Programmatic advertising transparency usually means one thing: knowing where the money goes. How much reaches the publisher, how much stays with intermediaries, which fees apply along the supply chain. That question matters, and the industry has made measurable progress on it.
But it is only half of transparency. Cost transparency tells you where your budget went. It doesn’t tell you what you got for it, how that result was measured, or who answers for it if it falls short.
Full transparency starts before the campaign runs: an agreed objective, a committed KPI, a clear way to measure it and explicit conditions. With that in place, an advertiser doesn’t need to inspect every individual bid to verify whether the campaign met its commitment.
Contents
1. What is programmatic advertising transparency?
2. Why cost transparency is not enough: the three layers of transparency
3. What an advertiser should be able to audit
4. Why autonomous media buying is not a black box
5. Case study: Alsea and Domino’s Pizza
6. Five questions to test the transparency of any media buying model
7. FAQ
What is programmatic advertising transparency?
Programmatic advertising transparency is an advertiser’s ability to know where its budget goes and what it buys. Most of the industry debate has focused on the first part: fees, intermediaries and supply paths.
That focus has solid reasons behind it. The 2020 Programmatic Supply Chain Transparency Study by ISBA and PwC found that, on average, publishers received 51% of advertiser spend, while 15% could not be attributed to any part of the supply chain, what the study called the “unknown delta”.
A second ISBA and PwC study, published in 2023 and based on 2022 data, found significant improvements: the unattributable share fell to 3%, compared with 15% in the original 2020 study, and the share of impressions that could be matched end to end rose from 12% to 58%.
Both studies focused on a defined group of premium advertisers, agencies, technology vendors and publishers, so their results should not be treated as representative of the entire programmatic ecosystem.
The industry has made measurable progress in cost transparency. But even when cost transparency is established, it still doesn’t answer whether the campaign delivered the intended business outcome.
Why cost transparency is not enough: the three layers of transparency
Transparency in media buying can be looked at through three layers: cost, execution and outcome. Most industry transparency efforts have focused on cost and supply chain visibility.
• Cost transparency answers: where did my money go?
• Execution transparency answers: what did the system or the team do with it?
• Outcome transparency answers: what result was committed, how is it measured and who answers for it?
An advertiser can have a fully traceable supply chain and detailed execution logs, and still not know whether the campaign delivered what the business needed. Every cent can be accounted for while the CPA drifts well above target.
Outcome transparency is the layer that closes that gap: it connects the money spent and the decisions made to the result the advertiser actually cares about.
What an advertiser should be able to audit
Whatever the buying model, an advertiser should be able to verify six dimensions. Each one should be explicit before the campaign starts.
| Dimension | What should be explicit |
| Spend | Where the budget goes and which fees apply |
| Objective | What the campaign is intended to achieve: reach, views, leads, sales |
| KPI | The metric (CPA, CPV, CPM, CPP) and a specific target agreed upfront |
| Measurement | Who measures the result, from which data, and whether that source is independent from whoever executes |
| Conditions | Budget, period, markets, channels and the inputs the result depends on, such as creative assets or tracking |
| Accountability | Who is contractually responsible for the outcome, and what happens if it isn’t met |
Different buying models provide different levels of visibility across these dimensions. The important question is not whether every bid is visible, but whether the advertiser can verify the economic and performance commitments that matter. How the five main buying models differ across eight criteria is covered in the five ways to buy media.
In an autonomous model, the split of decisions should also be explicit. The advertiser sets the objective, the budget, the brand limits and the markets. The system executes bidding, budget allocation, pacing and audience decisions within those limits.
One point of precision on accountability: an AI system doesn’t take on responsibility for a result. The provider does, through the contract. That is how Mainkore works, and we explain why the distinction matters in who answers for an AI decision and why a performance guarantee is not a performance target.
Why autonomous media buying is not a black box
Autonomous media buying is not opaque media buying. What changes is where the advertiser looks: at objectives, rules, measurement and accountability, instead of at each bid.
For an advertiser, the problem with a black box is not simply that individual decisions are difficult to inspect. It is that the advertiser cannot establish what the system is optimizing for, how its output is measured and who is accountable for the result.
Mainkore’s autonomous intelligence weighs 200+ variables per decision, in 20 milliseconds, 24/7. No person can review each of those decisions one by one, and the same applies to any system buying media in real time. What makes the difference is the frame around those decisions: what the system is allowed to do, which objectives it is optimizing for, how the result is measured and who is accountable for it.
That frame is auditable even when individual decisions are not practical to inspect manually. Financial audits, supply chain audits, brand safety audits and measurement audits all remain possible, and none of them requires reviewing every bid manually.
Case study: Alsea and Domino’s Pizza
Alsea has run more than 1,500 campaigns with Mainkore over two years in Spain and Mexico. In Domino’s Pizza, a marketing mix model built by a third party, Indaru, estimated ROAS gains of 11% to 29% by channel.
Alsea operates brands such as Domino’s, Starbucks, Chili’s, Foster’s Hollywood, VIPS, Ginos, TGI Fridays and P.F. Chang’s. Together with Dentsu as agency partner, it has invested more than $6 million through Mainkore across awareness, video, performance and social campaigns.
Before, Alsea worked with the usual model: agency, technology partners, buying platforms and manual optimization. It worked, but it came with three recurring problems: limited visibility into what was being bought and why, constant pressure on CPM, CPV and viewability, and results that were explained at the end instead of being known upfront. What Alsea wanted, in the words of Borja Cameron, CMO, was “to start by knowing what was reasonable to expect”.
Aggregated results reported by Dentsu and Alsea
Across more than 1,500 campaigns, Dentsu and Alsea report these results, measured against:
• 21% average reduction in total media cost.
• 24% improvement in unit-cost efficiency KPIs (CPM, CPV, CPA and CPP).
• More than 23% growth in ROAS.
These aggregated figures don’t isolate Mainkore’s contribution from other factors that changed over the same period.
Independent measurement: Domino’s Pizza marketing mix model
The Domino’s analysis estimates the contribution of each channel within the model. Indaru built a marketing mix model comparing ROAS by channel for Domino’s campaigns in Spain and Mexico between 2021 and 2023, before Mainkore, and between 2024 and March 2026, with Mainkore:
| Channel | 2021 to 2023 (before) | 2024 to March 2026 (with Mainkore) | Change |
| Display | 5.03x | 6.49x | +29% |
| Video | 3.43x | 4.39x | +28% |
| Social Meta | 5.54x | 6.13x | +11% |
| Social TikTok | 3.19x | 4.12x | +29% |
The gains were not uniform. Meta, the channel with the highest starting ROAS, improved the least. And a comparison between two periods can’t rule out every external factor: channels like TikTok matured significantly over those years.
What makes this measurement useful is that it was produced by a third party rather than by Mainkore itself.
Transparency is not asking the vendor to prove itself. It is designing measurement so that the vendor is not the only party able to prove the result.
Alsea’s advice for any CMO considering this model sums it up: test with rigor, compare against real benchmarks, demand transparency and scale only when results hold over time.
Alsea’s advice for any CMO considering this model sums it up: test with rigor, compare against real benchmarks, demand transparency and scale only when results hold over time.
Five questions to test the transparency of any media buying model
An advertiser can test the transparency of any media buying model with five questions. If the answers aren’t in writing, the transparency isn’t either.
1. Where does each euro go, and what does each intermediary keep?
2. What result am I buying, expressed as a KPI and a number?
3. Who measures that result, and with which data?
4. Under which conditions does the commitment apply?
5. What happens if it isn’t met, and who answers for it?
The first question covers cost transparency. The other four cover outcome transparency: the part of the commitment this framework is designed to make explicit.
FAQ
It is the advertiser’s ability to know where the budget goes and what it buys. The industry debate has focused on fees, intermediaries and supply paths. Outcome transparency, which result was agreed, how it is measured and who answers for it, matters just as much and is discussed far less.
More than a few years ago, at least among premium players. ISBA and PwC’s second study, based on 2022 data, found that unattributable spend had fallen from 15% in the 2020 study to 3%. Cost transparency has improved; outcome transparency is the next layer to make explicit.
Six dimensions: spend, objective, KPI, measurement, conditions and accountability. The first covers cost; the other five define whether the advertiser can verify the result it paid for.
A system where the advertiser cannot establish what it optimizes for, how its output is measured or who is accountable for the result. Decisions that are too fast to review individually don’t make a system a black box, as long as that frame is explicit and auditable.
Not necessarily. Bid-level data is useful for technical audits, but it doesn’t tell an advertiser whether the campaign met its objective. Verifying a commitment doesn’t require inspecting every individual bid.
By agreeing before the campaign on the KPI, its target, the measurement source and the conditions. Ideally, measurement relies on data the executor doesn’t control, such as the advertiser’s own analytics or a third-party marketing mix model.
Transparency isn’t about seeing everything. It’s about being able to verify what matters.
See what Mainkore can commit to for your KPI, and how it would be measured. Talk to our team
