đź’ˇ Tip of the Day
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Pricing shapes your unit economics, your acquisition loops, and your runway. A small change in price can lift or flatten demand, and the right level often depends on costs and the audience more than on competitors. This simulator gives you a quick way to test price points against a rough elasticity, costs, and demand to see revenue, gross profit, and net after fixed costs. It will not give you the final price. It will help you plan tests with a clear sense of trade-offs.
Quick start - enter costs, demand, and price points
List a few candidate prices. Add your variable cost per unit and fixed costs per month. Set a base monthly demand and an elasticity estimate. If you sell subscriptions, include churn; otherwise leave it at zero. Run the simulation. You will see units, revenue, gross profit, and net for each price point, plus a breakeven unit count at the base price. This is a planning sketch. Use it to pick two prices for an A/B test or to shape a three-tier set for a landing page experiment.
About elasticity - a useful fiction
Elasticity is a simplification that models how demand changes when price changes. In many markets, demand is not smooth and depends on anchors, endings, and packaging. Use elasticity to get a feel for direction, not to forecast precisely. If you do not know where to start, try -1.0 to -2.0 and look at how the shape of net changes. Then run real tests with guardrails. For frameworks on testing pricing, the short notes from Reforge on monetization experiments pair well with this kind of back-of-the-envelope model.
Costs, margins, and confidence
Price is only meaningful when you know your costs. Variable costs - hosting, fulfillment, fees - set the floor for gross margin. Fixed costs - salaries, rent, base software - set the breakeven point. In subscription models, churn pulls against price. If raising price increases churn, your net may not move the way you expect. Use this simulator to find prices worth testing, then track conversion, retention, and payback to see if the higher price holds. A modest increase that sustains retention can fund better support and product without a huge marketing push.
Packaging and endings - humans are not calculators
9, 19, 29 are not magic, but they are familiar anchors. Packaging matters as much as the raw number. If one tier includes priority support or a limit that removes a headache, many buyers will accept a higher price without shopping. Test versions that change the outcome, not just the number. Show price on an anchor - per seat, per month, per thousand events - that matches how your buyer explains the expense to a manager.
Comparison - competitor match vs value-based pricing
| Aspect | Match competitor | Value-based |
|---|---|---|
| Signal | Safe | Explains outcome |
| Margin | Constrained | Can expand |
| Differentiation | Low | High if tied to gains |
| Testing | Simple | Needs interviews and trials |
Bullet notes - tests that respect customers
- Always communicate changes early to existing customers and honor a grace period.
- Test price with clear value changes - better support, new limits, or features that save time.
- Watch payback and retention, not just conversion, when prices move.
- Keep one stable tier for teams that cannot tolerate surprise.
Real example - finding a higher, steadier price
A tool priced at $9 saw high churn and busy support. We added a $19 plan with priority support and raised the main tier to $12. The simulator suggested the mix could lift net at moderate elasticity, so we tested for six weeks. Conversion dropped slightly, but churn improved and net rose because more teams took the higher tier. Support load became predictable. No magic. Just a small price change tied to value that customers felt within the first week.
Two quick questions before you publish a price
- Do you have a test plan that looks at conversion, payback, and retention, not just week-one revenue?
- Are you changing the value story alongside the number so buyers understand the trade?
Price experiments should be polite and reversible. Use a quick model to pick candidates, run clean tests, and let cohorts tell you if a price is both higher and healthier. Over a year, these small improvements compound more safely than one big bet.