Web & CMS
A/B Testing
A/B testing is a method of comparing two versions of a page or element by splitting traffic between them to see which performs better.
It replaces guesswork with evidence about what actually drives conversions. For example, testing two headlines reveals which one generates more leads. Reliable results require enough traffic and a clear single variable to test.
Testing discipline is mostly statistical patience: pick one variable, size the sample before starting, and run to the planned end, the common failures are stopping at the first significant blip, testing five changes at once, and running experiments on traffic too thin to ever conclude. Tests need a hypothesis worth learning from, moving the price above the fold will lift conversions because visitors bounce hunting for it, so wins compound into understanding instead of trivia. Below a few hundred conversions per variant, skip formal testing: fix the obvious and watch the before-after.
Example
A landing page tests its headline: benefit version against price version, traffic split evenly, everything else frozen. After 410 conversions per side in three weeks, price wins by 19% with statistical confidence. The team's previous "test", changing headline, image and form the same week and eyeballing the difference, could never say which change did what, or whether the lift was noise.
Frequently asked questions
- How long should an A/B test run?
- Until the pre-calculated sample size is reached, typically several hundred conversions per side, and through at least two full weekly cycles to average out day-of-week behaviour. Stopping at the first significant-looking day is how noise gets shipped as insight.
- What should I A/B test first?
- The elements closest to the decision: headline, offer framing, form length, price presentation. One variable at a time, biggest suspected impact first. Below the traffic needed for significance, skip formal testing and fix what recordings show is broken.
Related terms
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