Playbook

Run growth experiments

Create a structured experimentation system that prioritises high-impact tests, produces valid results, and builds institutional knowledge with every cycle.

Playbook

Create a structured experimentation system that prioritises high-impact tests, produces valid results, and builds institutional knowledge with every cycle.

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Introduction

I have been testing since 2010, before most B2B companies even knew what a growth experiment was. I am CXL certified in experimentation, I built the testing programme for NU.nl (the largest news site in the Netherlands), and I have run A/B tests for dozens of brands across SaaS, e-commerce, and lead generation. Most companies either skip testing entirely or run random experiments without a clear plan. Both waste time. The ones that grow fastest have a simple system: a backlog of ideas ranked by potential impact, a clean process for running tests, and a habit of documenting what they learn so every experiment builds on the last. This playbook gives you that system. You will learn how to collect and prioritise experiment ideas, write hypotheses that actually teach you something, set up tests properly, and turn results into repeatable wins. No statistics degree required, just discipline and curiosity.

Chapters

Articles

  • Article

    Statistical significance is just the beginning. Learn how to interpret results correctly, avoid false positives, and turn winning experiments into permanent improvements across your growth engines.

  • Article

    Most experiments fail before they start because the hypothesis is vague or untestable. Learn how to write hypotheses that are specific enough to prove or disprove and tied to metrics that matter.

  • Article

    A folder full of interview notes is worthless if nothing changes. Learn how to spot patterns across conversations and turn what you heard into better copy, sharper ads, and stronger sales conversations.

  • Article

    A winning test means nothing if the setup was flawed. Learn how to configure experiments properly in VWO, ad platforms, and email tools so your results are actually valid.

  • Article

    Random testing wastes time and teaches you nothing. Learn how to collect experiment ideas systematically and prioritise them based on potential impact so you always know what to run next.

  • Article

    Build a knowledge base from past experiments so new tests build on proven insights instead of starting from scratch every time.

All 20 articles under A/B testing and experimentation
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