Monitor your ad variations

After you’ve started running ad variations, you can monitor their performance. By understanding how your ad variations are performing, you can make an informed decision about whether to replace your original ads with better performing ads.

This article explains how to monitor and understand the performance of your ad variations.

Before you begin

If you haven’t yet created an ad variation, read Set up an ad variation.


  1. Sign in to your Google Ads account.
  2. From the page menu on the left, click Experiments.
  3. Click Ad variations. You’ll see a table of ad variations you’ve created, along with information about each variation.

The number of affected ads, clicks, and impressions are listed for each ad variation. When you click on an ad variation, you’ll see a comparison of the performance metrics with the original ad.

About performance metrics in ad variations

The performance metrics you’ll see for your ad variations include four numbers:

  1. The first number is the value for each performance metric for the ad variation only. For example, your ad variation got X clicks.
  2. The percentage outside of the brackets "[]" is the difference between your ad variation and the original. For example, your variation got Y% more clicks than the original.
  3. Inside the brackets, the two numbers give an expected range with a 95% confidence interval. For example, you have a 95% chance of seeing a difference between A% and B%.

When a metric is marked by a blue asterisk "*" it's statistically significant. It's at least 95% likely that the impact on performance resulted from the change you made rather than from random chance.

Generally speaking, significance is affected by three factors:

  • The difference in performance between the original ads and the modified ads. Larger differences tends to increase significance.
  • The changes in performance. A campaign where clicks vary by 50% from day to day has more variability than a campaign where clicks vary by 2%. Large variability tends to decrease significance.
  • Total number of impressions in ad variation. The higher the impressions, the higher the statistical significance may be.
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