Set filters in Google Analytics

Google Analytics is the most popular web analysis tool. As a website operator, you can understand the behavior of your users on the site and subsequently define appropriate measures for page optimization. To ensure that you only find relevant data in your evaluation, it's wise to set certain filters.

Why are filters useful in Google Analytics?

Google Analytics captures all user data on the website without filtering. However, it is not always 100% effective to use all values for an analysis.
With the help of filters, you can initially channel the data through a kind of sieve so that your data view only shows the values you need.

Generally, filters can be categorized into the following main categories:

• Inclusive filter: only a defined filter pattern gets through

• Exclusion filter: the defined filter pattern is excluded

• Search & Replace: designations recorded by Google (e.g., Homepage = "/") are rewritten

• Lowercase: Reduction of duplicate results by using all lowercase letters

A common example of setting a filter is the exclusion of an IP address range. This allows you to exclude data from your company or agency network if it uses a static IP. The same applies to your IT service provider who regularly visits the site with a static IP address, without it being relevant for site optimization. This filter is not possible with dynamic IP addresses as they change every 24 hours.

Access from spam or bots can partly be excluded by filters. Many referral URLs are now known and published on portals that go to the site as spam and skew your traffic. Check the referrals that lead users to your website. It is typical to see 1 page/session, 0:00 time on site, and an almost 100% bounce rate.
When researching, you can get an overview of the referral URLs via the left navigation bar under Acquisition, All Traffic, Referrals.

Fig. 1: Display referrals in Google Analytics

But you can also detect spam behavior in the reports of Google Analytics under "Locations", "Website Content", and "All Traffic".

A common example of spam access is the following:

Fig. 2: Example of spam access

How to exclude the IP address range for your corporate network

If you want to check user behavior with the web analysis tool Google Analytics, accesses from your own network are obstructive. Internal page hits should not be equated with the character of a browsing user.

To prevent accesses from your own ranks from appearing in your data, set a filter as follows:

Navigate to your Google Analytics account and open the admin view:

Fig. 3: Admin view in Google Analytics

In the right column of the data view, you will already see the "Filter" field. Here you can now add a new filter.

Configure the filter. Give it a meaningful name, select "predefined" as the filter type, and use an "Exclude" filter. Choose the "begins with" command and enter the start of the IP address to be excluded in the last field. You should never use the "equals" command. Due to the data protection-compliant anonymization of the IP address, the fourth/last part (the last 3 digits) of the IP address is always truncated, so the complete sequence of numbers never reaches Analytics.

Fig. 4: Filter to exclude IP addresses

How to exclude referral URLs

If you found out through checking your referral URLs that certain accesses are done by bots and thus flow into your analysis as spam, you should create a filter to exclude this data. First, collect a list of these URLs or research a list and also create a custom "Exclude" filter.

Give it a meaningful designation and use the referral as the filter field. Then enter all the researched URLs into the filter pattern. Separate the individual URLs with a pipe (|). Add a backslash () before EACH dot, so the dot does not execute a command as a regular expression. Avoid spaces in the filter pattern entirely.

Unfortunately, Google Analytics provides only 255 characters in the filter pattern, so you may need to set up multiple filters to exclude referrals.

Test filters before applying

Once you've set the filters, they are immediately active. However, you cannot regain filtered data or reverse the sieving effect. We therefore recommend testing the filters in a test view before applying them to the live environment. Also use a third data view that collects all your website data without filters. This way, you can still reconstruct the correct values in case of possible errors.

How you should configure the data views can be found in our article Google Analytics Basic Configuration – What You Should Pay Attention To.

What we can do for you

The proper use of filters cleans up your data so that you can draw meaningful results from analysis with Google. If you need support with Web Analysis, we look forward to your inquiry.