How to Backup Google Analytics Data

With Google officially sunsetting Universal Analytics (UA), years of valuable historical website data are on a countdown to deletion. If you want to continue doing year-over-year reporting or reference past performance, it's time to create a backup. This guide will walk you through several methods for exporting and saving your Google Analytics data, from simple manual downloads to more robust, automated solutions.

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Why Bother Backing Up Your Google Analytics Data?

For years, Google Analytics has been the cornerstone of digital marketing, providing insights into website traffic and user behavior. The shutdown of the Universal Analytics platform on July 1, 2024, means all that historical data will be permanently erased. Saving it is about more than just data hoarding, it’s about preserving your business’s history.

• Maintain Historical Context: The most immediate reason is to enable year-over-year (YoY) analysis. Without a backup, you lose the ability to compare your current performance against past trends, seasonal patterns, and the impact of previous marketing campaigns.
• Data Ownership: Relying on one platform to store your data makes you vulnerable to changes you can't control. Owning a copy of your data gives you stability and the freedom to analyze it with any tool you choose, now or in the future.
• Deeper, Unsampled Analysis: Large websites often encounter data sampling in the GA interface, where reports are based on a subset of data for speed. Exporting your data can help you avoid sampling, allowing you to run analyses on your complete dataset for greater accuracy.
• Combine Data from Multiple Sources: When you export your GA data, you can merge it with information from other platforms like your CRM (e.g., Salesforce), advertising channels (e.g., Facebook Ads), or e-commerce platforms (e.g., Shopify). This allows you to build a comprehensive view of your entire customer journey.

Choosing the Right Backup Method

There isn't a single "best" method for everyone. The right choice depends on your technical comfort level, budget, the sheer volume of your data, and what you ultimately want to do with it. We'll cover four main approaches, starting with the simplest and moving toward the most sophisticated.

Method 1: Manual Exports (The Quick and Easy Way)

This is the most straightforward method and requires no special tools besides access to your GA account. It’s ideal for saving a few high-priority reports or for those who aren't comfortable with more technical solutions. However, it is extremely time-consuming for backing up entire websites.

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Step-by-Step Instructions:

1. Log into your Universal Analytics property.
2. Navigate to a report you want to save, like the Acquisition &gt, All Traffic &gt, Source/Medium report.
3. Set the date range in the top right corner. To minimize trips, choose a long period, such as an entire year.
4. Near the bottom of the table, find the "Show rows" dropdown and select the maximum number (usually 5000) to see as much data as possible on one screen. You will have to export page by page if you have more than 5000 rows.
5. Look for the "Export" link in the upper-right corner of the report.
6. Choose your preferred format: Google Sheets, Excel (XLSX), or CSV. CSV is often the most flexible for future use.
7. Repeat this process for every single report and date range you want to save.

Pros: Totally free, easy for anyone to do, and good for grabbing your most critical reports quickly.

Cons: Incredibly tedious and slow for comprehensive backups. It's easy to make mistakes or miss reports, and the exports are subject to GA’s data sampling. You are also limited to the aggregated data shown in the report, not the raw, user-level data.

Method 2: Google Sheets Add-on (The Automated Spreadsheet Approach)

If you're comfortable with spreadsheets, the official Google Analytics Spreadsheet Add-on is a huge step up from manual exports. It uses the Google Analytics API to pull data directly into Google Sheets, allowing you to configure reports and refresh them without endless clicking and downloading.

Step-by-Step Instructions:

1. Open a new Google Sheet.
2. Go to the main menu and select Extensions &gt, Add-ons &gt, Get add-ons.
3. Search for "Google Analytics" and install the official add-on by Google.
4. Once installed, navigate to Extensions &gt, Google Analytics &gt, Create new report. A sidebar will appear on the right.
5. Name your report and select the Google Analytics Account, Property, and View you want to pull data from.
6. Choose your Metrics (e.g., ga:sessions, ga:pageviews, ga:transactions) and Dimensions (e.g., ga:sourceMedium, ga:date, ga:landingPagePath). This part requires knowing the specific API names for your data points.
7. Click "Create Report." A new tab called "Report Configuration" will appear. You can further refine your settings here.
8. To run the report, go to Extensions &gt, Google Analytics &gt, Run reports. The data will populate in a new tab. You can also schedule reports to run automatically.

Pros: Free, significantly more efficient than manual exports, schedulable for automation, and puts your data in a familiar spreadsheet format.

Cons: Can hit API daily quotas if you're pulling massive amounts of data. The initial setup requires some learning, and you need to know the specific API names for metrics and dimensions. The data is still aggregated, not row-level.

Method 3: Custom API Scripting (The Developer's Route)

For those with programming skills (or access to a developer), using the Google Analytics Reporting API directly offers the most control and flexibility. This method involves writing code (e.g., in Python or JavaScript) to request data from Google and save it wherever you want, such as in CSV files, a database, or a data warehouse.

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The General Process:

1. Create a project in the Google Cloud Platform.
2. Enable the "Google Analytics Data API" or "Google Analytics Reporting API" for your project.
3. Create authentication credentials (like a service account key) that your script can use to securely access your data.
4. Write a script that uses these credentials to make requests to the API. Your script will specify the date ranges, metrics, and dimensions you need.
5. Your script will then process the API's response and save the data to a destination of your choice.

Pros: Complete customization over what data you pull and how it's formatted. It can be fully automated to run in the background. It's the most powerful way to extract large or complex datasets.

Cons: Requires significant technical expertise. The setup is complex, and you are still subject to API quotas and limits. You are also responsible for maintaining the code if anything breaks.

Method 4: Automated Data Pipelines & A Data Warehouse

This is the enterprise-grade solution and the most robust strategy for permanently preserving your data in an accessible and scalable way. It involves using a third-party tool, called a data pipeline or ETL tool (like Fivetran, Stitch, or Supermetrics), to automatically extract your GA data and load it into a cloud data warehouse (like Google BigQuery, Snowflake, or Amazon Redshift).

How It Works:

Think of it as setting up a permanent, automated bridge. Instead of manually pulling data, these tools connect to the GA API on one end and your data warehouse on the other. You set it up once, and the tool handles all the heavy lifting of pulling, cleaning, and storing the data on a set schedule.

While you can connect tools like Looker Studio (formerly Data Studio) directly to Google Analytics, they are primarily for visualization — they don't store your data. When the UA data is deleted, those dashboards will break. The pipeline/warehouse method creates a permanent, independent copy of your data that you can then connect your BI tools to.

Pros: Fully automated and reliable, designed to handle large volumes of data without hitting quotas, and stores your data in a structured, queryable format optimal for advanced analysis. It creates a single source of truth for your business data.

Cons: This is the most expensive option. You typically pay for both the pipeline tool and the data warehouse based on usage. The setup can also be technically involved, though less so than writing your own API scripts.

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What Data Should You Prioritize?

Trying to save everything can be overwhelming. It's better to focus on the key reports that you rely on for business decisions. Start by listing the questions you most frequently ask of your data. This will guide your backup strategy.

Here are some core reports to consider prioritizing:

• Overall Performance: Key metrics like Users, Sessions, Pageviews, and Bounce Rate over time.
• Acquisition Performance: Sessions by Source/Medium and by Channel to understand where your traffic comes from.
• Audience Data: Key information about your visitors like Country/City, and Device Category.
• Behavior Data: Your most important Landing Pages and Exit Pages.
• Conversion Data: Goal Completions and Ecommerce Transactions are essential. Make sure to include an associated dimension like Source/Medium so you can attribute conversions to their origins.

Final Thoughts

Saving your Universal Analytics data is a critical task to preserve your company’s digital history and ensure you can still analyze long-term trends. Whether you opt for simple CSV exports, automated Google Sheets reports, or a full-blown data warehouse, taking action now will save you from major reporting gaps in the future.

Moving, cleaning, and storing data manually is precisely the kind of tedious work that holds marketing and sales teams back. At Graphed, we automate this process by connecting directly to your tools like Google Analytics. We sync all your historical data for you, putting it right alongside a company's other crucial data from platforms like Shopify, Salesforce, and Facebook Ads. This means you can skip the complex setups and hours spent wrangling CSVs and instantly ask for visualizations and dashboards covering years of performance, all in one place. You can sign up for Graphed and have your historical UA data secured and ready for analysis in minutes.