Semalt: 3 Steps To Creating Accurate Google Analytics Reports

Some companies may experience a period of increased traffic, which then gradually declines. It could raise the alarm because nobody wants their referral traffic to fall. However, one primary cause for traffic decline is the filtering of internal traffic from Google Analytics reports. Also, implementing an exclusion filter for referral spam will reduce the traffic some more.

First of all, companies have many employees accessing their website from different computers on a single day. These visits may record as direct traffic to the website's analytics. In addition to this, referral spam has become so rampant that it outpaces the organic searches and skew data in the Google Analytics in the worst ways.

Alexander Peresunko, the Customer Success Manager of Semalt, assures that applying the two filters may result in a significant decline in traffic data. Nevertheless, as much as this is apparent, the company may be doing quite well in reality.

Importance of creating filters in Google Analytics

Spotting referral spam in Google Analytics is easy. For example, if the website currently has two views: the raw data, and the filtered views, there are website domains that appear in one and not the other. If you apply filters well in Google Analytics, referral spam only makes it to the raw data but not the filtered view.

Referral spam was a huge problem between the years 2015 and 2016. The most afflicted were small business owners since their link profiles are not as robust as those of larger companies. The result was that referral spam could take over the top referrals analytics reports.

The reports can record an increase in traffic while it was not in real sense. It is one of the implications of having referral spam traffic.

What do people know about referral spam?

Referral spam is fake traffic, or what most people call ghost traffic. The source of this traffic disguises itself as referral links from legitimate sites, which they are not in most cases, with one goal which is to trick the site's owner to click on their domain when it appears in the Google Analytics reports. Google is now ahead of the situation and has included a Bot filtering option in Google Analytics to make sure that it removes any known referral spam from appearing in the analytics reports.

Another source of skewed analytics traffic data is the internal traffic from the company itself, especially if it has a customer service department that has to walk customers through a step by step process on how to navigate, complete a form, or an application of some sort.

The best way to acquire a more accurate picture of the website data is to create a filtered view in analytics. The filters should help exclude any referral spam or internal traffic and allow you to view data devoid of misleading data.

Task #1 Creating the new view

Importance: A new view will help in filtering traffic without making any alterations to the raw data.

Description: It is a duplicate of all the analytics data from which you can make modifications to, and are visible once you open your Google Analytics account.

The steps to create a new view are as follows:

  • Log in to your GA account and select "Admin."
  • Click on "View" in the right column and select "Create new view."
  • Give your new view a name, then save and exit.
  • The next step is to change the name of the original or unfiltered view. You can see the different views you have in the Admin overview page. Click on your original view which will open a View settings page. Input the new name for your original view to something that would make sense to you like "Unfiltered view," or "Raw Data view."
  • Save the new settings.

Task #2 Turn on Bot Filtering

Importance: This is the view that allows you to exclude the referral spam traffic.

The process to follow is to head to the Admin Overview page to select the just created filtered view. Under the View Settings page, scroll down to "Bot Filtering" checkbox, check it, then save and exit.

The method is an excellent starting point for filtering spam, but some will still slip through. However, it still serves some purpose as it reduces the damaging effect nevertheless.

Task #3 Creating the filter to exclude internal traffic

Importance: For more accurate data, you must exclude any traffic coming from inside the company. Employees that make regular visits to the company website can significantly skew your data.

  • Search for "what is my IP" on Google.com to know what your current IP address is
  • In the new Filtered view, select Filters
  • Click on Add Filter
  • Choose to create a new filter. Input a preferred name for the filter, select "Predefined" in the Filter Type. Choose to "Exclude" the "traffic from IP addresses" and then select "that are equal to" from the "Select expression" box. Then enter the IP address as returned by Google.
  • Save and exit.

You can now record when you make changes with Google. View any graph data from the analytics report and then click on the arrow beneath the graph to "Create a new annotation." You can now record the annotation, remembering to include details for the change as well as the date.

Conclusion

Google Analytics is not 100% accurate when it comes to top-line traffic data. By creating a new view, you are in a better position to filter out any traffic and see the implications it has for your data.