Unlocking the Mystery: Why BigQuery and GA4 Total Users Can’t Match

Are you tired of scratching your head, wondering why the total users in BigQuery and GA4 don’t add up? You’re not alone! This common conundrum has puzzled many a Google Analytics enthusiast. Fear not, dear reader, for we’re about to embark on a journey to demystify this enigma and provide you with actionable steps to troubleshoot and reconcile the discrepancy.

Understanding the Basics: BigQuery and GA4

Before we dive into the nitty-gritty, let’s quickly review the fundamentals of BigQuery and GA4.

BigQuery: A fully-managed enterprise data warehouse service that allows you to analyze all your data using SQL-like queries. It’s a powerful tool for data analysis, providing unparalleled scalability and flexibility.

GA4 (Google Analytics 4): The latest generation of Google Analytics, designed to provide a more unified, intuitive, and machine learning-driven approach to web and app analytics. GA4 offers improved cross-platform tracking, advanced predictive analytics, and a more streamlined user interface.

The Mysterious Discrepancy: Total Users in BigQuery vs. GA4

So, why do the total users in BigQuery and GA4 often fail to match? There are several reasons for this discrepancy, and we’ll explore each one in detail.

Reason 1: Data Processing and Latency

BigQuery and GA4 process data differently, which can lead to temporary discrepancies. BigQuery typically processes data in near real-time, while GA4 may take some time to process and update its reports. This latency can cause the total users in BigQuery to be higher than those in GA4, especially during periods of high traffic or sudden spikes in user activity.

To mitigate this issue:

• Wait for a few hours to allow GA4 to catch up with the latest data.
• Use BigQuery’s built-in DATE_TRUNC function to aggregate data by hour or day, reducing the impact of latency.

Reason 2: Data Scope and Filtering

Different data scopes and filtering rules in BigQuery and GA4 can also contribute to the discrepancy. BigQuery might include data from multiple sources, such as website, app, or offline data, while GA4 might be configured to filter out certain types of traffic or users.

To resolve this issue:

1. Verify that your BigQuery dataset and GA4 property are configured to collect data from the same sources and scope.
2. Check your GA4 filters and ensure they’re not excluding any legitimate users.
3. Apply similar filtering rules in BigQuery using the FILTER clause to match the GA4 configuration.

Reason 3: User Identification and Cookie-Based Tracking

The way BigQuery and GA4 identify and track users can also contribute to the discrepancy. BigQuery relies on user IDs, device IDs, or client IDs, while GA4 uses cookie-based tracking by default.

To address this issue:

• Verify that your GA4 tracking code is correctly configured to set the _ga cookie.
• In BigQuery, use the REGEXP_EXTRACT function to extract user IDs from the user_agent or device_id columns.
• Apply the extracted user IDs to your BigQuery queries to match the GA4 tracking methodology.

Step-by-Step Troubleshooting Guide

Now that we’ve covered the main reasons for the discrepancy, let’s walk through a step-by-step troubleshooting guide to help you reconcile the total users in BigQuery and GA4.

Step 1: Verify Data Scope and Filtering

-- BigQuery query to verify data scope and filtering
SELECT 
  COUNT(DISTINCT user_id) AS total_users
FROM 
  `my_dataset.my_table`
WHERE 
  _TABLE_SUFFIX = 'my_suffix'
  AND device_category = 'desktop'
  AND country = 'USA';

In this example, we’re using a BigQuery query to count the distinct user IDs, applying filters for device category and country. Verify that your GA4 property has similar filters and scope configured.

Step 2: Check Data Processing and Latency

-- BigQuery query to check data processing and latency
SELECT 
  COUNT(DISTINCT user_id) AS total_users
FROM 
  `my_dataset.my_table`
WHERE 
  DATE_TRUNC(TIMESTAMP '2022-01-01', hour) = TIMESTAMP '2022-01-01 00:00:00';

In this example, we’re using the DATE_TRUNC function to aggregate data by hour, reducing the impact of latency. Verify that your GA4 reports are up-to-date and not experiencing any processing delays.

Step 3: Inspect User Identification and Cookie-Based Tracking

-- BigQuery query to inspect user identification and cookie-based tracking
SELECT 
  COUNT(DISTINCT REGEXP_EXTRACT(user_agent, r'(?P[a-zA-Z0-9]+)')) AS total_users
FROM 
  `my_dataset.my_table`;

In this example, we’re using the REGEXP_EXTRACT function to extract user IDs from the user_agent column. Verify that your GA4 tracking code is correctly configured to set the _ga cookie.

Conclusion

In conclusion, the discrepancy between BigQuery and GA4 total users can be attributed to differences in data processing, scope, filtering, and user identification. By following the troubleshooting guide and applying the recommended solutions, you should be able to reconcile the discrepancy and unlock a more accurate understanding of your users.

Remember to regularly monitor your data and adapt your approach as your analytics requirements evolve. Stay curious, keep exploring, and happy analyzing!

Reason	Cause	Solution
Data Processing and Latency	Different processing times and latency	Wait for a few hours, use DATE_TRUNC function, and verify GA4 report updates
Data Scope and Filtering	Different data scopes and filtering rules	Verify data scope and filtering configurations, apply similar filtering in BigQuery
User Identification and Cookie-Based Tracking	Different user identification methods	Verify GA4 tracking code, use REGEXP_EXTRACT function to extract user IDs in BigQuery

By understanding the underlying causes of the discrepancy and applying the recommended solutions, you’ll be well on your way to achieving a unified view of your users across BigQuery and GA4.

Frequently Asked Question

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