Google Ads A/B Testing: Your Guide to Market-tested Ads

A/B testing offers valuable data that can improve engagement and conversions by helping you make sound decisions based on what resonates with your audience. It has shaped the online world and continues to adapt to keep pace with evolving technologies.

Google Ads A/B testing is a powerful tool for gaining insight into what your audience prefers and responds to. You can use A/B testing to compare features of ads and many other kinds of digital and physical products, including social media posts, software, and web design. By creating two nearly identical options and sending them out to different audiences, you can compare the performance metrics between option “A” and option “B” to make a generalization about what works better for your audience. 

Major companies like Google, Microsoft, and Meta use A/ B testing. It is also common across industries that include mobile app development, e-commerce, social media, software development, and more, with a global market for A/B testing software estimated to be worth $720.6 million in 2024. What’s more, the market may grow at a rate of 10.2 percent through 2030 [1]. The rise of A/B testing software available on cloud computing is one of the drivers of that growth, as are the AI integrations that make A/B testing more efficient and help you better understand your test results. 

Explore the benefits of A/B testing and learn how to plan, set up, and execute Google Ads A/B testing. 

What is an A/B test? 

A/B testing is a strategy for collecting evidence about your audience's preferences by providing them with two options. Some users will see the “A” option while others will see the “B” option. You can compare performance metrics such as click-through rates, ad impressions, or increased revenue for both options to hypothesize that your audience preferred one option over the other. The important part is to make sure that options A and B are identical in every way except for the variable you want to test for, such as the headline, image, or ad copy. 

For example, you could conduct A/B testing if you create a product and want to try out different product names or designs. You could bring in two focus groups and present your product to each group in a different way. The groups may react differently, giving you insight into what your larger group of customers will respond to positively.

When it comes to ads, you can A/B test many different factors, including the content of the copy, headlines, and calls to action, in addition to design elements that influence how well they perform, such as which audience you are sending your ads to and where you place your ads.

Can you do A/B Testing in Google Ads?

You can run A/B tests in Google Ads directly through the Google Ad Manager or through Google Experiments to optimize your ads for best performance. When creating your ad in Ad Manager, you can designate an A/B test with the variable you want to test and select how much of your traffic you want the Ad Manager to allocate to each of the options in your test. It can give you more control over how large your sample size is. A larger sample size can provide more accurate data, but a smaller sample size can help you test ideas on segments of your audience rather than the whole group. 

You can also use Google Experiments to run A/B tests on ad campaigns that are already active or for experiments you want to keep separate from your main campaigns. The native tools for A/B testing in Google Ads and the options for experimentation in Google Experiments can help you save time, analyze your results more effectively, and organize the insights you learn from testing compared to manual A/B testing. You can also run A/B testing for Demand Gen campaigns, which can help you engage audiences across Google properties, such as Gmail and YouTube, in addition to search. 

Google Ads A/B testing example

Imagine running an ad campaign with an average click-through rate of 1 percent on your search ads. You may create a hypothesis that you can increase the click-through rate if you use a more engaging call to action (CTA) that offers customers a clear motivation to click your link. 

You can create an A/B test in Google Experiments using a second version of the ad with adjusted copy or design. Google Ads will then serve your experiment to your desired number of users and, after gaining enough data, present you with the results of your test. If more people clicked on your test link (the B option, testing the changes you want to make) you would see an increase in click-through rate on that ad compared to the ad you’re running. You can then update your ad with the new changes based on the data. 

How to use Google Ads for A/B testing

While the process looks a little different depending on the exact type of A/B test you want to conduct, you can easily set up A/B testing directly in Google Ad Manager or through Google Experiments. To create your Google Ads A/B test, begin by defining your goals, hypothesis, and testing variable, and then set up your test. Once your test gathers enough data, you can analyze the results to help you gain insight into the results of your experiment. 

Step 1: Design your A/B test

The first step in conducting an A/B test in Google Ads is determining what you want to test, predicting how your audience will respond, and setting a goal. This basic framework can help you set up a test for the prediction and help you understand what metrics you’ll need to measure to determine the success of your test. 

To design an effective A/B test, you will need to single out the specific variable you want to test. This means you should pick one element to test, even if your hypothesis is more complex. You can test design or campaign elements such as: 

• Ad layout

• Ad copy

• CTA language

• CTA button design

• Landing pages

• Target demographics

Isolating the variable you want to test is essential because if you make too many changes between option A and option B, you won’t be able to separate what precisely caused your audience to react differently. If the only difference between the two is a single variable, you can have more confidence that the change made an impact on your results. 

Next, you should set a goal for what success will look like. This helps you understand what metrics will be crucial to collect so you can determine the results of your test. You can test these elements against different outcomes, such as your click-through rate, bounce rate, the amount of revenue each ad generates, increased form submissions, and conversion rate. 

Step 2: Run your A/B test

After you’ve designed your test and you have a clear idea of your goals, your hypothesis, and the metrics you will use to measure success, you can set up your A/B test in Google Ads or Google Experiments. If you want to set up an A/B test on a new ad while setting up your ad campaign at the same time, you can run the test natively in Google Ad Manager. If you want to test ads that are already active, keep your testing separate from your main campaign, or experiment with a broader assortment of variables, you can use Google Experiments. 

To set up your A/B test in Google Ad Manager: 

• Log in to your Google Ad Manager account and select “Delivery > Native.” 

• Navigate to “Style your native ad” and select “Create A/B experiment.” 

• Configure your test with scheduling options and traffic allocation. 

• Click “Experiment” and configure A/B testing options. 

• Click “Continue,” address any other changes, and click “Save and finish.” 

To set up your A/B test in Google Experiments: 

• Navigate to Experiments (located in the left-hand page menu of your Google Ads account). 

• Select the type of experiment you want to run. You can choose from variations of your ad, such as using different text ads, experiments considering how different ad campaign settings impact performance, and experiments on video ad performance.

• Configure the settings for your A/B test using the options for each type of test, including factors such as how long the test will run and what audience demographics your test should target. 

Step 3: Collect and analyze the results

After you run your A/B test and Google has enough data to analyze, you can start to see the results of your experiment. It’s important not to act on the results you gain right away. Instead, wait to make conclusions once the test reaches an appropriate sample size. 

The exact benchmarks for your test will depend on many different variables, but you should look for at least a five percent change in your performance metrics before you draw a conclusion that your A/B test had an impact on performance. Anything less than that may not be significant enough for you to make a change. You can compare the results of your test against the goals you set in the planning stage to determine whether you want to move forward with widespread changes to your ad campaign or continue testing new ideas. You can make these changes—implementing the change campaign-wide or adjusting experiment parameters—directly in Google Ad Manager. 

Learn more about optimizing ad campaigns on Coursera

Creating an A/B test in Google Ad Manager or Google Experiments is a helpful way to consider how small changes to your ads can impact performance. If you want to learn more about optimizing ad campaigns to maximize your ad dollars’ value, consider exploring programs like the Google Digital Marketing & E-Commerce Professional Certificate, which you can use to develop a strong foundation in digital marketing. 

You might also explore the Meta Marketing Analytics Professional Certificate for the opportunity to learn how to collect, sort, evaluate, and visualize marketing data, summarize and analyze data using marketing analytics methods, design experiments and test hypotheses to assess advertising effectiveness, and use Meta Ads Manager to run tests, learn what works, and optimize ad performance.