Overcoming Common Google Analytics 4 (GA4) Pitfalls: A Guide to Smooth Data Capture and Analysis

Introduction Google Analytics 4 (GA4) is a robust analytics tool that provides valuable insights into user behavior and website performance. As you delve deeper into GA4, you may encounter some common pitfalls that can affect the accuracy and completeness of your data analysis. In this blog post, we will explore three critical issues: data sampling, data thresholding, and cardinality. Understanding these challenges and implementing appropriate solutions will enable you to make the most of your GA4 data.

1. Cardinality: Unraveling the Impact of Unique Values

Cardinality refers to the occurrence of unique values within a specific dimension in your GA4 reports. When a dimension contains more than 500 unique values in a single day, it can lead to a situation known as "cardinality explosion." This explosion occurs when the sheer volume of unique values overwhelms GA4's capacity to process and display data effectively.

A typical scenario that triggers cardinality issues is the usage of dimensions like customer_id or crm_id, which can easily generate a vast number of unique values. When this happens, your data analysis may become less meaningful and segmentation could become problematic.

To address cardinality concerns, prioritize data capture by focusing on relevant data points that provide valuable insights into user behavior. Instead of using unique identifiers like customer IDs, consider capturing attributes such as user group or user type. These attributes can facilitate more meaningful analysis, segmentation, and audience creation in GA4.

For GA4 360 users, there is an option to request an "expanded data set," which accommodates a higher number of unique values. Alternatively, GA4 automatically creates expanded data sets for reports that frequently encounter the (other) row due to high cardinality.

2. Data Thresholding: Ensuring Data Completeness and Accuracy

Data thresholding is a limitation that arises when a GA4 report reaches its maximum data capacity. This happens most commonly when the reporting identity is set to "blended," which is the default setting when creating a GA4 property. The blended reporting ID allows for cross-device reporting, but it can lead to missing data if the report exceeds its threshold. To avoid missing data due to thresholding, consider setting the reporting identity to "device based." This adjustment ensures a more comprehensive data capture and minimizes the risk of data loss in your reports.

For GA4 360 users, a more advanced approach involves creating a sub-property with the reporting ID set to blended or observed, while setting the master property to use "device only" reporting identity. This configuration allows for both cross-device reporting and complete data capture.

Alternatively, exporting data to Big Query provides access to raw event and user-level data, avoiding data loss and enabling more in-depth analysis.

3. Data Sampling: Unraveling the Impact on Data Accuracy

Data sampling is a technique employed by GA4 when the number of events in a report exceeds the quota limit for the property type (standard or 360). Rather than processing all data, GA4 uses a representative sample to provide directionally accurate insights.

For free Google Analytics users, the quota limit is 10 million events, while GA4 360 users can process up to 1 billion events without sampling. If your reports are subject to sampling, it's essential to understand its impact on data accuracy.

To address data sampling concerns, there are several options available:

a) Unsampled Reports (360 only): GA4 360 users can create unsampled reports, allowing for more accurate data analysis without sampling bias.

b) Adjust Population Size or Precision and Speed (360 only): GA4 360 users can customize their data sampling preferences to balance between precision and speed based on their specific needs.

c) Export Data to Big Query: Exporting data to Big Query provides access to raw event and user-level data, allowing for detailed analysis without sampling limitations.

Conclusion Mastering the challenges of data sampling, data thresholding, and cardinality is essential for deriving accurate and meaningful insights from your Google Analytics 4 data. By prioritizing data capture with relevant data points, setting up reporting identities appropriately, and leveraging the power of unsampled reports or Big Query exports, you can overcome these hurdles and unlock the full potential of GA4. Armed with this knowledge and the provided solutions, you can confidently optimize your website, understand user behavior, and make data-driven decisions to drive your business forward.