[UA→GA4] Archive your UA data with Google Sheets

All standard Universal Analytics properties started to sunset on July 1, 2023. You'll be able to access your UA property's data via the user interface and API until July 1, 2024.

Before you archive

There might be obvious business reasons to archive UA data. However, before attempting to archive as much data as possible for your UA properties, we suggest you consider the following:

  • You certainly don't need to archive all data at every granular level.
  • Data requirements will depend on how your business makes decisions.
  • The effort that you put into the data archiving and maintenance process should be worth the return you get out of that data.

A way to have a good coverage of your UA data requirements would be to first list out all stakeholders that depend on your UA property's data. Then, make a list of reports those stakeholders view on a regular basis and also a list of reports stakeholders and business leadership use for annual or quarterly planning. Determining the required dimensions, metrics, filters, and timeframe for each of these reports will give you a list that should cover most requirements your business will have from legacy UA data.

There are a few limitations around UA data archiving that you should keep in mind:

  • If you are using a standard UA property, you can't export hit level data to BigQuery.
  • There is no other way to export detailed hit level data.
  • For aggregate data, there is no way to export all combinations of metrics, dimensions, and timeframes.

How to archive UA data

One easy way for smaller properties to archive UA data is to create the report in the UI and export to Sheets or CSV from there. This can be done for reports with limited data since the export has a limitation of 5000 rows.

Our recommended way for archiving UA data is to use the Google Analytics Spreadsheet Add-on. First, install the add-on. Then, copy this template report we've created. Replace the 'View ID' in Row 3 with the View ID for your property and also update the date ranges. Note that adding significantly large date ranges might cause the add-on to fail. Then run the reports. This will export data for the most commonly used reports from the GA UI. Once the report is done running, you can directly query this data in BigQuery or create dashboards from it using Looker Studio. You will need to repeat this process for every View whose data you want to archive.

If neither of these solutions serve your business needs, we suggest you either build your own solution using the Google Analytics Reporting API v4 or reach out to one of our Google Marketing Platform Certified Partners for a customized solution.

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