Conversion Rate Calculator

Calculate website conversion rate and compare A/B test variants.

By Konstantin Iakovlev · Updated April 2026 · Source: SBA — Business Guide

Conversion Rate

2.50%

vs Industry Avg (2-3%)

Average

A/B Test Winner

Variant B

Detailed Results

Conversion Rate2.50%
Industry Average2.0% - 3.0%
Variant A Rate2.50%
Variant B Rate3.20%
Relative Lift (B vs A)+28.0%
Statistical SignificanceLikely reliable (>1,000 visitors)

Use the Conversion Rate Calculator above to calculate your results. Enter your values and see instant results — all calculations run in your browser.

Disclaimer: This calculator is for informational purposes only and does not constitute tax, financial, or legal advice. Results are estimates based on the information you provide and current rates. Always consult a qualified tax professional or financial advisor for advice specific to your situation.

How It Works

Conversion rate measures how well your calls to action and marketing actually turn attention into results. It is a central metric for any digital strategy, and the stakes only grow as global e-commerce is projected to reach $8.1 trillion by 2026, which makes each converted visitor more valuable. Beyond a single rate, the tool lets you line up A/B test variants side by side so decisions rest on data rather than instinct.

The math is straightforward: divide the number of conversions by the total number of visitors, then multiply by 100 to express it as a percentage. When you run an A/B test, the calculator applies that formula to each variant and reports the percentage difference between them, so the stronger performer stands out at a glance. The simplicity of the formula is what makes it such a dependable yardstick.

Two traps are worth watching. Mixing up total website visitors with unique visitors quietly distorts the math, so hold your metrics consistent across the calculation. The other is calling an A/B test too early, before the numbers reach statistical significance, which produces false positives. Let the test run long enough, and over enough traffic, that the outcome reflects real behavior instead of random noise.

Example: E-commerce Product Page A/B Test

  1. 1 Imagine you're running an A/B test on a new product page design. Variant A (original) received 15,000 visitors and generated 300 sales. Variant B (new design) received 16,000 visitors and generated 480 sales.
  2. 2 Using the calculator: Variant A Conversion Rate = (300 sales / 15,000 visitors) * 100 = 2.0%. Variant B Conversion Rate = (480 sales / 16,000 visitors) * 100 = 3.0%.
  3. 3 The calculator reveals that Variant B has a conversion rate of 3.0%, while Variant A has a conversion rate of 2.0%.
  4. 4 This means Variant B performed 50% better than Variant A (a 1 percentage point increase from 2% to 3% is a 50% relative increase). Based on this, you might consider implementing Variant B as your new product page design, potentially increasing your revenue significantly in 2026, where a single percentage point increase in conversion can translate to millions in additional sales for large e-commerce platforms.

Source: SBA — Business Guide · Last updated: April 2026

Frequently Asked Questions

What is a good website conversion rate?
The average website conversion rate across industries is 2-3%. E-commerce averages 2-4%, SaaS free trials 3-7%, lead generation forms 5-10%. Top-performing sites achieve 2-5x the industry average. A conversion rate above 5% is generally considered excellent.
How do I calculate conversion rate?
Conversion rate = (number of conversions / total visitors) x 100. If 50 out of 2,000 visitors made a purchase, your conversion rate is 2.5%. Track this for each traffic source separately, as organic search, paid ads, and email often have very different conversion rates.
How long should I run an A/B test?
Run tests until you reach statistical significance (typically 95% confidence) with at least 100 conversions per variation. Most tests need 2-4 weeks and 1,000+ visitors per variation. Do not stop a test early just because one variant is ahead, as early results are often misleading.

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