CRO and A/B testing: build a useful experiment programme
Build a CRO programme from customer evidence to a trustworthy A/B test. Learn how to choose hypotheses, measure outcomes and make release decisions.
An experiment should help you decide what to change and which assumption to leave behind. Explore research, experiment design and results, including low-traffic situations and guardrail metrics. The +57% conversion improvement for Whirlpool EMEA comes from my work; your own decision starts with your context and evidence.
Start with this guide
Build a CRO programme from customer evidence to a trustworthy A/B test. Learn how to choose hypotheses, measure outcomes and make release decisions.
Choose A/B test guardrail metrics that protect quality, performance and downstream outcomes. Define thresholds and decisions before launching a test.
Prioritise your experiment backlog using evidence, traffic and decision value. Build a practical queue that turns observations into useful A/B tests.
Plan low-traffic A/B testing around useful effect sizes, eligible audiences and uncertainty. Learn when to test, research, simplify or defer.
Design personalisation tests that compare a clear targeting rule with a useful default. Measure incremental value, guardrails and uncertainty.
Use qualitative research for CRO to identify friction and build stronger hypotheses. Plan interviews, task observations and a traceable evidence record.
Understand A/B test significance, uncertainty and peeking. Learn how fixed-horizon and sequential methods differ before calling an experiment a winner.
Read the reported Whirlpool EMEA conversion result with clear evidence boundaries. Use a practical framework to diagnose and improve your own customer journey.
Write an A/B test hypothesis from evidence, audience and expected behaviour. Use a practical worksheet to choose metrics and interpret a negative result.