Free tool Conversion rate calculator
Visitors and conversions in, rate out — with a confidence interval. Add a second group to compare variants and see whether the difference is more than noise.
- Rate and 95% confidence interval
- Two-proportion z-test for A/B
- Runs locally
Enter visitors and conversions
Use the same definition for both groups: the same visitor count (people, not page views) and the same converting event.
Group A
ControlConversion rate
21.2%
±1.6 pp · 95% confidence interval
Group B
Variant · optionalConversion rate
24.0%
±1.7 pp · 95% confidence interval
A vs B
Two-proportion z-test with pooled variance, two-tailed. Significant at p < 0.05.
The test assumes each visitor is counted once and the two groups were split at random. It says nothing about whether the lift matters to the business.
Three decisions before you type a number
The arithmetic is instant. Getting the inputs right is the work.
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1
Decide what counts as a visitor
Unique people who had the chance to convert — not page views, and ideally with bots removed. 2,400 product viewers in the sample, not 48,213 site visitors.
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2
Decide what counts as a conversion
One event, counted once per visitor: a paid order, a submitted form, a completed sign-up. The sample uses 508 paid orders.
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3
Keep the time window identical
Both groups should cover the same days. A variant that ran over a weekend is not comparable to a control that ran on weekdays.
The rate, the interval and the test
The rate alone hides how many people it is based on. The confidence interval puts the sample size back in, and the z-test asks whether two intervals really disagree.
- Rate = conversions ÷ visitors
- Interval width shrinks with the square root of the sample size — four times the traffic halves the width
- The z-test pools both groups to estimate variance under "no difference"
- p < 0.05 means a difference this large would appear by chance less than 1 time in 20
Conversion rate
p = conversions ÷ visitors
Sample: 508 ÷ 2,400 = 21.2%
95% confidence interval (Wald)
p ± 1.96 × √( p (1 − p) ÷ n )
Sample: 21.2% ± 1.6 pp → 19.5% to 22.8%
Two-proportion z-test
z = (p₂ − p₁) ÷ √( p̄ (1 − p̄) (1/n₁ + 1/n₂) ), p̄ = (x₁ + x₂) ÷ (n₁ + n₂)
Sample: z = 2.33, p = 0.020 → significant at 5%
What a "normal" rate looks like
Example ranges only — they vary with industry, traffic source and how strictly you define the conversion. Use them to sanity-check a number, not as a target.
| Goal | Example range | Typical visitor definition | What moves it most |
|---|---|---|---|
| E-commerce order | 1% – 4% | Sessions that viewed a product | Shipping cost, checkout steps, returning share |
| SaaS sign-up → paid | 2% – 8% | Accounts that signed up | Time to first value, onboarding, pricing clarity |
| Landing page lead form | 5% – 15% | Sessions on the landing page | Form length, offer, traffic intent |
| Content subscription | 0.5% – 3% | Article readers | Placement, frequency of prompts |
| Chat invite → conversation | 3% – 10% | Visitors shown a proactive invite | Timing, page, message relevance |
Example ranges, not benchmarks. A paid-search landing page and a blog post can differ tenfold on the same site.
How conversion rates go wrong
Most disagreements about a rate are disagreements about the denominator.
Page views as the denominator
One person reloading five times becomes five chances to convert. Count visitors, not views.
Bots left in
Crawlers never convert. Leaving them in the denominator drags the rate down and makes campaigns look worse than they are.
Stopping the test early
Peeking daily and stopping at the first p < 0.05 inflates false positives. Fix the sample size or duration first.
Different windows
A rate from a sale week against a rate from a normal week measures the sale, not the change you made.
Conversion rate questions
Still have a question?
Chat with the team behind TapCub — we usually reply within a few hours on business days.
Which significance test does the calculator use?
A two-proportion z-test with pooled variance, two-tailed, at the 5% level. It is the standard test for comparing two conversion rates with reasonably large samples.
How many visitors do I need?
It depends on the baseline rate and the lift you want to detect. As a rule of thumb, detecting a 10% relative lift on a 20% baseline needs a few thousand visitors per group. Smaller lifts need far more.
What does "not significant" mean?
That the observed difference could plausibly be chance. It does not mean the variants are equal — only that you have not shown they differ.
Why is my rate different from my analytics tool?
Usually the denominator: sessions vs visitors, bots included or not, and whether the converting event is counted once per person. Match the definitions before comparing.
See the rate for every step, automatically
Connect your site and TapCub builds the funnel from the events it already captures. Compare segments, channels and time ranges without a calculator.