A/B tests you can actually trust.
Hypothesis-led experiments, sized before launch, built and QA'd properly, and analyzed honestly. Including the ones that lose, because those teach you just as much.
Illustrative readout, not a client result.
The problem
Most A/B tests fail before they launch. Weak hypotheses, too little traffic, broken tracking and results called on day six.
The result is a dashboard full of "winners" that never show up in revenue. A testing program is only useful if it produces decisions you can rely on.
What we check before any test goes live
The hypothesis
Grounded in research, tied to one problem, with a predicted effect on one metric.
The sample
Baseline, minimum detectable effect, required sample and duration, calculated in advance.
The tracking
Primary metric and guardrails firing correctly in every variant, on every device.
The build
Cross-browser QA, no flicker, no layout shift, no broken checkout.
Our approach
A repeatable loop, documented so your team keeps the learning.
- 01
Prioritize
Ideas from research scored for impact, confidence and effort. Bold tests first.
- 02
Design
Variant design and copy, with a written test plan and stopping rule.
- 03
Build and QA
Built in your testing tool or server-side, checked on real devices before launch.
- 04
Run
Monitored for errors and sample ratio mismatch, never stopped early because it looks good.
- 05
Analyze
Primary metric, guardrails and segments, reported with uncertainty, not just a percentage.
- 06
Roll out and record
Winners shipped, every result added to a learning library that shapes the next tests.
Deliverables
Test plans
Hypothesis, metrics, sample size, duration and stopping rule for every experiment.
Built and QA'd variants
Production-ready experiments in your testing platform.
Result readouts
Plain-language analysis with segments, guardrails and a clear recommendation.
Learning library
Every test, win or lose, recorded so knowledge stays with your company.
Expected outcomes
Decisions, not opinions
Design debates settled by evidence from your own visitors.
Compounding gains
Validated wins stacked over months, each one measured against a clean baseline.
Lower risk
Big changes tested on a share of traffic before they reach everyone.
Case study Sample
+127% conversion rate for a home decor store
Research showed shipping costs appeared only at checkout and product pages lacked returns information. Those findings shaped the first round of experiments.
Sample figures until a verified client study is published.
A/B testing FAQ
How much traffic do I need to A/B test?
It depends on your conversion rate and the size of effect you care about. As a rough guide, a few hundred conversions per variant per test are needed for reliable results. We calculate it for your numbers before recommending a program.
Which testing tools do you use?
We work with the tool you already have, such as VWO, Optimizely, AB Tasty, Convert or Shopify-native testing apps, and can run server-side tests where your stack allows it.
How long does each test run?
Typically two to four weeks, always in whole weeks so weekday and weekend behavior are both included.
What happens if a test loses?
You keep the control, record why it lost and use that learning to shape the next hypothesis. A losing test protected you from shipping a change that would have cost money.
You already have the traffic.Let’s make it convert.
Get a clear view of where your funnel is losing customers, and what to test next.