19 Feb Six Ways to Clean Up Your Data In Google Analytics
You’ve invested in Google Marketing Platform, and now you’re ready to fine-tune it to meet your specific digital marketing goals. But first, you need to take a look at your data and make sure it’s both accurate and reliable. The good news is that you can clean up your data in your existing Google Analytics account! Let’s walk through six ways to clean up your data with Google Analytics.
#1 Organize Your Account Structure
The structure of your Google Analytics account defines how much flexibility your analytics have. Before diving into the data clean-up, make sure you have all essential Google Analytics Properties and Views in place. Since Google must process your data before displaying it, make sure to create all of your all Google Analytics assets when you first create the View. As a result, you only see data moving forward from that point in time.
Make sure you also carefully plan what you want to see in Google Analytics. Keep different businesses under different properties and multiple subdomains under the same property.
Regardless of the business type, you will need at least three Views in your Google Analytics account:
– Master View: the main view for all your google analytics
– Test View: a view that allows you to test your improvements safely
– Unfiltered View: unedited raw data is your backup in case of trouble
In addition to the main types of views above, you’ll also need to think about any optional views you might need, including:
– Separated Site Sections
– Excluding internal traffic
– Separated traffic i.e., your internal traffic)
– User ID View
Tip: Always create Views in advance: you will NOT see any historical data if you copy an existing View.
#2 Clean Up UTMs
Make sure that tagging is correct for all incoming traffic, with clear and unified naming conventions for all campaigns. Discrepancies significantly affect the results of your marketing efforts, so we suggest starting with a thorough UTM audit. The audit also helps show how you can improve your UTM architecture.
If there are multiple teams involved in your Email, Google Ads, Bing or other Social and Paid campaigns- you might see a variety of vaguely tagged traffic sources with vague descriptions in Google Analytics, which can make it hard to aggregate traffic properly. Align UTM parameters across teams and campaigns within a single document
Here are some suggestions for how you might structure the UTM parameters.
|Item||UTM Source||UTM Medium|
|Paid Search||google, bing||cpc|
|Display Advertising||dfa, dbm||cpm|
|Organic||yahoo, bing, aol||organic|
Tip: Make sure the Medium is consistent throughout all of your various email campaigns. If you need to differentiate between your campaigns and tactics – use Campaign Name (&utm_campaign) and Campaign Content (&utm_content) instead. If you try to separate the campaigns by Medium, you might receive inaccurate email tracking.
You can also check all of your tracking so far by selecting Acquisition -> All Traffic -> Source/Medium report and export data for the past six months. Now you can filter out any incorrect sources and share it with your team.
Unified UTM parameters allow you to set up accurate Channels (Custom Channel Groupings) and help you avoid losing data into ‘Other’.
Tip: Note that Google Analytics source/medium processing is case-sensitive; thus, Email and email show in separate lines.
#3 Clean-Up URLs
Once you’ve sorted your traffic correctly, you should also clean up page reporting so that you can correctly track the user journey. URLs tend to be messy and contain so many junk parameters that can make your Site Content reporting nearly useless. For example. a single page may have hundreds of URL variations due to parameters appending to your URL:
You can use this simple spreadsheet to get a list of permutations and then insert them in the query exclusion list separated by commas. You can also do it manually by pulling all Pages and extracting the permutations. However, the form will save you a ton of time!
Tip: Verify whether or not reporting uses the parameter or Goal condition before removing it. To exclude all unnecessary query parameters, go to Admin -> View Settings -> Exclude URL Query Parameters.
Tip: Always test-run the changes in your Test View before applying them to the Master View. You can’t reprocess this data once you enable the query exclusion.
#4 Audit Transactions
If you enable eCommerce reports within your Google Analytics, you may need to verify its accuracy. The best way to check the reliability of your eCommerce tracking is to compare it to the data stored in the back-end of your central eCommerce platform. We suggest exporting a few months of orders from your CRM and comparing them to the Google Analytics transactions. That way, you can define the level of discrepancy between both systems.
A 5-7% difference in orders and revenue is a good result for this test, and we suggest investigating anything above 5-7% to determine if tracking is functioning correctly. Look to see what missing orders in your Google Analytics account have in common. Is there a specific parameter or string (i.e., a coupon code, a discount, a price bucket, etc.) that breaks the eCommerce tracking on your confirmation page?
Tip: eCommerce implemented via Google Tag Manager tends to be more accurate than the classic inline code. You can learn about implementing the enhanced eCommerce plugin here.
#5 Exclude Internal & Dev Traffic
Sometimes internal traffic and traffic from the staging environment mix with the hits from the actual website. You may even sometimes see test transactions in the Master View, which can significantly affect your overall performance and KPI reporting.
To filter out internal traffic, you will need to implement the IP exclusion filter on your Master View. Do not edit the Unfiltered and Test view in this case since you will still need a completely raw View with all unfiltered data. You also need to see your internal team traffic in one of the Views for the test purposes. You may also want to create a separate view for test purposes in case you still want to see all incoming data in your Master View.
To avoid including dev traffic, create another Google Analytics property and use it as a sandbox for your QA environment. Then, place the UA id of this property in the tracking code of your staging site.
Tip: Ensure data consistency and comparability by making sure settings are identical in both Live and Test properties.
#6 Enable the Referral Exclusion List and Exclude Bot Traffic
Make sure you’ve enabled spider traffic and referral traffic exclusions for more accurate data. If you see your own site in the referral traffic, you may need to include it in the referral exclusion list. To do so, go to Admin -> Tracking Info -> Exclude Referral Exclusion List. It may take some time to disappear completely, but it will decrease gradually over time.
Tip: Check the referral paths – some of your pages may be missing the tracking, which results in referral traffic. Make sure you check the bot filtering box to exclude all known bot and spider traffic. However, we suggest disabling the feature in the Unfiltered view so that you can analyze the maximum amount of available data :
The Final Takeaway
Nothing can undermine an otherwise successful marketing program like inaccurate reporting and bad data quality. Honest data is the foundation of a proper analytics framework, so make sure you start with clean, accurate data from the very beginning. As a result, you’ll gain the most accurate insights into how users interact with your website and make better-informed marketing decisions.