How to Export and Safe Your Universal Analytics Data
An Essential Guide on How to Export Your Universal Analytics Data Before Deprecation
On the 1st of July 2024, Universal Analytics, the previous version of Google analytics 4 will officially cease to exist. Therefore, it’s important for businesses who are measuring user behaviour on this platform, to migrate the data to an alternative host. Thankfully, you can access the data until the 1st of july, which leaves you with some time to prepare. In this article, we will tell you what you need to do in order to safeguard your Universal Analytics data.
From July 2023, Universal analytics already stopped processing hits, and therefore became a useless tool to observe user behaviour. This decision was made by Google to be able to better respond to privacy regulation changes in the industry – something Universal Analytics was not built for. A new platform, Google Analytics 4, was built to answer the need. However, what you still were able to do was look at historical data in Universal Analytics to understand and compare the behaviour of your users from before July 2023.. If you’re one of those still evaluating historical data in UA, you need to buckle up for a big change to come. All data will be deleted, and Google has promised not to be kind; “While the standard sunset took weeks to complete, the full Universal Analytics shutdown will happen within a week” – source. This process will start on the 1st of July.
Trusting this is not an announcement subject to any delays (unlike the third-party cookie deprecation scheduled for 2024, but recently announced this has been moved to early 2025 – source), it’s best to start the export process today. Let’s look at the steps involved in order to safeguard your Universal Analytics data.
All users, including those accessing 360 properties, will lose access to the Universal Analytics user interface and API starting on July 1, 2024.
Universal Analytics Data Export Methods
1. Individual Report Exports (Best for Small Datasets)
The most time consuming method is to export your individual reports one by one. This method would only suit if you don’t need all data exported, but are happy with just a couple of reports you used to gain insights. To do this, you simply navigate to the report in Universal Analytics, select the preferred date-range and report settings, and click on ‘Export’. You can choose between a wide range of export types, which you can find here.
The report will automatically be stored on your computer.
2. Google Sheets Add-on for Analytics (For Smaller Properties)
There’s a handy Google Sheets add-on specifically designed for exporting Universal Analytics data. This is mostly recommended for smaller Universal Analytics properties, since the export has a limitation of 5000 rows.
If this is your method of preference, you can get started with these instructions to archive your data.
Get started: Install the Google Sheets add-on for Analytics and follow these instructions to archive your data.
Google has created a template for you to use for this method, which you can find here.
After you export the data, you can access the data from BigQuery (now free to use for Google Analytics 4 users), or in Looker (which can also be merged with Google Analytics 4 data) and use it for your future analysis.
3. Google Analytics Reporting API (For Large Datasets)
This is your go-to solution if you want to export a large amount of data from Universal Analytics.
Before getting started, it’s important to know there are versions of the API, so it’s important to use the following: Legacy Reporting API v4 (for Universal Analytics). This version is specifically designed for Universal Analytics properties.
The first step in this process is to identify the data you need. You can then use the API to submit a request. This request specifies the view (profile) in Google Analytics you want to access, the date range for the data, and of course, the dimensions and metrics you’re interested in.
How to create the API connection depends on your preferred coding language. In this article, you can find the start guides for all possible coding languages. This is where you developer can get started (look for this section at the bottom of the article):
A requirement to make this method work is to create a project in Google Cloud. If you’re hesitant about creating an account for Google Cloud, we would like to understate that having a Google Cloud project is also a prerequisite for Server Side tracking; a future proof first-party measurement solution for Google analytics 4. Read more here. Therefore, setting up the project is a little bit of work at the beginning, but it’s likely that you will benefit from this in the future.
4. BigQuery Export for Universal Analytics 360 Users
If you’re a 360 user, you most likely already have your account linked to BigQuery. From there, you just need to export the data to BigQuery. This article explains how to export Universal 360 data to BigQuery.
When you have completed this process, your data is safe; Bigquery data will not be affected after the deprecation of UA360. BigQuery is a cloud data warehouse service offered by Google Cloud Platform. It acts as a storage location for your exported GA360 data. It is a separate service from GA360 and functions as a data warehouse. As long as you manage your BigQuery project according to Google’s policies and billing, your exported GA360 data will be safe and accessible for analysis, even after the deprecation date.
If you’re a 360 user and you haven’t linked your property to bigquery yet, you can get started with the guide linked above, as it also explains how to link Universal Analytics 360 to Bigquery.
Important Limitations to Consider
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.
- UA uses data sampling for reports with a large volume of data. This means you might not see the complete picture, especially for reports covering long timeframes or with many dimensions. When exporting data, be mindful of these limitations and consider segmenting your data or adjusting timeframes to minimise sampling.
- For aggregate data, there is no way to export all combinations of metrics, dimensions, and timeframes.
- UA data exports are limited to 5,000 rows per report and specific file formats like CSV or TSV. If you have a large dataset, you might need to segment your data or use third-party tools that can handle bigger exports.
- For UA360 users: BigQuery Export supports backfilling data, but there are limitations. Refer to Google’s documentation for details on avoiding export failures and backfilling data.
By safeguarding your universal analytics data before the sunsetting on the first of july 2024 and understanding the limitations of the described methods, you can ensure a smoother archiving process for your UA data before Google sunsets the platform.
If you want to simplify the process, consider signing up for a GA4 UP subscription. With this subscription, we audit your GA4 setup, implement new feature releases and update you on new privacy releases (stay updated with GDPR / CCPA) for a small monthly fee. This subscription allows you to focus on your priorities while the experts handle the rest. Learn more about how we can support you here.
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