How to share results with your team

Document experiment learnings. Communicate outcomes clearly. Build institutional knowledge so the whole organisation benefits from tests.

How to share results with your team

Introduction

Experiments only compound if learnings spread. When results stay siloed in one person's head, the organisation runs duplicate tests, repeats failures, and misses patterns. Sharing creates institutional memory. This chapter shows you how to document experiments in a way that others can find and use, how to communicate results so non-technical stakeholders understand, and how to build a learning culture where every experiment makes the entire team smarter.

Document experiments in a searchable system

Communicate results to stakeholders clearly

Extract principles that apply beyond one test

Build a culture of learning from failures

Conclusion

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Build a scalable experimentation process

Turn CRO into a repeatable, collaborative workflow that consistently improves your funnel.

Playbook

Experimentation

Random experiments waste time and budget. A structured framework ensures every test teaches you something, even when it fails. Decide what to test, design experiments properly, analyse results accurately, and share learnings so the whole team gets smarter.

See playbook
Experimentation
Tools

Relevant tools

VWO
Tool

VWO

VWO provides A/B testing, personalisation, and behaviour analytics to optimise website conversion rates through data-driven experimentation.

Hotjar
Tool

Hotjar

Hotjar captures user behaviour through heatmaps, session recordings, and feedback polls to understand how visitors use your website.

Microsoft Clarity
Tool

Microsoft Clarity

Microsoft Clarity provides free session recordings, heatmaps, and user behaviour analytics without traffic limits or time restrictions.

Notion
Tool

Notion

Flexible workspace for docs, wikis, and lightweight databases ideal when you need custom systems without heavy project management overhead.

Growth wiki

Growth concepts explained in simple language

Wiki

A/B testing

Compare two versions of a page, email, or feature to determine which performs better using statistical methods that isolate the impact of specific changes.

Wiki

Hypothesis testing

Structure experiments around clear predictions to focus efforts on learning rather than random changes and make results easier to interpret afterward.

Wiki

Conversion rate

Calculate the percentage of visitors who complete desired actions to identify friction points and measure the effectiveness of marketing and product changes.