Spam traffic appearing in the referral section of Google Analytics has been a thorn in the side of many marketers for a while now. No matter what efforts are taken, it always seems like more junk referral sources keep appearing and throwing off website, traffic numbers. While there isn’t a bulletproof solution to completely stop these spam sources from making reporting harder, there are some preventative measures that can be put in place to help minify the issue. This three-part blog post will cover how to first identify these spam referrals sources, and then how to manage them in an efficient way.

Why is spam referral traffic showing up in Analytics?

The reason certain domains are spamming Google Analytics is to reach people who are decision-makers with a company’s marketing efforts. Where’s the one place you know a marketer will look at least once a week? Google Analytics. How do you get a marketer to notice you? Make it look like they got a lot of traffic. Then the marketer checks to see which source is giving them all that traffic, they see the spam URL and get curious about it, maybe even visiting that domain (which you should never do).

Because marketers are the targets, most of the domains will say “SEO”, “Leads”, “SEM”, or any other buzzwords marketers will likely recognize. If they don’t, they’ll likely have the typical spam user behavior pattern: lots of sessions, 100% bounce rate, 1.00 pages per session and 0:00:00 average session duration.

This typical behavior stopped being as frequent when spam sources started having seemingly normal behavior analytics, making it harder to distinguish them from legit traffic sources.

Why is it important to get rid of spam referral traffic?

Purity of data. When these delinquent referral sources create phony sessions in your analytics, the numbers get skewed and your power to make well-informed decisions becomes lessened. Depending on the level of traffic being analyzed, the impact could be significant. This could lead to assumptions that quality traffic is being driven to a site, when it’s nothing but spam.

Knowing which domains are actually spam

If you look in Google Analytics under acquisition, all traffic and then referrals, you’ll see a table list of sources that visited your website for the selected timeframe. Looking through this list, there’s bound to be a few sources that aren’t recognizable. But how to tell which ones are legit versus which ones are spam?

The one thing you never want to do is try and copy and paste the domain into a URL bar and see what website comes back. This could lead to viruses or malicious activity to infect your computer. Instead, use a URL checker to help you validate the security and trustworthiness of a website instead of visiting it yourself. The tool I personally use is provided by Scamadviser.

With this tool, all the history, ownership and key details about the domain will be revealed; even bringing attention to certain red flags. The most important metric to look at is the risk bar, which provides a very clear indicator of the level of risk. Anything in the red can be considered spam and should be added to a list, which can be created and maintained in Notepad or some other text editing software.

As an example, there’s a referral source called Leads411 dot com. Without any knowledge of what that source’s intentions are, it sounds like spam. But to be sure, putting the domain into Scam Advisers’ tool provides helpful insight. 59% risk level and a description that reads “Lead411 provides business email lists, company addresses, executive emails, and phone numb” (I assume it was going to says ‘numbers’, but it gets cut off there). This source is clearly trying to target a marketer while offering contact information to possible campaign targets.

With this identification process in place, it should be easier to mark which domains you want to filter. I make it a practice to keep an active list of all the junk domains to filter for use across many website analytics accounts, and add to it whenever a new spam referral source pops up every month. Now it’s time to learn how to filter these sources out.