The price that maximizes revenue may leave profit behind

A price that produces the highest sales revenue does not necessarily produce the highest profit. Extra sales require resources, and a price reduction often applies to units that would have sold at the previous price. Include the cost response before selecting a price from a demand curve.

ACCA's pricing discussion distinguishes marginal revenue from marginal cost and explains the profit calculation for a linear demand relationship. That framework is useful for checking a model's logic. It does not establish the demand curve for a real product; the company still needs evidence about how customers respond.

The following example uses an invented demand function and constant unit cost. Its purpose is to show why a revenue target and a profit objective can select different prices, and which assumptions need review before the arithmetic informs an actual commercial decision.

Assume monthly demand is Q = 1,200 - 6P, where P is the dollar selling price and Q is units. Each additional dollar of price reduces the modeled demand by six units. Assume variable cost is $50 per unit and fixed spending is $9,000 for the month.

Revenue is P multiplied by Q. Profit in this simplified model is (P - $50) multiplied by Q, less $9,000. Use the same price, demand population and period throughout. The calculation assumes all units in the compared population receive the selected price and that every demanded unit can be supplied.

Do not extend the equation indefinitely. At a sufficiently high price it produces zero or negative quantities, and at low prices it may predict more demand than the market or operation can support. Define a commercially relevant range and evaluate only feasible alternatives within the model's supported scope.

At a $100 price, modeled demand is 600 units. Revenue is $60,000, variable cost is $30,000 and profit after the $9,000 fixed amount is $21,000. This is the revenue-maximizing price under the assumed linear relationship.

At $125, demand falls to 450 units. Revenue declines to $56,250, but variable cost falls to $22,500. Profit becomes $24,750, which is $3,750 higher. The price sacrifices $3,750 of revenue while avoiding $7,500 of variable cost, so the lower revenue produces the higher profit.

Nearby prices help show the shape. At $120, the model produces 480 units and $24,600 of profit. At $130, it produces 420 units and the same $24,600 profit. The exact modeled optimum is $125, but the small difference from nearby prices may be less important than uncertainty about the demand slope.

Substituting Q into the profit formula gives -6P squared + 1,500P - 69,000. The same expression can be written as $24,750 - 6 × (P - 125) squared. Within this unconstrained model, moving away from $125 reduces profit because the squared term cannot be negative.

This algebra also makes the assumptions visible. Fixed spending changes the profit level but not the selected price while it remains unchanged across all alternatives. If a price and volume combination requires another capacity block, the fixed-cost function changes and the simple optimum may no longer apply.

A spreadsheet optimizer should be checked against a small table of feasible prices and boundary cases. Confirm that it is maximizing the intended profit measure, not revenue, gross margin percentage or profit per unit. Those objectives can select different answers even when every cell formula is correct.

Historical prices and quantities can move together for reasons other than the price itself. Promotions, seasonality, product changes, channel exposure and competitor availability may all affect demand. Two observed price points do not by themselves identify the causal response to a new price.

Use relevant commercial research or appropriately designed tests, and preserve the conditions in which the response was measured. A discount offered only to difficult customers is not comparable with a standard price offered to a broad audience. A launch surge may not describe repeat purchasing after the initial attention fades.

Show sensitivity to plausible demand curves. If small changes in the estimated slope move the optimum substantially, the precise calculated price is weak evidence. Management may prefer a limited price range, additional learning or a staged change rather than commit to a single number with unsupported precision.

Percentage payment fees and sales commissions change with price, while packaging or delivery may change with order size. Recalculate those costs at each alternative. The constant $50 variable cost in the example is a simplifying assumption, not a default treatment for every expense.

Capacity limits, minimum commitments and customer contracts can restrict the feasible set. A model that predicts profitable demand beyond available delivery capacity needs a constrained comparison. Likewise, a uniform price change cannot be applied immediately to customers whose agreed terms remain in effect.

Check effects on related products and future behavior where the evidence is material. A price may shift customers to another offer, change retention or alter support demand. Keep those consequences separate and supported; uncertain future benefits should not be inserted as guaranteed profit to make a chosen price appear optimal.

Record the selected price, eligible population, demand assumptions, cost assumptions and reason for choosing it. The best commercial choice may differ from the mathematical optimum because the business values simplicity, positioning or a specific customer relationship. State that reason directly so the model remains an honest comparison.

After the change, compare realized price, eligible demand, variable cost and contribution with the original case. Separate concurrent changes in exposure or service. The useful learning is whether the economic relationship held in the intended population, not merely whether total revenue happened to rise or fall.