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.

What is Google Analytics used for? 

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: 

• What country users are visiting from
• What kind of device they’re on
• How they found your website (for example, did they visit from social media or organic search)
• What pages they looked at
• Did they buy anything when they visited, or take a conversion action (like fill in a form). 

That’s just a tiny sample of the kinds of data you can look at in Google Analytics.  

Why does the quality of your Google Analytics data matter? 

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 If those maps are out of date or incorrect, you’re going to end up at the wrong destination, or in danger.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: 

1. Master
2. Raw
3. Test

To create filters, follow these steps: 

1. In Google Analytics, click “Admin” - the cog in the bottom left of your screen
2. You’ll then see 3 columns of information. Filters are created at “View” level which is the column on the right of the screen. 
3. Click “+ Create View” in the right hand column.
4. Type in a view name in “Reporting View Name”, adjust the time zone settings if needed and click “Create View”. 
5. Repeat this process again, and you’ll now have 3 views. Here’s how we suggest setting up each filtered view:

View 1: Raw data set (raw view)

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.

View 2: Test data set (test view)

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. 

View 3: Master data set (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. 

Some suggested filters

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: 

• Filtering out internal traffic. This might be your staff visiting the website while in your office or store. How staff use the website will be very different to customers, so you might want to filter out their use of the website so you don’t get the wrong perception about what customers search for on the website, or how they navigate through.
• Filter traffic from bots and crawlers. Again, bots and crawlers will be experiencing your website in a way which might skew your data. You can easily filter this in GA settings.
• Filter out spammy referral traffic. You might notice a spike in traffic on a certain date and when you delve into the data, you notice it’s from a spam website like free-traffic-seo.com. If these visits start to hit a high enough number, you’ll want to exclude them from your master view. 

Our suggested exclusions

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. 

You can find Referral Exclusions:

1. In Google Analytics, click “Admin” - the cog in the bottom left of your screen
2. You’ll then see 3 columns of information. Referral Exclusions are created at “Property” level which is the column in the middle of the screen.
3. Click “Tracking Info”
4. Click “Referral Exclusion List”
5. Click “+ Add Referral Exclusion”
6. Enter the domain you want to exclude from your referral traffic (for example paypal.com)
7. Click create

Get ready to migrate to GA4

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.