Personalization Glossary
A/B testing? Recommendation models? The personalization engine space uses several acronyms that may seem daunting, so we're here to help. You can find definitions to the most common e-commerce customer experience optimization terms here!
A/B/n Testing
A/B/n testing is where multiple forms of web pages are tested against each other to understand which one of the options delivers the highest conversion rate. The traffic is evenly distributed between the multiple versions of the page to evaluate performance.
In addition to the control found in A/B testing, this has at least 3 more variations of the website tested—perhaps, the primary graphic on the landing page. The traffic is directed to these 4 pages in an appropriate percentage.
A/B/n testing also can be contrasted with what’s referred to as multivariate testing. A multivariate test also compares multiple versions of a page directly, by testing all possible combinations of variations directly. Multivariate testing is more comprehensive than A/B/n testing and equates to running several A/B/n tests in parallel simultaneously.
A/B testing is a critical method to analyse and understand which particular version of the page converts best. In many cases, A/B testing is not just enough, some situations require us to test more than just two versions/variations of the page, A/B/n testing is the way to go.
What is A/B/n testing?
For any type of test to be done, there is always a control page against which every other version of the page is tested. In the A/B/n test you can test two, three or more versions of the page against the control page.
In the A/B test method, a method that’s commonly used, we often use, there is version A as the control and there is version B, a variation of the control. Similarly in an A/B/n test, version A can still be the control, but instead of testing only two variations, more variations can be tested.
The n in A/B/n refers to the number of variants being tested. N can be as few as 2 or as high as an “nth” — there is no limit to how many versions you can test against your control.
In A/B/n the “n” refers to the number of variations one can create of a specific page. N can be as small as 2 variations or as big as “nth”. N literally means that there is no limit to the number of variations that can be tested.
Why would you want to A/B/n test?
A/b/n testing essentially aids marketers to understand and identify which versions of the page are converting better. It is largely useful in scenarios where you want to publish and see which versions are performing and could be made as a permanent one.
Multivariate Testing vs A/B/n Testing
It’s critical to understand the difference between terms multivariate testing and A/B/n testing as they are commonly used juxtaposed one another or interchangeably.
In an A/B/n test there is only one change tested at a time, it can be any specific variable on the page, could be a headline, an image, etc, but in a multivariate test, testing can be done beyond single variable, it could be more intense, where you are changing multiple variables at once.
So while A/B/n testing is testing multiple variations of a page, it isn’t testing multiple variables. That’s an important concept to understand.
Hence while running an A/B/n testing it is critical to know that it’s not where you test multiple variables on the page but it is testing only testing multiple variations of the page with only minimal variables put to experimentation.
Why is A/B/n testing important?
A/B/n testing essentially allows us to look at any change we make and say we got that ‘spot on’ as it allows us to determine which designs and templates drive engagement and conversion among users. In situations where we have more than one competing ideas for what would be the best website layout, A/B/n testing can help us take a result-based, data-driven approach to settle this debate over most other unconvincing ways. Here, the pages in contention can be tested against one another at the same time and the data can be used to determine the winning variation.
In addition, these tests turn the spotlight on the features that convert better which can then be used in new tests on other pages in the site. This way one does not have to opt for one template entirely but can pick the best features from the other templates that tend to work just as well.