What is A/B testing?
A/B testing splits your audience so that half sees version A and half sees version B, then measures which one drives more of the result you care about, like clicks, sign-ups, or purchases. That result is often a conversion rate, meaning the share of visitors who take the action you want.
Teams use it to replace hunches and opinions with evidence when they have to pick between two options. Instead of arguing over which design feels better, they let real user behavior settle the question.
Those small gains add up. The median landing page conversion rate is 6.6% across all industries, based on Unbounce's analysis of 41,000 landing pages and 464 million visits (Q4 2024 Conversion Benchmark Report). Nudging that number even slightly can mean real revenue.
Demand for the tooling is growing, too. Research and Markets valued the A/B testing software market at USD 1.43 billion in 2026. It projects the market will reach USD 2.73 billion by 2032, a compound annual growth rate of about 11%.
Key characteristics
A/B testing works as a controlled experiment with a few defining traits. Nielsen Norman Group defines it as a randomized experiment, "a quantitative research method that tests two or more design variations with a live audience to determine which variation performs best."
- Two versions, one change: A is the control, your current version; B changes one thing, so any difference in results traces to that change.
- A randomly split audience: visitors are divided at random and at the same time, which keeps the comparison fair.
- One success metric: each test measures a single goal set in advance, such as conversion rate or click-through rate.
- A confirmed winner: results are checked for statistical significance, the confidence that a lift is real and not luck, before you act.
Example
Imagine a direct-to-consumer coffee roaster testing two versions of its product page. Version A shows a green "Add to cart" button, while version B shows an orange one.
The roaster sends half of its traffic to each version, measures which page earns more purchases, and keeps the winner. This kind of test is a core part of conversion rate optimization, the practice of steadily improving the share of visitors who take a desired action.
Related terms
- eCommerce analytics tools: the wider toolkit for measuring online performance that A/B testing belongs to.
- ABC inventory analysis: a different method, easy to confuse by name, that ranks inventory by value rather than testing web versions.
- demand forecasting: another method for planning with real data instead of assumptions.
FAQ
What is the difference between A/B testing and multivariate testing?
A/B testing compares two full versions that differ by a single change, while multivariate testing checks many combinations of elements at once. A/B tests are simpler to run and need less traffic to reach a clear result.
How long should an A/B test run?
Long enough to gather enough visitors and conversions for a statistically significant result, which usually means a few weeks that cover a full business cycle.
Is A/B testing only for websites?
No. It works just as well for emails, ads, app screens, and pricing, anywhere you can split an audience and measure a result.
The same evidence-over-guesswork mindset behind A/B testing drives operational decisions in Fishbowl AI Insights, where you can ask questions of your live inventory and manufacturing data in plain language.