One of the crucial parts of web analytics is to measure the right metrics in order to get data that is trustworthy. You are relying on this data to make important business decisions. The measurement of data should be accurate across users, campaigns and devices.
However, simple mistakes can create discrepancies in your Google Analytics data and your reports might give you the wrong information. This will lead to problems when you make your business decisions.
Here are some common mistakes that you can keep in check in order to generate accurate data reports.
Missing Tracking Code
Your Google Analytics tracking code must be on all your webpages. Sometimes this is overlooked especially with websites that have numerous pages. It is important to ensure that the code is not missing in any of the webpages or you might lose valuable data.
To fix this problem you can take help of various tools that can crawl your website and give a list of URLs’ on your domain that are missing the code. Screaming Frog is one such tool that can help you do a site-wide analysis.
Not Setting Up Cross-Domain Tracking
Cross-domain tracking in Analytics helps you to see sessions on two related domains as one session. For example, an e-commerce site that has a separate shopping cart site and while making a purchase the customer is sent from the e-commerce site to the cart site. If cross-domain tracking is not set-up Analytics will measure this as two separate sessions. This will give you wrong data and you will not be able to track your customer’s buying journey. For this reason, it is important to set up cross-domain tracking.
To set up cross-domain tracking for multiple domains, you will have to modify Analytics code for each domain.
Not Filtering Internal IP Addresses
Google Analytics by default will report all the traffic coming to your website which will also include your own team. You might have your team members visiting your website through multiple channels which will overestimate numbers and not give you accurate reports. This will impact your customer data especially if you are tracking any new marketing campaigns.
In order to exclude these internal visits, you can simply make those changes in your Admin section by creating a new filter to exclude internal IP addresses. You can modify or remove filters if there are any errors.
You can also set up filters to exclude other data sources like bots and search engine “spiders” from your reports. This will ensure that your data is as accurate as possible.
Internal Referral Exclusion
Another area where mistakes are seen is the Internal Referral Exclusion list. Referrals are important reports that present to you the traffic from external sources. It is important to exclude internal domains or self-referrals from site traffic reports if you want to find out which external sources are sending you traffic.
You can make these changes at account-level where you can add internal domains to the referral exclusion list.
Referral Spam
Websites tend to get “referral spam” which fills your analytics with fake data. Spam bots send fake referrer information with domain names that can be tracked by analytics and shows up in reports. This can bring in numerous URL’s linking to spam websites who are trying to improve domain ranking. This can mess with your reports. All websites will receive these spam links, but it is important to keep monitoring if there are numerous spam links in your reports.
Using Filters or Segments you can block these spam URL’s. Using Filters, you can include or block data that will be gone forever. If you accidentally filter out real referrals, then the data is not retrievable. On the other hand, Segments are subsets of sessions or users which can be turned on and off. They can be applied to past data and data which is left out can be recovered.
Missing backup and testing views
This one is more of a good practice that you can do in order to handle your Google Analytics data properly. Even if you have just one account and property, you need to make sure you have at least these three views:
Master view: You are likely to use this one more with various settings and filters
Backup view: This is a view that will have all default settings. If anything goes wrong with your master view or you accidentally excluded some data, you can always find your raw data here.
Testing view: You can test and play around with this view. It comes in handy if you are not sure about applying certain complex filters.
These are some of the common analytics mistakes you can avoid. If you have your GA set-up properly from the beginning, you can easily skip these mishaps and manage your data well. This will ensure that your data is coherent and trustworthy.
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