Growth experimentation framework for B2B teams

Introduction

Most B2B companies either test nothing or test everything. The first group makes changes based on gut feeling and wonders why results are inconsistent. The second group runs dozens of experiments without a clear thesis and learns nothing useful.

Effective experimentation sits in the middle. You form a hypothesis based on data and customer insight, you test it properly, and you document what you learn so the knowledge compounds over time.

I've been running A/B tests since 2012, starting with Optimizely and Google Optimize, now primarily using VWO. The tools have changed, but the fundamentals haven't. A good experiment starts with noticing something odd in the data, understanding why through customer research, and then testing a specific change to see if your theory holds.

This playbook covers how to build a systematic experimentation practice: collecting and prioritising ideas, writing hypotheses that are actually testable, setting up experiments that produce valid results, and documenting learnings so they don't disappear into a spreadsheet nobody reads.

The goal isn't to run more tests. It's to run better tests that teach you something regardless of whether they win or lose.

Chapters

1

Building your backlog

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.

Read chapter

2

Creating strong hypotheses

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.

Read chapter

3

Setting up experiments

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.

Read chapter

4

Analysing and acting on results

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.

Read chapter

Experimentation

tools

VWO

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

Rating

From

€

393

per month

Explore tool

My review

Hotjar

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

Rating

From

€

per month

Explore tool

My review

Microsoft Clarity

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

Rating

From

€

per month

Explore tool

My review

Notion

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

Rating

From

€

per month

Explore tool

My review

Books

Lean Startup

Eric Ries

Rating

A disciplined approach to experiments. Define hypotheses, design MVPs and learn before you scale.

My review

Hacking growth

Sean Ellis

Rating

A practical framework for experiments and insights. Build loops, run tests and adopt a cadence that ships learning every week.

My review

Wiki

Sample size

Calculate how many users you need in experiments to detect meaningful differences and avoid declaring winners prematurely based on insufficient data.

Read concept

P-value

Interpret experiment results to understand the probability that observed differences occurred by chance rather than because your changes actually work.

Read concept

Minimum viable test

Design experiments that answer specific questions with minimum time and resources to maximise learning velocity without over-investing in unproven ideas.

Read concept

Hypothesis testing

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

Read concept

Control group

Maintain an unchanged version in experiments to isolate the impact of your changes and prove causation rather than correlation with external factors.

Read concept

Statistical significance

Determine whether experiment results reflect real differences or random chance to avoid making expensive decisions based on noise instead of signal.

Read concept

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.

Read concept

Growth hacking

Deploy fast, low-cost experiments to discover scalable acquisition and retention tactics, learning through iteration rather than big bets.

Read concept

Other playbooks

Demand generation tools

Pipeline doesn't fill itself. These tools help you identify who to target, reach them at scale, and create content that earns attention in crowded markets.

Read playbook

Growth team tools

The wrong tools create friction. The right ones multiply your output without adding complexity. These are the tools I recommend for growth teams that move fast.

Read playbook

Sales pipeline tools

Deals slip through cracks when your sales stack doesn't work together. These tools keep your pipeline visible, your follow-ups timely, and your process tight.

Read playbook

Customer value tools

Acquiring customers is expensive. These tools help you keep them longer and grow their accounts so your acquisition costs actually pay off over time.

Read playbook

Marketing funnel tools

Traffic means nothing if it doesn't convert. These tools help you capture leads, nurture them automatically, and understand what's actually working in your funnel.

Read playbook

Hubspot configuration

HubSpot is powerful when configured properly and a mess when it's not. Set up your instance correctly from the start so your data stays clean and your team trusts the system.

Read playbook

Experimentation

Introduction

Chapters

1

Building your backlog

2

Creating strong hypotheses

3

Setting up experiments

4

Analysing and acting on results

Experimentation

tools

VWO

Hotjar

Microsoft Clarity

Notion

Books

Lean Startup

Hacking growth

Wiki

Sample size

P-value

Minimum viable test

Hypothesis testing

Control group

Statistical significance

A/B testing

Growth hacking

Related topic

Growth orchestration

Other playbooks

Demand generation tools

Growth team tools

Sales pipeline tools

Customer value tools

Marketing funnel tools

Hubspot configuration

Keep reading