What is price optimization?
Price optimization is choosing the price that best meets a goal, usually maximum revenue or profit, using data on how demand responds to price, along with costs and competition.
Price optimization models estimate how many units would sell at each candidate price, then pick the price that gives the best result within your constraints.
Price optimization techniques
- Elasticity modelling: estimate how sales respond to price from past price changes.
- Price testing: show different prices to comparable visitors and measure revenue per visitor.
- Competitor-aware optimization: include rival prices as a factor in demand.
- Constrained optimization: maximize profit within floors, ceilings and stock levels.
A price optimization example
A product sells 1,000 units a month at $20, and tests suggest an elasticity of −1.5. Raising the price 5% to $21 would cut sales about 7.5% to 925 units, and revenue would fall from $20,000 to $19,425. Lowering it 5% to $19 would lift sales about 7.5% to 1,075 units, and revenue would rise to $20,425. With elastic demand, the revenue maximizing price is below today’s, as long as the margin at $19 still clears the floor.
Price optimization vs dynamic pricing
Optimization answers “what should the price be?” from a model of demand. Dynamic pricing answers “how should the price change as conditions change?” in close to real time. Most systems combine them: optimization sets the target, dynamic rules adjust around it, and guardrails keep both within bounds.
Test before you change
Dynamic Pricing Engine simulates the revenue curve and the price that maximises it before anything goes live, then lets you run a price test with a clear verdict.
Last reviewed 2026-11-01.