Running an experiment allows you to compare one of your ad settings against a variation of that setting to see which performs better. Experiments help you to make informed decisions about how to configure your ad settings, and can help you to increase your earnings. Here are some examples of the types of experiments that you can run:
- Compare the performance of text-only ad units to ad units that have both text and display enabled
- Analyze the effect of changing the color of ad text on ad performance
- Determine the impact on your revenue of using ad serving, sensitive and/or general category blocks
There are two ways to create experiments. You can either:
- Create your own experiment - you set up the experiment yourself by choosing the ad setting and creating the variation.
- Create an experiment from a recommendation on your Home page - we set up the experiment for you based on the settings change described in the recommendation.
Life of a typical experiment
A typical experiment that you create yourself, follows these steps:
Before you create your experiment it’s a good idea to decide exactly what you’d like to test in the experiment.
Ad units: All ad type and text ad style settings are available for experiments. If there are multiple changes that you would like to try (e.g., change the text ad border color and background color), you may want to run two different experiments - first an experiment for the border color change and then an experiment for the background color change.Note that the following ad units are not eligible for use in experiments:
- Idle ad units
- Ad units that are already being used in another experiment
- Matched content units
Allow & block ads: The following ad blocking settings are available for experiments:
- You can either run ad unit settings experiments (maximum one experiment per ad unit) or a single global “Allow & block ads” experiment, but you can’t run both types of experiment at the same time.
When you create an experiment you:
- Select the original ad setting that you want to compare the experiment variation against.
- Select which settings you’d like to change for the variation.
- Decide whether you want Google to optimize the traffic split for your experiment. We highly recommend that you let Google optimize the traffic split for you, as this will maximize the efficiency of your experiment.
Learn how Google optimizes the traffic split for experimentsWe use a multi-armed bandit approach to optimize the traffic split for experiments. Each day, we take a fresh look at your experiment to see how the original and the variation performed, and we adjust the amount of traffic that each receives going forward. If one appears to be doing well it gets more traffic, and if one is clearly underperforming it gets less. The adjustments we make are based on a statistical formula that considers sample size and performance metrics together, so we can be confident that we’re adjusting for real performance differences and not just random chance. As the experiment progresses, we learn more about the original and the variation, and so do a better job in choosing the best-performing variation. Experiments based on multi-armed bandits are typically much more efficient than "classical" A-B experiments based on statistical-hypothesis testing. For more detailed information, see Multi-armed bandit experiments in the Google Analytics Help Center.
The “create experiment” page guides you through each step, and provides you with more information. When you’re finished setting up your experiment, you can start it.
Once your experiment is up and running, it’s up to you to monitor its progress.
After 24 hours, you should see data in both the:
Over time, you should see either the original ad setting or the variation outperform the other. To help you decide the right time to choose your winner, we calculate a confidence score for the two settings. This score indicates how likely the original or the variation is to be the better performing (i.e., the highest earning) setting in the long term.
- If you choose the original as the winner, then the settings of the original are retained.
- If you choose the variation as the winner, then we apply the settings of the variation to your account.
In either case, we stop splitting your traffic and collecting data, and your experiment ends.