If you’ve had a chance to read previous guides we’ve published on Google Analytics 4 (GA4), you probably know that it’s not a plug-and-play analytics tool like Universal Analytics.
There is a lot of information to absorb in order to properly set up GA4 and time is of the essence.
Because GA4 is a more complex tool, it is easy to make mistakes that can affect the accuracy and reliability of the data collected.
In this article, we’ll go over five common Google Analytics 4 mistakes that are easy to make and give practical tips to avoid them.
1. Data retention period not specified
GA4 has a default data retention period of two months. You have the option to set this to 14 months. The retention period is applied to custom reports in explorations, while data in the standard reports never expires.
Once the retention period has expired, the data will be automatically deleted. So if you do not change this setting when setting up GA4, you will no longer be able to run custom annual reports and will lose valuable historical data.
To change the retention period, navigate to data settings > data storage, and select 14 months from the drop-down menu.
You’ll also notice a checkbox that says “Reset user data on new activity,” meaning the 14-month data retention period is counted from the time of the user’s last visit.
In other words, every time a user performs a new activity, the retention period of their data is extended by another 14 months.
Honestly, I can’t think of a use case where you would disable this option, so I’ll leave it enabled.
2. High cardinality dimensions
High cardinality dimensions are dimensions that contain more than 500 unique values within a day. This can lead to challenges and limitations in data analysis within GA4.
Cardinality in GA4 can negatively impact data accuracy and reliability.
For example, if you track exact word count as a custom dimension on each article page, thousands of articles can have high cardinality because the word count can be different for each article.
How to fix high cardinality
To mitigate the effects of high cardinality in GA4, consider creating a range of values.
In the above example of a custom word count dimension, it doesn’t matter that much whether the article is 500 or 501 words. You can categorize values into ranges such as:
And instead of pushing too many different values, you only have five different dimensions.
As a best practice, you should always define custom dimensions with care.
Ensure that custom dimensions align with your analysis goals and consider their potential impact on data accuracy and resource consumption.
3. No association with BigQuery account
Linking to BigQuery was available in Universal Analytics 360, but not in the free version. With GA4, all users now have access to this premium feature.
Because it exports data to BigQuery from the moment you connect, it’s important to set it up from the start to have as much historical data as possible.
BigQuery has a major advantage over GA4 custom reports because data is never collected, whereas custom reports collect data when the exploration report has more than 10 million events.
To link GA4 to BigQuery, navigate to BigQuery Links in your GA4 settings.
To finish connecting to BigQuery, you need to create a BigQuery project where you need to enter your billing information.
It’s freemium and 10GB is free every month; If you exceed this number, you will be charged $0.02 per GB.
4. Failed to set up custom audiences
GA4 has powerful audience building features. For more information, see our guide to creating segments and audiences.
With GA4 Audiences, you can analyze specific segments of data to uncover valuable insights. For example, you can create audiences such as engaged users, subscribed users, or users who made a purchase in the last 30 days.
It’s a good idea to create audiences for your ICP and mark it as a conversion.
Because audience data isn’t retroactive, it’s important to define your audiences at the beginning of setup to collect historical data.
5. Using Universal Analytics automatic migration
GA4 is a completely different beast compared to UA, with a different data model.
While it offers the ability to automatically collect Universal Analytics events, you’d better not use it as it offers a chance to rethink your analytics and redesign your event collection architecture for better analytics.
6. Don’t rule out unwanted referrals
Ecommerce sites often have third-party payment processors hosted on different domains – and if they’re redirected back to the site after the user’s checkout, GA recognizes this as a new session because the redirect is different.
To avoid this and not falsify your conversion data, you must exclude such domains from referrals so that GA does not initiate a new session.
For example, at SEJ we have the shortlink domain “sejr.nl” which should be treated as the same domain – so we added it to our exclusion list.
Additionally, if you have subdomains and want to track across subdomains with the same GA4 property, you must exclude your own domain from referrals to maintain the same session when users navigate from a subdomain to your main domain.
7. Not choosing the right report identity
The following reporting identity options are available in GA4:
- device based.
The good news is that you can switch between these options at any time, which will be reflected in your custom exploration reports.
However, I would like to mention why it is important to choose the right option according to your business case.
If you don’t have login and user IDs on your website, then 99% of the time you should go with device-based, as the other two options can skew your conversion data.
The reason is user privacy. When Google signals are enabled, GA uses user IDs to track users across devices and then associates them when they are logged into their Google service accounts on different devices – and there is a chance that the user’s identity will be exposed.
In such cases, user data is hidden from the reports and data is modeled based on user behavior. Modeling data can introduce a degree of imprecision as it is an estimate rather than an exact measurement.
For modeled and observed options, you will often find in your reports that “data thresholds are applied” which impacts data accuracy.
You can try switching between these options and see how your data changes.
If you see a significant difference in the number of conversions between mixed observed identities and device-based, it may be preferable to use the latter option.
Device-based identity works similar to Universal Analytics tracking.
In summary, it is important to avoid common configuration errors when setting up Google Analytics 4 to ensure accurate and reliable data collection.
By understanding these potential pitfalls and taking the necessary action, you can make the most of GA4’s capabilities and generate meaningful insights for your website or application.
Additionally, GA4 requires ongoing maintenance and no one-time setup.
Failure to regularly monitor and analyze your data can result in missed opportunities and make it difficult to identify and fix problems in a timely manner.
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