Introduction To Spam in Google Analytics

There is one thing that the communication, information and data ages all having in common — spam.

Spam permeates emails, text messages, comment fields on websites and more; however, when it infiltrates an area that is designed for accuracy and specificity, it can wreak havoc. This especially true for small to mid-sized volume sites where the spam can significantly offer the numbers in metrics produced by different programs. Google analytics is a powerful tool that allows site owners to monitor the performance of their site in a way that they can identify what marketing modalities are performing up to par, and which one they need to abandon.

Unfortunately, when the spam infiltrates the analytic data and shows up in the reports, it can make it immensely difficult to get accurate readings. Now, for high volume sites that are seeing hundreds of thousands of visitors each day, the spam is more of a headache than anything; however, for sites with lower volume the numerical impact is significant enough to produce false results, which can become a serious issue.

The Different Types of Spam

When it comes to Google Analytics, all spam is not created equal. The truth is that most of the spam never actually arrives to the site, but it does provide false readings in the analytic reports. This is especially true with referral spam. The problem with referral spam is that it mimics website visitors, which will compromise the accuracy of the analytic reports produced through Google Analytics. Metrics are only as good as the accuracy of the numbers being processed, when the numbers are compromised, the metrics will produce false readings. As stated earlier, when the site has a high level of volume, the inaccuracy is measured in decimal points, but with lower volume sites, the impact is measured in whole numbers, which can be devastating to the efforts to optimize site performance.

The Impact of Referral Spam

While “Ghost” spam never actually visits a website, it should not be viewed as harmless, because it shows up in analytic reports as legitimate visitors, completely impact the readings in a negative manner. It is not unheard of for Ghost spam accounting for as much as 60 percent of the daily sessions, which wreaks havoc on the month-to-month reports.

Identifying Referral Spam

The first thing that you must understand when attempting to identify different types of referral spam is that there will some traffic anomalies that will appear in reports that are not the result of referral spam. As technology and the diversity in how traffic is generated continues to grow, the amount of dark traffic has increased in direct correspondence. Dark traffic may appear to be fake traffic, or it may be falsely attributed to a traffic generation source other than the one it actually originated through. It will be important to distinguish between dark traffic and referral spam.

Data integrity is a huge priority in creating metrics that can be dependable, so when these anomalies appear, it will be necessary to perform the due diligence in identifying the traffic source. While it will take a little extra work, it is absolutely worth it.

Avoid Complicated Methods

There are as many myths about referral spam as there are suggested solutions. The problem with both is that they serve to convolute the process of reducing spam more than they serve to help. The idea is to keep the filtering process as simple as possible.

Blocking and Filtering Spam

When fighting against referral spam, it is important to make sure that you are taking the necessary steps to protect your data. The last thing that you need is to corrupt your data in your attempts to attack spam.
One of the easiest steps that you can take to protect your data is to make sure that you properly configure your view readings to protect against misconfigurations.
Following are the most basic configuration set up that all sites using Google Analytics should have.

1:  Master View — which is a universal view where all filters are applied

2:  An Unfiltered View — This is a backup view that does not have any filters applied to it, which will make sure that there is at least one view in which the data will not be altered in any way.

3:  Test — Although a test view is optional, it is good for testing different filters to determine which one work best.
Once the data has been protected, then you must take the necessary steps to stop the spam from infiltrated your reports.

Fortunately, Google Analytics has some effective spam filters that can be applied to reduce spam infiltration. Because different types of spam appear differently in readings, it will require multiple filters to detect and block the spam, meaning that you will need to install ghost filters and crawler spam filters. Additionally, it is highly recommended that you also install filters that can detect fake language, which will help block the less common variety of spam.

Finally, Bot spam is becoming a real issue with Google Analytic reports. Bot spam is actually just another word for crawler spam, which perform a number of dark tasks, including content and data theft, comment spam, phishing links server jacking and more. Fortunately, Google Analytics has a built in feature for Bot filtering. It is highly recommended that you enable this feature to make sure that you have optimal protection against crawler spam.

Actually, the most effective filter against Ghost spam is the creation of a valid hostname filter designed to catch Ghost spam. When set up correctly, this type of filter will permanently halt spam from site-auditor sites, fake compliance cookie sites, spammers who pretend to be from legit sites and the vast majority of fake language trash, including, and vitality rules google.

When the system is functioning optimally, Google Analytics can be an immensely powerful tool to help site owners optimize their digital marketing efforts; however, when the reports have been corrupted by false traffic readings produced by different types of referral spam, it can literally wreak havoc on the efforts of the site owner to determine what methods are working and which ones are not.

Fortunately the steps set forth here should help significantly reduce the appearance of referral spam moving forward.  If you’re not sure how yours is set up then take the Google Analytics review for your site.

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