Growth experimentation framework for B2B teams

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

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.

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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.

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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.

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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.

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5

Run your monthly experiment cycle

Execute the full experiment loop each month: select from your backlog, launch, monitor, close, and document what you learned.

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6

Reprioritise backlog by impact and effort

Re-score your experiment backlog each quarter using updated data so you always run the highest-leverage tests first.

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7

Compound learnings across experiments

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

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8

Scale winning experiments across channels

Take validated wins from one channel and systematically test whether they work in others to multiply the impact of what already works.

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Growth 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

User behaviour analytics platform showing heatmaps, session recordings, and feedback from real website visitors. Understand exactly how people use your site.

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.

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P-value

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

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Minimum viable test

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

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Hypothesis testing

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

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Control group

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

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Statistical significance

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

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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.

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Monthly Recurring Revenue (MRR)

Track predictable monthly subscription revenue to monitor short-term growth trends and make faster decisions than waiting for annual revenue reports.

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Growth hacking

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

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Other playbooks

Data & dashboards

Build the dashboards and data pipelines that show your growth engines in one view so you can spot bottlenecks and make decisions in minutes, not meetings.

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Planning & project management

Set up project boards, sprint rhythms, and communication habits that keep growth work on track without endless status meetings or lost context.

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Increase pricing

Raise prices strategically through better packaging, value communication, and positioning so revenue grows without adding customers.

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Increase line items

Develop cross-sell and upsell motions that expand accounts by solving more problems for customers who already trust you.

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Increase contract length

Build retention strategies, success milestones, and renewal processes that keep customers committed for longer periods.

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Improve win rate

Strengthen your closing approach — objection handling, negotiation, and follow-through — so more proposals turn into signed contracts.

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Growth experimentation

Introduction

Chapters

1

Building your backlog

2

Creating strong hypotheses

3

Setting up experiments

4

Analysing and acting on results

5

Run your monthly experiment cycle

6

Reprioritise backlog by impact and effort

7

Compound learnings across experiments

8

Scale winning experiments across channels

Growth 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

Monthly Recurring Revenue (MRR)

Growth hacking

Related topic

Growth management

Other playbooks

Data & dashboards

Planning & project management

Increase pricing

Increase line items

Increase contract length

Improve win rate

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