How to Block Referral Traffic in Google Analytics

For any business with a website, Google Analytics should be seen as the “engine room” of your digital marketing campaigns. All website traffic is recorded within Google Analytics, all of those performance goals and KPI’s you set for your website are recorded here. Without Google Analytics business owners will struggle to get a full understanding of how their website is working for them or how it is converting into leads and sales.

Essentially Google Analytics tracks the number of visitors to your website each day, where the user came from, what they did, which pages they visited and how long they were on those pages. Visitors are broken down into 6 broad “channels” as follows:

• Organic – Natural traffic from the search results.
• Paid – Pay Per Click marketing adverts.
• Referral – Visits which came via another website (through a hyperlink to your site).
• Social – Traffic from social media websites.
• Direct – People directly typing your web address into the browser, or clicking a bookmark.
• Email – Traffic as a result of an email marketing campaign you may be running (requires tracking being set up in the mail shots).

Sounds simple, right? However, it may not all be as it seems. Traffic being reported may not be 100% accurate, this article discusses the problem with “referral spam”.

What Is Referral Spam?

Referral spam can come in many forms, all of it is totally useless to business owners and their marketing teams. It is a shady tactic that spammers employ to attract attention to their website or most commonly software or product. They do this by sending a small amount of automated traffic to your website from their own via a “spam bot” (web crawler).

This is actually Fake Traffic and often as you dig deeper you will see that the “visit” from these sources lasted a few seconds and didn’t interact with any other pages on your site other than the landing page (potentially artificially inflating your website bounce rate).

Why Would They Do This?

As a result, their website will show up in your Google Analytics referral traffic sources, their sole intention is that when you are looking at your own reports you notice their website address and visit their website.

Does It Hurt Your Website?

No, not really. Because these robots are on your site for a very short time, the likelihood of them making any real difference to the performance of your website is extremely slim. However, the main effect is to fill your Google Analytics with rubbish! This skews your data and makes your site performance metrics look worse than they actually are. It is a widespread problem, almost every website we work on has some level of Referral Spam being recorded. In addition, each week there seems to be new spam sources appearing.

FREE Spam Referral List for Your Analytics

Kumo has begun keeping a list of these sources, freely available to you to check against, and remove from your reports – found here. We aim to regularly update this list with new sources on a weekly/monthly basis.

https://docs.google.com/spreadsheets/d/17PHsfFEEYTvQjPbfnK1YgJnWqjublIBUIkNuO-80Zfk/edit?usp=sharing

How to Remove It from Your Google Analytics

There are two methods for removing referral spam from Google Analytics. The first, and by far the simplest, is to prevent analytics form recording traffic from the same source in the future, by setting up an exclusion. The other, more complicated method is to set up filters to block this traffic from the past days, weeks and months of GA data.

Initial Steps

For both methods the initial steps are the same, open your Google Analytics and browse to the Referral Traffic sources report (give yourself a fairly wide date range initially). The example below has 9 spam sources highlighted.

Open up a new notepad file and copy and paste these web domains into it. Work your way through your entire referral traffic report using the pagination at the bottom of the GA screen, this particular example shows 77 referring domains. Some of these sources you will know straight away that they are spam, uptime-eu.net, uptime-delta.net for example, others you may need to visit their site to verify they are spam.

Method 1 – Exclusion Only

Within Google Analytics, click on the “admin cog” icon at the bottom left. You will be presented with the screen below.

Select the your website from the “property” drop down box and click into JS Tracking Info, then click Referral Exclusion List. you will then see the following screen. Notice this example already has some spam sources excluded.

Click the red button and enter the referral domain one at a time into the following form & click create.

Once you have done this, GA will no longer report upon this traffic.

Method 2 – Filter Out & Exclude

The much more in-depth method is to take each spam source and set up a Google Analytics filter to remove it from your reports. Click on the “Admin Cog” icon again and this time, in the first column, click on “All Filters” (note our office IP exclusion, preventing traffic from our office being reported).

Click the red “Add Filter” button, where you will be presented with this screen. You should give the filter a useful name such as “Spam Referral Sources -1”. Make sure you select “Custom” as the filter type and, with “Exclude” checked, select “Referral” in the filter field box.

Filter Pattern – This is where is can get a little confusing, instead of filtering each domain one by one like the first method, you will filter them out in bulk. You do this by writing a string of domains separated by a pipe as follows:

cheap-trusted-backlinks.com|uptime-delta.net|uptime-eu.net|semalt.com|buttons-for-website.com

Here we are going to filter out these 5 domains:

• cheap-trusted-backlinks.com
• uptime-delta.net
• uptime-eu.net
• semalt.co
• buttons-for-website.com

This string should be copy and pasted into the “Filter Pattern” box, as per below. To filter out more spam sources you should make the string longer, I have only given 5 domains as a short example here. Also, please note that these filters also have a character limit in place for the string. If you run out of space, simply repeat the process and set up a second filter, a third, a fourth etc.

Don’t forget to apply this filter to the correct “view”.

Once the filter has been added it will do a similar job to the first method and remove them from being reported upon in the future. Crucially, it will still be visible in the “past”.

Remove the Spam from the past Days, Weeks and Months

The final piece to the puzzle is to add a “custom segment” to your analytics. To do this, browse to “Acquisition” on the left then select “All Traffic” then “Source/Medium”.

Click the “Add Segment” button.

On the screen below, click “Add Filter” and select “Sessions” and “Exclude” from the filter options.

Next select “Source” and “Matches regex”. The next thing you should do is copy and paste the same string of domains from the previous step, as the image below shows:

You will get an indication that the filter is working because the “Summary” circle on the right of the screen will update.

In my example, you can see that Analytics is now showing 99.71% of the total traffic or, in other words, we have successfully filtered out 0.29% of the traffic which was spam.

Click Save.

Your new Custom Segment will now be available from the drop down selections at the top of every Google Analytics screen, giving you the option to filter out this spam traffic from previous days, weeks and months.

Summary

Referral spam is extremely common and every single site we look at has been effected in varying degrees. There are two main methods for dealing with this spam in your referral reports.

First is to prevent it showing in the future by excluding it from future reports using the Referral Exclusion list built into GA.

Second and by far the most effective is to prevent it from showing in the future by use of Analytics Filters and also set up a custom segment to remove it from all past reporting.

Don’t forget to check out our FREE referral spam sources list – https://docs.google.com/spreadsheets/d/17PHsfFEEYTvQjPbfnK1YgJnWqjublIBUIkNuO-80Zfk/edit?usp=sharing