Rename and duplicate

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In this article

In this article, we’ll show you how to rename and duplicate experiences and variations. We’ll also cover the reasons why you might want to duplicate.


Rename an experience

Step 1

Open your experience from your one of your lists and select more.

Step 2

Select Rename, enter the name you wish to use for the experience and select Save

Rename a variation

Step 1

Open the experience from Experiences and select edit-button

Step 2

Select more next to your variation and select Rename. Enter the name you wish to use in the field provided and select Save


Duplicate an experience in the same or a different property

Duplicating is useful when you want to copy an experience as the basis for a new experiment or when you want to publish as a live experiment but keep a copy as a draft that you can continue working on.


You can only duplicate experiences to a different property if that property has the same experience type enabled.

Step 1

Open the experience from Experiences and select more.

Step 2

Select Duplicate, enter the name you wish to use for the experience in Name, and enter the preview url you wish to use for the experience in Preview URL

Step 3

Select the property you want to duplicate the experience to:

  • Select the property you’re currently working on to duplicate the experience to the same property


  • Select a different property you have access to

Step 4

Select Duplicate and then Done

Duplicate a variation

Duplicating an experience variation is a good option where you wish to test the effectiveness of a small change to an experience, for example, a different image or slightly different wording in a banner.

You can also duplicate a variation more than once. This allows clients looking to undertake A/B/n testing the opportunity to compare multiple versions of a web page against each other. In this type of test, traffic is split randomly and evenly distributed between the different variations to determine which variation performs the best.

Step 1

Open the experience from Experiences and select edit-button

Step 2

Select more next to your variation and select Duplicate


In an A/B test, how many variations should I test?

A/B tests (respectively A/B/n tests) compare one variation (respectively many variations) against a single control. Whether you want to run one A/B/n test or many A/B tests is a matter of preference.

It may seem faster to run an A/B test than an A/B/n test since your traffic will be less diluted amongst the variations. If you run a sequence of A/B tests, each A/B test in the sequence will complete faster than the A/B/n test that runs all the variations you want to test in parallel.

However, the A/B/n test will complete faster than the entire sequence of A/B tests. Indeed, it’s more efficient to run an A/B/n test, since the control group is shared by the variations. A/B/n testing also has the benefit that data in all the variations is collected during the same duration, and so the variations in the A/B/n test are all subject to the same seasonal effects.

That’s not to say you should always run A/B/n tests with as many variations as possible: diluting traffic into variations that are for all practical purposes, conducting A/A tests, is not an efficient use of traffic.

What’s an A/A test?

An A/A test is a test where we run two variants with no treatment whatsoever, that is, we run a test with two controls. We do not expect to observe any difference between the two variants (though this can happen by chance). It’s generally done as a sanity check for new clients or a new test, to iron out data issues.

What’s an A/B test?

A/B testing is a randomized experiment with two variants A and B, one of which is a control, and one of which is a treatment variant. The goal is to identify changes to web pages that increase an outcome of interest, for example conversion rate, or clickthrough rate on a banner.

What’s an A/B/n test?

This term is sometimes used in the industry to describe a type of multi-variate test. Multi-variant testing is hypothesis testing with more than two variants (that is, more than one treatment variant and a control). It’s popular in web-analytics because it’s perceived that it can be used to test lots of hypotheses quickly (though we believe it’s a bit more complicated than this).