The Ultimate Guide to Google Ads A/B Testing for 2025

Many advertisers believe their job is done once they launch their Google ad campaigns. They expect great results to start appearing automatically without any further effort. However, it’s a mistake to think this way. Launching campaigns is just the beginning. To get optimal results consistently, you will have to keep improving your campaigns and ads.

Talking about improving campaigns, you cannot just rely on guesswork. It is no longer effective. You need to use real data to guide your decisions, whether it’s changing ad copy, visuals, or any other variable.

That’s where Google Ads A/B testing comes in.

Using A/B testing, you can compare two versions of your ads or even test different campaign settings. Based on the results, you can identify which setup performs the best and can benefit your marketing objectives.

You might now wonder how to set up Google Ads A/B testing. Can it be done within Google Ads, or will a third-party tool be involved? Also, what variables can you test?

Through this guide, we will answer all such questions. We will discuss A/B testing in Google Ads in detail, methods to implement it, and how advertisers can interpret and apply the test results accurately.

What is A/B Testing in Google Ads? 

A/B testing in Google Ads, which is also known as split-testing, allows you to compare two different versions of your ads or campaigns to find out which one performs better in terms of achieving a specific goal.

In practice, you create two versions of your campaign or ad that differ by one or more variables. These variables can include, but aren’t limited to, Google Ads copy, images, videos, and bidding strategy. Both versions run either side by side or separately. After a defined period of time, you can compare their performance data and understand if the changes you made are resulting in a performance uplift. 

It removes guesswork and assumptions and allows you to update your campaigns or ads based on actual performance data. 

Note: This is the general process for A/B testing. The specific steps can vary depending on the method you choose.

Methods to Conduct A/B Testing in Google Ads 

We will now discuss the four most common methods for A/B testing in Google Ads.

Method 1: Manually Running Campaigns and Optimizing Based on Results

The first method is very basic and easy to follow, even if you are just beginning your Google Ads journey.  

You just need to create a campaign in your account, configure its settings properly, and allow it to perform for some time. This time period should ideally be around 4-6 weeks.

Once that phase ends, you need to review the performance data. Based on any insights you get, you can identify areas for improvement. Then, you can start optimizing your campaign assets, settings, or any other variable to get better results. 

After you have changed one or more variables, allow the updated campaign (or ad) to run for the same amount of time. Then, compare its results with the original one. 

Best for: This method is ideal for beginners who aren’t well-versed with Google Ads and don’t want to create Experiments. However, it lacks control over external variables like seasonality, your audience’s behavior, etc., making it difficult to offer accurate comparisons. 

Method 2: Google Ads Experiments (Recommended)

The most recommended and native Google Ads A/B testing method is creating Experiments in your ad account.  

With Experiments, Google allows you to run controlled tests by splitting your campaign’s traffic and budget between the original and test versions. This also enables you to compare the results of both versions over a set period and measure the impact of your proposed changes. 

Moreover, the most significant benefit of creating an experiment is that it only applies to a subset of your overall traffic. This means that your original campaign continues to perform as is. This reduces the risk of any negative impact of your proposed changes on overall results while still gathering valuable test data. 

Currently, Google supports the following types of Experiments: 

• Ad variations 
• Search Experiments (launched recently)
• Custom experiments for Search and Display 
• Video Experiments 
• PMax Experiments 
• App Experiments

Best for: Those advertisers who: 

1. Want A/B testing within the Google Ads interface
2. Rely on Google’s algorithms for accurate reports 
3. Don’t want to incur extra costs for A/B testing 

Method 3: Duplicating Campaigns and Ads

You can also conduct A/B testing in Google Ads by duplicating ad groups, assets, keywords, or any other variable from an existing campaign. 

Google allows you to copy and paste campaigns, ad groups, ads, audiences, etc., across the same or even different Google Ads accounts. By using this feature, you can keep the same ad or campaign structure and change only one variable you want to test. This could be an asset, a keyword, or any other element. You can then run the original and duplicated campaigns side by side with all other settings consistent.

Note: Whenever you copy a campaign, ad group or any other variable, ensure that you paste it in the correct location. By correct location we mean that if you are duplicating an entire campaign, then paste it within your own Google Ads account. If you duplicate an ad group, then you can do it only within a campaign and so on.

Over time, you can compare the performance of both campaigns and better understand which variation is driving better results. Once you identify the winning version, you can adapt to it.  

Best for: This method doesn’t provide statistically precise comparisons between ads or campaigns. That’s because if you duplicate an ad and just make some tweaks to its assets and run both the original and new ads together, it isn’t guaranteed that both will enter the same number of auctions. And thus, one version may have better metrics than the other simply due to how Google prioritizes ads.

This method is more suitable when you want to test bigger changes across multiple campaigns, where 40-50% of settings are changed. 

Method 4: Using Third-party A/B Testing Tools

Lastly, you can use third-party tools as well for Google Ads A/B testing. 

Some specialized tools, such as Optmyzr and Adalysis, allow advertisers to conduct advanced A/B testing of their ads and campaigns. 

Optmyzr is known for its AI capabilities, which allow you to automate ad variation tests and identify winning variations with ease. 

Whereas, when we talk about Adalysis, it allows you to create structured tests for different types of Google Ads such as RSAs, and manage them in bulk. 

Best for: Tools such as Optmyzr come with an additional cost, on top of your Google Ads spend. This cost can be quite significant at times (for example $209/month for Optmyzr). If you are comfortable with such high investments and want to go beyond the native Google Ads A/B testing experience, you can consider trying one. 

How to Set Up Google Ads A/B Testing?

In this section, we will learn how to set up Experiments in Google Ads. Of all the A/B testing methods we discussed above, this is one of the most effective and recommended methods. 

Depending on the campaign and the variable you want to test, you can create different types of experiments. We will cover the step-by-step process for creating custom experiments for demonstration purposes. 

Step-by-Step Process to Create Custom Experiments 

Before we process further, there are some important points you must note: 

• Custom experiments are only available for Search, Display, Video, and Hotel Ads Campaigns. For Shopping and App campaigns, you cannot create them.
• These experiments will use your original campaign’s traffic and budget.  
• You cannot use any deprecated ad type in your base campaign when creating custom experiments. Examples of deprecated ad types include Text and Expanded Text ads.
• You can schedule 5 experiments for a single campaign, but only run one at a time. 
• With cookie-based and search-based splits, you can decide the exact percentage of your Search or Display campaign’s traffic and budget that you want to dedicate to the experiment. 

However, which type of split is applicable and can be used depends on your campaign type. To read more about it in detail, as well as some other important information that you must be aware of before creating an experiment, visit this Google Ads help article.

  To set up a custom experiment: 

1. Click on the ‘Campaigns’ icon from the left-hand side menu, and under ‘Campaigns’, go to ‘Experiments.’

2. Click on the blue ‘+’ button, and select ‘Custom Experiments.’ 

3. Select the campaign type from ‘Display’ or ‘Search.’

4. Add the name of your experiment and also its description, if required, as it is optional.

5. Select the original campaign that you want to test through this experiment. 

Note: Google will duplicate the selected campaign and label it as your test campaign. Once the experiment begins, both the original and the test campaigns will run at the same time.

6. Then add a suffix (like _test) to the test campaign. This will differentiate it from the original campaign. 
7. Click on the blue ‘Save and Continue’ button. 
8. In the next step, Google will allow you to make changes to the duplicated campaign. So, you must update the variables you want to test (such as bidding strategy, conversion goals, etc.) at this stage. Once done, click on the schedule button. 

9. Then, select up to 2 Experiment goals that you wish to track as your success metrics. For example, you can choose “Increase in clicks” and “Decrease in cost per conversion.”

10. In the next step, select the Experiment split. This is basically to allocate a portion of your original campaign’s budget and traffic to this experiment. 

11. Moreover, from ‘Advanced options’, you can also select search-based or cookies-based split options. 

Note: We are seeing the option to choose between search-based and cookie-based split options, as earlier we had selected a Search campaign for this custom experiment. However, if we had chosen a Display campaign, then the ‘Advanced options’ setting wouldn’t have been visible. That’s because for Display campaigns, Google by default and always uses cookie splits.

12. Select the Experiment’s start and end dates. Remember, the entire duration cannot be more than 85 days. 

13. Within the ‘Enable sync’ setting, select ‘On’ if you want the changes that you make to your base campaign to automatically reflect in your trial campaign. If not, then select ‘Off’. 
14. Click on ‘Create experiment.’ And with that, you will successfully complete the process of creating a custom experiment..

Analyzing and Implementing Results 

Once your experiment starts running (or when it ends), you must review its performance data and check if it is driving better results. 

Let’s look at the step-by-step process which you can follow to view and analyze any experiment’s performance in your Google Ads account: 

1. Go to “Campaigns” from the left-hand side menu, and under “Campaigns”, click on “Experiments.” 
2. You will be able to view a list of all current running campaigns. 
3. Click on the one for which you want to check the performance report.

4. You will be taken to the ‘Experiment Results’ page, similar to the image shared below. On that page, you will get a scorecard and a time series performance graph. 

5. Moreover, you will also be able to see and compare the performance metrics of the trial campaign against the base (original) one. 
6. Based on this data, you can check if the new trial campaign, compared to the original campaign: 

• Got more clicks or impressions. 
• Was able to generate more conversions. 
• Had a lower CPA. 
• Had a higher conversion rate.
• Or in general did better with regard to any other KPI that matters for your business. 

For example, as we can see in the screenshot below, the trial campaign’s cost per conversion is $38.62 less than the original campaign. 

Moreover, the conversion rate at 21.69% is 66.7% better than the base campaign.  

Thus, in this scenario, it’s fair to say that the experiment was successful. You can either apply it to the original campaign or convert it into an entirely new campaign. 

Pro tip: Learn more about what to do when an experiment ends, and how to apply it to your base campaign on this Google page.

Example of A/B Testing in Google Ads

Suppose you have been running a Search campaign to promote the running shoes you sell through your e-commerce store. You want to check if changing the headline from “Shop Running Shoes Online” to “Buy Lightweight Running Shoes Today” will increase clicks and conversions. 

In such a case, you can easily create and run an Ad Variation Experiment. 

Note: Experiments are the native method to conduct A/B testing within the Google Ads ecosystem. Read more here.

All you need to do is: 

1. Select the specific Search campaign for which you want to test out a different heading
2. Create its variation where you replace the old headline with the new one
3. Then, set certain variation details such as start and end dates of the variations, experiment split, etc. 

That’s it. With this process, you would have successfully created the experiment. Once that experiment runs and ends, you can review the performance data and apply the ad variation (with the new headline) if it outperforms the original one.

Benefits of A/B Testing in Google Ads

We discussed one main benefit of A/B testing in Google Ads earlier in the blog, which is that it allows you to make data-driven optimizations to your campaign or ads. 

But there are some other important benefits as well, which include: 

1. Improved Ad Relevance: With A/B testing, you can test different variables of your campaigns or ads and analyze which one of them is resonating well with your audience. Once you identify which combination is the most effective, you can apply it to improve the relevance of your ads, which can also lead to an improved Quality Score. 
2. Better Ad Performance: When your ads are more relevant to your target audience, they will successfully garner more engagement. This can lead to better performance metrics such as higher CTR, more impressions, etc. 
3. Higher RoAS: With the help of A/B testing, you can funnel your ad budget toward those ad variations that can bring the best results. This means that you can get more conversions or sales without increasing your budget, which will result in a higher RoAS. 
4. Lower risk of changes: A/B testing with the help of methods such as Google Ads Experiments allows you to test your proposed changes on a small portion of traffic first. This means that if the change fails, then it will only have an impact on the results of the experiment’s split and not your entire campaign. 

Other Google Ads blogs you can read: Google Ads Call Tracking What are URL Parameters in Google Ads?

Best Practices for Google Ads A/B Testing 

Let’s quickly go through the list of best practices you should follow whenever you are conducting A/B tests for your ads or campaigns: 

1. Test one variable at a time: We recommend that you only change one variable (e.g., headline, bidding strategy, keywords, etc.) and don’t change the rest for the A/B test. This isolation will help you better track the impact on results. 
2. Use Equal Experiment Split: A 50-50 split is recommended as it will help you compare the performance of both campaigns accurately and minimize any type of bias. 
3. Ensure there is Enough Budget: One best practice that was shared with us by seasoned Google Ads product experts was to make sure that you have enough budget for the experiment. By ‘Enough’ we mean that the budget should be able to accommodate wide performance fluctuations and should always be sufficient to allow both the trial and base campaigns to run seamlessly and lead to conclusive results from the experiment. 
4. Run the A/B Test at Least for 3-4 Weeks: Don’t end a test prematurely. That often leads to incorrect conclusions. Rather, allow the test to run for at least 3-4 weeks so that sufficient data is accumulated, based on which you can make a decision. 
5. Check for Statistical Significance: Google will also highlight if your results are statistically significant. This means that the difference between the performance of both the base and trial campaigns is real and unlikely due to chance. 

If you notice that on the Experiments results page, Google is displaying “Statistically significant” with a confidence level (usually 80%, 90%, or 95%), you can trust the result and confidently apply the winning version. However, if it says “Not statistically significant,” then you must allow the experiment to run further, change the experiment split (if required), so that the results are more reliable and you can reach better conclusions. 

Bonus: Google Ads A/B Testing Ideas

After reading till this point, you would surely have a better idea about what A/B testing in Google Ads is and how to set it up. However, you must also know which types of A/B tests to create for your ads or campaigns. To help you with that, we will discuss some ideas in this section.

1. Ad Copy Variations: You can test out different Google Ads copies with new headlines, descriptions, and other assets. 
2. Landing Page URL: You can also add the link to a different landing page to check which one brings better results. 
3. Bidding Strategy: Check how manual CPC fares against smart bidding strategies such as Maximize conversions or Target CPA. 
4. Ad assets: Create and test out different types of sitelinks, callouts, and see which one helps you improve CTR. 

In case you are running a PMax campaign, then you can even A/B test the impact of text customizations such as Final URL expansion. 

5. Images and Videos: You can also analyze which types of image and video assets are garnering the highest engagement or conversions.  

Conclusions and Important Takeaways

A/B testing in Google Ads is important because it can help advertisers improve the performance of their ads and maximize return on ad spend with the help of real performance data. 

Some important takeaways from this blog include: 

1. A/B testing in Google Ads helps you compare two versions of your ads, campaigns, or any other variable and decide which brings better results. 
2. Some variables or settings that you can test via A/B testing include landing pages, bidding strategies, keywords, assets, etc. 
3. There are multiple methods to conduct these tests in Google Ads. However, using the native Google Ads split-testing method, i.e., Experiments is recommended.
4. Google allows you to create different types of Experiments, such as custom, PMax, Demand Gen, etc. 
5. Whenever you create an experiment it is recommended that you test out only one variable, in order to attribute the success or failure of the changes accurately. 
6. Moreover, allow your experiment to run for 3-4 weeks to gather meaningful and statistically significant data. 
7. Apply changes only when you find that the experiment goals have been achieved and the uplift is statistically significant.

Over to you! 

FAQs

1. Can we do A/B testing in Google Ads?

Yes, you can run A/B testing directly in Google Ads using the Experiments feature. It lets you test changes by splitting your campaign’s traffic and budget between the original and test campaigns. This way, you can compare performance and decide if your updates improve results.

2. What if I find that after the A/B testing, both versions perform almost the same?

That’s still a useful result. It means your test variable may not strongly affect performance. You can keep the original or try testing a different element that might produce more impact.

3. Can I stop an experiment early if I already see a winner?

It’s better to wait until the test completes or at least runs for 3–4 weeks. Early results can be misleading due to daily fluctuations. Waiting ensures more accurate and dependable insights.

4. Do Google Ads Experiments use my real budget?

Yes, Google Ads Experiments use your real campaign budget. The budget is split between your original campaign and the experiment. It’s not a simulation.

5. What’s the difference between a cookie-based and a search-based split in Google Ads Experiments?

In Google Ads A/B experiments, cookie-based split ensures each user sees only one version, either the original or the experiment, throughout their journey. This helps maintain consistency in user experience. In contrast, search-based split assigns users randomly on each search, so the same user may see both versions.

6. What is a confidence interval in A/B testing?

A confidence interval shows how likely your test results are true and not due to chance. For example, a 95% confidence level means there’s only a 5% chance your result is random. Higher confidence means more reliable results. We recommend keeping the interval at around 90–95% for optimal results.

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