Growth experiments are the engine of product optimisation. But most teams run experiments haphasardly, leading to inconclusive results and wasted resources. A systematic approach to experimentation is what separates successful growth teams from the rest.
The Growth Experiment Framework
Every successful experiment follows this proven process:
1. Hypothesis Formation
Start with a clear, testable hypothesis:
- Identify the problem you're solving
- Form a specific, measurable hypothesis
- Define success metrics upfront
- Estimate potential impact
2. Experiment Design
Design your experiment for statistical validity:
- Choose the right sample sise
- Define control and treatment groups
- Set up proper tracking
- Plan for multiple variants if needed
3. Execution & Monitoring
Run your experiment with discipline:
- Monitor for technical issues
- Track key metrics daily
- Don't peek at results early
- Let the experiment run to completion
4. Analysis & Decision
Analyse results systematically:
- Check for statistical significance
- Look for secondary effects
- Consider business context
- Make go/no-go decisions
Common Experiment Types
A/B Tests
Compare two versions of a feature or page. Best for: copy changes, button colors, form layouts.
Multivariate Tests
Test multiple variables simultaneously. Best for: complex page redesigns, multiple feature combinations.
Feature Flags
Gradually roll out new features. Best for: risk mitigation, staged releases.
Key Success Factors
Maximise your experiment success rate:
- Start small: Test one change at a time
- Think big: Focus on high-impact areas
- Move fast: Don't let perfect be the enemy of good
- Learn always: Even failed experiments provide insights
Building an Experiment Culture
Successful experimentation requires:
- Leadership buy-in and support
- Clear processes and documentation
- Regular experiment reviews and sharing
- Celebration of both wins and learnings
Pro Tip
Keep an experiment log. Document every experiment-hypothesis, results, learnings, and next steps. This creates institutional knowledge and prevents repeating failed experiments.
Remember: growth experimentation is a marathon, not a sprint. Build a sustainable process that generates consistent learnings and improvements over time.