Universal Analytics Historical Data: Export, Archive, and Analyze

Universal Analytics historical data is a treasure trove of insights waiting to be uncovered. You can export, archive, and analyze this data to gain valuable insights into your website's performance over time.

Exporting data from Universal Analytics allows you to retrieve data from the past 30 days, and you can export up to 500,000 rows per export. This gives you a good starting point for analyzing your data.

To get the most out of your historical data, it's essential to understand how to archive it properly. Archiving your data helps you to keep it organized and easily accessible for future analysis.

Analyzing your historical data can help you identify trends and patterns in your website's performance. By examining your data, you can determine what's working and what areas need improvement.

Exporting historical data from Universal Analytics is crucial for SEOs and business owners who want to preserve insights and trends. You can export data to spreadsheets, use the Google Analytics Reporting API, or import data into another analytics platform outside of Google.

There are several options for exporting historical data, including manual file downloads, using the Google Sheets add-on, and downloading data using the Google Analytics API. Manual file downloads are easy for most users to do, but they can be time-consuming and have a 5000-row limit.

Here are some key considerations for exporting historical data:

* Export MethodProsConsManual file downloadsEasy to do, freeTime-consuming, 5000-row limitGoogle Sheets add-onFairly simple to implement, fast to downloadRestrictive to a set timeframe, sampling issuesGoogle Analytics APIPulls data quickly once set upRequires web development knowledge and resources, API quota limitations

BigQuery is another option for exporting historical data, offering increased data insights and flexibility. However, it can be complicated for novices to set up initially and may require technical resources.

Exporting historical data from Google Analytics can be a bit of a challenge, but don't worry, I've got you covered. You can use the Google Reporting API to manually export your data, which can really speed up the process.

Using the Google Reporting API requires a developer, but it's worth it. Your data will be exported to Cloud storage, and you can connect it to Looker Studio from there. Once your historical data is in the API, a programmer can build dashboards to visualize it or integrate it with other apps.

Here are the steps to follow:

Note: You'll need to have some programming knowledge to use the Reporting API, but it's definitely worth the effort if you want to export large amounts of data quickly and easily.

Exporting Historical Data is a crucial step in preserving valuable insights about your website's performance. For many SEOs and business owners, historical data is essential for long-term analysis of trends and patterns.

Having a baseline for benchmarking and forecasting is also a significant advantage of preserving historical data. This helps you understand how your website's performance has changed over time.

Regulatory compliance is another important reason to preserve historical data. Businesses subject to the EU's General Data Protection Regulation (GDPR) might need to retain historical analytics data to prove they followed the rules about collecting and storing users' personal data.

If your website was launched shortly before Google sunsetted UA, there might not be much data to preserve. But if you have a longstanding domain with years of data in Universal Analytics, preserving some historical insights can be very helpful.

Here are some specific reasons why preserving historical data is important:

Backing up your Universal Analytics data is a crucial step in preserving your historical data. You can use a Python script to download UA data if your needs are complex.

There are four main options available for archiving your Universal Analytics data, each with its own pros and cons. You should choose a method based on your team's resources and skills.

The Google Sheets method can be a good option, but you may encounter sampling if you pull too much data at once or your report is too detailed. Sampling can limit the accuracy of your data.

Several factors play a role in the decision-making process when determining whether or not to back up your Universal Analytics (UA) data.

You can use Data Studio to visualize historical data from Universal Analytics (UA) and Google Analytics 4 (GA4) side by side. This allows for easy comparison of year-over-year results.

To create a Data Studio report, start with a blank report and select Google Sheets as the data source. Locate the spreadsheet with your historical data, such as "UA Historical Data _Traffic Acquisition_2021", and select it.

Data Studio will automatically create a table from your historical data. You can then copy and paste the table, change the data source to your GA4 account, and update the metrics and dimensions to match.

To compare primary metrics year over year, set the date range in the GA4 table to match your historical data. You can also use the Google Analytics Reporting API to manually export UA data and connect it to Looker Studio.

If you rely on constant or sporadic access to historical data, retaining it post-UA shutdown becomes critical for continuity.

The frequency of referencing older data is a clear indicator of its importance to ongoing operations. This suggests that if you regularly or occasionally use past data in current decision-making or performance evaluations, retaining it post-UA shutdown becomes critical for continuity.

You can analyze how frequently you reference past data by considering the following factors: how often you use older data, how it informs your current decisions, and how it's used in performance evaluations.

If your operations rely on constant or sporadic access to historical data, it's essential to retain it for future planning and trend analysis. This is because historical data can provide valuable insights and inform long-term strategy.

Here are some key considerations for evaluating the usage of your historical data:

Google Analytics 4 (GA4) and Universal Analytics (UA) have different data models, making it rough to compare the two.

You can create a Data Studio report to stack a historical data table on top of a GA4 data table, allowing you to see YoY results in one place.

To get started, open Data Studio and click to start a Blank Report. Then, select Google Sheets as your data source and locate the spreadsheet named “UA Historical Data _Traffic Acquisition_2021.”

The first row of your spreadsheet will automatically name your metrics and dimensions, so keep both boxes checked. For example, if your headers start at A15 and end at E62, your range will be “A15:E62.”

Data Studio will automatically create a table, which will look similar to the screenshot below.

To create the same table for GA4 data, copy and paste your table, then change the data source to your Google Analytics 4 account.

Preserving data is crucial for many businesses, especially those with longstanding domains and years of data in Universal Analytics. You may not need to care if your website was launched shortly before Google sunsetted UA, but for many SEOs and business owners, preserving some historical insights can be helpful.

Google encourages users to export their historical data, and while it's not possible to migrate data, it can still be saved. Google allows GA360 users to export Universal Analytics data to BigQuery, but this option is inaccessible for smaller organizations due to the cost.

To export historical data, you can use DIY methods or tools that can handle more complex requests. You can export data as .tsv (tab separated values) and open it in Excel or Google Sheets.

If you're looking to save data from historical sites, consider the options available.

BigQuery might not be the best choice for saving data, especially if you don't use it.

Saving data from historical sites requires careful consideration of the available options.

One option is to export data from the site, but this can be a complex process.

You can export data from historical sites, but it's essential to follow the correct procedures.

Data from historical sites can be fragile and easily damaged, so it's crucial to handle it with care.

Exporting data from historical sites can be a time-consuming process, but it's worth the effort.

Deciding how long to keep your historical data is a crucial step in preserving it. You may not need to care if your website was launched shortly before Google sunsetted Universal Analytics, but for many businesses, especially those with longstanding domains, preserving some historical insights is essential.

You might be wondering how far back you need to go. Many organizations have been using Google Analytics since the mid-2000s, and if that's the case, you may need to archive data from nearly 20 years ago.

Consider archiving back to 2018 or so to ensure you have pre-pandemic data, as the pandemic presented data anomalies for many companies. This will give you a solid baseline for benchmarking and forecasting.

Here are some key considerations to keep in mind:

You have several options when it comes to backing up historical data from Universal Analytics. You can use manual methods, or Free and Open Source solutions like Matomo, depending on your needs and technical skills.

Optimizing your backup process can be simplified with tools like PowerBI or BigQuery, which have many connectors available. Hevo and Electrik AI also offer pre-built data pipelines that export historical data from Google Analytics to a database file or data warehouse of your choice.

If you're not an Analytics 360 user, you'll need to create a Google API Console project and enable BigQuery. Then, you'll need to prepare your project for BigQuery export and set up a free trial of Supermetrics.

Here are some options to consider:

Several factors play a role in the decision-making process when determining whether or not to back up your Universal Analytics (UA) data.

Historical data is vital for future strategies, including marketing campaign planning, website design changes, and product development. Understanding how historical data might contribute to future strategies is essential.

If past performance data is projected to shape future actions, ensuring its availability post-UA becomes a strategic imperative. Evaluate your strategic planning processes and ascertain how historical data may fit into future roadmap development.

The Google Analytics Query Explorer and Google Analytics Sheets Add-On offer flexibility in extracting and utilizing historical data. You can manually input dimensions and metrics to execute a query, or create report configurations in Google Sheets to generate reports.

Being tasked with exporting Universal Analytics data can feel overwhelming, but it's better to save something than stay paralyzed by not knowing the exact right approach.

To get started, ask yourself some questions to help decide what to export. These include: how often you've used your UA data since setting up GA4, what you care about on your website, and what metrics you use for reporting.

Exporting your UA data to Google Sheets is a good starting point, as it's free and easy to do on your own.

Here are some suggested report exports to at least save something:

For each report, export the 12 most recent full months of data, one at a time. You can also apply a segment for Organic Traffic or set Default Channel Grouping as the secondary dimension for Behavior reports.

When planning your data archiving, it's essential to identify what matters to you. Prioritize downloading data that you regularly refer to, such as conversion and sales data.

To do this effectively, make a full list of the data you need to archive. This will help you stay organized and ensure that you don't miss any crucial information.

Prioritize your data by focusing on the most important metrics, such as conversion rates and sales figures. This will help you make informed decisions and drive business growth.

Here's a list of the types of data you may want to prioritize:

By focusing on the data that matters most to you, you'll be able to make the most of your data archiving efforts and drive business success.