• 1. About this Book
  • 2. 10 Reasons why Ecommerce Businesses Need to Have Google Analytics Set Up Correctly
  • 3. Quick Overview of Your Google Analytics Admin - Before You Set Up Your Ecommerce
  • 4. Setting Up Your “All Web Site Data” View in Google Analytics
  • 5. What the Heck are Parameters... And What do I do with the "Exclude Query Parameters" Field in Google Analytics?
  • 6. Adding Filtered Google Analytics Views Gives You Access to Better Marketing Data
  • 7. Setting up an “Include My Domain” Filter in Google Analytics
  • 8. Using Google Analytics Filters to Rid Yourself of Rage-Inducing Referral Spam
  • 9. Formulating Your IP Address Filter in Google Analytics
  • 10. Ensuring that Your Google Analytics Data is Accurate by Applying Lowercase Filters
  • 11. How to Remove Slashes From The End of your URLs in Google Analytics
  • 12. Fixing the Problem of Parameters in your Ecommerce URLS in Google Analytics
  • 13. Acquiring Your Ecommerce Store’s Unique Parameter List for Google Analytics
  • 14. How to Turn your Ecommerce Parameters into Custom Dimensions in Google Analytics
  • 15. Using your Parameter Custom Dimensions to Discover Ecommerce Opportunities
  • 16. Key Google Analytics Settings You Might Have Overlooked for your Ecommerce Configuration
  • 17. What are the Google Analytics Ecommerce Settings For and How are They Set Up?
  • 18. How to Turn on Ecommerce Tracking in Google Analytics
  • 19. Google Analytics Enhanced Ecommerce on popular Ecommerce Platforms
  • 20. Manually Adding Google Analytics Standard Ecommerce Transaction Tracking Code
  • 21. Manually Adding Google Analytics Enhanced Ecommerce Transaction Tracking Code
  • 22. Implementing Enhanced Ecommerce Features to Collect Game Changing Data For Your Ecommerce Store
  • 23. How Do You Use the Ecommerce Reports Built into Google Analytics?
  • 24. What is the Google Analytics Ecommerce Overview Report and What Should You Use It For?
  • 25. What is the Shopping Behavior Report and What Should You Use It For?
  • 26. The Importance of the Checkout Behavior Report in Google Analytics
  • 27. What is the Product Performance Report Used for in Google Analytics?
  • 28. This post has been deleted
  • 29. How can you see Individual Ecommerce Transactions in Google Analytics?
  • 30. What is the Time to Purchase Report in Google Analytics Used For?
  • 31. Deep-dive your Product Sales with the Google Analytics Product List Report
  • 32. Setting Ecommerce Goals in Google Analytics and Why This is So Important
  • 33. Adding Your Ecommerce Goals to Google Analytics
  • 34. Using Google Analytics Goals to Boost Your Ecommerce Conversion Rate
  • 35. Using the Model Comparison Tool in Google Analytics
  • 36. Segmenting Users - A Powerful Tool for Providing Data Insights
  • 37. Building Segments Using the Shopping Behavior Report
  • 38. How to Use the Segment Builder in Google Analytics
  • 39. How to Build Specific Criteria using Google Analytics' Segment Builder
  • 40. Google Analytics Segment Examples to Enhance Your Ecommerce Sales
  • 41. How to use Segmentation Analysis to Identify Opportunities and Increase Conversion
  • 42. Making the Most of the Demographics of Users When Looking at Ecommerce Data
  • 43. Google Analytics Segmentation Example - Transacted vs Did Not Transact
  • 44. Taking your Segmentation Analysis Further
  • 45. Bonus: Six Reasons Why Your Ecommerce Store Needs Google Tag Manager
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    How to Build Specific Criteria using Google Analytics’ Segment Builder

    How to Build Specific Criteria using Google Analytics’ Segment Builder

    As I’ve mentioned in previous posts, the segment builder providers you with a huge amount of potential because it can help you to compare different audiences to find insights that can drive your eCommerce strategy. But in order to use the segment builder effectively, you will need to understand some terminology, so that your segments mean what you think they mean.

    Here are a list of terms you will see used in the segment builder, and what they mean:

    Per User / Filter Users: This option enables you to segment based on specific users. This means that if customers visit your website for more than one session you will see all of their sessions in aggregate, even if they didn’t perform any eCommerce actions in some of the sessions.

    So, for example, “all users with a transaction” will show all the sessions by those users, even if the session did not contain a transaction.

    It is also important to note that filtering by users in Google Analytics has a 93-day limit; essentially, equivalent to three months. (You can get around this using other tools alongside Google Analytics).

    Per Session / Filter Sessions: This enables you to segment based on specific sessions (i.e. the specific visit in which the user did the thing that you are interested in). This means that if they visit your website for more than one session you will not see the other sessions.

    So, for example, “all sessions with a transaction” will show only the sessions with a transaction, and not the sessions before or after a transaction if the user came back to your website again.

    This is useful for identifying behaviours that are associated with another specific action, but not so useful if you want to understand your visitors’ behaviours overall. Filtering by sessions does not have a time limit, so you can also use this instead of users, if you need a segment that is longer than 93 days.

    Include / Exclude: This lets you control whether you are including or excluding the condition from your segment. So including the country of Australia would provide you with a segment with only Australians, whereas excluding the country of Australia would give you people from everywhere but Australia.

    Note: Include can have unusual behaviour. If you include A and B then the system will filter down to only data that is both A and B at the same time, not A and B separately. So be careful when you stack conditions with include, as you can end up with some highly misleading data.

    Contains: The word ‘contains’ means ‘does the text contain the text that you have typed?’; i.e. “men’s shoes” contains “shoes” and “kids’ shoes” contains “shoes”.

    Exactly Matches / Equals / Is one of: ‘Exactly matches’, ‘equals’ or the equals symbol ( = ) and ‘is one of’ means that the text or number that you have typed in your condition is exactly the same as the data stored in Google Analytics. In the case of ‘is one of’, it has to exactly match one of a list of items. You can also choose ‘does not exactly match’, ‘is not equal’, or ‘is not one of’ if you want to reverse the condition.

    Starts with, ends with, matches regex: These conditions let you be more specific about the text and narrow it down to specific options, without being forced to narrow down to just one option. For example, all SKUs that start with ‘D’ will show all the SKUs that start with the letter only. Matches Regex lets you be even more specific. Regex is short for Regular Expression. You can read the Google Analytics Regular Expressions here.

    Greater than ( > ), Less than ( < ), Greater than or equal to ( ≥ ), Less than or equal to ( ≤ ): These enable you to select from the data that is stored as numbers. So, for example, all sessions with transactions would require “include sessions where transactions > 0”.

    OR: The condition will be met if A is true, or B is true. This means that the segment will be bigger.

    AND: The condition will be met if A is true, and B is true at the same time. This means that the segment will be smaller.

    Product data: you can specify any product data, whether it be name, category, brand, variant, SKU etc. You can also choose more than one criterion from this section. So, for example, if you have categories of women’s shoes, kids’ shoes, and men’s shoes then you can specify products in categories containing “shoes”, and all three will be captured.

    As you can see, you can be very specific when building segments. It is important to build the segment logic carefully, because once you see the data in an aggregated form it is almost impossible to detect that you have made a mistake when constructing the segment. And this can cause you to infer results that just aren’t true.

    Now that you understand the lingo and how to build your segments, lets have a look at what segments to build.

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