How to Save Universal Analytics Historical Data

If you have data on Universal Analytics, do know that it won’t last forever. In 2023, this service will stop collecting new data, and users will lose access to the Universal Analytics interface and API on July 1, 2024. After that, all historical data will be deleted, and you will no longer be able to view or use it.

Imagine years of valuable statistics just disappearing! Sounds scary, doesn’t it? The only way to continue working with that data would be to download and save it. Let me show you how to do so effectively.

Why is Universal Analytics data important for business?

For many businesses, Universal Analytics is the only source of historical data. Although Google Analytics 4 (GA4) was released in October 2020, many businesses didn’t make the switch in time or didn’t set it up correctly, which left them without this historical analytics data.

Universal Analytics data are used to compare current statistics with past periods. The results of this analysis allow you to:

  • see seasonal trends, for example, by month, quarter, or year;
  • identify increases or decreases in audience, traffic, conversions, revenue, and other indicators;
  • analyze the impact of specific actions or campaigns and identify weaknesses, opportunities, and threats.

The old version of Google Analytics will soon be no longer available. Unless you save your data, you won’t be able to analyze your past strategies and make future decisions based on these insights. Besides, it’s nice to have a copy of past statistics as a record of your company’s history and achievements.

Different ways to save Universal Analytics data

The only way to save your data is to export it to another platform where you can store, analyze, and visualize it. There are two ways to do that.

Manual export

You have to select time periods and export data to Excel files, bit by bit. It’s easy and free of charge, but it comes with a number of risks:

  1. Missed insights. You won’t be able to export everything at once because Google samples the data over a long period of time. So you have to manually select smaller date ranges and download each one separately. It is very easy to make a mistake during this process.
  2. Limited analysis. If some of the data is dropped during the sampling process, you won’t be able to perform deep analysis. 
  3. Tedious process. You will spend many hours doing this, and there is a risk of not achieving the desired result due to mistakes.

Working with manually exported data is like reading random pages of a book. You won’t get the whole picture.

Automatic export

This method uses the API to export historical data from Universal Analytics. The API allows you to download and process large amounts of information in various formats.

You can use this method to collect and store your statistics in any database. The choice of database is yours and can include the storage you already use: cloud or local, without any restrictions.

Based on the experience of specialists at Netpeak Agencies Group, using Google Analytics Reporting API and BigQuery is the most advantageous approach.

Google BigQuery integrates with Google Analytics, GA4, Firebase, and Google Ads, allowing you to combine data from multiple platforms and create a complete picture of your business. BigQuery also provides a high level of security and reliability by using different permissions and roles to control data access. The service is both scalable and flexible without the need for large resources.

The analytics department of Netpeak Ukraine has developed a solution for automatic data export. It includes the following steps:

1. Create a Google Cloud account.

2. Enable the Google Analytics Reporting API in the Google Cloud Console.

3. Create a service account and upload a JSON key.

4. Grant Universal Analytics access to the service account.

5. Create a new dataset in Google BigQuery.

6. Copy the script and add the following information:

  • KEY_FILE_LOCATION — the path to the JSON key;
  • VIEW_ID the Universal Analytics view ID;
  • BIGQUERY_PROJECT — project name in BigQuery;
  • BIGQUERY_DATASET — dataset name in BigQuery;
  • BIGQUERY_TABLE — name of the table in BigQuery where the data is stored.

It is important to enter these parameters correctly; otherwise, the script will not function properly.

7. Run the script. After the script runs, the statistics from Universal Analytics are securely exported and stored in BigQuery.

In this dataset, you will get data about the number of

  • sessions and page views;
  • website users; 
  • new users by country, browser, channel group, origin, page, and device.

Customize the script accordingly to obtain other data. Review the documentation to determine which metrics and parameters you need. This resource provides a comprehensive list of available metrics and parameters.

If you choose to do an automatic export, it is better to leave the correct setup to specialists. Feel free to contact Netpeak, and Netpeak specialists will help you:

  • export your data from Universal Analytics to Google BigQuery for safe storage;
  • customize its visualization for different analysis purposes; 
  • combine Universal Analytics data with other data from Google and non-Google platforms.

Which reports can you recreate? 

If you have historical data, it’s easy to recreate old reports or even create new ones.

  1. Audience reports allow you to learn more about your users, including their age, gender, language, country, city, device, browser, operating system, interests, and more. Use this report for segmentation, personalization, and targeted advertising.
  2. Traffic reports show where your users are coming from: their sources, channels, campaigns, keywords, links, and more. Use this report to evaluate the profitability of your marketing campaigns and optimize your traffic acquisition strategies.
  3. Behavior reports show how users interact with your website or app, including their login pages, sessions, time on site, return rates, bounces, events, goals, and more. Use this report to analyze the user experience, identify problems and opportunities, and improve your products and services.
  4. Conversion reports help you understand how your users achieve their goals, such as purchases, registrations, downloads, subscriptions, and more.

Here are some examples of reports based on Universal Analytics data.

Funnel analysis

Page analysis

Marketing campaign performance review 


  1. Saving Universal Analytics data is a strategic step that helps you preserve historical data, understand seasonality, and have statistics at your fingertips. 
  2. There are two ways to save data: manually or automatically.
  3. The manual method is time-consuming and carries the risk of missing important insights. It is also more difficult to analyze the data manually.
  4. Automatic export uses the Google Analytics Reporting API and BigQuery.
  5. Once the data is stored, you can view reports on audience, behavior, traffic, and conversions and create new reports as needed.
Found a mistake? Select it and press Ctrl + Enter