What is A/B Testing?
A/B testing is a method of comparing two versions of a webpage, screen or feature to determine which performs better with real users. By splitting live traffic and measuring the results, teams replace guesswork with statistical evidence and steadily improve conversion.
How does A/B testing work?
A/B testing (also called split testing) shows two variants of the same page or feature to different segments of your audience at the same time. Half of your users see version A (the control) and half see version B (the variant), with traffic divided at random so the only meaningful difference between the groups is the change you are testing. You then measure a single, clearly defined metric - sign-ups, purchases, taps on a button - and let the data decide which version wins.
The randomisation is what makes the result trustworthy. Because users are assigned to each version by chance, outside factors such as the day of the week, a marketing campaign or seasonality affect both groups equally, so any difference in the outcome can be attributed to the change itself rather than to noise.
Why A/B testing matters
Most product and design decisions are opinions until they meet real users. A/B testing turns those opinions into measured outcomes, which protects teams from shipping changes that feel better but actually perform worse. For a digital product, even a small lift compounds: a one percentage point improvement on a checkout flow can represent a significant amount of revenue over a year. Testing on a fraction of traffic first also reduces risk, because a damaging variant is caught before it reaches your whole audience.
What can you A/B test?
Almost any element that influences user behaviour is a candidate for testing. Common examples include:
- Calls to action - button copy, colour, size and placement.
- Headlines and value propositions - the words that frame your offer.
- Page layout and information hierarchy - what users see first.
- Pricing presentation - how plans and prices are displayed.
- Onboarding flows - the number of steps before activation.
A/B testing best practices
Test one change at a time so you can attribute the result with confidence. Decide the success metric and the required sample size before you launch, not after you peek at the numbers. Run the test for full business cycles - usually one to two weeks - so weekday and weekend behaviour are both represented, and wait until the result reaches statistical significance before calling a winner. The most common mistake is stopping early on an exciting trend, which is how teams ship false winners.
How PixelForce approaches A/B testing
At PixelForce, A/B testing lives in Phase 3 - Post Launch Support, where our in-house Adelaide team iterates on a live product against real user behaviour rather than opinion. It is a core part of the app data analytics work we run for clients: instrument the product, form a clear hypothesis, test it against one metric, then ship the winner. Across 100+ products shipped, the teams that test consistently are the ones that compound small, measured wins into meaningful growth. When traffic is too low to test reliably, we say so and recommend qualitative user-centred design research instead - honest advice beats a vanity experiment.
Where this applies
The PixelForce services where A/B Testing matters most - explore how we put it to work in client products.
Related terms
Other glossary definitions closely related to A/B Testing.
Frequently asked questions
Run an A/B test for at least one to two full business cycles, which is usually one to two weeks, so that both weekday and weekend behaviour are captured. The exact duration depends on your traffic volume and the size of the effect you are trying to detect. Stop only once the test has reached your predetermined sample size and statistical significance, never on the first promising trend.
Statistical significance is the level of confidence that the difference between version A and version B is real rather than the result of random chance. Teams commonly aim for 95 percent confidence, meaning there is only a five percent probability the result happened by luck. Reaching significance requires a sufficient sample size, which is why low-traffic pages take longer to test.
A/B testing compares two complete versions that differ by a single change, making it easy to attribute the outcome. Multivariate testing changes several elements at once and measures how the combinations interact, which can reveal richer insights but needs far more traffic to reach a reliable result. Most teams start with A/B testing and move to multivariate testing only when volume allows.
There is no single number - the required sample size depends on your current conversion rate and the size of the improvement you want to detect. Smaller expected improvements need larger samples. Use a sample size calculator before launching, and be realistic: low-traffic products may need several weeks, while high-traffic flows can reach significance in days.
Avoid A/B testing when traffic is too low to reach significance in a reasonable time, when the change is a clear usability fix that does not need validation, or when a decision is driven by legal, accessibility or brand requirements rather than performance. In low-traffic situations, qualitative research such as usability testing often delivers better insight than a statistically underpowered experiment.
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