[GA4] BigQuery Export: How to Build Your First Dashboard Using Looker Studio

Creating a comprehensive dashboard using GA4 data exported to BigQuery and visualized in Looker Studio allows marketers and data professionals to gain deeper insights into user behavior and campaign performance. By integrating these tools, you can move beyond standard GA4 reports and unlock advanced analysis.

Looker Studio’s flexibility offers dynamic visualizations that are easy to share with stakeholders and marketing teams. The ability to build interactive dashboards means you can filter data, explore trends, and track performance in real time, enhancing your ability to respond to changes quickly.

This article is Part 7 in our series, "[GA4] BigQuery Export."

If you're following along, check out the previous articles in the series:

• Part 1: How to Connect Step-by-Step
• Part 2: Events Table Schema and Managing Dates
• Part 3: How to Query GA4 Event Data
• Part 4: How to UNNEST GA4 Event Parameters
• Part 5: Extract GA4 User Properties and Metrics
• Part 6:  BigQuery Mistakes and How to Prevent Them

Understanding GA4 BigQuery Export

Exporting data from Google Analytics 4 (GA4) to BigQuery allows users to access raw event-level data for more comprehensive analysis. Unlike standard GA4 reports, exporting to BigQuery allows you to retain all your data, perform advanced queries, and combine it with other data sources. 

This capability is essential for organizations that want to go beyond GA4's built-in reporting limitations.

Benefits of exporting GA4 data to BigQuery:

• Store your data in BigQuery (Google Cloud) or send it to data warehouses in AWS, Azure, or Snowflake.
• Combine and enrich your GA4 data with other marketing, CRM, or contextual datasets.
• Visualize data using tools like Looker Studio, Tableau, PowerBI, or other BI platforms.
• Perform advanced data analysis with SQL queries to uncover deeper insights.
• Use GA4 data as input for machine learning models to predict trends and optimize marketing strategies.

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Why Build Google Analytics 4 Reports in Looker Studio

Looker Studio is a powerful tool for visualizing and analyzing data from Google Analytics 4 (GA4). By creating GA4 reports in Looker Studio, you can turn raw data into insightful visualizations that help you monitor key metrics, track performance, and make data-driven decisions. 

Benefits of using GA4 Looker Studio templates:

• Time-saving: Pre-built templates provide a structure, eliminating the need to build reports from scratch.
• Consistency: Templates ensure that all reports follow a unified layout, maintaining a professional and consistent look across your dashboards.
• Ease of customization: Modify metrics, dimensions, filters, and visualizations in templates to focus on the most relevant data for your business needs.
• Efficient collaboration: Teams can collaborate seamlessly by sharing standardized templates, ensuring everyone works with the same visualizations and data points.
• Flexibility: Templates allow for easy updates and adjustments, enabling you to refine reports as your data analysis needs evolve.

Required Permissions for Connecting BigQuery and Looker Studio

When connecting BigQuery to Looker Studio, having the correct permissions is essential for accessing data and building reports. Permissions ensure that only authorized users can view or modify datasets and dashboards, maintaining data security and control.

To connect BigQuery as a data source in Looker Studio, users must have the necessary permissions in both platforms. You need the 

• BigQuery Data Viewer role for the dataset you want to use, which grants "Can view" access. 
• Additionally, you must have the bigquery.jobs.create permission to run queries that populate the reports. 
• If you created the dataset, you automatically have Owner access, granting complete control over the data and permissions. 

💡Learn how to effectively manage BigQuery IAM roles and permissions to ensure secure and controlled access to your datasets. This guide will help you understand the essential roles required for querying, viewing, and managing BigQuery data, enabling you to assign the right permissions and maintain data security.

How to Connect GA4 Data in BigQuery to Looker Studio

To start building a GA4 dashboard in Looker Studio, the first step is creating a data source. A data source connects your BigQuery dataset (which holds the exported GA4 data) to Looker Studio. The types of data sources in Looker Studio are:

1. Live Connection: Connects directly to external databases, providing real-time data for your reports.
2. Extracted Data: Copies a snapshot of data into Looker Studio, improving performance but without real-time updates.

This enables Looker Studio to visualize and analyze the raw event data from GA4. Please remember that certain permissions, including BigQuery Viewer and job creation rights, are required to create a data source.

There are two ways to connect GA4 data from BigQuery to Looker Studio:

1. Custom Query Connection: Allows you to write and use SQL queries to extract specific metrics and dimensions from GA4 data in BigQuery.

2. Pre-calculated Table Connection: You can connect pre-built reporting tables directly from BigQuery, streamlining the process for dashboards that are based on recurring reports or analysis.

Preparation for Building a Dashboard

Before building a dashboard, it’s important to do some preparation. 

• First, define the structure of your dashboard by deciding how you want to organize and present the data. 
• Second, identify the key metrics and indicators that need to be visualized, creating a clear report scheme. 
• Third, assess the complexity of the dashboard and the volume of data to be visualized. 
• Finally, determine the frequency of data updates required for your reports.

Once these preparatory steps are completed, you can move on to deciding the connection type for your dashboard:

• Simple Metrics and Small Data Volume: If the dashboard only needs to handle basic metrics and a small volume of data, you can use a standard connection to BigQuery without any additional processing. 
• Complex Metrics and Additional Data Sources: A Custom Query Connection is recommended if the dashboard involves more complex metrics or needs to combine data from multiple tables. 
• Complex Metrics and Large Data Volume: If the dashboard needs to visualize both complex metrics and a large volume of data, consider pre-calculating a table in BigQuery.

By properly preparing for the dashboard's requirements, you can ensure that the dashboard is efficient, accurate, and well-structured to provide meaningful insights.

Adding BigQuery Custom Query as Your Data Source

Before building a dashboard in Looker Studio using GA4 data from BigQuery, connecting the correct data source is essential. Follow these steps to add BigQuery as your data source and use custom SQL queries to retrieve and visualize specific GA4 metrics and dimensions.

Adding BigQuery Custom Query as Your Data Source:

Here’s a step-by-step guide to connecting BigQuery to Looker Studio:

• Open Looker Studio: Go to the Reports page and click on the Blank Report template to start a new report.

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• Complete Settings: If prompted, fill out the Marketing Preferences and Account and Privacy settings, then click Save.
• Add a Data Source: In the Add a Data Source window, click Create new data source.
• Select BigQuery: In the Google Connectors section, hover over BigQuery and click Select.

• Authorize Access: Authorize Looker Studio to access your Google Cloud project.
• Select Project and Dataset: Choose My Projects, select your project, and then navigate to the appropriate dataset and table.

• Connect the Data Source: Click Connect, adjust field properties, or create calculated fields as necessary.

• Add to Report: Click “Add to report” to make the BigQuery data available for use in your dashboard.

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• Edit Data Sources: You can also edit the data sources after creating the report from Resources > Manage Data Sources.

Once connected, you can use a custom SQL query to refine your GA4 data extraction:

• Write a Custom SQL Query: Start by writing a custom SQL query to extract specific metrics (e.g., session counts, conversions) and dimensions (e.g., traffic sources, regions) from GA4 data in BigQuery.
• Filter and Customize the Data: Use SQL to filter, aggregate, and customize your data, ensuring only relevant metrics and dimensions are pulled into Looker Studio.
• Build the Dashboard: Once the query is complete, use Looker Studio to create visualizations, tables, and reports based on the custom GA4 data you've retrieved.

💡Want to dive deeper into working with GA4 data in BigQuery? Check out our detailed guide on Unnesting GA4 Event Parameters in BigQuery. Understand how to efficiently handle nested event data, enabling you to extract valuable insights from your GA4 exports. It’s a must-read if you're looking to enhance your BigQuery queries and maximize the potential of your GA4 data!

Connecting a Pre-Created Flat Table

Connecting a pre-created flat table is an additional option when using BigQuery as your data source in Looker Studio. This approach simplifies data visualization using pre-aggregated or pre-processed data, reducing query load and improving performance.

Step 1: Create a Reporting Table in BigQuery

To avoid running a custom query every time, create a pre-calculated reporting table in BigQuery. Use SQL to aggregate, filter, or transform your raw GA4 data into a more reporting-friendly format. For example, you can set up scheduled queries to generate these tables daily, ensuring fresh data without the need for real-time queries.

Step 2: Schedule Updates with Scheduled Queries

To ensure your pre-calculated table remains updated, set up scheduled queries in BigQuery. These queries can run at regular intervals (e.g., daily or weekly) to refresh the data automatically. This approach keeps your Looker Studio dashboard up-to-date with the latest insights without requiring constant manual execution.

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Step 3: Connect the BigQuery Table to Looker Studio

In Looker Studio, select BigQuery as your data source and connect it to the pre-calculated table you’ve created. Instead of running live queries, this method reduces computational load by using already processed data, improving performance, and speeding up dashboard load times. 

Step 4: Build Your Dashboard

Using the pre-calculated data from your BigQuery table, build your dashboard in Looker Studio. This approach enhances performance since the data is fetched from a static table rather than running real-time queries. Your dashboard will load faster with the data already processed, offering a smoother user experience and quicker insights.

How to Build a Dashboard in Looker Studio with Visualizations and Metrics

Building a dashboard in Looker Studio allows you to visualize your GA4 data and extract meaningful insights. You can create dynamic reports featuring key metrics and dimensions with various customization options. 

Follow these steps to efficiently build and tailor your dashboard for optimal performance and data analysis.

Step 1: Add a Data Source

Begin by connecting your BigQuery table (from the previous steps) containing GA4 data prepared to Looker Studio. Depending on your setup, you can either select the table generated from a Custom Query or a Pre-calculated Table. Both options provide the necessary data for building your dashboard, ensuring it reflects either live queries or pre-processed data for visualization.

Step 2: Add Reports & Visualizations

To visualize GA4 data from BigQuery in Looker Studio, choose relevant charts and graphs based on the metrics you want to display. Here are some commonly used visualizations and how they can enhance your dashboard:

• Line Charts: Perfect for showing time-series data, such as daily active users or sessions over time.

• Bar Charts: Great for comparing categories, like pageviews by device type or traffic source.

• Pie Charts: Use these to illustrate proportions, such as session distribution by traffic source.

• Tables with Heatmaps: Ideal for a detailed view of metrics, such as bounce rates or conversion rates by country, with color gradients to emphasize trends.

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• Scorecards: Use these to showcase key performance indicators (KPIs) like total users, revenue, or sessions at a glance.

Example: Creating a Scorecard Visualization:

Let’s take an example of creating a Scorecard Visualization to highlight key metrics like impressions, revenue, or sessions in Looker Studio:

To create a scorecard visualization, follow these steps:

1. Add a Chart: Click on ‘Add a chart’ and select Scorecard from the list of visualization types.

• Set the Metric: In the settings panel on the right, choose the metric you want to display (e.g., ‘Impressions’) and the aggregation method (e.g., ‘sum’). We will be using “User Pseudo ID”.

• Select a Date Range: Define the default date range for the data display.

• Comparison Date Range: Add a comparison date range (e.g., ‘Previous period’) to highlight changes in metrics over time.

2. Style Settings: Enable ‘compact numbers’ under style settings for a cleaner look.

Repeat this process for other scorecards to show KPIs like revenue, clicks, or conversion rates. This will help create a more actionable, easy-to-understand dashboard with key data front and center.

Step 3: Add Metrics and Dimensions to Visualizations

In Looker Studio, adding relevant metrics and dimensions from your GA4 BigQuery data ensures your visualizations are insightful and actionable. 

Common metrics include:

• Users: Total number of users interacting with your site or app.
• Events: Actions taken by users, such as clicks or form submissions.
• Pageviews: Total number of page views recorded.
• Revenue: Total revenue generated from transactions.
• Conversion Rate: The percentage of sessions that resulted in a conversion.

Dimensions:

• Date: To visualize time-series data and trends over time.
• Device Category: Distinguishes between desktop, mobile, and tablet users.
• Geographic Location: Breaks down data by country or region.
• Traffic Source: Displays the source through which users were acquired (e.g., direct, organic, referral).
• Session Traffic Source (Last Click): Shows the traffic source from the last click interaction before a session.
• Collected Traffic Source: Indicates the source of the user's first acquisition.

For example, let's look at the Google Analytics for Firebase template. This template focuses on conversions, traffic source breakdowns, and event data related to Firebase app analytics. 

It includes metrics like: 

• Event Count 
• Conversions

Dimensions:

• Acquired Source
• Medium
• Campaign for tracking app user acquisition and behavior.

These elements help track user behavior and acquisition sources effectively in your dashboard.

💡 Looking to extract even more valuable insights from your GA4 data? Check out our detailed guide on Extracting GA4 User Properties and Metrics. Learn how to access and leverage key user data to enrich your visualizations, enabling more personalized and actionable analytics in Looker Studio.

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Step 4: Integrate Controls into Data Visualizations in Looker Studio

Controls help make your Looker Studio dashboard more interactive, allowing users to filter and view data based on specific criteria. Here's how to integrate controls:

Click on ‘Add a control’ and select the desired control from the list.

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You can use controls like:

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• Date Range: Allows viewers to change the date range and view data for specific periods.

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• Dropdown List: Lets users select individual dates or categories (e.g., using 'date_start')

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• Fixed List Control: Predefined list of options for viewers to select from.
• Input Box: Allows users to enter custom values or queries.
• Advanced Filter: Enables complex filtering with multiple conditions for in-depth analysis.

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There are also additional controls that you can use, like Sliders, Checkboxes, Data Controls, Dimension Controls. By combining these controls, you can create an interactive, user-friendly dashboard, allowing viewers to engage more effectively with the data.

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Steps to Customize Metrics and Dimensions in GA4 Looker Studio Templates

Customizing metrics and dimensions in GA4 Looker Studio templates allows you to tailor your reports for specific business needs. Adjusting key performance indicators (KPIs) and data dimensions allows you to create more relevant visualizations, track essential metrics, and make data-driven decisions based on personalized reports.

Step 1: Identify Key Performance Indicators (KPIs)

The first step in customizing your GA4 Looker Studio templates is identifying the Key Performance Indicators (KPIs) most relevant to your business goals. KPIs are critical metrics that provide insights into user behavior, engagement, and performance.

For example, in a Google Analytics for Firebase report, key KPIs could include:

• Event Count: Total number of tracked user actions.
• Conversions: Number of completed goals or actions, such as purchases or sign-ups.
• Acquired Source: Where the users originated from (e.g., organic, direct, referral).
• Session Duration: Time users spend in the app or on the website.

By focusing on these KPIs, you can ensure your dashboard highlights the most important data points for making informed decisions.

Step 2: Highlight KPIs in the Template

Once you've identified the key performance indicators (KPIs), ensure they are prominently displayed in your Looker Studio template. Use visualizations like scorecards or bar charts to emphasize essential metrics, such as event counts, conversions, and traffic sources. This helps viewers quickly identify key data points and assess overall performance without sifting through unnecessary information.

Step 3: Modify Metrics and Dimensions

To customize your GA4 Looker Studio template, modify metrics and dimensions to match your reporting needs. You can create custom formulas at the data source level or for specific graphs and tables (e.g., calculating conversion rates) and add dimensions like device type or location.

You can also Adjust filters and visualizations to reflect these changes, ensuring your dashboard provides targeted, actionable data.

The following window will let you add conditions to customize the filter based on your data needs.

Best Practices to Optimize Looker Studio Dashboards

Optimizing your Looker Studio dashboards ensures they deliver actionable insights in a clear, efficient, and user-friendly manner. With Looker Studio’s vast array of features, creating an optimized dashboard can seem overwhelming.

Here are some proven tips to help you get the most out of your Looker Studio dashboards:

• Start with a Clear Goal: Define the main objective of your dashboard before you begin. Whether it's tracking KPIs or monitoring trends, ensure every element supports your goal.
• Keep It Simple: Avoid overloading your dashboard with unnecessary charts and tables. Focus on the essential data and present it in a clean, concise manner for easy interpretation.
• Use the Right Visualization: Choose the appropriate visualization type for your data. For instance, bar charts work best for comparisons, while line graphs effectively show trends over time.
• Leverage Filters and Parameters: Utilize filters and parameters to allow users to customize the dashboard based on their needs, such as specific regions, products, or time periods.
• Set Up Alerts and Notifications: Implement alerts to notify you of significant changes, such as spikes or dips in data, so you can quickly respond to emerging trends or issues.
• Test and Iterate: Dashboards should evolve over time. Regularly test, gather feedback, and adjust based on user needs to improve performance and relevance.
• Collaborate and Share: Take advantage of Looker Studio’s collaboration features, inviting team members to contribute and share the dashboard.

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