How to Get Raw Data from Google Analytics

While the standard dashboards in Google Analytics 4 are great for a quick overview, they often hide the real insights you need. To truly understand customer behavior, build custom attribution models, or combine web data with sales info, you need to access your raw, unsampled data. This article guides you through the best methods for getting your raw data out of GA4, from the simple and quick to the powerful and automated.

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Why Standard GA4 Reports Aren't Always Enough

The default reports in Google Analytics are aggregates. GA simplifies your data into pre-calculated summaries to make reports load quickly. While this is useful for top-level metrics, it comes with a few key limitations:

• Data Sampling: For high-traffic websites or complex queries, GA4 may use a smaller, representative sample of your data to estimate the results. This can lead to inaccuracies when you need precise numbers.
• Limited Dimensions: Standard reports only allow you to combine a limited number of dimensions and metrics. If you want to analyze traffic source, device category, landing page, and a custom event all at once, you’ll quickly hit a wall.
• Inability to Join Data: You can't merge your raw GA4 data with data from your CRM (like Salesforce or HubSpot), your email platform, or advertising channels directly within the GA4 interface. This makes it impossible to track the full customer journey from ad click to final sale.

Accessing your raw data solves these problems. It gives you the full, unadulterated, event-level information you need to conduct deep, customized analysis.

Method 1: Exporting from the "Explore" Section (The Quickest Way)

If you just need a specific slice of data for a one-off report and don't want to set up any complex integrations, the manual export feature in the Explore section is your best bet. Think of this as a much more powerful version of the standard reports.

The "Explore" hub lets you build custom reports that go beyond the limitations of the standard dashboards. Once you've built a view you like, you can easily export it.

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How to Do It Step-by-Step:

1. Navigate to the Explore section in the left-hand menu of your GA4 property.
2. Start a new exploration by choosing a template like "Free form" or "Funnel exploration."
3. In the "Variables" column on the left, add the Dimensions and Metrics you need for your analysis. For example:
4. Drag and drop these dimensions and metrics into the "Tab Settings" column to build your report. You can place dimensions in "Rows" or "Columns" and metrics in "Values."
5. Apply any filters you need (e.g., filtering for only "google / cpc" traffic). Adjust your date range in the top left.
6. Once the report looks right, click the share and export icon (a square with an arrow pointing out) in the top right corner.
7. Choose your export format: Google Sheets, CSV, or PDF.

Best for: Quickly pulling a specific, custom-built dataset for a report or a quick analysis in Excel or Google Sheets.

Limitations: This method is still subject to sampling on very large datasets and is entirely manual. You have to rebuild and re-export the report every single time you need fresh data.

Method 2: Connecting GA4 to BigQuery (The Gold Standard)

For serious, ongoing analysis, setting up the native GA4 to BigQuery export is the most powerful and scalable solution. BigQuery is Google's cloud data warehouse, built to handle massive datasets. Connecting it to GA4 gives you a daily (or even real-time) feed of every single raw event that happens on your site, with no sampling whatsoever.

This allows you to run complex SQL queries, blend GA data with other data sources, and gain a complete picture of your business performance.

How to Set Up the BigQuery Export:

1. Go to the Admin section of your Google Analytics 4 property (the gear icon in the bottom left).
2. Under the "Property" column, find "Product Links" and click on BigQuery Links.
3. Click the blue "Link" button. This will start the linking process.
4. Click "Choose a BigQuery project" and select the BigQuery project you want to export your data to. If you don't have one, you'll need to create one first in the Google Cloud Platform Console.
5. Confirm your location and click "Next."
6. Configure your settings. Select the data stream(s) you want to export. You can also exclude specific events if you want to keep your BigQuery tables cleaner and reduce costs.
7. Choose an export frequency. You have two options:
8. Review your settings and click "Submit." Your data will start populating in BigQuery the next day.

Best for: Marketers and businesses that need a permanent, unsampled record of their data. It's the perfect foundation for building custom marketing dashboards, joining web data with your CRM, and performing truly advanced analysis.

Limitations: Requires some basic knowledge of SQL (or a tool that can query it for you) to make sense of the data. While BigQuery has a generous free tier, storing and querying very large volumes of data can incur costs, so it's wise to monitor your usage.

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Method 3: Using the Google Analytics Data API

The Google Analytics Data API is designed for developers or users leveraging business intelligence tools that need to programmatically fetch data from GA4. Instead of manually exporting reports, the API lets you build applications that request specific data and get a response back in a structured format (like JSON).

Unless you're comfortable with code or are configuring a BI tool, this probably isn't the method for you. However, it's good to know it exists because many third-party tools use this API behind the scenes to pull your data.

Steps for developers generally involve:

• Creating a project in the Google Cloud Platform Console.
• Enabling the "Google Analytics Data API."
• Creating service account credentials to authenticate your application.
• Writing a script (in a language like Python or JavaScript) to formulate a request specifying the dimensions, metrics, date ranges, and filters you need.
• Sending the request to the API and processing the data that's returned.

Best for: Building custom applications, integrating GA4 data into internal software, or automated, programmatic reporting pipelines.

Limitations: Requires a technical skillset and a deep understanding of GA4's schema (dimensions and metrics).

Method 4: Using Third-Party Spreadsheet Connectors

A great middle ground between the tedious manual process and the technical BigQuery setup is using a third-party add-on for Google Sheets or Excel. Tools like Supermetrics or the official Google Analytics spreadsheet add-on automate the process of pulling data directly into your spreadsheets.

These connectors essentially use the API for you via a user-friendly interface. You can set up queries to pull specific metrics and dimensions and schedule them to refresh automatically every day or every hour. This combines the flexibility of a spreadsheet with the automation of a more advanced solution.

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How It Generally Works:

1. Install the chosen add-on from the Google Workspace Marketplace or Microsoft AppSource.
2. Authorize the tool to access your Google Analytics account.
3. Use the add-on's sidebar to build a query, selecting the properties, date ranges, metrics, and dimensions you want to pull.
4. Run the query, and the data will populate directly into your spreadsheet cells.
5. Set up a "scheduled refresh" to keep your data up to date automatically.

Best for: Marketers and analysts who live in spreadsheets and want to automate their weekly or monthly reporting without the technical overhead of BigQuery.

Limitations: You are still at the mercy of API quotas and potential data sampling, though many tools have features to help mitigate this. The best tools are often subscription-based, adding another cost to your marketing stack.

Final Thoughts

Getting your raw Google Analytics data opens up a world of insights that are simply impossible to find in a standard dashboard. Whether you're using a quick manual export, an automated spreadsheet connector, or the powerful BigQuery integration, pulling your raw data is the first step toward making smarter, more data-driven decisions for your business.

Manually exporting CSVs, setting up complex data pipelines, or trying to query BigQuery can be a huge time sink, pulling you away from actually analyzing performance. This is precisely the friction we built Graphed to eliminate. We connect directly to your data sources, including Google Analytics and BigQuery, handling the technical setup so you don't have to. You can then simply ask questions in plain English - like "Which landing pages are driving the most conversions from Google ads?" - and get real-time dashboards instantly, skipping the manual reporting work entirely.