Technology
Basis Platform
Advertising Operating System
Communication Portal
Intelligent Collaboration
All Channel Activation
Activate Media on Every Channel
Consulting
Resources
Key Statistics:
Key Takeaways:
Marketing teams have moved quickly to adopt AI. But when it comes to delivering measurable returns on those investments, most organizations are still finding their footing.
For the second consecutive year, agency leaders named AI as their top investment priority, with more than three-quarters planning to increase their AI spend over the next 12 months. However, only about 29% of organizations across sectors say they can dependably measure ROI on their AI initiatives, and CEOs report that just 25% of their AI initiatives have delivered expected ROI.
Right now, the gap between investment and demonstrated impact is significant. Closing it requires a deliberate approach to how tools are selected, how a strong foundation is built before implementation, and how their impact gets translated into stories that resonate with stakeholders.
Getting real returns on AI starts well before a tool is ever deployed. Key steps include:
With a proliferation of AI solutions crowding the market, tool selection has become an increasingly consequential decision. According to Lauren Johnson, Effectiveness Lead at Basis, a deep understanding of how an AI tool works is the starting point for evaluating whether it will deliver meaningful results.
"If we are going to ask AI to help us evaluate datasets, we need to understand how it's executing that task," says Johnson. "What are its outputs based on? What historical data is it drawing from? Marketers need to understand how their tools work in order to assess whether they'll be able to drive the impact they're looking for."
Given the state of the market, deep evaluation is critical: Gartner has warned that many vendors are engaging in “agent washing,” or positioning existing products as agentic AI without adding genuine agentic functionality. Of the thousands of vendors pitching themselves as agentic AI providers, Gartner estimates only around 130 actually deliver on that promise.
A deeper understanding of how AI tools work also makes the case for seeking out specialized solutions over generic ones. Out-of-the-box AI solutions have strengths when used strategically, but specialized tools trained on large volumes of relevant industry data tend to produce more precise and differentiated outputs. An AI media planning tool trained specifically on advertising performance data, for example, can generate cross-channel recommendations grounded in real campaign outcomes—something a general-purpose model isn't equipped to do.
Data quality is a make-or-break factor for AI performance, but most marketing organizations aren't yet where they need to be. Only 21.4% of industry professionals describe first-party data as “foundational” to their organization's AI initiatives, and roughly one-third say first-party data plays little-to-no role in their current AI use at all. Half of leaders also say their businesses don’t have the technical or data stack readiness to support AI agent deployment.
Without clean, unified, accessible data, AI tools produce outputs that are generic at best and misleading at worst—neither of which supports the kind of differentiated results that justify continued investment. Leaders must treat data readiness as a strategic prerequisite. That involves:
Finally, understanding the impact of AI investments requires knowing exactly what success looks like before deployment. “Just like with anything in our world,” says Johnson, “assessing effectiveness starts with setting a goal for what you want the tool to do for you.” If saving time on a specific workflow is the goal, for instance, measure how long that workflow takes today and define a clear target for how much AI should reduce it.
Currently, only 40% of marketing professionals are using or planning to use defined KPIs specifically for their AI solutions. Crafting a phased roadmap for AI adoption, with concrete milestones and tool-specific performance targets, gives teams both a framework for evaluating impact and the foundation for communicating that impact to stakeholders.
How marketing teams use and communicate about their AI investments is just as important as laying the groundwork for them to succeed. The teams best positioned to demonstrate impact tend to:
To achieve maximum value from AI tools, humans must consistently scrutinize what they produce. The technology can provide weak or inaccurate outputs if trained on low-quality data, and of course, there’s the matter of hallucinations: One study found that close to half of marketers spot inaccuracies in AI outputs several times a week.
“AI tools are not going to tell you when they're wrong,” notes Johnson. “Teams need to continually push back against and stress-test AI outputs in order to get real value.”
Leaders should nurture a team culture where pushing back on AI outputs is both expected and encouraged. When that standard is set by leadership and integrated into how teams operate on a daily basis, it drives higher-quality AI usage across the board. Teams that operationalize this approach are better positioned to extract demonstrable value from their AI investments over time.
Proving AI’s value to stakeholders is a significant challenge for marketing teams in 2026. In fact, only 41% of marketers can confidently prove out the ROI of their AI investments, down from 49% in 2025.
This is where the goals and KPIs set during the planning phase become essential. Tracking performance against those benchmarks over time—whether that’s hours saved on a specific task, improvement in campaign performance, or lift in a business outcome—gives teams the evidence they need to make a credible case for AI’s impact.
Beyond quantifying AI’s impact, marketing leaders must strategize around using that evidence to craft compelling narratives for stakeholders. For one thing, while AI can (and should) drive meaningful improvements in speed and cost, leaders should focus more on how their AI investments contribute to business outcomes. AI efficiency gains can be significant and are worth including in these stories, but positioning AI’s value primarily around efficiency risks reinforcing the perception of marketing as a cost center rather than a strategic growth driver.
Connecting investments to business outcomes is also what tends to land with senior decision-makers. The most resonant executive narratives tie major investment asks to concrete business drivers that connect marketing’s AI investments to the revenue and business outcomes that sales and finance leaders care about.
Another key to making those narratives credible is grounding them in a deep understanding of the tools themselves. “Executives want to hear that we're using AI, but they also want to know that it's grounded in real data,” says Johnson. “They want to know what’s powering these tools and that they can trust the outputs.”
That deep understanding is part of what makes an AI narrative credible at the executive level. Leaders who build expertise in how their tools work, what data powers them, and how that functionality is advancing business goals will be poised to earn sustained buy-in.
The ability to drive and demonstrate ROI on AI tools is quickly becoming one of the clearest dividing lines between marketing teams that lead and those that fall behind.
Ultimately, three factors determine whether AI delivers ROI in marketing: Selecting tools with a deep understanding of how they work, fueling them with the data infrastructure they need to perform, and translating their impact into narratives that resonate with stakeholders.
—
Want more insights on how marketing teams are approaching AI? Our AI and the Future of Marketing report synthesizes findings from a proprietary survey of professionals across leading brands and agencies, covering adoption trends, workforce shifts, and the biggest barriers teams are still working through.
Advertising automation is the use of software and AI to plan, activate, optimize, and reconcile ad campaigns with reduced manual intervention. An AI advertising platform brings these capabilities into a single system, spanning media planning, buying, optimization, measurement, and billing across programmatic, search, social, and CTV, rather than a stack of single-purpose point tools. This guide explains what advertising automation delivers in 2026, where vendor claims outrun reality, and how to choose an AI advertising platform.
Key Takeaways:
The best tools for automating media operations are systems that unify campaign planning, activation, optimization, reporting, and reconciliation across programmatic, direct, search, social, and CTV. But while these systems are optimal, many advertisers in 2026 are operating from a far different place, piecing together a stack of single-purpose point solutions.
According to Basis’ 2026 Advertising Agency Report, 36.8% of full-service and media agencies now manage ten or more tools to run their clients’ campaigns, more than double the 2024 share. In the same Basis research, agencies named inefficient processes (44.1%) and siloed, disconnected systems (40.4%) as their top operational challenges.
Today’s advertisers face a market full of vendors promising automation, and a buying environment where the line between genuine operational lift and AI-flavored marketing copy is increasingly blurry. This guide breaks down what advertising automation can actually deliver in 2026, where marketing promises run ahead of platform reality, and how media teams should evaluate platforms for the next stage of automated advertising: the autonomous era.
Advertising automation maturity has four tiers: rule-based automation, semi-automated workflows, algorithmic optimization, and autonomous advertising. Advertising automation lives on this spectrum, and knowing where a platform actually sits is the first filter for any serious evaluation.
At one end: rule-based automations, such as “if-then” logic that pauses a campaign at a spend cap or fires a pacing alert when delivery falls behind. At the other end: autonomous advertising, wherein AI systems plan, execute, and optimize campaigns end-to-end with humans in an oversight role rather than an execution role.
Most of what the industry markets as “automation” today sits in the middle of that spectrum. Rule-based and semi-automated workflows deliver reliable, measurable time savings—and for many teams, they are an impactful capability a platform can offer.
The teams getting the most out of automation today are the ones evaluating across all four tiers. They also scrutinize every ‘autonomous’ claim, checking where a platform truly lands on this spectrum rather than where its marketing says it does.
A practical way to categorize where a platform’s automation actually operates:
| Automation Tier | What it does | Maturity in 2026 |
| Rule-based automation | Predefined if-then triggers and actions (ex. pause at a spend cap, fire off a pacing alert) | Reliable, generally transparent, widely available |
| Semi-automated workflows | Templates, bulk operations, and guided processes that cut manual steps but still need human initiation | Common today |
| Algorithmic optimization | Machine learning adjusts bids, budgets, and targeting within human-set parameters | Maturing, in production |
| Autonomous advertising | AI plans, executes, and optimizes independently, with humans in oversight rather than execution | Emerging, evaluate on a platform-by-platform basis for real capability |
In 2026, four categories of advertising automation are delivering genuine, measurable time savings for media teams right now: automated planning, automated performance, automated measurement, and automated billing. Each of these maps to a stage of the campaign lifecycle where manual work has historically consumed the most hours.
| Automation category | What it automates | Measured result with Basis |
| Automated planning | Turning briefs into omnichannel media plans | Compass by Basis builds media plans 50% faster |
| Automated performance | Bid optimization and mid-flight budget reallocation | SmartBid drives 36% lower CPA and 35% higher CTR |
| Automated measurement | Normalizing cross-channel data into client-ready reporting | Unified dashboards across programmatic, search, social, and CTV |
| Automated billing | Reconciliation and invoicing from plan to payment | Basis delivers a 15% average reduction in time to collect |
Automated planning: AI-driven media planning translates briefs into omnichannel strategies in minutes, pulls in past performance to sharpen recommendations, and exports presentation-ready plans without the manual build—all within the same platform where campaigns are eventually activated. The 2026 Advertising Agency Report found that only 29.1% of agencies currently use AI for media planning and 22.1% for media buying strategy—among the lowest adoption rates of any AI use case, despite both being among the highest-leverage. AI advertising platforms that handle planning natively, like Compass by Basis, allow teams to create media plans 50% faster.
Automated performance: This is where many teams already feel the impact: algorithmic bid optimization, real-time budget reallocation, and continuous mid-flight optimization that’s difficult for human teams to replicate at scale. For instance, Basis’ SmartBid AI optimization tool drives a 36% decrease in cost per acquisition and 35% increase in CTR for clients deploying it across their programmatic campaigns. This is also a tier where ‘AI-powered’ claims can be easy to test. Rather than asking whether a platform optimizes bids and budgets (since nearly all of them do), ask how much it moved CPA and CTR and how consistently.
Automated measurement: Pulling performance data from a fragmented stack, normalizing metrics across channels, and assembling client-ready reports is one of the most time-intensive jobs in media operations. Unified dashboards that aggregate programmatic, direct, search, social, and CTV data into a single view eliminate hours of weekly spreadsheet work with live reporting teams can actually trust, and they connect into the tools agencies already run on—such as Looker, Datorama, TapClicks, and Ninjacat—rather than forcing a rebuild.
Automated billing: Reconciliation and invoicing remain among the least glamorous, most time-consuming, and most error-prone work in media operations. Spend has to be matched, contracts have to tie out, and invoices can’t go out until the numbers agree. Platforms that offer automated billing, like Basis, treat this as a closed loop from first media plan to final invoice, keeping planning, activation, reporting, and reconciliation in one system rather than handing data between disconnected tools and finance teams. With Basis, this results in a 15% average reduction in time to collect. Many ‘automation’ pitches skip this stage entirely, so it’s worth asking a vendor how much of the plan-to-payment cycle actually runs without manual intervention.
Not every automation claim holds up under operational scrutiny (especially when AI is involved), and the gap between marketing and infrastructure is where many evaluations break down. That said, the right kind of skepticism is not anti-AI. Rather, it’s a demand for specifics: What the AI actually does, where it operates in the workflow, what data is powering it, and what it measurably changes.
Separating real automation from marketing comes down to recognizing a few recurrent patterns:
Red flag: “AI-powered” with no specifics
What model powers each capability? The “AI” label has become so broadly applied that it has lost meaning without further elaboration. A platform using a rules engine to automate a workflow is not the same as one using machine learning to optimize in real time, yet both often market themselves as AI-driven. When evaluating platforms, ask what specific model powers each capability, what data it trains on, how often it retrains, and how its recommendations get validated.
Red flag: black-box optimization
Can you inspect, override, and audit the AI’s decisions? Some platforms surface AI recommendations that the buyer cannot inspect, override, or audit. That works fine in a demo. It is a serious problem in a campaign where questions like “Why did we spend $10,000 on this placement?” need an answer the team can actually defend. Look for platforms that expose the logic, show the inputs, and let humans intervene at any step.
Red flag: optimization biased toward the platform’s own inventory
Does the AI favor its inventory or your outcome? Walled-garden AI tools—particularly the ones bundled with single-channel ad products—often optimize toward their own inventory by default. When evaluating an omnichannel platform, the question to ask is whether a platform’s optimization logic is built to favor inventory it controls or built to favor the marketer’s outcome. Some platforms own inventory and bias toward it. Some own inventory and choose not to. Some have no inventory stake at all. Basis, for instance, owns Basis DSP but does not optimize toward it; media decisions in Compass and SmartBid are made against marketer outcomes, not internal inventory preference. That distinction becomes even more important as autonomous systems take on a larger share of budget allocation decisions.
Red flag: instant, zero-effort onboarding
What setup does meaningful automation actually require? Vendors sometimes imply that automation works out of the box with no configuration, training, or workflow adjustment. In practice, meaningful automation requires setup: defining rules, mapping workflows, integrating reliable data sources, and training teams.
When choosing an AI advertising platform, evaluate it against six criteria that map to real operational lift: workflow coverage, integration depth, honest maturity-tier assessment, human-in-the-loop control, time-to-value, and total cost of ownership.
These criteria typically sort platforms into four broad groups. Where a platform lands shapes both its automation ceiling and whether its optimization works toward the marketer’s outcome or its own inventory:
| Platform type | Best for | Automation ceiling | Risk of inventory bias |
| Unified omnichannel platforms | Unified media planning through reconciliation across all channels | Algorithmic optimization across channels, with a foundation built toward autonomous workflows | Depends on platform |
| Legacy DSPs with AI add-ons | Programmatic buying with bolted-on optimization | Algorithmic optimization | Depends on vendor |
| Walled-garden / single-channel tools | Deep automation within one channel | Channel-specific optimization and automation | High (often optimizes toward own inventory) |
| Point solutions | A single workflow (ex. planning, reporting, or reconciliation) | Task-level automation | Depends on vendor |
Autonomous advertising runs on infrastructure, data, and interconnectivity. This is also the foundation for agentic advertising, where AI agents take on planning and optimization tasks independently while humans set strategy and guardrails. Whether described as autonomous or agentic, the operational prerequisites are the same. The teams ready for the next several years of AI evolution are the ones with the operational layer already in place: a connected platform that automates the campaign lifecycle, AI applied transparently against unified data, and human judgment looped in intentionally.
That is the foundation Basis is built around. Compass for AI-powered planning. SmartBid for performance optimization. Unified dashboards and automated billing reconciliation closing the loop from plan to payment. All inside a single omnichannel platform—connecting programmatic, search, social, site direct, and CTV—with the auditability and oversight media teams need to actually trust the AI underneath it.
What is the best AI advertising platform for agencies in 2026?
The strongest AI advertising platforms unify planning, activation, optimization, reporting, and reconciliation across programmatic, search, social, direct, and CTV in one system, rather than bolting AI onto a single channel. Evaluate them on workflow coverage, integration depth, and transparent, unbiased optimization. Basis is one omnichannel example built around this model.
What is advertising automation, and how does it work?
Advertising automation is the use of technology—including rule-based logic, algorithmic optimization, and AI-driven decisioning—to streamline tasks across the paid media campaign lifecycle: planning, trafficking, bid optimization, cross-channel reporting, and financial reconciliation. It reduces operational hours, minimizes errors, and frees talent up to focus on strategy.
How is advertising automation different from marketing automation software?
Advertising automation focuses on paid media operations across programmatic, search, social, direct, and CTV. Marketing automation software manages email workflows, lead nurturing, and CRM sequences. The two categories solve different problems and serve different teams within an organization.
Which platforms automate campaign planning and activation?
Platforms that automate planning and activation natively, rather than offering planning as a standalone module, are the ones delivering the most lift today. Look for AI-driven media planning that translates briefs into omnichannel strategies, exports presentation-ready plans, and activates campaigns with one click against live line items. Compass by Basis is one example, and teams that use Compass build media plans 50% faster.
Can advertising automation work across programmatic, search, social, direct, and CTV in one platform?
Yes. The key distinction is whether the platform offers true workflow integration—where data flows automatically between planning, activation, and reporting—or simply provides multi-channel access through separate modules. A unified omnichannel platform like Basis eliminates the manual data transfers and context-switching that fragment cross-channel campaign management.
What advertising tools reduce time spent on campaign reconciliation?
Reconciliation tools that match delivered spend against contracted terms, flag discrepancies automatically, and push actuals into ERP systems eliminate hours of spreadsheet work and reduce billing errors. Platforms like Basis handle reconciliation natively rather than requiring exports to a separate finance system.
What is autonomous advertising, and which vendors lead the category?
Autonomous advertising is AI-driven planning, activation, optimization, and reconciliation across the full campaign lifecycle, with humans in an oversight role. The leaders in the category are omnichannel platforms with AI built natively into the campaign lifecycle, unbiased regardless of inventory ownership, and transparent in how their optimization logic works.
How do I measure whether advertising automation is delivering real ROI?
Track operational metrics before and after implementation: hours spent per week on campaign setup, reporting, and reconciliation; error rates in trafficking and reconciliation; time from campaign brief to activation; campaigns or accounts each team member can manage; reduction in billing discrepancies. Compare those savings against the total cost of the platform.
Media is being rebuilt from the ground up. In this episode of Adtech Unfiltered, Axios media correspondent and CNN media analyst Sara Fischer joins Noor Naseer to unpack how AI, consolidation, creator-led businesses, and shifting consumer behavior are reshaping media.
They discuss why audience attention is more fragmented than ever, what publishers are getting right (and wrong), how AI is changing the economics of journalism, and why trusted brands still matter. It's an inside look at the trends shaping the future of media, advertising, and the business models that will determine who thrives next.
AI-powered strategic media planning tools are software platforms that analyze client briefs and automatically generate structured media plan drafts, including channel recommendations, budget allocations, and strategic guidance.
These tools address one of the biggest challenges facing agencies today: inefficient planning processes that drain time, create inconsistency across teams, and prevent planners from focusing on strategic work. As AI adoption accelerates across the industry, understanding how these tools work and who benefits most from them matters for teams looking to stay competitive.
Key Takeaways
An AI-powered strategic media planning tool is software that analyzes client briefs and generates a structured media plan draft, including channel recommendations, budget allocations, and tactical suggestions.
This technology works by interpreting natural language from client briefs and matching campaign goals with industry benchmarks and channel-specific insights. Rather than starting from a blank page, planners receive a structured plan they can refine and customize based on client needs as well as their own strategic and creative judgment.
For example, a tool like Compass by Basis reads a client brief, identifies campaign objectives and target audiences, and generates a complete omnichannel media strategy—including prioritized audience segments, competitive context, channel-by-channel budget allocations with visual breakdowns, and campaign flighting—all within a conversational interface inside the platform where campaigns get activated. Planners can refine the strategy through follow-up prompts before building it into client deliverables or moving into activation.
Though powered by many of the same technologies, these tools differ from more general AI assistants or chatbots because they're purpose-built for media planning workflows. They understand advertising terminology, channel dynamics, and how to structure plans that translate directly into campaign execution. Compass, for instance, is built on Basis’ proprietary IMPACT omnichannel framework—a methodology used across thousands of successful media campaigns—rather than relying on generic AI reasoning alone.
According to the IAB State of Data 2025 report:
And, data from Basis’ 2026 Advertising Agency report finds:
This gap highlights where AI adoption has lagged most: operational planning, not creative execution.
Agencies who begin to implement AI toward operational inefficiencies now can gain a strategic edge, freeing up their teams to focus on strategy rather than manual tasks.
AI planning tools follow a structured process to convert briefs into actionable media strategies:
1. Brief Upload and Extraction
The planner uploads a client brief—whether a structured planning document or a simple prompt—and the tool extracts and summarizes key information. This includes campaign objectives, target audiences, budget parameters, KPIs, geographic focus, and timing constraints. In Compass, this extraction step is visible in the interface, so planners can confirm the tool understood the brief correctly before strategy generation begins.
2. Audience Strategy and Prioritization
The tool builds prioritized audience segments based on the brief, going beyond basic demographics. Each segment includes targeting rationale, recommended channels for reaching that audience, and messaging direction. This creates a strategic foundation where audience strategy, channel selection, and messaging are connected from the start.
3. Strategic Framework and Competitive Context
The tool generates a broader strategic framework that includes competitive context, key challenges, and a recommended approach, rather than just a channel list. This strategic layer guides the channel and budget recommendations that follow, grounding them in campaign-specific logic rather than general best practices. The framework also documents the channels the tool evaluated but chose not to recommend, with the reasoning behind each decision. This gives planners a defensible rationale to share with clients, showing that the recommended mix reflects deliberate trade-offs rather than default choices.
4. Channel Mix and Budget Allocation
The tool recommends a channel mix with specific budget allocations, including dollar amounts and percentage breakdowns with rationale for each channel. Visual outputs like budget allocation charts make it easy to see how spend is distributed and share recommendations with stakeholders. The tool can also generate multiple budget scenarios at different investment levels, each with its own channel allocation and rationale. When a client adjusts the budget or asks to see options, planners have ready-made tiers to work from instead of rebuilding the plan each time.
5. Campaign Plan and Flighting
AI maps out campaign flighting with budget allocation by phase, accounting for seasonal moments, tentpole events, and how different channels should ramp up or down throughout the flight. Channel-specific timing guidance ensures the plan reflects real-world campaign dynamics, not just even budget distribution.
6. Measurement and KPI Framework
The tool builds a measurement framework that maps KPIs to each channel’s role in the funnel, complete with relevant benchmarks. Planners get primary and secondary metrics for each channel, along with the business outcome each metric ladders up to. Having these benchmarks built in saves planners from researching performance standards across channels and gives clients a clear view of how success will be measured from the start.
7. Refinement and Strategy Delivery
The strategy generates within a conversational interface where planners can ask follow-up questions, request deeper analysis on specific sections, or adjust recommendations through natural prompts. The result is a complete, structured strategy that planners can refine and use to build client-ready deliverables. Because Compass lives inside the Basis platform, the strategy and eventual campaign activation share the same system, reducing the manual handoffs and reformatting that typically separate planning from execution.
When orchestrated by agentic AI media planning tools, this entire process can happen in minutes. What traditionally required multiple planning sessions, spreadsheet modeling, and cross-referencing past campaigns now generates automatically, giving agency talent more time to focus on strategic refinement and client-specific nuances.
AI media planning tools deliver several concrete benefits that address some of agencies’ most pressing operational challenges:
The time savings from AI planning tools come from automating specific tasks that eat up planners' days.
Take benchmark research. Manually researching industry benchmarks for CPMs, CTRs, and conversion rates across different channels takes significant time. AI tools have this data built in and automatically apply relevant benchmarks based on campaign parameters.
Budget modeling works similarly. Testing different budget scenarios manually requires rebuilding spreadsheets for each variation. AI tools can generate multiple budget allocation models instantly, letting planners compare approaches without manual calculation work.
Channel analysis is another time sink. Evaluating which channels make sense for a specific audience and campaign goal requires cross-referencing multiple data sources. AI planning tools synthesize this analysis automatically, presenting channel recommendations with supporting rationale.
Then there’s plan documentation—formatting decks, documenting strategic rationale, and creating presentation-ready outputs. AI-powered planning tools produce formatted plans that planners can review and refine rather than building from scratch.
With so many manual tasks wrapped up in drafting media plans, time savings derived from using AI-powered planning tools can add up fast. For instance, teams can create media plans 50% faster when using Compass by Basis, and that time savings can then shift to strategic consultation, client communication, or campaign optimization.
Consistency in media planning creates several advantages for agencies:
Standardized Strategic Approach: AI tools encode best practices into their planning logic. Every plan starts from the same strategic foundation: proven frameworks for audience targeting, channel selection, and budget allocation. This doesn't mean every plan looks identical, but it ensures no planner misses critical strategic considerations.
Quality Baseline for Junior Planners: Junior team members can often struggle without senior guidance. AI tools give them access to senior-level strategic thinking, helping them develop better plans while learning. The tool serves as a training resource that improves plan quality across experience levels.
Reduced Errors: Manual planning risks introducing errors such as calculation mistakes, overlooked channels, and misallocated budgets. AI tools eliminate these mechanical errors, catching issues before plans reach clients. This improves client trust and reduces the costly back-and-forth of fixing mistakes.
Scalable Quality Control: As agencies grow, maintaining consistent plan quality becomes harder. AI tools scale that quality automatically—the hundredth plan generated gets the same strategic rigor as the first.
These consistency benefits matter even more when you consider the tech stack complexity most agencies face. More than one-third (36.8%) of agencies now juggle 10+ tools in their tech stack—up dramatically from 17.3% in 2024—and managing that many disconnected systems can create inconsistency. When AI planning tools integrate into unified platforms where planning connects directly to activation, consistency extends beyond plan creation into execution. The fewer handoffs between systems, the fewer opportunities for plans to get lost in translation.
One concern about AI tools is the "black box" problem, i.e., a lack of visibility or understanding around how the AI reaches its recommendations. But well-designed AI planning tools address this through transparency features, providing rationale into their reasoning as well as ample opportunities for human interaction, iteration, and oversight.
IAB research finds that 51% of brands worry they don't have enough transparency about how agency partners use AI. Transparent AI tools that clearly show their work help address this concern: Agencies can demonstrate their value and provide visibility into their strategic process.
The key difference between manual and AI-powered media planning is how planner time is allocated: manual planning prioritizes mechanics, while AI planning prioritizes strategy.
| Aspect | Manual Media Planning | AI-Powered Media Planning |
| Speed of Drafting Initial Plan | Hours to days per campaign | Minutes per campaign |
| Benchmark Research | Manual lookup across multiple sources | Automatic application of relevant benchmarks |
| Consistency | Varies by planner experience and approach | Standardized strategic framework across all plans |
| Budget Modeling | Manual spreadsheet work for each scenario | Instant generation of multiple allocation models |
| Junior Planner Support | Depends on senior availability for guidance | Built-in access to senior-level strategic thinking |
| Measurement Setup | Research benchmarks and build KPI framework manually | KPI framework with channel-level benchmarks generated automatically |
| Error Rate | Higher risk of calculation and oversight errors | Reduced mechanical errors |
| Time Allocation | More time on mechanics, less on strategy | More time on strategy, less on mechanics |
| Scalability | Requires adding planners to handle more volume | Same team handles increased planning volume |
| Knowledge Transfer | Lost when team members leave | Captured in the tool |
| Planning-to-Activation Handoff | Manual export, reformatting, and rebuilding in activation platform | Strategy built inside the same platform where campaigns get activated |
Using AI-powered media planning tools doesn’t mean replacing planners. Rather, it allows planners more time to scale their work effectively and efficiently, while simultaneously providing them with more time to focus on the deep, strategic work best completed by humans. Manual planning forces planners to focus on mechanical tasks. AI planning shifts that time to strategic consultation, creative collaboration, and client relationship building.
This shift matters because 54.0% of agencies report more strained client relationships compared to two years ago. When planners spend less time on administrative work, they have more capacity for the client-facing strategic work that strengthens relationships.
AI-powered media planning tools are best suited for agencies and brands managing planning complexity, scale, or constrained resources.
Agencies managing multiple clients across various industries handle significant planning volume. AI tools help these agencies scale planning operations without proportionally scaling headcount, improving profitability while maintaining quality. They're particularly valuable when agencies need to pitch new business quickly or accommodate compressed timelines.
Organizations adding junior planners benefit from AI tools that give newer team members strategic scaffolding. Instead of requiring constant senior oversight, junior planners can produce quality work more independently while learning planning fundamentals.
Any agency where inefficient processes or disconnected systems create operational friction will benefit from AI planning tools. Given that 48.9% of agency leaders cite inefficient processes as their top challenge, this includes a significant portion of the industry.
AI planning tools deliver the most value when integrated into platforms where planning connects directly to activation. When the same system that generates the plan also executes it, data flows seamlessly—no manual transfers, no disconnected spreadsheets, no reconciliation work. This integration addresses the silos/disconnected systems problem that 40.4% of agencies identify as a major challenge.
The ideal scenario combines AI-powered planning with all-channel activation capabilities and AI that extends across the entire media buying process, from brief to activation to optimization. When these capabilities exist within a single platform rather than requiring multiple point solutions, agencies avoid the tech stack bloat that creates new inefficiencies (or accentuates existing ones).
Agencies looking to adopt AI-powered media planning tools should follow a structured approach:
Document how much time planning currently takes and where bottlenecks exist. Identify which parts of the planning process consume the most time and which would benefit most from automation. This assessment creates a baseline for measuring improvement.
Don't add another disconnected tool to an already complex tech stack. Look for AI planning capabilities that integrate with existing systems or exist within unified platforms. The planning tool should connect seamlessly to wherever campaigns get activated—whether that's programmatic buying, publisher-direct placements, or search and social platforms (or ideally, a platform that combines all of these in one).
Test AI planning on a small set of campaigns before rolling out across all clients. Choose campaigns that represent typical planning challenges, such as finding the right mix of channels, moderating complexity, and meeting realistic timelines. This pilot phase helps teams learn the tool and build confidence before scaling.
Invest in training so planners understand what the tool can do and how to refine its outputs effectively. Focus on explaining the logic behind recommendations so planners can make informed decisions about when to accept, modify, or override AI suggestions.
Create clear workflows for how AI-generated plans get reviewed and approved. Define who validates outputs, what criteria determine plan quality, and how feedback gets incorporated to improve future plans. This process maintains quality control while scaling efficiency.
Track metrics that matter: planning time per campaign, error rates, client feedback on plan quality, and planner satisfaction. These measurements justify the investment and identify areas for continued optimization.
Once the pilot proves successful, expand AI planning to additional teams and client accounts. Gradual rollout allows for learning and refinement without disrupting operations.
The investment priority is clear in the data: 77.7% of agency leaders plan to increase AI investment in the next 12 months, with automation tools tied for the second priority at 44.7%. Agencies moving quickly on AI planning implementation gain competitive advantage while others wait.
Basis Compass is purpose-built to solve the operational challenges agencies cite most: inefficient processes and disconnected systems. Here’s what sets it apart:
Compass gives agency employees back the time they need to do the strategic and creative work that clients are seeking, while automating the spreadsheet juggling that drains valuable hours from every week.
The shift to AI-powered media planning represents an opportunity to amplify human judgment, while reducing the manual tasks that slow teams down. These tools handle the mechanical work that drains time and creates inconsistency, giving planners capacity to focus on strategy, creativity, and client relationships. For agencies facing increasing complexity, tighter timelines, and pressure to do more with the same resources, AI planning tools offer a practical path forward.
The agencies that integrate these capabilities thoughtfully, particularly within unified platforms that connect planning directly to activation, will differentiate themselves through both efficiency and quality. They'll respond to briefs faster, produce more consistent work, and give planners more time for the strategic thinking that clients value most.
What is an AI-powered strategic media planning tool?
An AI-powered media planning tool is software that analyzes client briefs and automatically generates a structured media plan draft, including channel recommendations, budget allocations, and tactical suggestions. Planners can then refine and customize this draft based on client needs and their own strategic judgment.
Do AI media planning tools replace human media planners?
No, human oversight remains essential throughout the process. The typical workflow is AI generates a draft, then the planner reviews, refines, and approves it, keeping strategic decision-making with the planner while automating mechanical tasks.
How much time can AI planning tools save agencies?
Manual media planning can take hours or days, while AI tools can reduce this to minutes. Teams using Compass by Basis, for example, create media plans 50% faster than with manual processes.
Are AI-generated media plans transparent?
Well-designed tools avoid functioning as a black box by showing their reasoning behind each recommendation. Platforms like Compass explain the rationale for channel and budget decisions and let planners see which data sources informed them.
Can AI tools generate multiple budget scenarios?
Yes, AI planning tools can instantly generate multiple budget allocation models at different investment levels, each with its own rationale. This lets planners present clients with options without rebuilding the plan from scratch each time.
What data do AI media planning tools use to build strategies?
They combine campaign objectives and parameters from a client brief with industry benchmarks, performance data, and proprietary frameworks. Compass, for instance, is built on Basis's IMPACT omnichannel campaign framework, a methodology used across thousands of successful media campaigns.
How does AI media planning improve consistency across agency teams?
AI tools encode best practices into their planning logic, so every plan starts from the same strategic foundation regardless of which planner builds it. This gives junior planners access to senior-level strategic thinking while reducing calculation and oversight errors.
Why does connecting media planning to activation matter?
When planning tools integrate into the same platform used for activation, data flows seamlessly without manual transfers or reformatting. Compass lives inside the Basis platform, allowing planners to move from a brief to an active campaign without switching systems.
Who benefits most from AI-powered media planning tools?
Mid-to-large agencies managing high planning volume, teams with growing numbers of junior planners, and organizations facing inefficient processes or disconnected tech stacks see the most benefit. These tools are most valuable for teams using unified advertising platforms where planning connects directly to activation.
How is Compass by Basis different from a general AI chatbot?
Compass is purpose-built for media planning workflows rather than relying on generic AI reasoning, using Basis's proprietary IMPACT framework alongside industry benchmarks. It operates within the same platform where agencies activate and manage campaigns, so plans move directly into execution without manual handoffs.
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).
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.
Key Takeaways:
____________________________________________________________________________________
Remember when researching a purchase meant toggling between about 20 different tabs on your laptop? You’d run a string of keyword searches on Google, scroll through the results, open tabs for all the promising-sounding options, and review. Your collection of tabs might exist for days, weeks, or even months, growing and shrinking along with your research until you finally felt informed enough to pull the trigger (or until your browser decided to stage an intervention and crash).
Now think about the last item you bought. Maybe you asked ChatGPT for product recommendations and made a purchase after reviewing them. Or perhaps an AI summary at the top of a Google search compared three options for you, and you picked one without having to do any additional research. Sound familiar?
The path that moves a person from “I might need this” to “I bought it” looks almost nothing like it did a decade ago, or even three years ago. It’s fragmented, nonlinear, and increasingly shaped by algorithms and AI. For advertisers, that shift changes what it looks like—and the underlying technology it requires—to reach consumers effectively in key moments of influence.
Not long ago, the customer journey was relatively straightforward. A customer became aware of a product, considered and evaluated it, and finally made their decision and completed the purchase. Advertising mapped neatly onto that path. A billboard or TV spot built awareness, while a well-placed search or display ad nudged a shopper toward a decision. Advertisers could reasonably predict where a customer was headed and meet them there.
Today’s journey looks quite different: It bends, loops, scatters across channels, and rarely starts or ends where advertisers expect.
Discovery now happens everywhere, all the time. Most shoppers say they discover new products at least once a week, and that discovery is spread across TikTok FYPs, Instagram feeds, AI summaries, retail apps, and beyond. This discovery also often happens across multiple devices at the same time, with the majority of media consumers across every generation saying they now browse the internet or use apps on their phones while watching TV. With content so readily available, advertisers are competing for attention that is splintered across screens and digital spaces. That makes showing up intentionally and consistently across channels even more important.
The research phase has changed as well, evolving into a multi-touch, multi-channel endeavor. Consumers now research a product three or more times before buying, and nearly a quarter research five times or more. They also turn to a variety of sources for their research: online reviews and listicles, social media, recommendations from family and friends, in-store visits, search engines, AI, and beyond. For advertisers, that scatter makes presence across channels less of a “nice-to-have” and more of a requirement, since there’s no longer a single place where decisions get made.
Purchase has also grown more unpredictable. More than 30% of shoppers say they research online but buy in-store, a pattern that makes attribution especially difficult. When someone discovers a product through a TikTok creator but buys it at Walmart, connecting that sale to the original touchpoint—or any other touchpoints along the way—is a real challenge for advertisers trying to understand what’s working. Without a connected view of those touchpoints, advertisers risk crediting the wrong channel and misallocating their next dollar.
In addition to the rising complexity of digital media, AI is also playing a major role in the evolution of the customer journey. Among people who use AI to shop, it now ranks as the second most influential shopping source—trailing only behind search engines and outranking retailer sites, apps, and recommendations from family and friends.
And adoption is climbing quickly. AI now plays a role in 86% of shoppers’ retail journeys. Nearly half of AI shoppers use it most or every time they shop, with 80% saying they anticipate relying on it more moving forward. People who use AI for shopping are also finding real value in the tool: 81% say AI makes the job easier, 77% say it makes them more confident in their decisions, and nearly 90% report it helps them find products they wouldn’t have known about otherwise.
AI also tends to expand the path to purchase rather than shortening it. After an AI interaction, shoppers tend to add more steps to their customer journey, often in an effort to validate their choice before buying. Though AI certainly does streamline some stages of the path to purchase, it also adds steps that weren’t there before. And each of those new steps is another opportunity for advertisers to connect with shoppers on their way to making a decision.
Zero-click search is reshaping the journey further. As AI summaries and chatbot responses answer questions directly in the results, fewer users click through to a brand’s site at all. That doesn’t mean those impressions stop mattering, however: Ads appearing alongside AI-generated summaries still influence decisions, even without a click. It does mean advertisers have to rethink how they measure influence and where they show up, since a growing share of discovery and decision-making now happens inside environments where AI shapes what consumers see, hear, and trust about a brand.
Adapting to how the customer journey has evolved starts with recognizing and accepting the complexity of it. CTV, retail media, short-form video, AI chatbots, AI search summaries, and more are all live, simultaneous touchpoints, each with its own signals and rules. Advertisers who try to manage each in isolation will likely struggle to keep up. The teams adapting best treat these channels as one connected system, planning and buying across them together rather than each in isolation.
Accomplishing this depends on a few capabilities. One is real-time visibility and reporting. When AI tools can compress discovery, evaluation, and purchase into minutes, advertisers need to see what’s resonating as it happens (not days later in a reconciled report) so they can move budget toward what’s working during key moments of impact.
That kind of visibility is hard to come by when data stays fragmented. Nearly half of agency marketers use eight or more tools to manage campaigns, and more than a third manage 10 or more. Even more, fewer than one in five industry professionals describe their first-party data as extensive and well-structured. This leaves teams to piece together the path to purchase from incomplete inputs across systems that weren’t necessarily built to talk to each other.
Speed is another key capability, in both execution and planning. Shoppers today move through different steps quickly and across channels, which means bid strategies, creative, budget allocation, and the media plans behind them all need to keep pace. Automated, AI-powered optimization that makes continuous, goal-aligned adjustments, powered by live performance signals, can be the difference between capitalizing on the channels where target audiences are spending time and missing those opportunities entirely. That same speed matters earlier in the campaign process, too. Considering how dynamic the customer journey is today, teams that can build and adjust media plans quickly—rather than rebuilding them manually each quarter—stay aligned with how consumers actually behave. AI-powered tools are increasingly helping compress that planning work so agency talent can focus on strategy over manual setup.
Taken together, these capabilities underscore what adapting to the modern customer journey requires: A strategy built around how consumers behave today, and the infrastructure to execute it.
In a journey this fragmented and fast-moving, the infrastructure beneath a team’s advertising workflows matters as much as the strategy on top of it. But not all infrastructure is created equal, and “unified” can mean different things in practice.
Real visibility across channels means little if teams must continually switch between tools to access data, billing, and reconciliation systems. Real-time optimization falls short if the platform powering it can’t handle the complexity of true omnichannel work. For example, a platform that unifies programmatic but treats search, social, and site direct buys as afterthoughts isn’t unified in the way that advertisers need to adapt to the complexity of the 2026 customer journey.
The advertisers best positioned for navigating it are the ones working from a single, unified platform that connects programmatic, search, social, and CTV, supported by infrastructure stable enough to make agile, cross-channel activation reliable at scale.
In 2026, the customer journey is fragmented, nonlinear, and shaped by AI at every turn. To reach people in moments of meaningful impact, advertisers need visibility across channels, the speed to act on what they see, and the connected infrastructure to make both possible.
The days of the tidy linear funnel and the slow, self-directed path to purchase aren’t coming back. Today’s customer journey calls for a different kind of toolkit, one well-suited for media fragmentation, AI, and the speed at which today’s consumers move. The advertisers who invest now in unified, real-time infrastructure—the kind that brings every channel into a single view and acts on customer signals as they happen—will be the ones who keep pace as the journey keeps changing.
__
Looking for more information on how to adapt your media planning for the modern customer journey? Check out Beyond the Funnel: A Better Way to Plan Media.
Every programmatic impression travels through a chain of intermediaries before it reaches a person, and most advertisers can't see what happens along the way. That blind spot carries a measurable cost. Only 43.3% of programmatic ad spend reached a quality impression—viewable, measurable, fraud-free, and clear of made-for-advertising inventory—in Q1 2026. For the lower-performing half of advertisers in the ANA's benchmark, the figure drops to 32.1%, meaning more than two-thirds of every dollar was wasted.
That gap isn't random. It separates advertisers who actively manage supply quality, measurement coverage, and inventory curation from those who don't. Transparency is what makes that management possible. Yet most advertisers still can't see exactly where their money goes once a bid is placed, which intermediaries extracted fees along the way, or whether their ads ran in environments that match their brand standards.
This guide covers what programmatic transparency means in 2026, how independent DSPs and walled garden platforms compare on the dimensions that matter most, what a layered brand safety and suitability approach looks like in practice, and how to evaluate any platform's fraud protection claims with appropriate skepticism.
Key Takeaways
A programmatic advertising platform is transparent when buyers can see and verify the full path their money takes from a bid to a publisher's page: which intermediaries handled the impression, what each one charged, and what the publisher actually received. That visibility is called supply path transparency, the ability to trace every step an ad impression takes from publisher to DSP and verify each intermediary's legitimacy.
Programmatic advertising, of course, is the automated buying and selling of digital ad inventory through real-time bidding across display, video, audio, native, CTV, and DOOH. A typical transaction routes every impression through multiple intermediaries, each extracting fees and introducing a potential point of failure along the way. The quality of that supply chain determines the quality of the campaigns it supports.
IAB Tech Lab standards (ads.txt, app-ads.txt, and sellers.json) establish the baseline, letting publishers declare which sellers are authorized to represent their inventory and letting buyers verify every intermediary in the supply chain. Transparency goes beyond compliance with those standards. A transparent programmatic platform gives buyers four things:
Independent DSPs and walled gardens serve different purposes, and the transparency profile of each reflects that. Most advertisers run both.
| Evaluation Criteria | Independent DSPs (ex. Basis) | Walled Garden DSPs (ex. DV360, Amazon DSP) |
| Supply-path visibility | Publisher-level pricing, hop counts, deal IDs, multi-SSP reporting | Limited; platform controls most visibility into inventory sourcing |
| Domain-level reporting | Standard | Often aggregated or restricted to platform-defined metrics |
| Data ownership | First-party data exportable and portable across buys | Data generally retained within the platform |
| Cross-platform measurement | Native integrations with third-party verification and cross-channel measurement | Measurement primarily constrained to platform ecosystem |
| Third-party verification support | Native integrations with multiple vendors; open-systems reporting | Supports select vendors, often with platform-mediated reporting |
| Buy-side neutrality | No media ownership; no inventory bias | Own and sell media; inherent revenue-vs.-transparency conflict |
| PMP access | Broad open-web PMP inventory and DSP-agnostic deal access | PMP access may be limited to platform relationships |
| Fraud protection scope | Covers programmatic and open-web channels natively | Strong within platform; fragmented across other channels |
| Log-level data access | Impression-level log data available in leading independent DSPs | Limited or inconsistent; walled gardens generally restrict LLD access or provide it selectively |
| Minimum spend | Varies; accessible at agency and enterprise scales | Some managed services require high minimums (ex. Amazon DSP managed service: $50K/month) |
\Each model solves a different problem. Walled gardens deliver strong in-platform reach and performance for search, social, and commerce outcomes. Independent DSPs provide transparent, hands-on control over data, optimization, and cross-channel execution, along with supply path efficiency and access to high-quality open-web inventory where visibility, accountability, and brand safety are critical. For agencies and brands running campaigns across audio, CTV, display, DOOH, native, and video, a platform like Basis—which unifies those channels in a single workflow with consistent brand safety enforcement—can close the gap that fragmented stacks leave open.
Brand safety is the practice of ensuring ads are delivered in environments that align with an advertiser's standards for content, quality, and legitimacy across all channels. It combines content adjacency controls, supply-path validation, inventory curation, and pre- and post-bid verification to protect brand reputation and keep media investments in trusted environments.
No single control is sufficient. The platforms with the strongest brand safety and suitability offerings layer six mechanisms:
A practical note on trade-offs: tighter brand safety controls reduce available inventory and can increase CPMs. Higher-quality placements carry higher costs, and well-calibrated controls account for that trade-off.
Ad fraud is any deliberate activity that prevents proper delivery of ads to real human audiences, including bot traffic, domain spoofing, click injection, and ad stacking. Fraud protection encompasses the tools and processes used to identify and block these threats across the buying lifecycle, with a strong emphasis on pre-bid controls and supply-path validation to prevent invalid impressions before a bid is placed, alongside post-bid detection and remediation.
The most common fraud techniques in programmatic environments include:
Fraud protection operates at three points across the buying lifecycle:
A private marketplace (PMP) is an invite-only programmatic auction where select advertisers access premium publisher inventory through pre-negotiated deal terms. PMPs give advertisers a more direct, controlled path to premium inventory, reducing the supply hops, fraud exposure, and brand safety variability that come with open-exchange buying.
PMPs don't eliminate fraud entirely, and layered verification remains necessary regardless of buying method. But they reduce the attack surface significantly, and spending trends reflect that shift. PMP spending grew nearly 13% in 2025 against roughly 3% for the open exchange, per eMarketer—a gap that reflects advertisers' growing prioritization of inventory quality, brand safety, and supply chain accountability over bid-price savings.
When evaluating a platform's PMP offering, the size of the pre-negotiated deal library matters, but so does how it's organized. Platforms that maintain curated deal groups—by vertical, channel, content category, or audience type—save media buyers the time of evaluating individual deals from scratch. Basis maintains 2,000+ pre-negotiated deals organized in a browsable library that buyers can activate within the same workflow used for open exchange buying. Troubleshooting tools that surface setup issues before campaigns launch catch problems before they cost impressions.
Programmatic guaranteed (PG) deals extend this logic further. PG combines the predictability of insertion-order-based buys—fixed pricing, guaranteed impressions, direct publisher relationships—with the flexibility and automation of programmatic media. Inventory control is complete: the buyer always knows exactly where ads are appearing, and all PG buys consolidate into the DSP invoice rather than generating separate publisher invoices. Platforms with established PG relationships—Basis' partners include Equativ, Tubi, Beachfront, Google, Connatix, Magnite, OpenX, and FreeWheel—can accelerate deal setup considerably compared to negotiating publisher relationships from scratch.
Supply path optimization (SPO) is the strategic process of selecting the most efficient, transparent, and high-performing route for digital advertising transactions to flow from advertiser to publisher. Its goal is to find the best path to the target audience while maximizing value and minimizing waste. The demand for that visibility is broad-based: 88.3% of agency professionals say digital advertising needs more transparency, per Basis' 2026 Advertising Agency Report.
SPO has historically been framed as a cost-cutting exercise: fewer hops, lower CPMs. The 614 Group's study pushes back on that framing directly. When SPO is treated as a race to the bottom, it harms publishers, degrades inventory quality, and ultimately undermines advertiser outcomes. The more durable frame is optimization toward outcomes—using supply path visibility to improve ROAS, inventory quality, and brand safety at the same time, not just to shave a few basis points off CPMs.
The ANA Q1 2026 Benchmark puts numbers to what that difference looks like. The higher-performing cohort converted 54% of its spend into quality impressions, while the lower-performing cohort converted just 32.1%. Headline CPM tells only part of the story. Once waste is accounted for, the higher cohort's TrueCPM (the effective cost per quality impression) was $7.46. The lower cohort's TrueCPM was $19.04, or 2.6 times more for the same quality impression. The Benchmark found that a $1.95 difference in headline CPM becomes an $11.58 TrueCPM gap once non-measurable and non-viewable spend is stripped out. Tighter supply curation and better measurement coverage drive that difference, not better-negotiated rates. The higher cohort also runs a far more concentrated supply footprint: 32,998 unique domains and apps versus 67,049 for the lower half. More domains mean more exposure to hard-to-measure, hard-to-verify inventory, and more waste.
That reframe has practical implications for platform selection. The 614 Group study found that most buyers don't want more data; they want insights they can act on. Platforms that surface supply path intelligence as actionable reporting, rather than as data exports that require engineering to interpret, are the ones that make SPO a repeatable operational practice rather than a periodic project.
To identify where a platform stands on supply path transparency, the 614 Group's SPO research synthesized feedback from senior marketers and agency leaders into eight questions every buyer should ask their DSP.
Three tensions show up consistently when agencies and brands evaluate programmatic platforms on brand safety and transparency.
Transparency vs. ease of execution: Independent DSPs provide supply-path visibility and cross-channel control, and the best ones are built to minimize the operational overhead that complexity can create. Basis is designed to consolidate omnichannel campaign management, reporting, and brand safety controls in a single workflow, reducing the expertise barrier without sacrificing transparency. Walled gardens simplify execution within their own ecosystems but limit cross-platform visibility and data portability in ways that compound over time.
Safety vs. scale: PMPs offer strong open-web control through pre-approved publisher relationships but constrain available impressions compared to the open exchange. That trade is deliberate: PMPs exchange raw scale for quality and control. The optimal allocation depends on campaign objectives, not a fixed formula.
Cost vs. control: Higher brand safety, verification, and managed service support increase costs through higher CPMs, platform fees, or minimum spend requirements. Consolidating channels and controls within a unified platform can reduce the total cost of that control by eliminating tool fragmentation and operational overhead.
A portfolio approach works best. Walled gardens for intent-driven search, social, and commerce outcomes. Independent DSPs and curated PMPs for open-web reach, supply-path transparency, and brand safety mandates. The two models are complementary, not competitive.
Applying these practices rarely requires switching platforms, but it does require active management.
What is the difference between brand safety and fraud protection?
Brand safety ensures ads appear in appropriate, high-quality environments by controlling content adjacency and publisher context. Fraud protection prevents invalid or deceptive traffic, including bots, domain spoofing, and click injection. The two are distinct but complementary, and strong platforms address both through integrated controls rather than treating them as separate programs.
Which programmatic advertising platforms provide the most transparency?
Independent DSPs provide the most supply-path transparency, including domain-level reporting, publisher-level pricing, and cross-platform auditability. Walled garden platforms like DV360 and Amazon DSP offer strong in-platform measurement but limit visibility into supply chain economics and restrict data portability. Basis is an example of an independent omnichannel platform with a built-in DSP that provides publisher-level pricing, deal-type comparison reporting, and multi-SSP visibility without sell-side conflicts.
Which DSPs have the best brand safety and fraud protection?
The DSPs with the strongest brand safety and fraud protection combine pre-bid filtering, post-bid verification, native integrations with multiple verification vendors, and human monitoring. Basis integrates with DoubleVerify, Comscore, Peer39, and Protected by Mediaocean for third-party verification, with pre-bid brand safety layers and a dedicated RTB Operations team for ongoing inventory quality monitoring.
How do advertising platforms prevent ad fraud?
Fraud protection operates at three points: pre-bid filtering (screening inventory before a bid is placed), in-flight monitoring (continuous analysis during delivery), and post-bid analysis (reconciling impressions against verification data). The strongest platforms, including Basis, combine proprietary detection with independent third-party verification, ads.txt and sellers.json enforcement, and human review for sophisticated fraud patterns that automated systems miss.
Are private marketplace deals safer than open auction inventory?
Yes. PMPs offer more control, direct publisher relationships, and higher-quality inventory than the open exchange. But they are not completely immune to fraud, so layered pre-bid and post-bid verification is still recommended regardless of buying method.
Which programmatic platforms offer the best pre-negotiated private marketplace deals?
Platforms that maintain large curated PMP libraries with deal-group organization by vertical, channel, or content category give media buyers the fastest path to brand-safe premium inventory. Basis maintains 2,000+ pre-negotiated PMP deals, including programmatic guaranteed relationships with publishers across CTV, display, audio, and video. Deal management, troubleshooting, and activation happen within the same workflow as open exchange buying.
How do independent DSPs compare to walled garden platforms for transparency?
Independent DSPs provide greater supply-path visibility, cross-platform auditability, and first-party data portability. Walled garden platforms offer strong in-platform targeting and measurement but constrain visibility and data ownership to their ecosystems. The TAG TrustNet LLD Register illustrates this gap: Basis provides full log-level data with all required data fields; Amazon Advertising provides no LLD support; major social walled gardens—Meta, TikTok, X—are listed as unknown. The two models work best in combination: walled gardens for intent-driven in-platform outcomes, independent DSPs for open-web reach with supply-path transparency.
What is supply path optimization, and how does it relate to brand safety?
Supply path optimization (SPO) is the practice of evaluating and refining the routes through which inventory is purchased to prioritize high-quality, transparent, and efficient supply. Cleaner supply paths—fewer hops, more curated deals, tighter domain footprints—directly reduce non-measurable and non-viewable inventory, which is where most programmatic waste occurs.
Do we still need third-party verification if we buy through a PMP or walled garden?
Yes. Layered verification is industry best practice regardless of buying method. PMPs reduce fraud risk through vetted inventory, but they don't eliminate it. Walled gardens measure within their own ecosystems, which creates both coverage gaps and an inherent conflict of interest. Independent verification from accredited vendors provides the audit layer that makes fraud protection claims auditable.
When does PMP buying make more sense than open auction buying?
When brand safety, viewability, inventory quality, and supply-path transparency matter more than maximum scale or the lowest CPMs. PMPs are the appropriate primary channel for campaigns with explicit brand safety mandates, for advertisers in sensitive categories, and for any program where inventory context is as important as audience targeting.
Key Takeaways:
Brand safety and suitability look very different today than they did just a few years ago, and most advertisers’ strategies haven’t kept up with the new pace.
Online spaces increasingly characterized by harmful and polarizing content, the proliferation of AI-generated media, reduced platform moderation, and the growing complexity of digital advertising have combined to raise the brand risk profile for advertisers.
Closing that gap requires a mindset shift from leaders. Instead of treating brand safety as a box to check once campaigns are live, brand safety and suitability must be approached as a strategic consideration built into media planning from the start.
The brand safety and suitability environment has evolved considerably in recent years. The open web has grown more volatile, with offensive language, controversial content, and hate speech on the rise. Just between 2024 and 2025, the share of offensive content online rose by 72%. Considering that 64% of global consumers say the genre of content surrounding an ad influences how they perceive it, the increasing hostility of online spaces creates significant content adjacency issues for advertisers.
The emergence of generative AI and subsequent proliferation of AI-generated content online has exacerbated such concerns. A 2025 Basis study found that a full 100% of marketers and advertisers agree that AI presents a brand safety and misinformation risk, and 53% of media experts in the US cite advertisements’ proximity to gen AI content as a top media challenge this year.
Social media has grown particularly contentious, especially as major social platforms have rolled back their content moderation policies in recent years. Close to two-thirds of marketers running campaigns on social feel concerned about the brand suitability of those ad placements.
April Weeks, Chief Media Officer at Basis, says the combination of these and other factors has raised the stakes for brand safety and suitability. “The risk has increased,” says Weeks, “and to adapt, advertisers must treat brand safety and suitability as brand-specific governance issues that are integrated into the media plan.”
Beyond content adjacency issues, wasted spend is a major concern when it comes to programmatic investments.
The ANA’s latest Programmatic Transparency Benchmark found a considerable gap in how effectively advertisers convert their spend into working media. Higher-performing advertisers directed 54% of their programmatic investments toward impressions that were measurable, viewable, and free of invalid traffic and made-for-advertising (MFA) content. Lower-performing advertisers converted just 32.1%—in other words, more than two-thirds of their spend was wasted.
The platforms marketing teams use for programmatic advertising have a considerable impact on how effectively they’re able to direct their spend. For example, platforms that prioritize supply path optimization (SPO)—offering supply chain visibility, neutral buy-side transparency, and brand safety controls built into the buying process—help advertisers convert more spend into quality placements.
“The best DSPs clean up the supply chain before an advertiser even bids—vetting publishers, filtering out bots, and removing invalid traffic up front,” notes Lindsey Freed, SVP of Media Investment at Basis.
Successfully addressing brand suitability, brand safety, and programmatic waste in today’s media environment requires marketing leaders to think about these issues differently than they have in the past.
“Historically, brand safety meant not showing up next to negative content,” says Dan Wilson, GVP of Integrated Client Solutions at Basis. “Today, it's about safeguarding your brand's integrity: considering where your ads are placed, the quality of the surrounding content, and what's suitable for your brand, audience, message, and moment in the customer journey.”
Legacy brand safety approaches were characterized by post-campaign verification, a reliance on platforms to manage risk, and blunt controls like broad keyword blocks or genre-level content blocking. As the complexity of the digital media environment has grown, Weeks says that advertisers must take on more responsibility, taking the time to craft nuanced brand safety and suitability strategies that are engrained into the planning process.
Leading advertisers are now incorporating pre-bid tools alongside post-bid verification, adding solutions to block MFAs and other low-quality websites, and accounting for channel- and platform-specific risks. Social listening, for example, has become essential given the polarization of content on social platforms.
Content adjacency approaches are also becoming more nuanced. The most successful advertisers are moving away from binary “safe vs. unsafe” thinking, and towards more granular, context-specific approaches. Rather than applying a blanket block on all news content, for instance, advertisers can use inclusion lists of trusted publishers paired with contextual targeting to ensure ads appear alongside news the brand is comfortable with, and within trusted editorial environments. “It’s about approaching it from a lens that isn’t black and white,” says Wilson.
Technology is evolving to support advertisers in these more granular approaches. For example, newer solutions can go beyond keyword matching, using contextual and semantic analysis to assess whether content is actually suitable and incorporating real-time signals to reduce waste.
Ultimately, success depends on leaders shifting their mindset, considering brand safety and suitability in the planning phase, and addressing them through a nuanced, multi-pronged approach.
Crafting a brand safety strategy suited to the complexity of today's media landscape takes real investment. Auditing legacy approaches, building channel-specific controls, and evolving workflows and tech stacks all take time. For leaders willing to invest that time, however, the potential returns are significant.
"The opportunity amongst the complexity is there," says Wilson. "The question is, will advertisers take the time to find it?"
—
For a deeper look at how supply path optimization supports stronger brand safety outcomes, check out The Case for Supply Path Optimization as Strategic Priority.
The local branch of a marketing & advertising agency holding company based in San José, Costa Rica wanted to compare Basis’ campaign efficiency—specifically with time and cost savings—against working with separate media owners.
To do so, they used Basis to boost brand awareness and visibility for two leading client brands in the beverage industry to their target consumers.
Basis implemented a strategic, data-driven digital out-of-home (DOOH) campaign that included:
Basis delivered a data-driven DOOH campaign in Costa Rica that simplified execution, reached 665k users, and proved the platform’s power to reduce costs and maximize efficiency.
The results:
Strategic Virtual Roadmap
Basis designed a journey roadmap of high-traffic zones and pinpointed 13 key screens that aligned with target audience’s mobility patterns, maximizing reach with the right audience at the right time.
Precise Activation with PMPs
Basis used three distinct private marketplace deals for precise targeting and scheduling across a combination of indoor and outdoor screens.
Centralized Execution Across Media Vendors
Basis enabled seamless coordination and exact time scheduling across multiple vendors, reducing complexity and boosting efficiency.
Integrated Post-Campaign Reporting
Paired with a measurement provider, Basis provided a detailed report that covered campaign performance metrics and qualitative audience data like consumer demographics and device type.