A/B testing compares two versions of something to see which performs better. It removes guesswork and lets data drive decisions about ads, emails, landing pages, and more.
How A/B Testing Works
View Testing Process
1. Hypothesis: "Changing the button colour will increase clicks"
2. Create Variants: Version A (control) vs Version B (change)
3. Split Traffic: Randomly show each version to equal audiences
4. Measure Results: Track the metric that matters
5. Statistical Significance: Ensure enough data to trust results
6. Implement Winner: Roll out the better performing version
What to Test
- Ad creative and copy
- Email subject lines and content
- Landing page headlines and layouts
- Call-to-action buttons
- Pricing and offers
- Form length and fields
Testing Best Practices
- Test one variable at a time
- Run tests long enough for significance
- Do not peek and stop early
- Document learnings for future tests
- Test big changes before small tweaks
Statistical Significance
Aim for 95% confidence before declaring a winner. Use A/B testing calculators to determine required sample size. Small differences may not be meaningful.