What is A/B Testing?
A/B testing compares two versions of a webpage, email, or other marketing asset to determine which performs better.
Definition
A/B testing (also called split testing) is a randomized experiment comparing two versions of a marketing element to determine which drives better results. One version serves as the control (A), while the other is a variation (B) with a single change. Traffic is randomly split between versions, and statistical analysis determines the winner. A/B testing removes guesswork from optimization.
Why A/B Testing Matters
- Provides data-driven answers to optimization questions
- Reduces risk of implementing changes that hurt performance
- Reveals actual user preferences vs assumed preferences
- Builds organizational learning and optimization culture
- Compounds over time into significant performance gains
How A/B Testing Works
Traffic is randomly split between two versions. Each visitor sees only one version. Statistical analysis compares conversion rates or other metrics to determine if the difference is significant or due to random chance.
Best Practices for A/B Testing
Test one variable at a time for clear learnings
Calculate required sample size before starting
Wait for statistical significance before ending tests
Run tests for at least one full business cycle
Document and share learnings
Prioritize tests by potential impact and confidence
Frequently Asked Questions
How long should an A/B test run?
Until you reach statistical significance (usually 95% confidence) with adequate sample size. This typically takes 2-4 weeks depending on traffic volume.
What should I test first?
Start with high-traffic pages and elements that directly impact conversion: headlines, CTAs, forms, pricing, and key page sections.
Related Terms
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