Is the tool aimed against agencies?

No. It is the shared calculation basis for both. A good agency becomes stronger because it can explain its strategy transparently; a weak one stands out without anyone having to attack it. Both roles are legitimate - the SME can check a proposal, the agency can set it up transparently.

What exactly does "neutral" mean here?

Neutral like a calculator: the model has no agenda and no channel interest, the formulas are open. But it doesn't make the inputs 'true' - in marketing there is no objectivity. The assumptions are still set by people; the tool is the neutral ground on which both lay them open and compute them.

Why does the JSON exchange secure the neutrality?

Because the strategy basis is a local file that both sides hold - no exclusive dashboard controlled by one party alone. No one can change the basis in the background without the other side seeing it on their copy. Data sovereignty on both sides is the precondition for the model to work as a shared basis.

Does the model prove a strategy works?

No. It computes assumptions forward and checks their plausibility - it doesn't measure a causal marketing effect. In operation, real numbers replace the assumptions, and plan and actual can be placed side by side. The causal proof of effect is a later, separate level.

Do you need your own data or a data-science team?

No. Without your own numbers, benchmark ranges serve as a starting point; as soon as real values are available, they replace the assumptions. The layer is meant precisely for those who have no enterprise MMM but should still think their allocation through mathematically.

A strategy basis both sides sign off on

The collaboration between SME and agency today usually begins at execution - and that is exactly where the SME is the less-informed party. YourValidator starts a level earlier: as a neutral calculation layer on which both set up their strategy together, before budget flows. Not a tool against the agency, but a common ground both sign off on.

Why the collaboration is lopsided today

The Information Gap

Without its own data science team, an SME has little choice but to take the agency’s word for it. Budget requests, channel recommendations, reporting-all of these are difficult to verify when the other party holds the data used in the calculations. This isn’t a matter of bad faith, but rather a structural asymmetry: one side has the model, the other does not. YourValidator enables valid reconciliation of planned and actual figures.

Correct the asymmetry: Request the model.

The next time your agency asks for “more budget for Channel X” without providing a cross-channel mathematical justification, simply send them your local YourValidator JSON file with your proposed calculation:

“Here are our calculated assumptions. Enter your campaign assumptions directly into this model and send me the updated file. If the numbers add up, the budget will be approved.”

A professional agency will gladly accept this invitation to a mathematical dialogue.

Neutral like a calculator

The layer is neutral in the sense a calculator is neutral: no channel interest, no agency agenda, open formulas. What it is not: an oracle. It doesn't make the entered numbers "true" - in marketing there is no objectivity as such. The assumptions are still set by people.

That is exactly where its value for the collaboration lies: as soon as both sides use the same model, both think and act on the strategy level. The discussion shifts from "more budget on channel X" to "which assumption leads to which result" - traceable for both, instead of asserted by one side.

The model has no agenda. The assumptions are laid open by both.

JSON exchange ensures neutrality

A neutral ground is truly neutral only if neither side can shift it. That is why a local JSON file accessible to both sides forms the strategic foundation - not an exclusive dashboard controlled solely by one party.

The SME can share its scenario with the agency, and the agency shares its scenario with the SME. Both have access to the same file; both can open, review, and further process it.

A common foundation - no more, but also no less

Marketing success cannot be guaranteed by either an agency or a tool. What can be documented, however, are the assumptions made by SMEs and agencies, the decisions they made along the way, and where the plan deviates from reality. YourValidator makes this visible and transparent. It is neither a contract nor a guarantee, but rather a shared basis for calculation so that both parties are on the same page regarding the numbers.

Tip: Transfer the JSON file encrypted.

How the collaboration works

Handover. One side builds a first model and passes it on as a JSON file - the SME to the agency or the agency to the SME. Both start from the same state.
Validate the base assumptions together. Conversion rates, order value, margin, saturation, ramp-up times - both check the assumptions together; plausibility and benchmark ranges help spot the unrealistic.
Fix one shared model. The agreed state is saved as a master plan - one truth both have signed off on, not two separate Excel versions.
Implement. Specialists carry out the measures - one or several agencies, partly in-house - on the basis of a clear brief instead of an open question.
Swap model numbers for real data. In operation, real values replace the assumptions. Plan and actual can be placed side by side - the basis grows with you, but stays the same for both.

Why a shared calculation logic for plan and actual matters, including when AI agents are used: Plan & Actual.

Set up a shared strategy model and share it as a file: try the tool

What this means for both sides

This software provides a mathematical foundation to ensure that planned and actual figures are always reconciled between the company and the agency on the same logical basis.

For the SME

It is no longer the less-informed party. It can trace proposals before approval, make assumptions visible, compare scenarios and keep its own data basis independent - even when switching agency. Having-to-trust becomes being-able-to-compute.

For the agency

A good agency becomes stronger: it can derive its strategy transparently, justify assumptions openly and obtain approvals on a shared basis - instead of presenting against distrust. The layer doesn't filter out the good agencies, but the blanket promises.

Where the layer ends

The limit - deliberately carried along, so the basis stays solid for both sides.

Simulation, not proof of effect. The layer computes assumptions forward and checks their plausibility. It doesn't causally prove what the marketing actually achieved.
Plan against actual, not cause. The comparison with real data shows deviations - not why they arose. The causal proof (experiments, MMM on time series) is a later, separate level.
Upstream, not replacing. YourValidator stands before a professional measurement infrastructure, not in its place. For many SMEs the upstream level suffices for a long time; for larger ones it is the entry point.

Try it free: set up the shared calculation basis for your collaboration - no sign-up

Frequently asked questions

How does YourValidator help SMBs and agencies work together?

Both sides work with the same calculation and the same assumptions. The agency shows how its recommendation comes about; the SMB sees what the budget depends on before approving it.

Is the tool directed against agencies?

No. Interests between SMBs and agencies can differ without anyone acting in bad faith. Agencies that work strategically benefit from clients who can follow their reasoning.

How do both sides exchange a plan?

Via a local JSON backup file that both sides can open and edit. There is no shared server. Saving and exchanging backups is a Pro feature; the calculation itself is free.

Which assumptions should be agreed on together?

Typically conversion rates, average order value, margin, budget per channel, saturation and ramp-up time. Benchmarks serve as starting values until real figures are available.

Does plan vs. actual explain why results differ?

No. Plan vs. actual (Pro) shows where results deviate from the plan. It does not explain the causes; that requires analysis of the actual campaigns.