How to Use Google Analytics Filters and Segments

Google Analytics offers many insights into your website’s performance. However, the massive volume of data available can be overwhelming. This is where filters and segments come to the rescue, serving as two indispensable tools in the arsenal of any data-driven marketer or website owner.

These powerful features enable you to navigate the data maze, extract meaningful insights, and uncover hidden opportunities. But how are they different? And how do we use these differences to create strategies that help us make data-driven decisions? Read on to find out.

What is the difference between segments and filters?

Segments and filters each serve a unique purpose in helping users analyse and extract insights from their data:


Segments are a dynamic way to temporarily isolate subsets of your data for analysis and reporting. They allow you to focus on specific sets of users or sessions based on various criteria, such as demographics, behaviour, traffic sources, or user interactions. The beauty of segments lies in their non-destructive nature; they don’t alter your original data in any way. Instead, they provide a filtered view, allowing you to explore how a particular subset of your audience behaves within the existing dataset.


In contrast, filters are a more permanent means of data manipulation. Filters are applied at the view level in Google Analytics, and they serve to exclude, include, or modify incoming data before it is processed and stored. Once a filter is set up, it affects all future data collected in that view and cannot be undone or reversed for historical data.

Important note: It’s crucial to exercise caution when implementing filters, as mistakes can lead to irreversible data loss or inaccuracies.

How will this impact my data?

The use of segments and filters in Google Analytics can have significant impacts on how data is analysed, reported, and stored:

Segments allow you to temporarily focus on particular user groups, behaviours, or traffic sources. By using segments, you gain valuable insights into the behaviour and preferences of specific user segments. This can help you understand the effectiveness of marketing campaigns, identify user journey bottlenecks, and tailor content to better suit your audience’s needs.

  • Segments enable you to compare different subsets of your audience side by side.
  • Segmented data can be used in custom reports and dashboards, allowing you to create tailored reports focusing on specific segments.
  • They do not permanently alter your data. Once you remove the segment, you return to your original dataset, ensuring data integrity and preserving historical records.

Filters, however, will permanently impact incoming data at the view level. They can exclude or include specific traffic sources, modify URLs, and perform other data transformations. Filters are often used for data cleaning and customisation. As a result, they must be used cautiously, as incorrect configurations can result in data loss or inaccuracies. Once data is filtered, it cannot be retrieved for historical periods.

  • Filters enable you to customise data to align with your specific reporting and analysis needs.
  • They are applied at the view level, affecting only the data within that particular view. Multiple views can have different filter configurations.

When to use segments and filters

A segment is the best way to isolate specific metrics, channels, or devices while applying the segmentation to historical data. For example, imagine you want to examine how many people have visited your website over the past three years from Facebook on their tablets.

This scenario is where segments shine. You can create a custom segment that filters data to show only sessions that meet your criteria, such as sessions originating from Facebook and on tablet devices. This segment allows you to:

  • View historical data for the specified period to understand how this particular segment of users has interacted with your site over time.
  • Create additional segments to compare this group’s behaviour with others, helping you identify trends, patterns, and opportunities.
  • Run custom reports and dashboards to provide a focused view of metrics tailored to your audience.

While segments are ideal for temporary data isolation and analysis, filters are your go-to tool for permanently changing how your data is collected and processed in Google Analytics. You can filter to exclude traffic from specific IP addresses, which is valuable for removing internal traffic from your data. This ensures that your data accurately reflects external user interactions. Filters can be used to:

  • Identify and exclude known bot traffic, helping maintain data accuracy and focus on genuine user engagement.
  • Rewrite URLs to make them more readable and consistent in your reports. Transform data for lowercase or uppercase conversions, search and replace operations, and more to standardise data presentation.

A wider range of options for data analysis and isolation within Google Analytics

Segments allow for a wide range of criteria for data selection. You can create them based on various factors, such as user demographics, behaviour, traffic sources, and more. This versatility enables you to craft particular segments, like “Returning Mobile Users from Organic Search” or “Visitors Who Completed a Purchase.”

Segments support complex statement logic, allowing you to combine multiple conditions with AND, OR, and NOT operators and permit text string matches, making targeting specific URLs, page titles, or campaign parameters easy.

Filters are more straightforward and focused on permanent data alterations, like excluding traffic from specific IP addresses or rewriting URLs. It is important to note that filters cannot handle complex logical conditions, text string matches, or numerical comparisons.

So, when it comes to in-depth data analysis and insights, segments are the more powerful tool, allowing you to dig deeper into your data to understand user behaviour and performance metrics. But as you can see, both options can be very useful when analysing your data; give them a try and benefit more from the data available to you!

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