A/B testing is an experimental approach to user experience design, which aims to identify changes to web pages that increase or maximize an outcome of interest / KPI (e.g., click-through rate for a banner advertisement). As the name implies, two versions (A and B) are compared, which are identical except for one variation that might impact a user’s behavior. Version A might be the currently used version, while Version B is modified in some respect.

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A/B Testing
Definition of "A/B Testing" by Chat GPT: A/B testing, also known as split testing, is a method of comparing two versions of a webpage, email, app, or other digital asset to determine which one performs better. It involves creating two versions (A and B) that are identical except for one variation that might affect a user's behavior. The versions are then shown to separate groups of users, and their responses are analyzed to determine which version is more effective in achieving a specific goal, such as click-through rate, conversion rate, or user engagement. A/B testing is commonly used in marketing, product development, and user experience optimization to make data-driven decisions and improve performance.
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