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A/B/n Testing

A technique for evaluating many web or mobile app iterations side by side to see which works better.

A/B/n Testing

What is A/B/n testing?

A/B/n testing is a sort of website testing in which multiple variants of a single web page are compared against each other to identify which version of the page has the highest conversion rate. The goal of this kind of test is to identify which variation of the page works the best by randomly and uniformly dividing the traffic that visits the website amongst the many versions of the page.

A/B/n testing is an extension of A/B testing in which two or more versions of a page are tested against each other. Whereas, in A/B testing we compare only control and variant.

In A/B/n testing, the number of different iterations being evaluated, denoted by the letter “N,” can range anywhere from two to the “nth” version.

There is also the possibility of comparing A/B/n testing with multivariate testing. A multivariate test is a type of test that evaluates many copies of a page at the same time by evaluating each and every combination of conceivable variants simultaneously. The purpose of multivariate testing is to evaluate the effects of changes made to particular elements on a page. MVT is more comprehensive than A/B/n testing. In A/B/n testing, totally distinct versions of a page are pitted against one another to see which one performs better.

Why is it vital to test with A, B, and n?

Through A/B/n testing, you may determine which website design encourages the highest level of engagement and conversions from your target audience of users. You are able to test numerous pages simultaneously against one another and utilize the data to choose which variation of the page you should proceed with.

When a company has more than one competing notion for what the greatest website layout would be, it may employ A/B/n testing to evaluate each idea and make a choice based on solid data that reveals how one version did better than others.

A/B/n testing reveals not only which version of a page is the most successful, but also which version of a page fared the worst in terms of its overall performance. It is feasible to come up with ideas as to why certain features convert better than others by evaluating the pages that are not performing well, and these lessons can then be implemented into new testing on other pages of the website to see whether they have any effect.

Possible drawbacks associated with A/B/n testing

If you test too many different variations, you risk further dividing website visitors among all of the many possibilities. In the process of reaching a statistically significant result, we will have to increase the amount of time and traffic that is necessary on the pages you are testing. This may lead to something called “statistical noise.”

When doing numerous A/B/n tests, it is important to remember not to lose sight of the wider picture. This is one of the considerations that should be kept in mind. Even if certain factors achieved the best results in their respective tests, this does not always indicate that they would perform well when coupled with one another in a new experiment. Consider carrying out multivariate testing in order to examine all possible variants and guarantee that any improvements made to top-level metrics will trickle down to lower levels of the conversion funnel.

A rule of thumb is, do 10 A/B tests for every 1 A/B/n test

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