A/B testing compares two or more versions of a page or flow by splitting live traffic between them and measuring which one performs better against a goal agreed in advance. The method is simple. The discipline around it is where most programs fall apart. Tests get launched to settle an internal argument, stopped the day a dashboard turns green and quietly forgotten when the result is flat.
We run testing as a program rather than a string of one-off experiments. Each test starts from a question the business wants answered, such as whether shoppers need shipping costs earlier or whether a shorter product page drops detail that buyers rely on.
How a test is built
- Evidence first. Hypotheses come from analytics, session recordings, support tickets and user research, not from a list of best practices copied from another store.
- One primary metric. A single measure decides the test and is agreed before launch. Secondary metrics are watched for harm, not used to rescue a losing variant.
- Sample size and duration. We estimate how much traffic the test needs to detect a change worth acting on, and we say so plainly when the answer is too long to be useful.
- Clean implementation. Variants are built and checked across devices and browsers so flicker, broken layouts or tracking gaps do not skew the result.
- A fair read. Every test is written up whether it wins, loses or shows nothing, with a recommendation on what to do next.
When traffic is limited
Plenty of brands with $10 million to $350 million in annual revenue do not have the volume to test a button colour on a single template. That changes what you test rather than whether you learn. We focus on bolder changes to high-traffic templates such as the product page, cart and checkout. Where a classic split test would run too long, we combine smaller tests with qualitative research or use staged rollouts and watch the numbers closely.
What you keep
Every test lives in a shared log with its hypothesis, design, result and decision. Over time that log becomes a record of what your customers respond to, which tends to be worth more than any single win. It also stops the same idea being tested three times by three different people.
Testing sits inside our conversion rate optimization service. Reliable results depend on conversion tracking and attribution you can trust, and if your traffic is modest it is worth reading whether A/B testing is worth it on a low-traffic site.
