Pricing A/B Test Design Planner
Design statistically valid pricing experiments with sample size calculations. Enter values for instant results with step-by-step formulas.
Worked Examples
Example 1: SaaS Monthly Plan
Problem:Current: $99/month, 3.5% conversion. Testing $119/month. 50,000 monthly visitors. Want 95% confidence.
Solution:Price increase: about 20.2%\nExpected conversion using the calculator's elasticity model:\n3.5% × (1 - 0.303) ≈ 2.44%\n\nRequired sample size:\nAbout 4,017 visitors per variant\n\nAt 50,000 monthly visitors, the calculator estimates enough traffic in about 1 week.\n\nRevenue per visitor:\nControl = 3.5% × $99 = $3.47\nTest = 2.44% × $119 ≈ $2.90\n\nThis scenario reduces revenue per visitor.
Result:$99→$119 | 3.5%→2.44% expected | Revenue/visitor falls | About 1 week needed
Example 2: E-commerce Product
Problem:Product at $49, 5% conversion. Testing $59 (about a 20.4% increase). 100,000 monthly visitors.
Solution:Expected conversion using the calculator's elasticity model:\n5.0% × (1 - 0.306) ≈ 3.47%\n\nRevenue per visitor:\nControl = 5.0% × $49 = $2.45\nTest = 3.47% × $59 ≈ $2.05\n\nRequired sample size is modest at this traffic level, so the calculator reaches power in about 1 week.\n\nThis test points toward weaker revenue at $59.
Result:$49→$59 | Revenue likely decreases | About 1 week to power | Consider a smaller increase
Example 3: Subscription Annual Plan
Problem:Annual plan $499, 1.2% conversion. Testing $599. Only 10,000 monthly visitors.
Solution:Price increase: about 20.0%\nExpected conversion using the calculator's elasticity model:\n1.2% × (1 - 0.301) ≈ 0.84%\n\nBecause traffic and conversion are both low, required sample size is much larger.\nThe calculator estimates roughly 11 weeks to statistical power at this traffic level.\n\nA smaller price step or longer test window is safer.
Result:Low power situation | About 11 weeks needed | Consider a smaller price change
Frequently Asked Questions
Why A/B test pricing instead of just raising prices?
A/B testing provides data-driven confidence. You learn the actual elasticity of your customers, minimize risk of revenue loss, and can optimize incrementally. Blind price changes may lose significant revenue or leave money on the table.
How long should a pricing A/B test run?
Long enough for statistical significance—typically 2-8 weeks depending on traffic. Must cover full business cycles (weekday/weekend, pay periods). Stopping early for positive results inflates false positives. Plan duration before starting.
What's a good minimum detectable effect for pricing tests?
Typically 10-20% relative change in conversion or revenue. Smaller effects require huge sample sizes. If a 5% revenue change doesn't matter to your business, don't design tests to detect it—you'll wait forever.
Should I test multiple prices simultaneously?
A/B/C or A/B/n tests can be valuable for finding optimal price points. However, they require more traffic and complexity. Start with A/B for simplicity, then iterate. Each additional variant increases sample size requirements.