How to AB test Google ads? Whether you like it or not, Google tools have become a standard in marketing and web development. Lots of them are free, intuitive and available globally. 

That’s why this article will focus on AB testing inside Google Ads (using a native tool called Experiments) and using Google Optimize outside Google Ads testing ideas.

What are AB Test Google Ads?

Google optimize AB testing, known also as split testing or bucket testing, is a research methodology consisting of a randomized experiment to find out if our hypothesis is true or not. To put it simply, you divide your audience into two segments. 

One of them is a control group and for them, nothing has changed. The second one is an experimental group that sees a different content, website, email, ad or anything else you want to test.

AB tests take time and money and might be complicated to conduct but it’s the safest and more effective way of implementing changes. If you ever wondered why it takes so long to launch a new medicine or vaccine, one of the reasons is a requirement of a series of successful AB tests. But let’s get back to marketing.

Read also: How to Add Google Product Category to Shopify Quickly?

How to do AB Test Google Ads?

How to split test adwords? There are a few factors that need to be taken into consideration before starting the test. It may be tempting to treat careful planning as a waste of time. The test will last some time so one likes to start as soon as possible. But the more time you spend on planning the better results you’ll have.

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If you have written down ideas on what to improve in your marketing funnel, you need to decide what to do first. There are many ways to answer it, one of the common frameworks is the ICE model. It has three variables:

  • Importance – how important is the testing element? How much can it improve the general result? How many users are affected by this element?
  • Confidence – how sure are you that the idea is going to work compared to the control version? How much evidence do you have? How many users suggested it?
  • Ease – how many hours will it take to achieve it? Do you need additional spending to produce it? Are there any other risks involved?


Let’s say the existing conversion rate on your landing page is 10%. You expect your new landing page to have a 12% conversion rate. It means your MDE is 20% because 12% is 20% better than 10%. You need this estimation to predict how long your test needs to last. The higher your MDE is, the faster you will have the answer.

Other important factors are the number of users and the number of conversions for each version. Again, the higher, the better.

Run a pre-test analysis and check how much time you need to run your test to get a significant result. Very often you’ll find there’s no point in test Google ads URL simply because you don’t have enough users or conversions to do it in a reasonable time.

For example, you’re testing a landing page and you have 200 users and 20 conversions weekly, so your conversion rate is 10%. To be sure in six weeks that your experiment version is a success it needs to have a conversion rate of 48.57% better, which in this case is around 15%. Spoiler alert: it’s a big improvement and often it’s hard to achieve it.

What if your experiment landing page improvement is less than that? It means you still can see the difference but it will take you longer to detect it. That’s why the MDE is so important.

Testing Environment

You can test different segments at the same time but only if one test doesn’t have any influence on the other. Plan ahead not only for your tests but also for other tests other people in your team might be doing. 

It may be very disappointing when in the middle of an important test of two different ads you need to interrupt it because you need to start with new Christmas communication no one told you before. 50% of a test is 0% of learning and profits.

What are the Benefits of Doing Split Tests on Google Ads?

Google AB testing tool is a randomized experiment using two or more variants of the same web page (A and B). Variant A is the original and variant B through each contains at least one element that is modified from the original.

Before you can start testing, you need something to test on. Rather than randomly testing items on your homepage or adapting your checkout flow, start small. Test a change to a Call To Action (CTA), change the color of a button or remove an extraneous form field. Once you’re comfortable creating variants and experiments, you can expand the scope of your testing.

Make the Right Decisions

You might have strong intuition and knowledge about your products and users, but AB tests can give you and your team clarity about customer preferences. When you have the opportunity to support your decisions with data, why not to do it?

Improve Constantly

Even if you improve your conversion rate by 5, it will have a great difference after a few iterations. Let’s say your conversion rate is 3% and you did 20 tests during the year and only half of them increased your CR.

Implement in a Safe Mode

As shown in the previous example, testing is a safe way for improvement. Even if your hypothesis is wrong, in the long term, the risks are low. You would just be losing out on many opportunities if you did not implement AB testing. Of course, in reality, testing has its costs as well, but it’s nothing compared to implementing changes without testing.

Learn Along the Way

AB testing means gathering high-quality data and it allows you not only to improve your results but also to learn why you did (or did not) improve. You can test your hypothesis and then spread the knowledge across the organization. A common example is an exchange of knowledge between the sales/customer service and marketing/product departments.


Please remember, there are lots of more factors and nuances in AB testing. This article covers the most important areas from a practical point of view. 

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