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How A_B-testing Works ?

A/B testing is a method for comparing two versions of a webpage, app, or other marketing asset to determine which one performs better.  By showing two variants to different, randomly selected groups of users, businesses can use statistical analysis to identify the more effective version.



How A/B testing works: A step-by-step process

Collect data and establish a goal.  Before running a test, use analytics tools to understand your current performance and find areas for improvement.  Define a clear, measurable objective based on this data, such as decreasing the bounce rate on a landing page or increasing the number of subscribers to a newsletter. 

Establish a hypothesis. Develop a theory about why your current version isn't meeting your goal and what change might improve it.  For instance, you might hypothesize that "changing the button color of the call-to-action will increase clicks".

Create variations.  A "control" (Version A), which is the original, and at least one "variant" (Version B), which includes the particular change you are testing, are both necessary. To ensure reliable results, only change one element between the control and variant.

                                                                       
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Divide up your audience. Divide your audience randomly into two or more groups using testing software. One group will be shown Version A, and the other will see Version B.  This random assignment eliminates bias and guarantees that the change you made is to blame for any performance differences. 

Execute the test. Give the experiment enough time to collect enough data to be statistically significant. To take into account the patterns of traffic on a daily and weekly basis, many tests are run for at least two weeks.

 Analyze the results.  Review your data to determine if one version performed significantly better than the other based on your initial goal.  

Statistical significance is key, as it indicates that your results are reliable and not just due to random chance.

 Act and repeat.  Use a variant as your new standard if it is the clear winner. If the results are inconclusive, you can use the insights to form a new hypothesis and run another test.  

A/B testing is an ongoing process of iterative improvement.

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