GA4 Limitations Every Analyst Must Know: Solutions and Strategies

In the ever-evolving landscape of digital analytics, Google Analytics 4 (GA4) has emerged as a questionable, yet potentially powerful, tool for deciphering user behavior and shaping data-driven strategies. We previously wrote a blog post on six reasons why organizations are doubting their GA4 reporting capabilities. Understanding the limitations of GA4 is essential for making the most of your data insights. In this blog post, we delve into some of the noteworthy limitations that every analytics professional should be well-versed in. From landing page report issues to data retention constraints, migration hurdles, cardinality limits, thresholding considerations, and Looker Studio limitations this blog post aims to empower you with the knowledge needed to navigate the intricacies of GA4 successfully.

Landing Page reports returning high (not set) data

The landing page reports section is a great place to view your top-performing landing pages (at least it used to be in GA-UA). While it is still possible to view your top landing pages in GA4, it is not without its limitations.

The biggest issue with this report is that a lot of the pageviews get clubbed under a row “(not set)” and you might think to ignore it since the count may not be high. That isn’t the case. For our own website, the (not set) sessions in the landing page report are about 13 percent of total sessions. But why does this happen?

This happens because a user left the page open in the browser until the session expired and returned to engage with the website later. In this case, the page_view event wasn’t loaded so GA4 was unable to determine the landing page when the session restarted. Hence, for the new session, the landing page is “(not set)”. For instance, let’s say a user lands on the homepage of your website, this page will be attributed to the landing page visit. Now, the same user leaves the page open until the session expires and then engages with the website again. In this case, a new session will be started but the landing page visit this time will be attributed to “(not set)”.

Limited data retention capabilities

Data retention refers to a period in time when data is retained by Google Analytics in your account. The data retention period can be configured in the admin section of Google Analytics. This will determine how long data will be retained before it is automatically deleted from your account. Universal Analytics allows the following data retention periods:

1. 14 months
2. 26 months
3. 38 months
4. 50 months
5. Do not automatically expire

However, in Google Analytics 4 Standard, you only have two data retention periods to choose from – 2 months and 14 months. The impact of this is a little confusing, but what this means is that if you want to pull up Explorations from beyond 14 months, you won’t be able to do so. On the other hand, you will be able to view this data in Standard Reports Another workaround for this problem is to import your data into Google BigQuery which will help give you more control over your data retention.

Limited migration support from GA-UA to GA4

One of the biggest problems of migrating to GA4 is the actual migration itself. The method by which data is being collected doesn’t make it easy either. All the existing tags need to be recreated for the new GA4 platform. As of July 2023, Universal Analytics has stopped receiving fresh data. This means that all the new data will be moved to your GA4 property and the historical GA-UA data will not be available in the new GA4 property which could complicate things when pulling reports from Universal Analytics and Google Analytics 4.

Cardinality Issues

Cardinality refers to the number of unique values that can be assigned to a dimension. For instance, the Device dimension can only have three values – desktop, tablet, and mobile. Therefore, the Device dimension has a cardinality of three. Some dimensions such as Device, Gender, and Age have a fixed cardinality while other dimensions such as Page path and screen class, Landing page + query string, and Item ID can have a high number of values. Hence, these dimensions are expected to have high cardinality.

Google Analytics basically sets the limits on the amount of data a particular table can include. Any data collected past the permitted limit will be grouped under “(other)”. For example, if the limit for the table is 500 and you have 550 rows of data, then the last 50 rows of data will be grouped under “other”.

These limits vary based on the report as some dimensions that are expected to have higher cardinality will have higher limits such as the Item ID. Tables that use more dimensions are likely to encounter these cardinality issues. You can workaround this issue by creating a BigQuery export and querying the data there.

Thresholding issues

Google Analytics 4 has a feature that ensures that a user’s identity isn’t revealed when viewing data. This thresholding occurs in the user count is low in a report. Sadly, there is no way to remove thresholding but there is a workaround. You can change the reporting identity in the configuration settings to device-only reporting. This way GA4 will utilize the device id or the app instance id (for apps) which are not subject to data thresholds.

Cannot import Google Analytics 4 segments into Looker Studio

Segments are a very crucial part of digital analytics tools as they allow you to create subsets of your data that can be analyzed and derive insights from. Therefore, when it comes to analyzing and visualizing data in Looker Studio, segments can come in handy.

While UA allows you to create import your segments from Google Analytics, there is now a Google Analytics segment import feature in GA4. In fact, if you go to the “Manage Segments” section of your Looker Studio dashboard, you will see a blank screen with no option to add a new custom segment.

Why Google would include a segment list in Looker Studio without the option to create or import a custom segment is a mystery that only Google can answer. Maybe they might include it in the future but for now, the only other option is to import your audiences into Looker Studio.

The problem with this, however, is that audiences are not retroactive and only collect data from the day that they are created.

Conclusion

As a digital analytics expert, your role involves harnessing data’s power and understanding the nuances that shape interpretation. Google Analytics 4, while cutting-edge, isn’t exempt from constraints affecting analysis. Landing page reports, data retention, migration complexities, cardinality challenges, and thresholding considerations all contribute to GA4’s limitations. Your expertise should extend beyond recognizing these limitations; it involves devising innovative strategies that work around these barriers. By working within these confines and using workarounds like BigQuery exports and adjusted reporting identities, you can navigate GA4’s limitations while capitalizing on its strengths. In this dynamic data era, if you need help deciphering GA4’s intricacies to drive meaningful insights and catalyze impactful decisions, our digital analytics experts are here to assist.