A method of comparing two versions of a webpage or app against each other to determine which one performs better.
Marketing & SEO
In our reference library
Also known as split testing, A/B testing randomly assigns users to a control group (A) or a variant group (B) and measures a predefined success metric to determine the more effective version. This data-driven approach removes guesswork from design and marketing decisions, allowing teams to make incremental improvements based on statistical evidence. It is widely used to optimize conversion rates, user engagement, and overall product experience. Running continuous A/B tests helps businesses refine their offerings and maximize return on marketing efforts.
Why A/B Testing matters when choosing software
A/B Testing can affect software selection differently depending on the workflow, team size, and category. Use the definition above as the starting point, then check how the concept appears in the products you are evaluating. In practical terms, look for the controls, limits, integrations, reporting, or operating assumptions that are directly related to A/B Testing. A useful comparison should explain what the concept means, where it matters, and what evidence a buyer can verify before committing.
How to evaluate it in a real product
Start with the workflow that depends most on A/B Testing. Identify the requirement, ask the vendor for the relevant documentation or configuration details, and test the requirement with realistic sample data where possible. Then compare the result against alternatives rather than treating a marketing label as proof. Related concepts in this category include KPI, SaaS.
Concept Visualization
- 1Testing two different email subject lines to see which gets more opens
- 2Comparing landing page headlines to improve conversion rates
- 3Testing call-to-action button colors for higher click-through rates