A/B testing (also split testing) compares two versions of something to see which performs better. Users are randomly shown version A or B. Metrics indicate which version is superior.
A/B testing is data-driven optimisation. Instead of debating which version is better, testing reveals actual user behaviour.
Testing Fundamentals
A/B tests have:
Control: The original version (A).
Treatment: The modified version (B).
Metric: What is measured - conversion rate, click rate, time-on-page.
Participants: Users randomly assigned to A or B.
Duration: Time the test runs.
Test Design
Good tests are:
- Single Variable: Testing one thing at a time. Multiple changes confound results.
- Clear Hypothesis: What do we expect to happen?
- Adequate Sample Size: Enough participants for statistical significance.
- Appropriate Duration: Running long enough for patterns to emerge.
Statistical Significance
Results must be statistically significant. Is the difference real or random variation?
Typically 95% confidence (5% chance of error) is required. With small differences, larger samples are needed.
Common Metrics
Conversion Rate: Percentage of users completing desired action.
Click-Through Rate: Percentage of users clicking.
Average Order Value: Average amount users spend.
Time-on-Page: How long users spend on page.
Bounce Rate: Percentage of users leaving without action.
Test Types
Multivariate Testing: Testing multiple variables simultaneously.
Split URL Testing: Different page versions at different URLs.
Redirect Testing: Redirecting traffic to test pages.
Running Tests
Tests typically run 1-4 weeks. Longer tests capture weekly patterns. Shorter tests run faster.
Premature stopping (after few days) risks false conclusions.
Tools
Tools including Optimizely, Google Optimize, Convert, and VWO run A/B tests.
Mobile A/B Testing
Mobile testing is more complex. Screen sizes vary. Interactions differ. Testing must account for mobile differences.
Multivariate Testing
Testing multiple changes simultaneously. More complex but can find interactions between changes.
Segment Analysis
Understanding how different user segments respond to variations. Some segments might respond differently.
Iterative Testing
Most optimisation is iterative. One test leads to another. Small improvements compound.
Test Ideas
Good test ideas come from:
- Analytics: Where do users struggle?
- User Feedback: What frustrates users?
- Competitive Analysis: How do competitors solve problems?
- Best Practices: Industry standards
Avoiding False Positives
Multiple tests increase false positives. Accounting for this statistically is important.
Documentation
Documenting test results, findings, and recommendations is important for learning.
PixelForce's Testing Approach
PixelForce uses A/B testing optimising products. Data-driven decisions improve outcomes.
Ethics of A/B Testing
A/B testing must be ethical. Users should not be deceived. Results should be used to improve products, not manipulate users.
The Future
AI may identify test opportunities automatically. Personalisation may replace A/B testing in some contexts.
A/B testing remains powerful for optimisation. Data-driven decisions improve products.