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A/B/n Testing

A/B/n Testing

A/B testing is an incredibly powerful tool to determine which version of a page performs best. But sometimes, rather than testing two versions of a page, you want to take your test a step further! You might want to test three, four, or even more pages head to head.

That’s where A/B/n testing comes in handy!

What is A/B/n testing?

A/B/n testing is a type of A/B test (also called a split test) in which you test two or more variants against a control.

In a traditional A/B test, you set a version A as the control and you test a version B as a variation against that control. In an A/B/n test, you still use version A as your control, but you can test two or more variants against the control.

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.

Why would you want to A/B/n test?

A/b/n testing helps marketers understand which version of a web page, landing page, or experience converts the best. This can be extremely powerful when it comes to deploying multiple versions of a page.

Let’s consider an example. Your company is considering a full redesign of your homepage — you have a few design options that you think will outperform your current page, but you don’t know which option will convert at the highest rate.

With an A/B/n test, you can deploy all the new variations of the homepage against the original and see what page connects best to your customer base. Rather than making the change based on a gut feeling, you can use data to determine the winner.

With the test, you search for which version of the page has the highest conversion rate, and once you reach a statistically significant result (>95%), you can declare a winner.

Problems with A/B/n testing

With every test you deploy, you’re essentially trying to improve a metric and reach statistical significance. An issue with A/B/n testing is that the addition of another variant increases the time (and traffic) required to reach statistical significance.

If you want to keep reaching results in a reasonable period of time, we recommend that you keep the number of variants in your test at a reasonable level.

A/B/n testing vs multivariate testing

You might hear the terms A/B/n testing and multivariate testing thrown around interchangeably, but it’s important to note that they aren’t exactly the same.

An A/B test (or an A/B/n test) is used to test one change at a time. That can be a headline, a button, an image, or even a whole page. A multivariate test, on the other hand, is a bit more complex. It’s when you’re modifying 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.

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