About this tool
The Conversion Rate Calculator divides conversions by visitors to give the conversion rate, and solves the other way round: enter a rate and a visitor count to see the conversions it should produce, or a rate and a conversion goal to see how much traffic you need. It is for marketers and product teams reading analytics, for anyone setting a conversion target who wants to know what relative lift that target implies, and for people comparing two landing pages, emails, or ad variants who need to know whether the difference they see is real or noise. The lift panel turns a target rate into the percentage improvement required and the extra conversions or saved traffic it would mean. The A/B panel runs a two-sided two-proportion z-test, shows Wilson 95% confidence intervals for both variants, and states the verdict in plain language.
How it works
Conversion rate = conversions ÷ visitors. Lift needed = target rate ÷ current rate − 1. For the A/B test the pooled rate is p = (conversions A + conversions B) ÷ (visitors A + visitors B), the standard error is √(p(1−p)(1/nA + 1/nB)), z = (rate B − rate A) ÷ standard error, and the two-sided p-value is 2 × (1 − Φ(|z|)) using a standard normal CDF approximation accurate to about 1e-7. Each variant's 95% confidence interval uses the Wilson score method: centre (p̂ + z²/2n) ÷ (1 + z²/n) with half-width z ÷ (1 + z²/n) × √(p̂(1−p̂)/n + z²/4n²), z = 1.96. Verdicts follow the conventional thresholds p < 0.01, p < 0.05, and p < 0.10.
Frequently asked questions
What is a good conversion rate?
It depends on what counts as a conversion and where the traffic comes from. E-commerce purchase rates commonly sit between 1% and 4% of sessions; lead forms on paid landing pages often run 5% to 15%; free-trial sign-ups and email clicks are different again. Your own trend and the rate of your best-performing segment are more useful yardsticks than any cross-industry number.
What does the p-value in the A/B test mean?
It is the probability of seeing a difference at least as large as the one observed if the two variants actually converted at the same rate. A p-value of 0.06 means a gap this size would arise by chance about 6% of the time. It is not the probability that B is better, and a result that is not significant is not proof that the variants are equal; it usually means the test needs more traffic.
Why use Wilson intervals instead of the usual ± formula?
The textbook Wald interval, p̂ ± 1.96√(p̂(1−p̂)/n), misbehaves at low conversion rates and small samples: it can dip below zero and its real coverage is often well under 95%. The Wilson score interval stays inside 0 to 100% and holds close to its nominal coverage even with a handful of conversions, which is exactly the situation most conversion tests are in.
How much traffic does an A/B test need?
Enough that the smallest lift you care about would show up as significant. A rough rule for a two-sided test at 95% confidence and 80% power: visitors per variant ≈ 16 × p(1−p) ÷ d², where p is the baseline rate and d the absolute difference to detect. To detect a lift from 2% to 2.5% (d = 0.005) that is about 16 × 0.0196 ÷ 0.000025 ≈ 12,500 visitors per variant.