How to use Segmentation & A/B Testing in Google Analytics to Generate Better Results

by Shane Barker

Last updated on July 25th, 2017

If you’re involved with conversion rate optimization, you’re probably familiar with the term “A/B testing.” This is basically a test to compare the performance of two versions (A and B) of a web page. A/B testing is a highly effective method used by marketers to increase website conversion rates. You could test multiple designs and select the one that garners the most customer conversions.

You can read how to conduct A/B testing using Google Analytics in my previous post. Now I’m going to talk about how segmentation in Google Analytics can get you better results. The purpose of this detailed guide is to show you how to make effective improvements to your website and gain more conversions. First, let me explain to you what segmentation in Google Analytics is and how it will put your goals within reach.

What Is Segmentation In Google Analytics?

In the traditional world of marketing, customers are divided into various segments based on shared characteristics. Similarly, Google Analytics allows you to group your website visitors into different segments based on their common qualities and needs. By default, the Google search engine collects a wide variety of user characteristics, including screen sizes, Internet browsers, referring sites, and types of pages viewed. You can then use these characteristics to segment your visitors and run an accurate A/B test.

Advanced segments meme

Why Do We Need Segmentation?

Using segmentation in Google Analytics offers a multitude of benefits for optimizing your website’s conversion rate. The main benefits are as follows:

  1. Many people make the mistake of focusing on the wrong elements when running an A/B test. Using segmentation in Google Analytics, you can concentrate on pages with the most traffic or those that yield the most conversions. The sequencing option in your segmentation enables you to find out which pages were visited by those who made a purchase. Therefore, you’ll gain a better understanding on which pages and elements you should prioritize for your A/B tests.
  2. Personalization is an important trend in Internet marketing. Segmentation allows you to deliver personalized content to your visitors based on their characteristics. With the help of segmentation in Google Analytics, you can create individualized email marketing campaigns and unique special offers based on their needs or buying habits.
  3. Since segmentation lets you see which pages receive the most views by paying customers, this helps you direct your marketing in a conscious manner. You’ll be able to shape your future campaigns based on insights you get from the data. For instance, you can determine the channels that bring in the maximum number of high-value users. Then you can make a wise decision about where to focus your latest campaign.

How To Apply Segmentation

To use segmentation in Google Analytics, you simply need to click on the “Reporting” tab and begin with any report. In the image below, you can see there are several different segments to choose from. Pick whichever one and click the “Test” button. Now you’ll be able to find out the percentage of users and sessions matching the particular segment filters.

How To Apply Segmentation 1

When you click on “New Segment,” you can create your own segment with filters of your choosing. Currently, Google Analytics allows you to have up to 1,000 segments per account but no more than 100 segments for any single view. Review the image below to see the options for different categories you can use to configure the filters for each segment. After creating a new segment, click on “Preview” to determine how the current report is affected by this particular segment.

How To Apply Segmentation 2

Let’s take a look at an example to help you gain a better understanding of segmentation in Google Analytics. The following image shows how enabling the segmentation of traffic source (referral vs. organic) brings about a change in the traffic report. It shows that the referral traffic is much higher than organic traffic.

segmentation result in Google analytics

Notice that actual conversions are driven by organic traffic despite there being a higher quantity of referral traffic going though. The below image shows that the conversion rate from organic traffic is 14.1% higher than that from referral traffic. This is an excellent example of how segmentation in Google Analytics can aid you in analyzing your visitors better.

segmentation result in Google analytics

Idea For Segment Types

For those who are just starting to use segmentation in Google Analytics for A/B testing, I suggest you focus on these few key types of segments:

  1. Traffic source – This will help you understand how your visitors behave offline. By segmenting your visitors based on traffic source, you can gain helpful insights into the variations of their journey. You can also determine the value they generate.
  2. Location/geography – This segment helps you see your visitor characteristics and how your performance varies in your target locations.
  3. Visitor type – Find out how your visitors engage with your brand by segmenting them based on their types. It could be on whether they are new or returning, registered or non-registered, and so on.
  4. Landing page type – This helps you understand which landing pages perform the best and track the ones that lead to a conversion. You can see how visitors arriving on a particular landing page are behaving throughout the website.
  5. Action taken – With the help of this segment type, you can review which visitors ended up making a purchase and how they behaved before completing the conversion.
  6. Content viewed – This is an excellent way to observe how visitors behave on-site with segmentation in Google Analytics. You can determine which particular pages are more likely to encourage visitors to buy.
  7. Engagement – Different pages in your website may have varying levels of customer engagement. Creating a segment type based on engagement helps you make improvements to pages that produce less engagement.
  8. Demographics – By segmenting your visitors based on demographics, you’ll acquire a deeper understanding of how your target group is interacting with your site.
  9. Technology platform – This helps you determine visitor device characteristics, such as browser type, screen resolution, and mobile apps. As a result, you’ll be able to make design improvements and technology changes accordingly.
  10. Value – One of the classic segmentation techniques is to segment your visitors based on their value. You can then identify the source of these visitors and their journey towards your conversion goals.

It’s suggested that you compare the performance of various segments and try to determine why certain segments are performing better. There are a multitude of options for segmentation in Google Analytics. It’s up to you to determine the choices that are most beneficial to your business. A segment that’s crucial for a certain industry may not be so for another. Make a careful and informed decision about which segment types you should build for your A/B testing.

Incorporate Segmentation Into A/B Testing And Play As A Ninja

For the segmentation to work, you need to integrate every different testing tool you use with Google Analytics. Popular testing tools like Optimizely, VWO, and Convert all come with built-in Google Analytics integrations. You need to have the data for every test sent to Google Analytics. After you’ve integrated Google Analytics with your testing tools, you’ll be able to look at any test result using the Custom Reports.

For a more accurate result, try creating segments for each test variation just by following above steps. Incorporating segmentation in Google Analytics to your A/B testing enables you to keep a close look at how different audience segments interact and respond to different test variations. In turn, this helps you understand your visitors better. You can then choose improvements that increase your ROI, which is the end goal of every online business.


When conducting A/B tests, avoid relying on a single source of data even after you’ve adopted segmentation in Google Analytics. Don’t be satisfied with only a quick look at the overall outcomes. Take your analysis further because the more data you have, the better decisions you will be able to make. Start by integrating the testing tools of your choice with Google Analytics and creating custom segments based on your business requirements.

Please share your experiences in the comment section below so that everyone can understand the power of segmentation in Google Analytics.

One Comment


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Shane Barker

Shane Barker is a digital marketing consultant, named the #1 social media consultant in the nation by PROskore Power Rankings. He has expertise in business development, online marketing and is an SEO specialist who has consulted with Fortune 500 companies, government agencies, and a number of A-list celebrities.


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  1. Georgi Georgiev says:
    June 13, 2017 at 3:30 am

    Hi Shane,

    This is some good advice on segmentation, but I think there are two caveats that need to be mentioned, even if only briefly.

    First, if you want to run an A/B test on just a segment of your population, you may need to wait longer for results (unless the potentially higher baseline doesn’t offset that in the sample size calcualtion). So this needs to be factored into the decision on whether or not to test on each segment.

    Second, when you say “Incorporating segmentation in Google Analytics to your A/B testing enables you to keep a close look at how different audience segments interact and respond to different test variations.” it’s probably worth mentioning that you’d need to plan for that analysis up-front, so you can dedicate appropriate statistical power. Otherwise it is very likely that you’d miss true differences between the segments. Also, when doing such analysis there are some p-value adjustments that need to be made, otherwise it becomes a fishing expedition that is guaranteed to result in some nominally statistically significant “findings”, which are in fact not warranted by the data.


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