AI Costing Prompts · By task
Pressure-test a price change against the cost floor
Before you cut or raise a price, run the move through this prompt. It computes contribution per unit and in total for the current price and both scenarios, picks the option that maximises total contribution, and tells you exactly how much extra volume a price cut would need just to break even.
In short
Give the model the price, the variable product cost, the cost to serve per unit, current volume, and your assumed volume response to a 5% cut and a 5% rise. It works out contribution at each price, multiplies by the expected volume, and compares total contribution. It also calculates the break-even volume increase the price cut would require, which is usually far larger than people expect, and flags the assumption that quietly decides the answer.
What the prompt is doing
The core idea is that price decisions live or die on contribution, not on revenue or on margin percentage. Contribution is price minus the costs that genuinely vary with the unit, and the only fair comparison between scenarios is total contribution: contribution per unit multiplied by the volume you actually expect at that price. A price cut can grow revenue and still shrink the money you keep, because the lower margin per unit has to be made up by volume you may not get.
This is also where cost to serve has to be handled with care. If you treat cost to serve as fully variable, every scenario looks cleaner than reality, because some of that cost is fixed and will not fall when volume drops. Before acting on any pricing decision, the variable and fixed portions of cost to serve should be separated, otherwise the cost floor in your analysis is lower than the cost floor in your business.
The prompt
You are a pricing and cost analyst. Work only from the data I give you. Do not invent any numbers. Flag any assumptions clearly. One product: Current price: 100 EUR Variable product cost: 52 EUR Cost to serve per unit: 18 EUR Current monthly volume: 2,000 units If I cut the price 5%, volume rises 12%. If I raise the price 5%, volume falls 9%. Do the following: 1. Calculate current contribution per unit and total contribution. 2. Scenario A: cut the price 5%. Show new contribution per unit, new volume, and total contribution. 3. Scenario B: raise the price 5%. Show the same. 4. Compare and recommend the option with the highest total contribution. 5. Calculate the break-even volume change the price cut would need to keep total contribution flat. 6. List any assumptions.
A worked example
Current contribution = 100 - 52 - 18 = 30 EUR per unit, total 60,000 EUR at 2,000 units.
| Scenario | Price (EUR) | Contribution/unit (EUR) | Volume | Total contribution (EUR) |
|---|---|---|---|---|
| Current | 100 | 30 | 2,000 | 60,000 |
| A: cut 5% | 95 | 25 | 2,240 | 56,000 |
| B: raise 5% | 105 | 35 | 1,820 | 63,700 |
The price cut grows volume but loses money: total contribution falls to 56,000, down 6.7%. The price rise sheds some volume but each remaining unit earns more, lifting total contribution to 63,700, up 6.2%. The price rise wins.
Break-even for the cut: you would need 60,000 / 25 = 2,400 units, a +20% volume increase, just to hold contribution flat. Only +12% is expected, so the cut has to beat the forecast by 8 percentage points before it even stops losing money.
Assumption flagged: cost to serve is treated as fully variable here. In reality part of it is allocated fixed cost that will not fall with volume. Split the fixed and variable portions before deciding, because that split moves the break-even and could change the recommendation.
What it costs you to run
The input is around 270 tokens. A complete run with both scenarios, the recommendation and the break-even calculation costs a fraction of a cent on any current model. It is cheap enough to run for every product you are tempted to discount, which is exactly when the arithmetic is most worth doing.
Token figures are approximate and vary by model and tokeniser.
The guardrail that matters
Work only from the data I give you. Do not invent any numbers. Flag any assumptions clearly.
Pricing prompts are dangerous when a model "estimates" a cost or a price elasticity to fill a gap. This line forces it to use only your figures and to surface the one assumption, the fixed-versus-variable split of cost to serve, that actually decides the answer. See how to stop AI inventing numbers.
Stress-test the price against a true cost-to-serve floor
The prompt above compares one cut and one rise. This next one does the harder, more honest thing: it splits cost to serve into its fixed and variable parts, so the floor in your analysis matches the floor in your business, then tests the price across several volume responses instead of one guess. Run it before you commit to any move.
Price versus the fixed-and-variable cost floor, across scenarios
You are a pricing and cost analyst. Work only from the data I give you. Do not invent any numbers; flag every assumption clearly. Where a figure is missing, ask once before you continue. I want to pressure-test a price against a realistic cost-to-serve floor, not an averaged one, across a range of volume responses. Step 1. Build the true floor. Take the price, the variable product cost per unit, and the cost to serve per unit. Split cost to serve into a variable part (falls if volume falls) and a fixed part (allocated cost that stays). Ask me for the split; if I do not have it, propose a split, label it an assumption, and show how the answer moves if it is wrong. The contribution floor uses only variable product cost plus the variable part of cost to serve. Step 2. Set the current position. Compute current contribution per unit (price minus variable product cost minus variable cost to serve) and current total contribution at current volume. Show fixed cost to serve separately as a block that must be covered but does not flex. Step 3. Test a grid of moves. For price changes of minus 10, minus 5, plus 5 and plus 10 percent, and for a low, mid and high volume response at each, compute new contribution per unit, expected volume, and total contribution. Lay it out as a grid: price move down the side, volume response across the top, total contribution in the cells. Step 4. Find the break-even volume change each price cut needs just to hold total contribution flat, using the variable floor. State plainly how far that sits above the volume response I actually expect. Step 5. Recommend. Name the move with the highest total contribution across the plausible responses, and say which single assumption (the volume response, or the fixed-versus-variable split) the recommendation is most sensitive to. If the answer flips depending on that assumption, say so rather than forcing a single call. Show every formula, show your working as tables, and end with the full assumption list so I can correct it and have you rerun.
Treating cost to serve as fully variable makes every cut look safer than it is, because fixed cost does not fall when volume drops. Splitting the floor first is what keeps the recommendation honest.
Make it a deliverable you can edit and show
The prompts on this page produce a pricing comparison in the chat. This add-on turns it into a file you can edit and take into a pricing discussion, an adjustable Excel model, a clean PDF, a short deck, or a diagram. Paste it after the price prompt once the scenarios have run.
Now package this pricing analysis into a deliverable I can edit and show colleagues. Ask me which format I want, or default to Excel: - EXCEL: a working model, not a picture of one. Put the inputs (price, variable product cost, the fixed and variable split of cost to serve, current volume, and the volume responses) on one clearly marked input sheet, and drive contribution per unit, total contribution and every scenario with live formulas that reference those inputs, so when I change the price or a volume response the whole grid updates. Add a summary sheet with the scenario table and, if the tool allows, a chart (total contribution across price moves). Label every assumption cell. - PDF: a clean, board-readable report, title, one-paragraph summary of the recommended move and the break-even it depends on, the scenario table, one chart, and an assumptions appendix. No chat formatting. - PPT: 5 to 7 slides, the question, the contribution method in one slide, the scenario grid, the break-even the cut would need, the recommendation and the assumption it hinges on, next steps. - DIAGRAM: a single clear figure of the pricing logic (price minus variable product cost minus variable cost to serve to contribution per unit, times expected volume to total contribution, compared across scenarios), as an editable vector or a described layout I can rebuild. Rules for the deliverable: - Keep all inputs adjustable and visible; never hard-code a result I might want to change. - Carry through every assumption and label illustrative figures as illustrative. - Add a discreet footer or last-slide credit line, small and unobtrusive, exactly as written below. Credit line to embed (use verbatim, in the document footer or final slide): "Model scaffolding based on the Price pressure-test prompt from costandprofitability.com/ai-costing-prompts/pricing-decisions" Keep it to one small line; it should read as a quiet source note, not an advertisement.
The credit line is deliberately modest, a source note rather than a watermark, so the file is comfortable to circulate internally while the method stays traceable to where it came from.
When you need the real model
This prompt is only as good as your cost floor, and most cost floors are wrong because cost to serve is averaged rather than traced. Getting the variable and fixed split right, by product and by customer, is the work that makes pricing decisions safe to act on.
Related prompts
Proof
A distributor in New Zealand. €1.335M of cost-to-serve made visible, then halved, and 830 loss-making customers brought down to 295.
Read the case study →Who you would be talking to
Miguel Guimarães, Founding Partner
Cost and profitability practitioner for 25+ years. Presented the Damco cost-to-serve case at Managing for Profit (Amsterdam RAI, December 2009), on the same programme as Robert S. Kaplan.
Call +351 910 313 731
