Marketing Glossary

A/B/n Testing

A technique for evaluating different versions of a website or mobile app to discover and evaluate which performs the best.

What is A/B/n Testing?

A/B/n testing is a sort of internet testing in which multiple variations of a website page are tested to see which has the best conversion rate. To evaluate which variation performs the best, the traffic is split randomly and equally between the multiple versions of the webpage.

A/B/n testing is a variation on A/B testing in which two variations of a page (model A and model B) are compared. An A/B/n test, on the other hand, compares more than two variations of a page at the same time. The number of variants getting tested is indicated by the letter “N,” which might range from two to the “nth” iteration.

Multivariate testing can be combined with A/B/n testing. A multivariate test is used to compare many versions of a webpage at the same time by evaluating all conceivable variations at the same time. Multivariate testing is more extensive than A/B/n testing and is used to evaluate changes to particular page elements. A/B/n testing can be used to compare two fully different variations of a page.

What is the Significance of A/B/n Testing?

A/B/n testing can help you figure out which web design generates the most user conversion rates and engagement. You can compare numerous pages at the same time and use the results to decide which variation to adopt.

It can be used to evaluate every idea and produce a choice based on solid data that indicates how one variant outperforms others when a company possesses conflicting theories for what the greatest website layout would be.

Not only does A/B/n testing show which iteration of a website page is the most effective, but it also reveals which variation of a website page is the least successful. It is feasible to come up with suggestions as to why particular aspects convert better than others by evaluating these low-performing pages, and these insights can subsequently be implemented into new testing on other pages.

An Example of A/B/n Testing:

When Electronic Arts introduced a new edition of their renowned SimCity series in March of 2013, it was a concrete example of A/B/n testing in operation. EA used an A/B/n test to compare and contrast three different variations of their pre-order page to discover which one worked better.

During the test, the EA team discovered that the variation of their website page without a specific promotion offer at the top scored 43 percent in comparison to the other alternatives.

Not only did EA notice a significant boost in pre-orders as a result of the analysis, but they were also able to utilize the insights gained from the experiment to other sections on their site, resulting in increased conversion rates across the board

(To learn more about this EA Case Study, click here)

A/B/n Testing’s Potential Drawbacks

Experimenting with too many variants (when only one can be chosen) will further separate traffic to a website. This can affect and considerably extend the time and traffic needed to arrive at a statistically meaningful result, as well as introduce “statistical noise” into the process, which can be quite painful to navigate.

Another thing to keep in mind when performing several A/B/n tests is to keep the broader picture in mind. Just because separate factors worked well in their respective studies does not automatically guarantee they would work well together. Consider conducting multivariate testing to evaluate all possible scenarios and ensure that changes to top-level metrics are carried through the conversion funnel.