Model pricing tiers, freemium conversion, and price elasticity with revenue maximization analysis and A/B test impact projections.
Most pricing debates come down to one number: how much volume a price change buys or costs you. This tool takes your current price and volume, an elasticity estimate, and your tier or freemium structure, then solves for the price that maximizes revenue and projects what an A/B-tested price change would do to the bottom line. The surprise is that the revenue-maximizing price rarely sits at a round number or at either extreme, and even a strongly elastic product's best move is usually a modest cut, not a slash. Everything computes in your browser from the numbers you enter.
A concrete input and expected output from the current implementation.
Input
Current price: $50 Current volume: 500 units per month Estimated price elasticity: -2 (a 1% price increase cuts volume 2%)
Expected output
Revenue-maximizing price: $37.50 (25% below current). Projected volume at that price: 750 units per month (+50%). Projected monthly revenue: $28,125, up from $25,000 today (+12.5%).
Modeling demand through the current point at the stated elasticity, revenue peaks where the implied elasticity reaches -1, the unit-elastic point. A 25% cut gains 50% more volume, and 37.50 x 750 = 28,125 beats 50 x 500 = 25,000. Cutting further would still gain volume but add no revenue.
You enter current price and volume plus an elasticity estimate, the percent volume change per percent price change. The tool models demand through that point and computes revenue across candidate prices, reporting the price where revenue peaks. That peak occurs where the curve's implied elasticity equals -1, the unit-elastic point at which a further cut adds nothing.
For tiered pricing you enter each tier's price, subscriber count, and upgrade rate; the tool rolls these into blended revenue and recomputes it as you move users between tiers. Freemium scenarios treat free-to-paid conversion as the variable and show revenue across conversion rates. A/B projections take a tested price and its observed conversion delta and annualize the impact across the full customer base.
When demand is elastic, meaning the elasticity magnitude exceeds 1, a cut gains enough volume to offset the lower price and revenue rises; when demand is inelastic, the reverse holds and a raise wins. Between the two sits the unit-elastic point where revenue peaks, and that is what the tool returns as the maximum.
No. Elasticity is an estimate you supply from historical price changes, tests, or published benchmarks, and the output is only as good as that number. The honest workflow is to try a range, for example -1.2 to -2.5, and treat the recommendation that survives across all of them as the robust answer.
The example and behavioral notes were checked against the browser implementation. Standards and primary references below define the relevant format, formula, or platform behavior.
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