Multi-adgroup testing using labels

Modified on Fri, 31 May 2024 at 07:43 AM


1. What is multi-adgroup label testing?
Multi-adgroup label testing is aimed at split testing sets of ads (across multiple adgroups/campaigns). Each set of ads shares (or not shares) a common label(s).

Multi-adgroup testing can be very useful for low volume accounts that don’t accrue enough data to reach statistical significance using single-adgroup testing. 

2. How do I run multi-adgroup label tests?

Multi-adgroup test results are produced in 2 ways:

2.1 Manually (run by you)

Just like single-adgroup testing, you can run multi-adgroup tests as often as you need as shown below:

1) Select which campaigns (and optionally specific adgroup labels) as well as the date range for this test.  A common date range means Adalysis will choose a date range during which all ads within all sets were active.

2)  Provide the labels which each set of ads should or shouldn't have.  

3) Click on this button to run this test.  

4) If this is a test you want Adalysis to run for you daily, click this button to save this test definition in the Daily results of your defined tests screen.

Tip 1:  Care needs to be taken when using a common test date range. If one matching ad out of hundreds was enabled only a few days ago, it will cause the whole test to run using only the last few days of data. 

Tip 2: When testing image Ads, you can also specify the image size

2.2 Automatically (run by Adalysis daily)

Unlike single-adgroup testing, you will first need to define a test before Adalysis can run it daily for you.

Once you run some tests manually (as described above in 2.1) and found one you want to keep running daily, you can do so by using the Save & Run this test daily option in the Run Tests screen or define the test here as shown below:

3. Understanding the test result data


1) The result of your last test run

2) Details of each included/excluded label you provided and how many adgroups and ads it was found in.  You can see the individual ads found for each label. Included labels are in blue while excluded labels are in red.

Once you open the test result details, you will see the aggregate performance of each label (over date range used) and the confidence the algorithm has in each of the metrics.  The performance boost figures are projected based on all ads with the losing label(s) getting paused

4. What actions can I take when analyzing a test result

You can pause all ads that use the losing label(s) for a specific metric.

Tip: Whenever losing ads are paused, a copy of the test result will be automatically archived for future reference.

5. Viewing historical test results 


History of Multi-Adgroup Test Result Changes

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