Google Analytics Basics – Segments

Did you know that you can break down your Google Analytics data in hundreds of different ways? In our last post, we covered the basics behind site activity. In this post we will go further and learn about how we can break down our site visitors into segments.

A segment is a subset of your Analytics data. For example, of your entire set of users, one segment might only show users from a particular country or city. Another segment might be users who purchase a particular line of products or who visited a specific part of your site.

Read more about segments.

Google Analytics comes with the following predefined segments at the time of this writing.

  • Bounced Sessions
  • Converters
  • Direct Traffic – activity obtained when the user typed the url into their browser
  • Made a Purchase
  • Mobile and Tablet Traffic – any traffic from mobile and tablet
  • Mobile Traffic – mobile traffic only.
  • Multi-session Users – Users who have visited the site more than once.
  • New Users – first time visitors.
  • Non-bounce Sessions
  • Non-Converters
  • Organic Traffic – found your site through organic search( ie. google or bing search)
  • Paid Traffic – traffic obtained through adwords.
  • Performed Site Search
  • Referral Traffic – referred from another website( non search)
  • Returning Users
  • Search Traffic
  • Sessions with Conversions
  • Sessions with Transactions
  • Single Session Users – Users who have only visited your site once.
  • Tablet and Desktop Traffic – activity from tablet or desktop devices.
  • Tablet Traffic – activity from tablet only.


In many cases, these predefined segments may suit your needs. However, using a tool called the segment builder, you can create your own segments(also know as advanced segments).

By using advanced segments you can gain greater insight into your web traffic. For example, by using the segment builder, you can set up a custom segment in order to get conversions by referrer. Referrer being the referring site( ie Facebook, Twitter, etc). Conversions can only be determined if you have set goals ( an important subject, but a topic for another day ).

We will now create a segment for English speaking male visitors between the ages of 25 and 34 who use the Windows operating system. To add an advanced segment go to your Google Analytics dashboard and open up a report. In my case, I opened the Audience Overview report. From this screen, you can add a new segment.

Google Analytics add segment

Now we can see all the current segments available to us. The sidebar on the left shows all the different categories available including our current custom segments and the pre built ones. In our case we will create a new segment.
Google Analytics create custom segment

Now we can begin breaking down the demographic data. On the left are the different categories of segments. Since we currently have “Demographics” selected we can begin this process here. Lets check off “25-34” and “Male” and then note that we only want only users who have the language containing the letters “en” for English. You will notice on the right, our segmentation data that we selected is summarized for this segment.
Google Analytics segments demographics

You might be wondering how Google gets user demographic data. This data can come from three possible sources:

  1. The third-party DoubleClick cookie.
  2. The Android Advertising ID.
  3. The iOS Identifier for Advertisers (IDFA)

You can learn more about these on Google’s documentation.

Next, we will select the “Technology” tab on the left hand side of this screen. We include in the text box that we only want to this segment to give us users that have the operating system “Windows”.
Google Analytics technology segment

Finally, lets give this segment a name and save the data. We can now use this segment on our reports.
Google Analytics add segment title

Google Analytics segments are powerful tools. By building custom segments, not only you have much more control over the data you collect, but you can also draw more accurate conclusions about what the data is telling you.