AI Dictionary
A/B Testing
Definition
Comparing two versions of a model or system to determine which performs better.
Deep Dive
A/B testing, also known as split testing, is a research methodology used to compare two versions of a webpage, app feature, marketing email, or other digital asset to determine which one performs better against a specific goal. It involves showing one group of users version A (the control) and another group version B (the variant), then collecting data on how each group interacts with their respective version. The goal is to identify changes that lead to improved outcomes, such as higher conversion rates, increased engagement, or reduced bounce rates.
Examples & Use Cases
- 1A website comparing two different call-to-action buttons (e.g., "Learn More" vs. "Get Started") to see which generates more clicks
- 2An email marketing campaign testing two distinct subject lines to determine which yields a higher open rate
- 3A mobile app testing two different onboarding flows to see which leads to higher user retention
Related Terms
Multivariate TestingHypothesis TestingConversion Rate Optimization (CRO)