The Secret Weapon for Growth Marketers: Minimum Viable Experiment
Article written by
Stuart Brameld
Where Did the Concept of the Minimum Viable Experiment Come From?
The idea of the minimum viable experiment (MVE) has its roots in the Lean Startup methodology, popularised by Eric Ries. The Lean Startup approach emphasises rapid experimentation, validated learning, and iterative product releases. It's all about testing assumptions quickly and efficiently.
What is a Minimum Viable Experiment in Growth Marketing?
In growth marketing, an MVE is a small, controlled test designed to validate a hypothesis with minimal resources. It's about learning fast and failing faster, so you can pivot or persevere based on real data. For more context on agile approaches to marketing and growth, see The Minimum Viable Test (MVT): for marketing & growth. For a smart decision-making lens for choosing which experiments to run, read Two way door decisions.
Traditional Approach vs. Minimum Viable Experiment
- **Time**: Long-term planning vs. Short-term, quick iterations
- **Resources**: High resource allocation vs. Minimal resources
- **Risk**: High risk due to large investments vs. Low risk due to small-scale tests
- **Learning**: Slow, often post-launch vs. Fast, continuous learning
- **Flexibility**: Low, hard to pivot vs. High, easy to adjust based on feedback
How is the MVE Useful?
An MVE helps you test ideas without committing significant resources. It reduces risk and maximises learning. By focusing on small, quick experiments, you can gather valuable insights and make data-driven decisions.
"At Growth Method we strongly believe that speed is the most influential factor on team outcomes. Growth marketing teams need to be shipping work and launching new programmes in days, not months. The minimum viable experiment or MVE is a great way to think about approaching your work in order to minimise risk and maximise learning."
- Stuart Brameld, Growth Method Founder & B2B Marketing Advisor.
Relevant Principles and Frameworks
Build-Measure-Learn Loop
This loop is a core principle of the Lean Startup methodology. It involves building a simple version of your idea, measuring its performance, and learning from the results. This iterative process helps you refine your approach based on real-world data.
Cynefin Framework
The Cynefin framework helps you understand the complexity of your environment. It categorises problems into five domains: simple, complicated, complex, chaotic, and disorder. Understanding where your problem lies can guide your experimentation approach.
How to Build a Minimum Viable Experiment
Define Your Hypothesis: What do you want to test? Be specific.
Identify Metrics: What will you measure to validate your hypothesis?
Design the Experiment: Keep it simple. Focus on the core elements.
Execute Quickly: Speed is crucial. Launch your experiment as soon as possible.
Measure Results: Collect data and analyse it.
Learn and Iterate: Use the insights to refine your approach.
Example of an MVE in Growth Marketing
Imagine you want to test if a new homepage headline will increase conversions. Here's how you could approach it:
Hypothesis: Changing the headline to "Unlock Your Growth Potential" will increase sign-ups.
Metrics: Measure the sign-up rate.
Design: Create two versions of the homepage, one with the current headline and one with the new headline.
Execute: Use an A/B testing tool to split traffic between the two versions.
Measure: After a week, compare the sign-up rates.
Learn: If the new headline performs better, consider making it permanent. If not, test another variation.
Conclusion
Minimum viable experiments are a powerful tool for growth marketers. They allow you to test ideas quickly, minimise risk, and maximise learning. By adopting an MVE mindset, you can drive faster, more effective growth.
Growth Method is the only work management platform built for growth marketers. We integrate directly with analytics platforms including Google Analytics, Salesforce, HubSpot, PostHog and others to pull your analytics data right alongside your experiments using AI and natural language.
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Article written by
Stuart Brameld
Category:
Experimentation