Opinions are cheap and redesigns are risky. A/B testing replaces both with evidence. We design, run and read tests that prove what actually lifts conversions before you commit to a change.
Every redesign is a bet. A/B testing lets you place small bets, measure them properly and only roll out what wins. That protects revenue and settles internal debates with data.
We hold tests to real statistical significance, so a result is a result. No calling winners early, no shipping noise as if it were signal.
Every test begins with a written hypothesis tied to a real funnel problem, not a hunch. We define the metric, the audience and the minimum sample before launch, build and QA both variations, then let the experiment run untouched until it reaches significance over a full business cycle.
Results are logged whether they win, lose or flatten, so the programme builds a library of what your visitors respond to. Winners ship to all traffic and compound, losers save you from a costly rollout, and every readout sharpens the next hypothesis instead of starting from zero.
One test at a time
Always-on testing
Multiple properties
We work with the major experimentation platforms and can use your existing stack or recommend one to suit your traffic.
Usually 2 to 4 weeks, long enough to reach significance and cover a full business cycle. High-traffic pages resolve faster.
We report it honestly, learn from it and feed it into the next hypothesis. Not every test wins, and that is part of the process.
No. We build and QA the variations. We loop in your developers only when a change touches core platform code.
Yes. On pages with enough traffic we run parallel or multivariate tests, but we isolate variables so each result stays readable. On lower-traffic pages we sequence tests one after another to protect statistical validity.