Transparent by design

The arithmetic is explicit.
The counterfactual is yours.

MarginTrial is a deterministic contribution accounting tool. It makes the commercial assumptions visible so you can review and explain them.

1. Set the comparison

For each product–campaign row, supply the expected units without the offer and the gross units sold with the offer. Baseline units are multiplied by promotion days divided by baseline days when both day fields are provided. A day-count adjustment does not correct seasonality, trend, product availability, or a different customer mix.

2. Calculate contribution

Baseline contribution = aligned baseline units × (regular price − variable unit cost)

Promotion contribution = gross promotion units × (promotion price − variable unit cost) − returned units × promotion price − fixed campaign spend + supplier funding − later displaced units × regular unit contribution

Contribution change = promotion contribution − baseline contribution

Variable unit cost should include product cost and relevant per-unit fulfilment and payment costs. Fixed campaign spending and supplier support are allocated once across product rows. Support already deducted from unit cost should not be entered again as funding.

3. Decompose the change

These components sum to the contribution change. This is an accounting bridge, and its ordering does not identify separate causal mechanisms.

4. Solve for break-even response

The minimum gross promotion volume must recover baseline contribution plus fixed campaign spending minus funding. The calculation holds observed refund and supplied displacement rates constant as volume changes. When contribution per extra unit is nonpositive, more volume may deepen a loss; a minimum-volume target is then undefined or funding can create a maximum viable volume.

5. Inspect uncertainty

The scenario studio varies gross promotion units, promotion price, promotion unit cost, spending, funding, and expected baseline units. Price and volume are independently varied. A price slider does not predict demand; use a defensible demand estimate and set volume accordingly.

Limits that matter

Explore the synthetic demo to inspect each bridge component and its source assumptions.