Difference between revisions of "A/B Testing"
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Latest revision as of 10:06, 8 April 2022
Full Title or Meme
A stratagem for comparing two User Experience options by running both in parallel and comparing results.
The difficulty in determining the impact of changes on the User Experience at a Web Site has lead to a range of techniques, the easiest to implement being the simple expedient of just enabling any proposed, and pretested User Experience as a live version of an already deployed Web Site, or other rendering of a User Experience. Under the assumption that pre-testing has shown no problems, the best approach is to introduce it to a small of Users and ask for feedback from the user or just to monitor their reactions to the site. The larger the change, the more likely to take a while before the user response can be fairly evaluated.
The wiki page Hunting discusses algorithms for finding the best solution and maintaining the performance of the web site where the internet Ecosystem is constantly changing. The general approach is to mimic natural selection of Evolution and determine the correct parameters to use to get a good rate of change of the User Experience to maximize the User retention and growth over time. it is unlikely that the a constant rate of change will work, so it is likely that an overall strategy of normal slow evolutionary change with occasional bouts of Disruption will be required.
- Wikipedia, A/B Testing. (current) https://en.wikipedia.org/wiki/A/B_testing