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 or split testing is a marketing technique used to compare two versions of a web page, email, or other types of marketing content to determine which one performs better. By randomly showing different versions to different groups of users and measuring the response, marketers can identify which version leads to higher conversion rates, click-through rates, or other desired outcomes. The results of A/B testing can then be used to optimize marketing campaigns and improve overall performance.
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