Written by: Briony Cullin
Published: 6 April, 2022
Google Analytics is the go-to option for most website owners and marketing managers. Did you know that over 86% of websites that use an analytics tool are using Google Analytics? However, the quality of the data being collected in Google Analytics varies and it's easy to fall into a bad data lake without even realizing it.
This article delves into the why and how of improving your data in Google Analytics.
Important note: the Google Analytics (GA) landscape is changing in 2022 and 2023 with the forced migration of GA4. We still have more than 12 months with Universal Analytics or GA3 which is what this blog post covers. A separate post is coming up all about GA4.
If you’re new to the world of website analytics, you might be unsure how GA fits within your website tools. Google Analytics is a website analytics service to track and report on website traffic. It’s by no means the only website analytics tool available, but it is the most popular.
Generally, you’ll be able to see the following kind of information once you’ve set up in Google Analytics on your website:
That’s just a tiny sample of the kinds of data you can look at in Google Analytics.
Google Analytics data informs business and marketing decisions, so if your measurement isn’t accurate, you might find yourself making bad or flawed decisions. Think of Google Analytics like an in-car GPS system. If those maps are out of date or incorrect, you’re going to end up at the wrong destination, or in danger.
Making sure the data you’re collecting in Google Analytics is accurate is going to be the first step to getting the most out of your data.
First up, we’re going to create some filters. Our goal will be to create three filtered views:
To create filters, follow these steps:
Having a raw data set is the ideal starting place. This means that you have a data set which is untouched, unfiltered and raw. In the future, you might need to refer back to this original data set for comparison purposes.
Think of this as a testing environment where you can test out new filters or exclusions you’re thinking of applying to the master view.
This will be your daily working data set that you’ll refer to most often. This is where we’re going to focus on our efforts to create some filters and exclusions.
The filters you want to apply to your master data will depend on your website and business needs. Some examples of filters to consider include:
Again, referral exclusions are going to be specific to your website and business.
If you’re an ecommerce website and have been collecting GA data for a while, you might start to notice some referral traffic from payment gateway sources like PayPal or Stripe. This obviously isn’t the actual referral source of the traffic, but because of how the data is processed through GA, it’s showing that PayPal is the referrer.
Using the Referral Exclusion list, you can set domains as exclusions which means you should then be able to see the actual referral source. Check your referral traffic and make a list of the domains you want to exclude in your master view.
As we mentioned in the introduction, Google Analytics 3 or Universal Analytics is being sunset on 1 July 2023. This means your current setup of Google Analytics will stop tracking data altogether. You’ll want to ensure that you’ve migrated to GA4 well before this date.
Contact us if you need support with your GA4 migration.
Briony's background as a lawyer in Australia is a uncommon but very handy foundation for her attention to detail and enthusiasm for improving rankings & ROI.
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