Conversion Rate Calculator
Marketing ToolsConversion rate, required traffic, lift — and whether your A/B result is real.
Conversion rate
2.5%
50 of 2,000 visitors
conversions ÷ visitors × 100
- Visitors per conversion
- 40
- visitors ÷ conversions
- Non-converting visitors
- 1,950
- visitors − conversions
How this is calculated
conversion rate = conversions / visitors x 100 visitors needed = target conversions / (rate / 100) relative lift = (rate_b - rate_a) / rate_a x 100 A/B significance, two-proportion z-test: p_pool = (c_a + c_b) / (n_a + n_b) SE = sqrt( p_pool (1 - p_pool) (1/n_a + 1/n_b) ) z = (p_b - p_a) / SE p = 2 (1 - CDF(|z|))
How it works
Conversion rate is conversions divided by visitors. That part is arithmetic. The hard part is knowing whether a difference between two rates means anything, and that is a statistics question rather than a division one.
The significance mode runs a two-proportion z-test. It pools both variants to estimate what the conversion rate would be if the change had no effect, works out how much random variation you would expect at your sample sizes, and asks how unlikely the observed difference would be under that assumption. A 95% confidence result means you would see a gap this large by chance roughly one time in twenty.
Two cautions the number itself will not give you. First, sample size: below roughly 30 conversions per arm the normal approximation strains, and the tool flags it. Second, peeking: checking a running test repeatedly and stopping the moment it crosses 95% dramatically inflates false positives. Decide the sample size before you start, then look once.
Worked example
Control 1,000 conversions from 50,000 visitors; variant 1,250 from 50,000
- p_a = 0.02, p_b = 0.025
- p_pool = 2250 / 100000 = 0.0225
- SE = sqrt(0.0225 x 0.9775 x (2/50000)) = 0.000938
- z = 0.005 / 0.000938 = 5.33
- p is far below 0.05
25% relative lift, significant at over 99.9% confidence
How to use it
- Use Conversion rate for a straightforward rate from visitors and conversions.
- Use Traffic needed to work backwards: how many visitors it takes to hit a conversion target at your current rate.
- Use A/B significance to compare two variants. Enter visitors and conversions for each, then read the confidence and the verdict.
- Watch for the underpowered warning — it means the result is not yet trustworthy however good the confidence figure looks.
What this tool does not do
- The z-test assumes each visitor is independent and randomly assigned. Uneven traffic splits, or returning visitors counted twice, break that assumption.
- It tests one metric at a time. Testing several and reporting the winner inflates false positives.
- It cannot detect a novelty effect, where a change performs well simply because it is new.
Frequently asked questions
How do you calculate conversion rate?
Divide conversions by visitors and multiply by 100. 50 conversions from 2,000 visitors is a 2.5% conversion rate.
What is a good conversion rate?
It varies enormously by industry, traffic source and what counts as a conversion. Ecommerce commonly sits between 1% and 3%; a high-intent lead form can be far higher. Your own trend over time is more useful than any published benchmark.
How do I know if my A/B test result is real?
Run the significance test. It tells you how likely the observed difference is to be chance alone. The convention is 95% confidence, meaning roughly a one-in-twenty risk of a false positive.
How many conversions do I need for a valid A/B test?
As a rule of thumb, at least 30 conversions per variant before the maths behind this test is dependable, and considerably more to detect small differences. This tool warns you when either arm is below that.
Why should I not stop a test as soon as it hits 95%?
Because if you check repeatedly and stop at the first crossing, you give randomness many chances to produce a false positive. Fix your sample size in advance, then evaluate once.
What is the difference between absolute and relative lift?
Going from 2% to 2.5% is a 0.5 percentage point absolute increase and a 25% relative lift. Marketing reports usually quote the relative figure because it is larger — make sure everyone knows which is on the slide.
What does the p-value mean?
It is the probability of seeing a difference at least this large if the change actually had no effect. A p-value of 0.05 corresponds to 95% confidence. Lower is stronger evidence.
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Last updated 3 September 2026.