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Segmentation is about separating parts of a whole. Speaking of segmentation in Google Analytics it can be briefly summarized as allocate a part (segment) of data from the entire data pool. For example, by creating a segment containing only user who'd visited the website via mobile phones instead of having all the users analyzed. The segmentation is done to make more detailed analysis of smaller data portions and to compare various segments. For example, we could verify whether tablet users have any problems with the purchase process on a website or if the blog is being visited by regular customers.

Analytics provides a number of standard segments. However, they shouldn't obscure the fact that segmentation is already a marketer's creative work over an issue. In other words, you know best what you want and you configure segments to find any answer you're looking for. In this chapter you'll learn to use segments, create them in Google Analytics as well as see some analysis examples.

Anyone who gets acquainted with segmentation makes it their primary tool of analysis.


The above menu is available right under the title of any report. Summary of all data assigned to "all sessions" segment is seen by default. Simply select "+ Add segment" button to use the segmentation mechanism. The following menu will appear:

Active segments are highlighted with blue color. Let's run the first test. Select 2 segments in your Google Analytics: "Conversion sessions" and "Abandoned sessions". Choose "all sessions" to deselect them. Finally, click the "apply" button. You should see this info:

Before you go further, note the important info displayed under the segments names. In my example it's 1.10% and 69.70% respectively. It refers to the percentage of contribution of the selected segment to entire website traffic. This gives us 2 information:

  1. How large is the scale of the phenomenon - is the segment significant and is it reasonable enough to analyse it,
  2. Some segments should represent 100% of data - it is to carefully divide the whole into selected segments.

Our quick test is to show us how do all reports look like after segmentation.

First thing that stands out in the report are the great amount of data. All the metrics of each segment are presented. In this example we have 2 segments. If you set more of them, the report will display its respective multitude.

In addition to the standard segments, you can add freely configured segments. Here is where the fun begins.


There is a "+ Add segment" button right in the same place where the enable/disable segment option appears. After it is pressed, the wizzard appears:

To create your own segment one should name it, set criteria to be taken into account in the segment.

It is possible to configure and join together criteria of the new segment. Criteria are divided into several groups:

  1. Demographic data with ability to determine parameters like: gender, age, language, categories, market segment and locations.
  2. Technology is where we set the info like i.e. operating system, hardware used to browse the Internet, screen resolution.
  3. Behavior is where the criteria of past sessions, transactions and time can be configured.
  4. Date of first session is a range of dates in which the user had paid his first visit on our website e.g. date of TV campaign etc.
  5. Traffic source is about media campaigns, sources and keywords configuration.
  6. E-commerce refers to parameters options concerning transaction details e.g. products, revenue, products categories.

There are 2 features for advanced users:

  1. Available conditions are to use logic to combine different criteria (and, or). We may also use the base-wide dimensions, not just those shown above.
  2. Sequences to connect criteria in sequences (something had happened before and something had happened later). We may also use the base-wide dimensions, not just those shown above.

You may create diverse segments by using this wizzard e.g. a segment comprising all women that had visited the website between January and February and purchased items via tablet for at least 200 zł.

Each and every criteria option can be tested before saving it - to do that, select "Test" button at the bottom. A percentage of segment contribution throughout all of the traffic will appear right after.

Saved segment can be analyzed separately or compared with others.

For advanced users

With every segment we can create remarketing lists in Google Analytics! This means that it is possible to target your advertisement only at female users of your website who haven't yet made any purchase. The only limit is your creativity.



History of Full Customer Journey raised in one of the previous chapters referred to how business should be oriented on various states of the customer. The most interesting segmentation will map these behaviors. I propose to create the following segments:

  1. Window shoppers - no purchase - Behavior / Sessions >2; Behavior / Transactions = 0
  2. Single purchase - Behavior / Transaction = 1
  3. Multiple purchase - Behavior / Transaction > 1
  4. Brand fans - Behavior / Session > 5; Behavior / Transaction >2; E-cmmerce / Revenue > 1000

Why is it divided like that? Simple. It is for finding best marketing strategies leading to window shopping up to brand fans. It enables analysis of where are the best customers from and how they use our website. In fact, however, all Analytics reports reviewed with enabled customer type segment will provide us loads of great answers.


  1. Women - Demographics data / Gender
  2. Men - Demographics / Gender
  3. Young customers - Demographics / Age
  4. Mature customers - Demographics / Age
  5. Territory division by context - Demographics / Location

By using age, gender and geographic segmentation it is possible to examine shopping behavior of different groups, compare them and check the size of the current target audiance.

Note: demographic and interests data incorporate only a portion of your users. It may vary between 10 to 20% for different categories.


  1. Mobile users – Technology / mobile devices (including tablets) / Yes
  2. Small screen mobile users (telephone) - Technology / hardware category, contains, mobile
  3. Desktop users - Technology / Hardware category, contains, desktop
  4. Windows users - Technology / Operating system / Windows
  5. Mac OS users - Technology / Operating system / Macintosh
  6. Users web browsers division - Technology /
  7. User abandoning cart - Advanced / Conditions / Filter, Session, Include, Exit page, contains, [cart URL address e.g. /cart]

Why is it divided like that? It is to determine whether there is a correlation between the type of device, browser or operating system used by customers with the bounce rate and e-commerce conversion. The division gives us ability e.g. to easily check whether the website's mobile version meets all the requirements and whether users do actually use it. We can check whether the website structure works fine with different OS and various browsers and has similar site traffic ratio.

Interestingly, the last parameter is to perform a "reverse" check of your website. Thanks to the segment concerning users that had abandoned the cart, various reports lead to reasons of this phenomenon.

Segmentation allows you to take a closer look on your customers to learn more about their behavior.

You can perform any segmentation that's material from the perspective of your business. Apart from what I've shown above, a division due to traffic source winds up as an interesting idea. Due to separate segments dedicated to individual sources e.g. SEO, adwords, facebook, mailing, you can compare user behaviour from these sources. Learn which channel is more or less efficient. Get them compared together.

So let's end this chapter with an example of a unusually helpful analysis:


Create the following segments:

Activate the report: Behavior / Website content / All pages. Select the "bounce rate" section at the drop-down menu located above the graph. You will get the following report:

First of all, pay attention to the users distribution of these 3 devices types. 86% are desktop users and approximately 13% are mobile devices users. 13% is already a significant group of potential customers. For that reason, mobile version of a website shouldn't be underestimated.

The chart presents comparison of users abandonment rate (missed chances) on various devices - from mobile phones to desktops. We can take blue line refering to desktop as a base. Desktop and tablet users have a similar behavior. Small screen users have the biggest bounce rate balance. As a result, we have a reason to further analysis of this state of affairs: why mobile phone users leave this website?

Now, take a look at the table below the graph and let's find the shopping cart page (enter URL address of your website's cart in the search box area placed at the right side above the table). Let's check the % of various devices generating abandoned cart:

In case of this store, no one using mobile device had ever abandoned the shopping cart because none of them had reached the cart. This is a clear "red light" and the reason for an immediate control. It may turn out that there is no possibility to place an order or add any item to cart via mobile phone or tablet.

I encourage you to continue to use this feature.


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