A/B/n testing is when you test several versions of a web page by sending them all an even distribution of traffic to see which one has the highest conversion rate. By doing this, you’re able to see which page converts the best.
This type of testing is similar to A/B testing where you only test 2 versions of a page, but A/B/n testing compares more than 2 versions of a page at the same time where “N” is the number of versions of the page. A/B/n testing is similar to multivariate testing, however the difference is that multivariate tests compare all possible combinations of variations of a page at once, making it more extensive than A/B/n tests. Multivariate tests are used to see is there are changes in certain page elements an A/B/n tests are used to test versions of a page which are completely different.
Why A/B/N Testing Is Important
A/B/n tests allow you to test completely different versions of a webpage which allows you to see which design led to the most conversions or engagement. By testing several web pages at once, it allows you to determine which one you should use moving forward. If your team has several ideas on your website layout an A/B/n test would allow you to test them all at once to see which one performs best. From there you would have a “winning” page. Along with determining which page performs the best, you can see which one performs the worst so you can create a hypothesis on why it didn’t perform well and try not to use those design features on other pages on your site.
A downside of A/B/n testing is if you have too many page variations, it could split traffic to your website too drastically. This means it would take longer to reach statistical significance for your test. When A/B/n testing you should also be mindful that just because one page didn’t perform well doesn't mean it doesn't have elements that would work when combined with elements of the other pages.
Taplytics allows you to create accurate A/B/n tests in a user-friendly way. To learn more about A/B/n tests with Taplytics, reach out to us.
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