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How to Connect to Google Analytics API

Cody Schneider•

Tired of being limited by the standard Google Analytics dashboard? The Google Analytics API lets you pull your raw website data directly into other tools, from spreadsheets to custom dashboards, giving you complete control over your reporting. This guide will walk you through the most practical ways to connect to the Google Analytics API, from simple, no-code solutions to more advanced methods.

Why Connect to the Google Analytics API Anyway?

You might be wondering why you'd go through the trouble when you can just log into Google Analytics. The standard interface is great for a quick overview, but pulling your data via the API unlocks a ton of new possibilities.

  • Build Custom Dashboards: You're no longer confined to the widgets and layouts Google gives you. Pull your GA data into tools like Google Sheets, Looker Studio, or Power BI to build reports that show exactly what you and your stakeholders want to see, in the format you prefer.

  • Combine Data Sources: Your website traffic is only one piece of the puzzle. The real insights happen when you combine GA data with information from other platforms. By using the API, you can place your traffic and conversion data right next to your ad spend from Facebook Ads, sales data from Shopify, and lead data from Salesforce to see the full picture of your performance.

  • Get Unsampled, Granular Data: For sites with high traffic, Google Analytics often uses "sampled" data in its reports to load them faster. This means the numbers you're seeing are an estimate, not a full count. Using a direct API connection, especially through BigQuery, allows you to work with raw, unsampled data for more precise analysis.

  • Automate Your Reporting: Stop wasting time every Monday morning downloading CSVs and pasting them into a spreadsheet. An API connection allows you to create live, auto-updating reports. Your data refreshes automatically, so you can spend your time analyzing insights, not wrestling with exports.

A Quick Primer: Understanding GA APIs and Key Terms

Before diving into the "how," let's quickly clarify two versions of the API you might see mentioned. Most of this guide focuses on the latest version for Google Analytics 4.

  • Google Analytics Data API: This is the current and primary API for pulling report data from Google Analytics 4 properties.

  • Universal Analytics Reporting API (v4): This is the legacy API for the old Universal Analytics (UA). Since UA has been deprecated, you should be focusing on the GA4 Data API for any new projects.

When you start pulling data, you'll constantly encounter two fundamental concepts:

  • Dimensions: These are the attributes or categories of your data. Think of them as the "what." Examples include Country, Traffic Source, Device Category, or Page Title.

  • Metrics: These are the quantitative measurements - the numbers you're actually counting. Think of them as the "how many." Examples include Sessions, Users, Conversions, and Event Count.

A typical API request essentially asks Google: "Show me [Metrics] broken down by [Dimensions] for a specific time period."

Method 1: The No-Code Way Using Add-ons and Connectors

For most marketers and business owners, this is the best and easiest place to start. You don't need to write any code, you just use a pre-built tool that handles the connection for you. The most popular choice is the official Google Analytics add-on for Google Sheets.

Connecting GA4 to Google Sheets

This lets you pull your GA data directly into a spreadsheet, where you can then build charts, pivot tables, or custom calculations.

  1. Install the Add-on: Open a new Google Sheet. Go to Extensions > Add-ons > Get add-ons. Search for "Google Analytics" and install the official one (it will likely be the first result). Grant it the necessary permissions.

  2. Create a New Report: Once installed, go to Extensions > Google Analytics > Create a new report. A sidebar will appear on the right.

  3. Configure Your Report:

    • Give your report a name (e.g., "Monthly Traffic by Source").

    • Select the correct Google Analytics Account and GA4 Property.

    • Choose your Dimensions (e.g., sessionSourceMedium) and Metrics (e.g., sessions, totalUsers, conversions). You can search for the ones you need.

    • Set the Date Ranges (e.g., "Last 30 Days").

  4. Run the Report: Click "Create Report." A new tab called "Report Configuration" will appear. Don't edit this directly! Go back to Extensions > Google Analytics > Run reports. The add-on will now run your query and drop the data into a brand-new tab in your sheet.

  5. Schedule Automatic Refreshes: To automate it, go to Extensions > Google Analytics > Schedule reports. You can set it to run every hour, day, week, or month, keeping your data fresh without you having to touch it.

Other Popular Connectors

  • Looker Studio (formerly Data Studio): This is Google's free data visualization tool. It has a native "Google Analytics" connector that allows you to link your GA4 property in just a few clicks and start building interactive dashboards.

  • BI Tools (Power BI, Tableau): Major business intelligence tools have built-in connectors for Google Analytics. You typically select "Get Data," search for the Google Analytics connector, and sign in to authorize access.

  • Third-Party Connectors (Supermetrics, Zapier): Tools like Supermetrics are extremely powerful for pulling GA data into Sheets, Excel, or BI tools, often with more user-friendly templates and options than the free add-on. Zapier can trigger workflows based on GA goals, though it's less for bulk reporting.

Best for: Marketers, analysts, and business owners who want to automate reporting in spreadsheets and BI dashboards without writing code.

Method 2: The Developer Way Using a Custom Script

If you need maximum flexibility or want to integrate GA data into a custom application, you'll need to interact with the API directly using a programming language like Python or JavaScript. This process is more technical and involves setting up access through the Google Cloud Platform.

Here is a high-level overview of the steps involved, using Python as an example.

  1. Create a Google Cloud Project: Go to the Google Cloud Console and create a new project.

  2. Enable the API: In your project, navigate to the API Library and search for the "Google Analytics Data API." Enable it.

  3. Create a Service Account: To allow your script to authenticate securely, you need to create a service account. Go to IAM & Admin > Service Accounts. Create a new account, give it a name, and download the JSON key file. Treat this file like a password!

  4. Give the Service Account GA Access: Copy the email address of the service account you just created (it looks like my-service-account@...iam.gserviceaccount.com). In Google Analytics, go to Admin > Account Access Management and add this email as a user with at least "Viewer" permissions.

  5. Write Your Code: Now you can install the Google API client library for Python and write a script to make the request.

Here’s a basic Python example of what pulling Sessions by Country might look like:

Best for: Developers, data scientists, and engineers who need to integrate GA data into custom software or perform complex programmatic analysis.

Method 3: The BigQuery Way for Unsampled Data

For businesses with serious data analysis needs, an even better option exists: linking GA4 directly to Google BigQuery, a serverless data warehouse. When you do this, Google automatically exports all your raw, event-level data from GA4 into BigQuery every day.

This isn't using the API in the same way as the other methods, instead of "requesting" reports, you're getting a complete, granular copy of your data that you can then query with SQL.

Steps to Connect:

  1. Link GA4 to BigQuery: In the GA4 admin panel, under your Property settings, you'll find "BigQuery Links." Click this and follow the prompts to link to your Google Cloud Project.

  2. Choose Your Data Streams and Frequency: Select which web or app streams you want to export and whether you want a daily export or a near real-time "streaming" export (which has costs).

  3. Query Your Data in BigQuery: Once a day's worth of data has arrived, you can log into BigQuery and use Standard SQL to run queries against it. Everything from a page view to a purchase is stored as a separate event line.

A simple query to count sessions by traffic source might look something like this:

Best for: Data-heavy businesses, e-commerce sites, and data teams who need access to raw, unsampled data for deep, complex analysis.

Choosing the Right Method for You

Feeling a bit overwhelmed? Don't be. Here’s a simple rule of thumb:

  • If you just want to automate reports in a spreadsheet or dashboard, use Method 1 with a no-code connector like the Google Sheets add-on or a Looker Studio connection.

  • If you are a developer building a custom app, use Method 2 and a client library in your language of choice.

  • If you have a data team and need to analyze raw user behavior at a granular level, use Method 3 and the BigQuery export.

Final Thoughts

Connecting to the Google Analytics API opens up a powerful world beyond the standard user interface. Whether you're using a simple Google Sheets add-on to automate a weekly report or leveraging BigQuery for deep analysis, pulling your data out lets you build a truly customized and comprehensive view of your business performance. The key is to start with the method that matches your current needs and technical comfort level.

But what if pulling data could be as simple as just asking a question? All these methods, even the "easy" ones, still require setup, configuration, and a basic understanding of metrics and dimensions. That's precisely why we built Graphed. We wanted to eliminate the need to deal with API credentials, configuration screens, or code entirely. You just connect your Google Analytics account once with a few clicks, then ask questions in plain English like, "Show me traffic by channel for the last 90 days as a line chart," and get your answer instantly.