How to Set Up and Export GA4 User Properties to BigQuery

Having lots of data is one thing, but turning it into useful insights is what really matters. If you're working with Google Analytics 4 (GA4), getting a clearer picture of your users can be a game-changer for making better decisions.

Setting up and exporting user properties allows marketers, analysts, and businesses to more precisely segment their audiences and develop targeted strategies. This guide will show how to set up and export GA4 user properties to BigQuery, unlocking deeper insights and enabling more advanced analysis.

Configuring GA4 user properties helps organizations better understand user behavior and refine marketing strategies to drive growth.

• Segmentation: GA4 allows for precise user segmentation, helping businesses more effectively target their marketing based on user behaviors and characteristics.
• Personalization: With these properties, companies can offer personalized experiences that boost engagement, satisfaction, and conversion rates.
• Deeper Insights: Detailed insights into user preferences and behaviors improve predictive models, support smarter decisions, and help optimize marketing efforts.

In addition, user properties allow you to track specific behaviors and characteristics, giving you a clearer picture of how different segments engage with your content. This improves campaign measurement and targeting and helps tailor your marketing efforts more precisely, leading to better performance and business growth.

Exporting GA4 user properties to BigQuery allows businesses to dig deeper into their data, uncover valuable insights, and make smarter decisions faster.

• Faster, Tailored Reporting: Instantly generate custom reports and dashboards that align with your specific business goals- whether it's tracking customer lifetime value, monitoring ROI on marketing campaigns, or identifying upsell opportunities. Quick access to these insights helps teams respond promptly to performance changes and market shifts.
• Advanced Analytics: Use tools like Python, R, SAS, and BigQuery ML to create predictive models that identify customer trends, enabling smarter marketing, sales, and product development strategies.
• Seamless Data Integration: Combine your GA4 data with Google Ads, AWS, or Azure, creating a single source of truth across all platforms. This streamlines campaign tracking and cross-channel analysis, leading to more effective ad spend and customer engagement.
• Operational Efficiency: Automate repetitive reporting tasks, freeing up time for your team to focus on high-impact activities like refining customer segments or improving product offerings.
• In-Depth Queries: Perform complex queries to analyze user behaviors in ways GA4's interface might not support. This enables more granular insights to drive actions like improving website conversion paths or identifying key drop-off points in customer journeys.
• Unified Customer View: Merge data from various sources to get a 360-degree view of customer interactions, from ad clicks to post-purchase engagement. This holistic view allows for more accurate targeting and retention strategies.

These advantages highlight how BigQuery can transform data handling and analysis, leading to more efficient and insightful business practices.

Before setting up user properties in GA4, it’s important to understand the prerequisites for a smooth and efficient process.

1. First, verify that you have access to your GA4 account with ‘Edit’ permissions. This will allow you to make changes to the configuration settings.
2. Next, familiarize yourself with the user data you want to track, such as demographic information, user preferences, or behaviors. Ensure that this data complies with GA4's privacy guidelines and terms of service.
3. Lastly, consider whether you must implement tagging or use Google Tag Manager (GTM) to pass your custom user properties to GA4.

Setting up GA4 user properties is crucial for tracking and analyzing specific user attributes important to your business. By configuring these properties correctly, you can segment your users more effectively.

The process involves several steps, including transmitting the data to Google Analytics, ensuring GA4 is configured to accept these parameters, and testing your setup to confirm everything is working as expected.

First, you need to either create a new GA4 event tag or edit an existing one in your Google Tag Manager (GTM) account. This tag will send user properties, such as the client ID, to GA4.

To send the client ID custom parameter (with user scope) to GA4 along with an event:

1.1: In the tag configuration screen within GTM, find the section labeled User Properties. This is where you will define the user properties you want to send to Google Analytics.

1.2: In the Property Name field, enter user_id (or choose another descriptive name that aligns with your tracking needs). In the Value field, select the variable that holds the User ID.

1.3: If you haven't already created a variable in GTM to capture the User ID, you’ll need to set one up. To do this, navigate to the Variables section in GTM, create a new variable, and configure it to capture the User ID from your website or application.

1.4: After entering the property name and value, ensure all other necessary configurations are completed, then click Save to finalize the tag settings.

Once you start sending the User ID as a user property to GA4, you need to configure GA4 to accept and recognize this user property.

2.1: Navigate to your GA4 property and locate the Admin gear icon in the bottom-left corner of the interface to access the Admin settings. In the Admin section, find the Data Display column, and click on Custom Definitions.

2.2: Here, you’ll need to create a custom dimension that corresponds to the user property you defined in GTM. Click on Create Custom Dimension, then enter the name of your user property, such as user_id , in the Dimension Name field. Set the Scope to User, and if desired, add a brief description in the Description field.

After setting up everything, it's crucial to verify that your configuration is working correctly.

3.1: Go to Google Analytics, within your GA4 property. In the left-hand menu, click on Data Display, then select DebugView.

3.2: In DebugView, you can monitor real-time data being sent to GA4. Look for the event get_user_data and check that the user_id user property is being correctly logged.

3.3: Ensure that the user property is correctly associated with the event in the DebugView. If everything is set up correctly, GA4 will now track and log the User ID as a user property for the specified event.

This detailed setup ensures that the User ID is correctly tracked and can be used for more granular analysis in GA4.

Setting up user data tracking collection in the GA4 interface is crucial for collecting and analyzing user-provided data. This process allows you to gather consented, first-party data from your website, which can then be used to enhance the accuracy of your measurement data and power advanced Analytics features.

Enabling this feature allows GA4 to export data to tables like pseudonymous_users_YYYYMMDD and users_YYYYMMDD and handle parameters for the event_YYYYMMDD table.

Steps to Set Up User Data Tracking Collection in GA4:

1. Activate User-Provided Data Collection:
   • Go to the GA4 Admin panel and navigate to Data Collection and Modification.
   • Under User-Provided Data Collection, click Turn on to activate the feature for your property.
2. Configure Automatic Data Collection: Enable the Collect automatically detected user-provided data option to allow GA4 to detect and collect user-provided data on your website automatically.
3. Review and Confirm the Policy: Review the User-Provided Data Policy thoroughly, then click Turn on to finalize the activation.
4. Identify and Configure Data Collection Method:
   • Adjust your gtag.js configuration to collect user-provided data.
   • Update your Google Tag Manager container with variables that collect user-provided data.
   • Set up user-provided data collection for offline interactions using Measurement Protocol.
5. Implement the User-ID Feature (Optional): Consider implementing the User-ID feature alongside user-provided data collection for more accurate user reporting, especially for e-commerce sites.
6. Manage and Adjust Settings: Disable user-provided data collection through the GA4 Admin panel or by adjusting your Tag Manager or Measurement Protocol configurations if needed.

Setting up user data tracking in GA4 ensures your property is prepared to collect and process crucial user information, enabling more detailed analysis and better decision-making capabilities.

Exporting user properties from GA4 to BigQuery is a powerful feature that enables you to analyze and model user data more deeply. This functionality creates specific tables in BigQuery, where each row represents a unique user, and provides a detailed view of user-scoped fields like current audiences, lifetime value (LTV), predictive scores, and more.

Here are the steps to export User Properties from GA4 to BigQuery.

• Begin by navigating to the BigQuery Link section under Product Links in the GA4 Admin panel.

• Scroll down to the "User data" section and check the box to enable the daily export of user properties.

When user data export is enabled, two new tables are created in your BigQuery project:

• Pseudo ID Table: This table contains a row for every pseudonymous identifier. It is updated whenever any of the fields associated with a user change, but it does not include user IDs associated with Pseudo IDs.
• User ID Table: This table contains a row for every user ID. Similar to the Pseudo ID table, it updates with any field changes but does not include Pseudo IDs.

The user data export includes any user whose data has changed on a given day. This means the number of users in the export may exceed the value of the Active Users metric, as it includes users whose data was modified, not just those who were active.

The exported user data in BigQuery includes various fields:

• occurrence_date: The date when a record change was triggered.
• last_updated_date: The date when the record was updated.
• user_id: ID for the User-ID namespace in reporting identity (User ID table only).
• pseudo_user_id: ID for the Pseudonymous namespace (Pseudo ID table only).
• user_info: Contains timestamps of the user's last activity, first touch, and other critical interactions.
• audiences: Information on audience memberships, including start and expiry timestamps.
• user_properties: Records key and string values of user properties.
• device: Details on the user's device, such as operating system and model.
• geo: Geographic information including city, country, and region.
• lifetime_value: Tracks the lifetime value metrics.
• predictions: Predictive scores like purchase and churn probabilities.

By setting up the export of user properties from GA4 to BigQuery, your organization can perform advanced data analysis, create predictive models, and gain valuable insights into user behavior.

💡Looking to better understand the structure of GA4 data exports to BigQuery? Explore our detailed guide on the GA4 BigQuery event table schema and dates management.

Once you’ve exported user properties to BigQuery, you can leverage them in several powerful ways:

• Segment Users: Group users by behavior, demographics, or preferences to create more targeted marketing strategies.
• Predict Churn & LTV: Use predictive models to estimate churn rates and lifetime value (LTV), allowing for proactive customer retention efforts.
• Analyze Behavior Patterns: Run advanced queries to spot trends in user behavior, helping you optimize user experiences and engagement.
• Merge Data: Combine GA4 data with other datasets to get a complete view of customer interactions across platforms and touchpoints.

Imagine a B2B SaaS company that provides project management software to mid-sized enterprises. One of its goals is to reduce customer churn-clients canceling their subscriptions, while increasing customer lifetime value (LTV). Using predictive modeling, the company analyzes historical data such as user activity, product usage patterns, and customer support interactions.

For example, it looks at which companies actively use advanced features, how frequently teams collaborate on projects, or whether customers have reduced their engagement or reached out with unresolved support issues. Based on these insights, the SaaS provider can identify clients that are at a higher risk of canceling their contracts.

With this list of at-risk customers, the company can take targeted actions. This could mean reaching out to help customers unlock more value from underused features, or offering strategic account reviews to improve product fit. These proactive measures can significantly lower churn rates.

Simultaneously, the SaaS company calculates the LTV of different customer segments, such as high-volume enterprise clients versus smaller firms.

By understanding which segment brings in the most long-term revenue, the company can optimize its resources, focusing retention efforts on high-LTV clients representing a bigger portion of future revenue while refining its sales strategies to acquire more valuable customers.

Connecting GA4 user properties with BigQuery can really improve how businesses use their data. It allows for more accurate user segmentation, personalized experiences, and better insights for making informed decisions. With this integration, companies can run advanced analyses that guide smarter strategies and help optimize user interactions, leading to better business results.

The OWOX Reports Extension makes data analysis easier by integrating multiple sources directly in your browser and allowing you to query GA4 data right from Google Sheets. It automates pivot tables and charts, giving you deeper insights into your GA4 data.

Perfect for marketers and analysts, the extension simplifies creating custom reports and dashboards, delivering real-time insights to keep up with changing market conditions. With OWOX BI, you can streamline reporting and get more value from your data.