A/B Testing
Data-driven decisions instead of gut feeling: A/B testing shows you with statistical confidence which variant of your shop converts better.
A/B Testing That Moves Conversion Rates
We derive hypotheses from real user data, set up tests with methodological rigour, wait for sufficient sample size, and interpret results so you understand what you've learned — not just which number is larger. Every test becomes a building block of your optimisation knowledge.
The essentials of A/B Testing
- We derive test hypotheses from real user data — heatmaps, session recordings, funnel analyses, and user interviews — instead of gut feeling.
- Each hypothesis names an observed problem, a proposed solution, and a measurable expectation, so every test has a clear objective.
- We calculate the required sample size before the test starts and only stop at statistical significance — stopping early leads to false-positive findings.
- We document every result in a central test log with hypothesis, result, significance, and the derived action.
- Every test becomes knowledge about your users — even a losing test yields an insight, not just a number.
Hypothesis Development
An A/B test without a hypothesis is guesswork. We derive test hypotheses from heatmaps, session recordings, funnel analyses, and user interviews. Each hypothesis names an observed problem, a proposed solution, and a measurable expectation. Every test has a clear objective — and you know what you learn, regardless of how it turns out.
Test Setup & Tooling
We set up A/B tests with established testing tools, configure correct audience segmentation, and ensure variants are distributed evenly and consistently. Sample ratio mismatch and other common implementation errors are actively checked before a test starts.
Statistical Analysis
A test is not finished when one variant leads, but when the sample size is sufficient for statistical significance. We calculate the required sample size before the test starts, monitor continuously, and stop only when reliable results are available. Stopping early leads to false positive findings.
Learning Documentation
Test results are documented in a central test log: hypothesis, result, statistical significance, and derived action. This knowledge base makes every test result the foundation for future hypotheses. Over months, an institutional understanding of what works for your users develops.
From gut feeling to proven insight
Methodically sound A/B testing follows a fixed sequence — each phase is a prerequisite for the next. Skipping one risks producing results with no statistical value.
Hypothesis development
A concrete, falsifiable hypothesis with a clear expectation is derived from analytics data and usage patterns — not from opinions.
Sample size calculation
Before launch, the required sample size and minimum runtime are calculated to guarantee statistical validity.
Test setup & tooling
Variant and control group are cleanly implemented, tracking validated, and the tool configured so no data gaps occur.
Statistical evaluation
Evaluation happens only after the target sample is reached — with significance level, confidence interval, and contextual interpretation rather than a simple number comparison.
Learning documentation
Result, hypothesis, context, and interpretation go into the test log — win or lose — as institutional knowledge for all future projects.
Every step generates knowledge that sharpens the next test.
What determines a test's learning value
Not all factors contribute equally to the reliability and insight value of an A/B test. This weighting shows where focus must lie.
- Hypothesis qualityDetermines whether a result yields insight or just a number
- Sufficient runtime & sample sizePrevents the peeking problem and ensures statistical validity
- Consistent result documentationTurns individual tests into cumulative organisational knowledge
- Clean tracking setupData gaps invalidate any test regardless of hypothesis quality
- Choice of testing toolInterchangeable — methodology matters, not the tool
Relative weighting
A weak tool with a strong hypothesis beats a strong tool with a weak hypothesis every time.
What matters for A/B Testing
Clean A/B testing lives and dies by the discipline not to stop too early. Ending a test the moment one variant leads falls victim to the peeking problem, because without a pre-calculated sample size and sufficient run time the results are not statistically reliable. Decisions on that basis can even worsen conversion, even though the number looked good at the moment you stopped.
The quality of the hypothesis matters more than the tool. The testing tool is interchangeable, but a hypothesis cleanly derived from data with a clear expectation yields insight even when the tested variant loses. That is exactly the difference between learning and guessing: a good hypothesis explains the result, a bad one leaves only a number.
Every result is knowledge about your users, even a losing one. If you do not document tests, you lose that knowledge at the next staff change and retest the same hypotheses without noticing. A maintained test log is therefore institutional capital and one of the most valuable resources in the whole CRO process.
Good testing interprets results instead of just announcing the bigger number. The real question is not which variant won but what you learned about your customers' behaviour. That way every test becomes a building block of a cumulative understanding that makes each further hypothesis sharper and each further test more valuable.
Early stopping leads to wrong conclusions
Stopping a test as soon as one variant leads falls into the peeking problem. Without a pre-calculated sample size and sufficient runtime, results are not statistically reliable — and decisions based on them can actually worsen conversion.
Hypothesis matters more than tool
The A/B testing tool is interchangeable. The quality of the hypothesis determines the learning value of the test. A hypothesis cleanly derived from data with clear expectations yields insights even when the tested variant loses.
Test log is institutional capital
Every test result — won or lost — is knowledge about your users. Organisations that don't document results lose this knowledge with the next personnel change and retest the same hypotheses. A maintained test log is one of the most valuable resources in the CRO process.
Decisions backed by data
With us you're always at the cutting edge of technology and benefit directly from our developer expertise. Together we analyze your shop, identify key areas and develop tailor-made solutions. Your goals and expectations are at the center of our work.
Developers, not resellers
Your shop is built by developers who really understand the code. We pass nothing to subcontractors.
Shopware down to the detail
Architecture, API integration and performance from hundreds of project hours.
One team, every discipline
Development, design and marketing come from one team that works without friction at the handoffs.
Built for growth
We build measurably for conversion, load time and revenue.
Partner, not vendor
We stay on after launch and keep developing your shop continuously.
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