Google Analytics 4 (GA4) represents a significant shift from the previous version, Universal Analytics (UA), offering a more comprehensive and privacy-centric approach to data collection and analysis.
GA4 provides many features and benefits, making it a crucial tool for digital marketers and website owners.
First and foremost, GA4 uses an event-based data model, allowing you to track a wide range of user interactions beyond page views. This includes clicks, form submissions, video engagement, and more, giving you a richer understanding of user behavior on your site.
With GA4, you can track user journeys seamlessly across websites and apps. This unified view of customer behavior helps you better understand how users interact with your brand across different devices and platforms.
GA4 is designed with privacy in mind. It offers features like data retention controls, IP anonymization, and the ability to comply with data regulations such as GDPR and CCPA. This helps you respect user privacy while still gaining valuable insights.
GA4 leverages machine learning to provide predictive metrics, such as churn probability and revenue potential. These insights can help you anticipate customer needs and optimize your marketing strategies accordingly.
However, all these GA4’s robust data collection capabilities also demand careful management.
This is where data retention becomes crucial.
Let’s explore how to optimize your GA4 setup for maximum insights while ensuring compliance with privacy regulations.
What is Data Retention in GA4?
Data retention in Google Analytics 4 (GA4) refers to how long your tracked event data is stored and available for reporting before it’s automatically deleted. It’s a crucial aspect of data management, as it directly impacts your ability to analyze historical data and gain valuable insights into user behavior over time.
However, you can customize your GA4 data retention settings based on your business needs and compliance requirements.
When deciding on your data retention settings, take into account factors such as:
- Legal and privacy regulations (e.g., GDPR, CCPA)
- Data analysis requirements
- Data storage costs
- Frequency of data exports
It’s important to note that once data reaches the end of its retention period, it’s permanently deleted and cannot be recovered. Hence, if you need to maintain data for longer durations, you should consider exporting and storing it externally.
What’s the Default Data Retention Period in Google Analytics 4?
The default GA4 data retention period is set to 2 months for user-level data and the default GA4 event data retention is 14 months. This means that after 2 months, user-specific data like user ID and user properties will be deleted, while event data will remain available for 14 months.
| Data Type | Default Retention Period |
| User-level data | 2 months |
| Event-level data | 14 months |
User-level data includes information like user properties and identifiers, allowing GA4 to deduplicate users across devices.
Event-level data encompasses the individual events and parameters sent to GA4, such as page views, clicks, and transactions.
It’s important to note that the default data retention in GA4 (2-month) for user-level data means you can only generate user reports, such as Lifecycle and Demographic reports, based on the last 2 months of data. However, you can still see event data beyond 2 months, up to the 14-month data retention limit.
Can You Customize Data Retention Policies in GA4?
Yes, GA4 allows you to customize the retention periods for both user and event data. You have two options:
- Retain user-level data for 2 months and event-level data for 14 months (default)
- Retain user-level data for 14 months and event-level data for up to 14 months
If you choose the 14-month option for user-level data, you can generate user reports based on the entire 14 months of data. This can provide deeper insights into long-term user behavior and trends.
Remember that you can set the event-level data retention to any period between 2 and 14 months. For example, you can keep event data for 6 months if you don’t need the entire 14-month history.
When deciding on your data retention settings, consider factors like:
- Reporting needs and the timeframes you typically analyze
- Privacy regulations and data minimization principles
- Data storage costs and quota limits
How to Configure Data Retention Settings in GA4
You can configure these settings to align with your reporting needs and privacy regulations.
How to Reduce or Increase the GA4 Retention Period
As we saw earlier, GA4 retains user-level data for 2 months and event-level data for 14 months. But you can customize these retention periods based on your specific requirements:
Log into your GA4 property and navigate to “Admin” in the bottom left corner.
In the “Data collection and modification” box, click on “Data retention”
Under “User and event data retention,” you’ll see the default setting of 14 months. To change this:
- Select “2 months” for shorter retention or “14 months” for the default period.
- If you have the necessary permissions, you may see an option for “25 months” retention.
You can choose between the default “2 months” or “14 months” for user-level data retention.
Click “Save” to apply your changes.
Remember that historical data beyond the new retention setting will be deleted permanently when retention periods are reduced. Increasing retention periods allows for more historical data analysis but may impact storage costs and privacy considerations.
Extended Data Retention for GA4 360 Users
If you’re using the premium version of Google Analytics 4, GA4 360, you’ll have access to extended data retention options beyond Suppose you’re using the premium version of Google Analytics 4, GA4 360. In that case, you’ll have access to extended data retention options beyond the standard 2 months for user-level data and 14 months for event-level data. With GA4 360, you can retain user and event data for up to 50 months, giving you a much longer historical view of your data.
The extended data retention in GA4 360 allows you to:
- Perform year-over-year comparisons and identify long-term trends
- Analyze user behavior and customer journeys over a longer time period
- Build more accurate predictive models using machine learning
- Comply with industry-specific data retention requirements
Keep in mind that extending your GA4 data retention period will increase your data storage costs. You’ll want to balance your needs for historical data analysis with your budget considerations.
It’s also important to ensure your extended GA4 data retention settings align with your organization’s privacy policies and any applicable regulations, such as GDPR or CCPA. Be transparent with your users about how long you retain their data and provide them with options to delete it if requested.
Best Practices for Managing GA4 Data Retention
Set Appropriate Retention Periods Based on Business Needs
When setting data retention periods in GA4, you’ll need to carefully consider your business needs. If you’re in an industry with longer sales cycles or need to analyze user behavior over extended periods, opt for longer retention settings. On the other hand, if you’re primarily focused on short-term metrics and don’t require historical data beyond a certain point, shorter retention periods can help you manage your data more efficiently.
It’s crucial to balance having access to the data you need for meaningful insights and avoiding unnecessary data hoarding. Regularly review your data usage patterns and adjust your retention settings accordingly.
Maintain Compliance with Data Protection Regulations
When managing GA4 data retention, staying compliant with relevant data protection regulations such as GDPR, CCPA, and others is essential. These regulations often have specific requirements about how long you can retain user data and for what purposes.
Ensure you’re familiar with the regulations applicable to your business and carry out retention policies that align with those requirements. This may involve setting different retention periods for various types of data or user segments based on user consent, data sensitivity, and legal obligations.
Conduct Periodic Data Reviews and Audits
Conduct periodic reviews and audits to ensure your GA4 data retention practices remain effective and compliant over time. Regularly assess your data retention settings, usage, and compliance with internal policies and external regulations.
During these reviews, consider questions such as:
- Are we retaining data for longer than necessary?
- Are we collecting and retaining data that we’re no longer using?
- Do our retention practices align with our privacy policies and user consent?
- Are there any new regulatory requirements we need to address?
How to Maximize Data Retention in GA4
Here are some strategies to make the most of your data retention settings.
Resetting Data Retention on New Activity
Resetting data retention on new activity is a GA4 setting that allows you to control how long user-level data is stored. When enabled, every new event triggered by a user resets the data retention clock, potentially extending the lifespan of that user’s data beyond the initially set retention period.
Consider enabling this feature if you prioritize recent user interactions over historical data. But if you need complete user journey analysis, keep it disabled.
When to Use It
- If your audience is highly engaged and frequently interacts with your site or app, resetting data retention can be beneficial. It ensures that you have the latest data for these active users.
- For businesses requiring long-term trend analysis or cohort studies, this setting can help preserve valuable user data.
- Keeping user data fresh can improve audience targeting if you heavily rely on custom audiences based on user behavior.
- Helps comply with privacy regulations by deleting inactive user data.
Potential Drawbacks
- Extending data retention can increase storage costs, especially for high-traffic websites or apps.
- Depending on the nature of your business and the data collected, storing user data for extended periods might raise privacy concerns.
- Managing large datasets can become more complex over time.
- Can skew long-term trend analysis by focusing on recent activity.
How to Enable/Disable
Navigate to your GA4 property settings and navigate to “Admin” in the bottom left corner.
Under the “Data collection and modification” box, click on “Data retention”
In the “User and event data retention” section, you’ll find the option to “Reset user data on new activity.”
Toggle the switch to enable or disable the feature.
Save your changes.
Manual export GA4 Data
While GA4 offers robust data retention settings, manually exporting data is a fundamental method for preserving historical records, but it is not the most robust or scalable.
When to Use Manual export:
- Manual export is a practical option for small datasets.
- Manual export can suffice if you only need to preserve data intermittently.
- Anyone without programming or technical expertise can use this method.
Limitations:
- Manually exporting large datasets could be more efficient and time-consuming.
- Human error can lead to data inconsistencies or omissions.
- Manual exports often provide a snapshot of data at a specific point in time, with limited granularity, hindering long-term trend analysis.
- This method requires manual intervention for each export.
We do not propose this as a solution to retain your data.
Google Analytics Reporting API: Programmatic Data Extraction
The Google Analytics Reporting API offers a programmatic approach to extract GA4 data. This method is ideal for larger datasets, frequent exports, custom data analysis, and development resources.
When to Use the Reporting API:
- For exporting large datasets and substantial amounts of data.
- When regular data refreshes are necessary.
- To create tailored reports based on specific requirements.
- To integrate GA4 data with other systems or platforms.
Challenges:
- Requires technical expertise, programming knowledge and understanding of API concepts.
- There are API rate limits and API usage quotas you need to adhere to avoid service interruptions.
- Extracted data may require cleaning and transformation before analysis.
BigQuery Export of GA4 Data
For organizations handling massive datasets and requiring advanced analytics capabilities, BigQuery Export is a powerful solution. BigQuery offers scalable storage and lightning-fast query performance as a cloud-based data warehouse.
Set up a BigQuery project and enable the API.
Create a dataset in BigQuery to store your exported data.
In GA4, go to Admin and click “BigQuery links” in the “Product links” box.
Select the BigQuery project and dataset you created.
Choose the data streams you want to export.
Set the frequency of data exports (daily or streaming).
Save your settings and start exporting data.
By regularly exporting your GA4 data, you’ll have a backup of your historical data that you can query and analyze outside of GA4 data retention limits.
Why Maya Insights is Better Compared to Other GA4 Export Options
Let’s face it, wrangling GA4 data can feel like herding cats. It’s complex, time-consuming, and often leaves you scratching your head. That’s where Maya Insights swoops in to save the day.
Here is the TL;DR version of it
| Manual Export | Google Analytics Reporting API | BigQuery Export |  | |
| Integration and Automation | ⚠️ Requires manual effort, prone to errors | 🔧 Requires setup, custom scripts | 🔧 Complex setup, needs SQL knowledge | ✅ Fully automated, no coding required |
| Real-Time Data Synchronization | ❌ Not supported, data can be outdated | 🔧 Possible but complex | 🔧 Supported but with high setup complexity | ✅ Real-time synchronization, always up-to-date |
| Ease of Use | 🔧 Labor-intensive, inefficient | 🔧 Requires technical skills | 🔧 Steep learning curve | 👍 User-friendly, intuitive interface |
| Analytics and Visualization | ⚠️ Manual processing, third-party tools | 🔧 Needs BI tool integration | 🔧 Advanced but requires SQL and BI tools | 👍Integrated with Power BI, customizable dashboards |
| Cost Efficiency | 💸 High manual labor cost | 💸 Development and maintenance costs | 💸 High costs with data volume and queries | ✅ Affordable, reduces manual effort and development costs |
| Scalability | ❌Not scalable for large datasets | ⚠️Scalable but resource-intensive | ⚠️Scalable with costs | ✅Scalable, automated handling of large datasets |
Advanced Features | ⚠️Limited | ⚠️ Limited to API capabilities | 🔧 Advanced, requires expertise | ⭐ Advanced analytics, visualization, cross-channel attribution |
Comprehensive Integration and Automation
- Manual export requires continuous, time-consuming manual effort to export data regularly.
- While Google Analytics Reporting API automates data extraction, it still demands significant setup, maintenance, and custom data management scripts.
- A BigQuery Export provides extensive storage and analysis capabilities but involves complex setup and maintenance, particularly for users who need to become more familiar with SQL or Google Cloud Platform.
Maya Insights automates the entire data collection, integration, and visualization process. It seamlessly connects to various data sources, including Google Analytics 4, and pushes data directly into BigQuery without requiring extensive manual intervention or coding skills.
Real-Time Data Synchronization
- Data from Manual Export can quickly become outdated, as manual processes do not support real-time updates.
- While Google Analytics Reporting API allows for regular data pulls, setting up real-time synchronization can be complex and resource-intensive.
- The BigQuery Export supports real-time data export, but it requires a complex setup and can incur significant costs.
Maya Insights ensures that your data is always up-to-date with real-time synchronization, continuously providing current and relevant insights.
Ease of Use
- Manual export requires repetitive manual tasks, making it inefficient and error-prone.
- The Google Analytics Reporting API requires knowledge of API usage and scripting, which can be a barrier for non-technical users.
- A BigQuery Export involves a steep learning curve for setting up and managing data exports and queries.
Maya Insights offers an intuitive interface with pre-built integrations and no-code data connection capabilities, making it accessible even to users with limited technical expertise.
Advanced Analytics and Visualization
- Your data from a Manual Export must be manually processed and visualized using third-party tools like Excel or Google Sheets.
- Data from Google Analytics Reporting API must be imported into a BI tool for visualization, adding an extra layer of complexity.
- The BigQuery Export supports advanced analytics but requires SQL knowledge and additional BI tools for data visualization.
Maya Insights integrates directly with tools like Power BI, providing advanced analytics and visualization out-of-the-box. It offers customizable dashboards and pre-made report templates that simplify data interpretation and reporting.
Cost Efficiency and Scalability
- Manual export is labor-intensive and inefficient, needing to be more scalable for large datasets.
- The Google Analytics Reporting API, while efficient, can become costly due to the need for development and maintenance resources.
- A BigQuery Export is powerful, but the cost can escalate with the volume of data and query usage.
Maya Insights combines the best of all worlds by offering scalable solutions that reduce the need for extensive manual effort or costly development. It is designed to be affordable while providing robust data management and analytics capabilities.
What’s next
Now that you understand the importance of configuring data retention settings in GA4, it’s time to take action and ensure you get the most out of your data while staying compliant. Here are some practical steps to optimize your GA4 data retention:
- Review your current data retention settings: Look closely at your GA4 property’s data retention settings. Are they set to the default 2 months? If so, consider extending the retention period to 14 months to balance insights and compliance.
- Assess your business needs: Consider how long you need to retain user and event data for analysis and reporting. Do you need data beyond 14 months? If not, avoid unnecessarily hoarding data that could pose compliance risks.
- Ensure compliance with data protection regulations: If you’re subject to GDPR, CCPA, or other data protection laws, ensure your GA4 data retention practices align with these regulations. Consult with your legal team or a privacy expert if needed.
- Conduct periodic data audits: Review your GA4 data to identify unnecessary or outdated information. Delete or anonymize data no longer needed to keep your analytics lean and compliant.
- Explore data export options: Take advantage of the opportunity to optimize your GA4 data retention with a powerful tool designed to make your data work for you. Try Maya Insights today and transform how you handle and analyze your marketing data.
Sign up for Maya Insights and start your journey toward better data management and insights.