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These two tests are not opposites, but two ways of approaching the same problem - the need to test your ideas.
Multivariate testing is used if you have many variables to test at once and if your userbase is big enough to create multiple segments.
A/B testing involves splitting your audience/user base into two groups. The two groups receive two versions of a feature. After a specified time, the product manager will look at the data gathered to know which version should be used for the entire user base.
The point of running a test is to use data to best improve your product.
The biggest mistake you can make after running an A/B test is to just pick the winner and forget about the test.
Testing is about understanding the reason why. Your users have given you more information about themselves, and to serve them properly you need to take advantage of that. Also make the data available to other teams as they may gain unique insights from it.
If you’re a larger organization, Optimizely may be suitable for deploying powerful tests and managing the insight you glean from them.
If you’re on a tight budget, many developers like to keep it simple with Google experiments.
Before you deploy the test, you need to make sure it meets the minimum acceptance criteria.
Every test in your backlog needs to have a detailed:
Once you've set the minimum criteria, you can deploy.
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