A/B testing
A/B testing shows two versions of a page to different visitors at random and measures which produces more conversions. It is the standard way to prove a change caused an improvement rather than coinciding with one.
In practice
The mechanism is randomisation. Because visitors are assigned to a version by chance, differences in who saw what average out, and a difference in outcome can reasonably be attributed to the change rather than to the season, a campaign or a competitor's bad week. That is the whole value, and it is a real one.
The catch for established B2B companies is sample size. Reaching a conclusion you can trust needs enough conversions in each variation, and conversions are what B2B sites have least of. A site with 3,000 visits and 25 enquiries a month is not going to resolve a 10% difference in any useful timeframe, and a test stopped early because the numbers looked good is worse than no test, because it produces a confident wrong answer.
There is a common workaround worth naming and avoiding: testing a step further up the funnel, such as clicks on a button rather than enquiries. It gets you statistical power quickly and it answers a different question. A variation that wins more clicks and fewer qualified enquiries is a loss you have measured as a win.
Where testing does earn its place in B2B is on the high-traffic exceptions. A paid landing page taking sustained ad spend, a pricing or contact page every visitor passes through, or an ecommerce path with real transaction volume can all produce enough events. The rest of the site is usually better served by conversion rate optimization methods that do not need statistical power.
A rough check before you buy anything: take your monthly conversions, divide by two for the two variations, and ask whether that number would convince you on its own. Most B2B teams find the honest answer is no, and that is worth knowing before signing an annual contract for a testing platform.
Common questions
How much traffic do you need for A/B testing?
It depends on conversions rather than visits, and on the size of the difference you want to detect. Detecting a small improvement takes many hundreds of conversions per variation. Most established B2B sites do not produce that in a reasonable window, which is why qualitative methods usually pay better there.
What is the difference between A/B testing and multivariate testing?
A/B testing compares two or more complete versions. Multivariate testing varies several elements at once and estimates the effect of each. Multivariate needs substantially more traffic than A/B for the same confidence, so if A/B is already marginal for your volume, multivariate is not the answer.
Can we A/B test if we have low traffic?
You can run the test; you usually cannot trust the result. The safer approach at low volume is to fix defects you can identify directly, use session replay and form analytics to find where people leave, and reserve testing for the few high-traffic pages such as a paid landing page.