In this post I go over the new Google Analytics Content Experiments, a tool that can be used to create A/B tests from inside Google Analytics. This tool has several advantages over the old Google Website Optimizer, especially if you are just starting the website testing journey. Content Experiments provide a quick way to test your main pages (landing pages, homepages, category pages) and it requires very few code implementations.
Here is a quick overview of the most prominent features that will help marketers get up and running with testing:
- Only the original page script will be necessary to run tests, the standard Google Analytics tracking code will be used to measure goals and variations.
- Google Analytics advanced segments can be used to segment results based on any advanced segment.
- Improved statistical engine for analyzing experiments, which will help making decisions faster about the winning/loosing pages.
- Tests results will not appear for at least 2 weeks, a mechanism to encourage statistical significance.
- Tests will automatically expire after 3 months to prevent leaving tests running if they are unlikely to have a statistically significant winner.
- "Dynamic Traffic Allocation" functionality: traffic will be shifted away from low-performing variations, over to higher performing ones. This feature can't be turned off. This is to prevent poor-performing variations from doing extensive damage)
Below is a step-by-step guide on how to use Content Experiments to create A/B tests.
Create A New Experiment
In order to create a new experiment, navigate to the Content reports and click on the Experiments link on the sidebar. You will see a page that shows all your existing experiments. Above this table you will find a button Create experiment. Once you click on it you will reach the following page.
In this page you can add all the URLs of your original page and the variations you would like to test. You will see thumbnails of the page, which helps you making sure the URLs are correct.
Click Next.
Set Experiment Options
In the page above you will be offered a drop down with a list of all your profile goals, which can be used as a goal for your tests. If there is not an existing goal that is also the conversion you want to measure for this specific test, you will have to create a new goal to use in the test.
In addition to that, you can set a percentage of your visits that will be included in the test. If you are testing radical alternatives to an important page, it is recommended you don't try it on 100% of your traffic, it can damage your conversions... You can choose 100%, 75%, 50%, 25%, 10%, 5%, 1%. You can also add notes to your experiment.
Click Next.
Add And Check Experiment Code
As mentioned above, you will need to implement one code in order to use this tool. In the page above you can chose to either get the code to implement immediately or to send it to your webmaster.
Click Next and your pages will be verified. If they are not you will see the following error message.
Note that you will be able to skip validation if you want, just click on skip validation and continue. But it is recommended that you check the code to understand why you are getting an error and then try validating again.
Review Experiment
This page is a review of your article, showing all the decisions you took along the process. You can either Save and run later or Run experiment now.
Experiment Results
In the screenshot above we see how you can navigate through a running test. We have the following capabilities:
- Advanced Segments: as mentioned above, this is an extremely valuable feature, it enables you to understand better how each variation performs for each segment of visitors on your website.
- Stop Experiment
- Re-validate
- Disable Variation
- Conversion Rate: gives you the option to check the test results using alternative metrics.
And below we see the results page of a test with a winning version, the Road Runner (purple line), with a lift of 28.5% lift in conversions as compared to the original.
Reviewing All Experiments
Any time you want to review your experiments just visit http://onbe.co/IXznAO
Concluding Thoughts
All in all, Google Analytics has made a great job out of this new testing capability, especially for marketers that are still not testing often. For marketers that are more advanced there are still quite a few features missing. Here is a wishlist for future versions of this tool:
- Multivariate Testing capability
- E-commerce transactions as goals
- Remove limit of 5 variations per test
- Remove limit of 12 tests per profile at this time
- Option to use the standard tracking code for everything (one ring to rule them all)
What are you waiting for, start testing!
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