Transitioning from GA3 to GA4: Your Complete Guide to Mastering Google Analytics Updates
Published: 4/15/2024
The Google Analytics Team notified users earlier in April that Google Analytics 4 will fully replace Universal Analytics properties on July 1, 2024. After this date, all Universal Analytics services and APIs will be discontinued, making these properties inaccessible. GA3 users are urged to migrate to Google Analytics 4 by following the provided migration guide. Exporting any data from Universal Analytics properties soon is also recommended, as it will be permanently deleted and unrecoverable after the service ends. Product integrations using Universal Analytics data will also cease to function, affecting things like ad campaigns and API requests. Users with Universal Analytics 360 properties should use the BigQuery integration to export historical data by June 30, 2024. Lastly, any projects within the Attribution (beta) will be deleted.
Export your GA3 data
- Manual Export Method: The initial step involves manually exporting data from GA3. This process includes navigating to critical reports such as Acquisition or Audience reports within GA3. Customize these reports based on requirements like geographic segmentation, page groupings, or dimensions like landing pages. Click ‘EXPORT’ at the top right corner and choose an appropriate file format (PDF, Google Sheets, Excel, or CSV). It’s vital to methodically repeat this process for each report within your GA3 property you deem essential to your business.
- Utilizing Export Tools: Leverage tools for bulk data export to enhance efficiency. Google Analytics Dev Tools, for example, offers a straightforward interface for this purpose. For an integrated approach to data analysis, the Google Analytics Sheets Add-On can be used to directly connect GA data with Google Sheets, streamlining the data management process.
- Data Storage and Organization: Post-export, the focus shifts to systematic data storage. Organize the exported data in a structured manner, ensuring it’s easily navigable and understandable for future use. Label each file descriptively, categorizing them by year, data type, or other relevant metrics. This organization is crucial in ensuring that historical data is stored, readily accessible, and usable for future analysis and decision-making.