If you’ve had a chance to read our previous guides 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 one needs to absorb in order to be able to set up a GA4 properly, and time is ticking.
With GA4 being a more complex tool, it is easy to make errors that can hinder the accuracy and reliability of the data collected.
In this article, we will explore five common Google Analytics 4 errors that can easily occur and offer practical tips to avoid them.
1. Not specifying the data retention period
The GA4 comes with a 2-month data retention period by default, and you have the option to set it to 14 months. A retention period is applied to custom reports in explorations, while data in standard reports never expires.
Once the retention period has passed, the data will be automatically deleted – meaning that if you don’t change this setting while setting up GA4, you won’t be able to run custom reports on an annual basis and will lose valuable historical data.
To change the retention period, go to data settings > retention date, And in the drop down menu choose 14 months.
You’ll also notice a checkbox that reads “Reset user data on new activity,” which means that the 14-month data retention period is counted from the moment the user last visited onwards.
In other words, every time a user engages in a new activity, the data retention period is extended by another 14 months.
Honestly, I can’t think of a use case when you choose to turn this option off, so I keep it on.
2. Highly relevant dimensions
Dimensions with high cardinality are dimensions that contain more than 500 unique values in a single day. This can present challenges and limitations in data analysis within GA4.
The cardinality in GA4 can negatively affect the accuracy and reliability of the data.
For example, when tracking exact word count as a custom dimension on each article page, you may end up with too many items if you have thousands of articles because the word count can be different for each article.
How to fix a high core relationship
To mitigate the impact of the high cardinality of GA4, consider creating an array of values.
Using the above example of the custom word count component, it doesn’t matter much whether the article will be 500 or 501 words. You can group values into ranges like:
And instead of paying a lot of premium values, you’ll only have five different dimensions.
Also, as a best practice, always select custom dimensions wisely.
Ensure that custom dimensions align with your analysis goals and consider their potential impact on data accuracy and resource consumption.
3. Not linking to a BigQuery account
Linking to BigQuery was available in Universal Analytics 360 but not in the free version. With GA4 now, all users can access this premium feature.
Since data is being exported to BigQuery from the moment you connect, it is important to set it up at the beginning to get as much historical data as possible.
BigQuery has a huge advantage over GA4 custom reports in that the data is never sampled, whereas in custom reports the data will be sampled if there are more than 10 million events in the exploration report.
To link GA4 to BigQuery, go to BigQuery Links in GA4 Settings.
To complete linking to BigQuery, you will need to create a BigQuery project which will require you to enter your billing information.
It’s free, and 10GB is free every month; They will charge you $0.02 per GB if you exceed this number.
4. Failing to set up custom audiences
GA4 has strong audience building capabilities which you can read more about in our guide on how to create segments and audiences.
With GA4 Audiences, you can analyze specific segments of data, enabling you to derive valuable insights. For example, you can create target audiences like engaged users, subscribers, or users who made a purchase in the past 30 days.
It is recommended that you create your own ICP audiences and mark them as conversion.
Because audience dates are not retrospective, it is important to define target audiences at the beginning of preparation in order to collect historical data.
5. Use automatic migration from Universal Analytics
GA4 is a completely different beast compared to UA, with a different data model.
While it provides an option to collect Universal Analytics events automatically, it is better not to use this, as it is an opportunity to rethink your analytics and design your event collection architecture afresh for better analytics.
6. Not excluding unwanted referrals
E-commerce sites often have 3rd party payment processors hosted under different domains – when they are redirected back to the website after the user completes the payment, the GA will detect that it is a new session because the referral is different.
To avoid this and not distort your conversion data, you need to exclude such domains from referrals so that the GA does not start a new session.
In SEJ, for example, we have the short link domain “sejr.nl”, which should be treated as the same domain – so we’ve added it to our exclusion list.
Also, if you have subdomains and want to track across subdomains using the same GA4 site, you need to exclude your domain from referrals in order to keep the same session when users go from one subdomain to your main domain.
7. Failure to choose the correct reporting identity
The following report identification options are available in GA4:
- device based.
The good news is that you can switch between these options at any time, and it will be reflected in your custom discovery reports.
But I would like to mention why it is so important to choose the right one according to your business situation.
If you don’t have login and user IDs on your website, then in 99% of cases “device-based” should be used, because the other two options would distort your conversions data.
The reason is user privacy. With Google beacons enabled, GA uses user IDs to track users across devices, then matches them if they’ve signed into their Google service accounts on different devices — and there’s the potential for the user’s identity to be exposed.
In such cases, it hides user data from reports and form data based on user behavior. Data modeling can introduce some level of inaccuracy because it is an estimate rather than an exact measurement.
With typical and notable choices, you’ll often notice “data limits apply” in your reports which have implications for data accuracy.
You can try switching between these options and see how your data changes.
If you notice a big difference in the number of conversions between mixed, observable, and device-based identities, it may be best to use the latter option.
Device-based identity works similar to how Universal Analytics tracking works.
In conclusion, it is crucial 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 necessary measures, you can make the most of GA4’s capabilities and derive useful insights for your website or application.
Additionally, GA4 requires ongoing maintenance rather than a one-time setup.
Failure to regularly monitor and analyze your data can lead to missed opportunities and make it difficult to identify and address issues in a timely manner.
Featured image: Cast Of Thousands / Shutterstock