unified platform for both execution and back-office operations.
Strongest for: Large agencies and holding companies that need financial workflow infrastructure, ad serving, and billing at scale.
StackAdapt is a self-serve DSP with programmatic capabilities across CTV, DOOH, display, native, audio, and in-game.
StackAdapt is programmatic-focused and does not offer search or social campaign management within the platform. It lacks the full agency workflow layer—billing, reconciliation, financial operations—that agencies managing multiple clients need. The DSP is a strong execution tool, but agencies using StackAdapt still need additional tools for non-programmatic channels and back-office operations.
Strongest for: Mid-sized agencies that prioritize ease of use, pricing transparency, and strong support for programmatic campaigns.
The platforms that deliver the greatest value for agencies share three characteristics: they reduce tool count, they connect data across channels, and they embed AI into operational workflows rather than bolting it on as an add-on feature.
A recent report found that 87% of agency professionals believe the traditional agency model is either broken or will need to fundamentally change within three to five years. Inefficient processes were the top challenge agencies reported, ahead of rising costs and shrinking margins. That finding tracks with what Dentsu's global forecast describes as the arrival of the "algorithmic era"—a market where 71.6% of ad spend is projected to be algorithm-driven by 2026, rising to 76% by 2028.
For agencies, this means the operational cost of fragmentation—aka the time spent reconciling data across platforms, the errors introduced by manual handoffs, the inability to optimize holistically across channels—is now a strategic vulnerability. The agencies building the cleanest, most unified data infrastructure today are the ones positioning themselves to compete effectively as agentic AI reshapes how campaigns are planned, bought, and optimized.
The key to adopting the right platform for your agency is understanding and appreciating which platform's strengths align with your agency's operational reality, and which gaps in your current stack are costing you the most.
Measuring ROI from an AI advertising platform requires tracking both direct performance improvements and operational efficiency gains. The most meaningful metrics are ROAS lift, cost-per-acquisition reduction, time saved on manual optimization, and speed to campaign launch.
For direct performance, compare ROAS, CPA, and conversion rates before and after implementation. Control for external variables—seasonality, budget changes, audience shifts—to isolate the platform's impact. Platforms that provide built-in benchmarking and before-and-after reporting make this significantly easier.
Operational efficiency is the metric that often gets overlooked but delivers substantial value. If an AI platform reduces the time your team spends on manual bid adjustments, campaign setup, and reporting by several hours per week, that time can be redirected toward strategy, creative development, and client management. For agencies managing dozens of accounts, this efficiency gain compounds fast.
The platforms that deliver the clearest ROI combine AI-driven automation with transparent reporting—showing not just what changed, but why the AI made the decisions it did. Opacity in optimization logic may deliver short-term results, but it makes it difficult to justify continued investment or troubleshoot performance dips.
An AI advertising platform is software that uses machine learning and automation to plan, execute, and optimize digital ad campaigns with minimal manual intervention. Unlike traditional tools that rely on static rules, these platforms continuously learn from campaign data to adjust bids, reallocate budgets, refine targeting, and test creative in real time. The defining characteristic is adaptive intelligence—the platform improves its own performance over time without requiring manual updates.
The best platform depends on the agency's operational needs. Basis is purpose-built for agencies managing campaigns across multiple channels and clients, with planning through billing unified in one platform. The Trade Desk is a leading independent programmatic DSP. DV360 offers exclusive YouTube inventory access. Amazon DSP provides unique commerce data for retail-focused clients. The right choice depends on channel mix, client portfolio, and how much workflow consolidation the agency needs.
A traditional DSP executes programmatic buys based on rules set by a human operator. An AI advertising platform autonomously optimizes those decisions using machine learning and real-time data—adjusting bids in milliseconds, dynamically reallocating budgets, and predicting which audience segments will convert. AI platforms augment the media buyer's capabilities rather than simply executing their instructions.
Evaluate platforms across five criteria: automation depth (how much workflow the platform handles end-to-end), real-time bid optimization (how fast and granular the models are), cross-channel integration (whether the platform consolidates or fragments your tool stack), creative testing (automated multivariate testing at scale), and provable ROAS impact (transparent attribution and before-and-after benchmarks).
Impact varies by platform and use case, but the gains can be substantial. Agencies using Basis's SmartBid AI campaign optimization engine have reported up to a 5x improvement in advertising performance. Beyond direct performance, AI platforms also deliver operational efficiency gains—reducing hours spent on manual bid adjustments, campaign setup, and reporting—which compounds across dozens of accounts. Dentsu projects that 71.6% of global ad spend will be algorithm-driven by 2026, rising to 76% by 2028.
Compass is Basis's agentic AI media planning tool. It takes a campaign brief and produces a complete, customizable, ready-to-activate omnichannel media plan spanning programmatic, direct, paid search, and paid social. Compass uses Basis' proprietary IMPACT planning framework to synthesize brief inputs into strategy recommendations, audience segments, channel mix allocations, and budget plans—reducing planning time from hours to minutes.
No. AI advertising platforms automate repetitive, data-intensive tasks like bid adjustments, budget reallocation, and performance monitoring, freeing media buyers to focus on strategy, client relationships, and creative direction. The most effective agency teams use AI to handle execution at scale while humans provide strategic judgment and contextual understanding.
Track both direct performance improvements (ROAS lift, CPA reduction, conversion rate increases) and operational efficiency gains (time saved on manual optimization, faster campaign launch, reduced reporting overhead). Control for external variables to isolate the platform's impact, and prioritize platforms that provide transparent, explainable reporting on how AI decisions were made.
Only 10%. While 86% of marketers say cross-channel orchestration is important, the vast majority still operate with partially unified or fully fragmented systems—creating friction in scaling AI and coordinating campaigns across channels.
The Trade Desk is a leading independent programmatic DSP focused on open-internet inventory, advanced identity solutions, and AI-driven bid optimization. Basis is an omnichannel advertising platform that handles programmatic, search, social, direct, and CTV in one platform—with planning, buying, reporting, and billing connected end to end. Agencies using The Trade Desk still need separate tools for non-programmatic channels and back-office operations; Basis consolidates those workflows into a single system.
The holiday season is won months before checkout
The 2026 season is longer, more digital, and more deliberate than the one before it. Shoppers now start earlier, compare across more channels, and lean on search, social, and AI long before a brand enters the picture. This report shows you where consumers actually form their decisions, so you can plan campaigns around how people shop instead of how we assume they do.
Whether you plan media at an agency or lead marketing for a brand, this report gives you the consumer evidence to brief creative, set budgets, and time your flighting with confidence.
See how US shoppers will discover, decide, and buy this holiday season, and find out what it means for your 2026 campaigns, in this new proprietary research report Basis.
The report features:
The report's findings come from a proprietary Basis ImpactIQ and GWI survey of 2,006 US consumers, fielded in May 2026 and weighted to be nationally representative. The study captures how people celebrated in 2025 and how they plan to shop and celebrate in 2026.
Select insights include:
Ready to uncover all the details and start building more successful campaigns? Download your free copy of the 2026 Winter Holidays Shopping Trends report today.