Easy Steps to Export Data from Google Analytics 4

Exporting data from Google Analytics 4 can be a straightforward process if you follow the right steps.

First, you need to sign in to your Google Analytics account and navigate to the property that contains the data you want to export.

To do this, click on the property dropdown menu and select the property from which you want to export data.

Once you've selected the property, click on the "Reports" tab and then click on "Export" at the top right corner of the page.

From there, you can select the data you want to export and the format in which you want to receive it.

To set up Google Analytics 4, you'll need to create a new property in the Google Analytics console. This involves selecting the website or app you want to track and creating a unique property ID.

Google Analytics 4 is automatically enabled for new properties, so you don't need to manually switch from Universal Analytics. You can view your property ID in the Google Analytics console under the "Admin" section.

To set up Google Analytics 4, you'll need to have a Google account and access to the Google Analytics console.

To set up API access for Google Analytics 4, you need to create a new project if you don't have any existing project. This is a crucial step before you can enable the Google Analytics API for your project.

You'll also need to authenticate by creating credentials like an API key or OAuth client ID. This will allow you to access the Google Analytics API and start exporting your data.

To determine which API to use, consider the type of data you need to access. If you want to report on aggregate data that's available in the Google Analytics 4 UI, the Google Analytics Data API (GA4) is likely the best choice. This API is used by many third-party data transfer tools and visualization tools, and it's a great option for medium-sized projects or those that don't require unique data analytics capabilities.

Here are some key differences between the standard property limits and Analytics 360 property limits for the Google Analytics Data API (GA4):

If you need to access historical data or require more advanced analytics capabilities, the BigQuery Export might be a better option.

Installing the Google Analytics Spreadsheet Add-on is a straightforward process that allows you to export data directly to a spreadsheet, giving you control over your data.

To install the add-on, go to Extensions >> Add ons >> Get Add ons on your Google Sheets page. This will take you to the Google Workspace Marketplace where you can browse and install various add-ons.

The Google Sheets add-on for GA4 offers a convenient way to export data directly to a spreadsheet, giving you options like organizing and analyzing data within Google Sheets, scheduling regular data exports, and customizing report settings to fit specific needs.

One of the benefits of using the add-on is that it provides flexibility and saves time. However, it's helpful to have a basic understanding of GA4 dimensions and metrics to get the most out of your reports.

Many add-ons offer a free tier for a limited number of queries, with paid plans available for extended use. This means you can try out the add-on without committing to a paid plan, and then upgrade if needed.

Here are the steps to install the Google Analytics Spreadsheet Add-on:

By following these steps, you can easily install the Google Analytics Spreadsheet Add-on and start exporting your data to Google Sheets.

Universal Analytics (UA) is being sunsetted, which means it will no longer process data after July 1st, 2023.

You have 12 months to access your UA data, which means you can export it until July 1st, 2024.

GA4 combines most of UA's functionalities with Google's mobile development platform, Firebase, and has seen extensive updates centered around its machine-learning base.

Here are some key differences between UA and GA4:

Google automatically started creating GA4 accounts for brands that hadn't done so yet in March 2023, to expedite the process of making GA4 the prominent Google Analytics version for everyone.

Exporting data from Google Analytics 4 can be done in several ways, including connecting to the Google Analytics Data API (GA4) or using BigQuery Export. This API is likely the most heavily utilized data extraction capability, and it's used by all third-party data transfer tools and visualization tools that integrate directly with GA4.

The Google Analytics Data API (GA4) offers several benefits, including ease of use and capabilities. It's typically used for exporting historical data, especially when BigQuery isn't connected or for KPI reporting. However, if the data will be accessed heavily, historical data access beyond what the GA4 data retention setting allows is required, or accessing raw data is pivotal due to advanced analytic needs, then the BigQuery Export is probably the best option.

If you're new to coding, you can still leverage the Google Analytics APIs with the help of API connectors like JSON Client by Coupler.io. This allows you to fetch data via the REST API and import it seamlessly into platforms like Google Sheets or BigQuery.

Configuring parameters is a crucial step in exporting data from Google Analytics 4. You'll need to provide all the necessary details required, such as metrics, dimensions, and sort.

To do this, you'll want to access the property from which you want to export data and configure the required parameters. Once you're done, you'll be able to export your data.

The Google Analytics API offers several APIs that can help you enrich your analytical capabilities. These include the Core Reporting API, which grants access to a vast array of report data within Google Analytics.

Here are some of the APIs available in the Google Analytics API:

With the right parameters configured, you'll be able to export both real-time and historical data from Google Analytics. This can be done via the REST API and imported into platforms such as Google Sheets or BigQuery.

The Google Analytics API offers several APIs that enrich your analytical capabilities beyond the standard Google Analytics UI. The Core Reporting API grants access to a vast array of report data within Google Analytics.

You can use the Google Analytics API to fetch datasets as it provides a purely technical way to fetch and manipulate data that offers flexibility beyond what is available to you through standard Google Analytics web interface or add-ons. This can be especially useful for organizations who want to report on aggregate data that is available and seen in the GA4 UI.

The Google Analytics Data API (GA4) is likely the most heavily utilized data extraction capability of the three options. It is used by all third-party data transfer tools and any visualization tools that integrate/connect directly with GA4. Connecting to the Google Analytics Data API (GA4) is typically best for organizations who want to report on aggregate data that is available and seen in the GA4 UI.

Here are the quota limits for the Google Analytics Data API (GA4):

The Google Analytics API also offers the Realtime API, which enables access to real-time data for immediate insights. This can be useful for getting a snapshot of current user behavior.

Using the Query Explorer is a straightforward process that allows you to interactively query and export data from Google Analytics 4 without writing any code.

To access the Query Explorer, visit the Google Query Explorer tool and log in to your existing Google account. You'll need to use the account that has access to the desired property containing the data.

Once you've logged in, you can run the query by clicking the 'Run Query' button. After running the query, you can export the data in various formats such as CSV, JSON, or HTML, and then download it by clicking the "Download" button.

To access the Query Explorer, start by visiting the Google Query Explorer tool and logging in to your existing Google account. This will give you access to the tool and allow you to begin working with your data.

You'll need to log in using the account that has access to the desired property which contains the data. This is an important step, as you won't be able to access the data without the correct account credentials.

When using BigQuery Export, you might notice some differences between the data it provides and what you see in the Google Analytics interface. This is because BigQuery Export gives you access to raw event and user-level data, without any value additions made by Google Analytics.

The data from BigQuery Export might not match the data in the Google Analytics interface, so it's essential to understand these differences. To bridge this gap, you can refer to the article "Bridging the gap between Google Analytics UI and BigQuery export."

One key difference is that BigQuery Export does not include user-attribution data for new users, specifically traffic_source.name, traffic_source.source, and traffic_source.medium. This is because BigQuery streaming export doesn't include these data points.

If you've set up BigQuery Export to export data daily, you'll notice that a table is created each day. However, this daily export does not include the missing user-attribution data for new users.

To summarize the differences between BigQuery Export and the Google Analytics interface, here's a quick rundown:

BigQuery is a serverless and cost-effective enterprise data warehouse that works across clouds and scales with your data. It's a great place to store and analyze large amounts of data from Google Analytics 4.

To import data into BigQuery, you'll want to connect GA4 to BQ without the need for a Google Analytics 360 license. This is a great option for large, advanced projects that require data manipulation or a large historical time period.

BigQuery provides one of the only places where organizations can access their backend user IDs sent to GA4, helping to tie together the full user journey. This is especially useful for projects that need to adhere to privacy regulations.

Here are some key benefits of importing data into BigQuery:

BigQuery is a powerful tool that can help you unlock insights from your GA4 data. With its flexibility and durability, you can combine online and offline data to gain a deeper understanding of your users.

Exporting data from Google Analytics 4 can be a bit of a challenge, but don't worry, we've got you covered. Integration complexity is one of the main hurdles, requiring you to align metrics and formats across different platforms.

Data limitations are another issue, with row or query limits restricting the amount of data you can export at once. This can be frustrating, especially if you need to export large datasets.

Manual effort is also a challenge, as regularly exporting data manually can be time-consuming. I've seen businesses struggle with this, especially those that need frequent data updates.

Complex formatting is another problem, as exported data may need further cleaning and formatting to be usable in other tools. This adds extra steps to your workflow, but there are solutions to help you overcome these challenges.

Here are some common challenges and their solutions: