Price and Volume Sensitivity: Test Both Inputs

A price increase improves contribution per sale when unit costs stay fixed, but it may also change the number of sales. A price-volume sensitivity table keeps those two assumptions visible. It does not predict customer behavior. Each cell asks what the operating result would be if a particular price and volume combination occurred.

Build the grid from one consistent cost model

Assume a hypothetical product has $24 of variable cost per unit and $6,000 of fixed monthly costs. Test prices of $40, $44, and $48 against monthly volumes of 300, 400, and 500 units. At $44 and 400 units, contribution is $20 per unit and operating profit is 400 × $20 − $6,000 = $2,000.

The $40-to-$44 change is a 10% price increase measured against the original $40 price. Contribution per unit rises from $16 to $20, a 25% increase measured against the original $16 contribution. Those percentages differ because they have different denominators. Neither is a margin or markup percentage.

Find the volume that preserves contribution

At the original $40 price and 400 units, total contribution is $6,400. At $44, the same contribution requires $6,400 divided by $20, or 320 units. That is 20% fewer units than 400. This is a mathematical threshold under unchanged unit costs and fixed costs, not a prediction that customers will accept the higher price or that a 20% decline is acceptable for other reasons.

If percentage-based payment fees change with price, include them in the unit contribution formula rather than holding the entire variable cost flat. If higher volume triggers a new shift or warehouse charge, the fixed-cost assumption must also change in the affected cells. A clean grid should not hide those step changes.

Choose scenarios with evidence

Use actual quote outcomes, conversion records, order histories, or a defined test to select plausible volume ranges. Do not label an invented demand response as a market elasticity. Separate customer segments when a price change may affect them differently, especially if their variable costs and order sizes also differ.

Preserve negative results and identify combinations outside available capacity. A profitable cell that requires 500 units cannot be treated as feasible if only 350 can be delivered. The table estimates operating profit for the stated period; it does not establish cash timing, long-term demand, or guaranteed earnings. Connect the selected scenario to a capacity plan and collection schedule before using it operationally.

Method references: OpenStax: Break-Even Sensitivity Analysis. Accessed October 22, 2026. All dollar figures are hypothetical worked examples.