Google Analytics 4 & Google Sheets: how to integrate your data

This tutorial is designed to guide marketing professionals on how to seamlessly integrate their Google Analytics data with Google Sheets using Catchr. By following these steps, you will be able to visualize and manipulate your marketing data more effectively, enabling you to make data-driven decisions with ease.

How to Connect Your Google Analytics Account to Google Sheets

Add a Source on Catchr

Begin by navigating to the Catchr homepage. On the navigation menu to the left, select Source, then choose Add a Source located at the top right corner of the screen. From here, select Google Analytics as the platform you wish to connect, and proceed with the necessary steps to identify and authenticate the integration between Catchr and Google Analytics.

Create a New Query

Open your Google Sheets document and locate the Catchr plugin in the menu bar. Click on Catchr, then select New Query to initiate the creation of a new data query dedicated to importing your selected Google Analytics data.

Select Your Data Source

In the query creation window, select Google Analytics as the source of your data. If Google Analytics does not appear in the list, click on the "+" icon to add it as a new source.

Select Your Account(s)

Proceed to the "Select account(s)" tab, and pick the Google Analytics account(s) you are interested in extracting data from.

Choose Metrics and Dimensions

Continue by selecting the specific metrics and dimensions you wish to include in your Google Sheets by navigating to the respective tabs named Metrics and Dimensions.

Metrics:

• Pageviews: Tracks the total number of page views.
• Unique Pageviews: Counts distinct visitors per page.
• Bounce Rate: Measures the percentage of single-page sessions.

Dimensions:

• Country: Identifies the visitor's country of origin.
• Device Type: Specifies the category of device used.
• Browser: Indicates the web browser utilized.

Set Date Range

Choose your desired date range for data extraction by visiting the Date Range section. Catchr provides predefined options like "Last month" or "This year," but also allows for custom date range selection.

Apply Filters

To get more precise data, apply filters under the Filters tab by providing the specific field, operator, and value for your filter criteria.

• Field: country
• Operator: equals
• Value: United States

Schedule & Customize Data Refreshness

Finalize your setup by customizing your data refresh options under the Options tab, where you can schedule automatic refreshes and decide whether to append or combine rows for your queries.

In conclusion, following these steps will allow you to successfully integrate your Google Analytics data into Google Sheets, where you can further analyze and leverage your marketing insights.

Example of Google Sheets You Can Create to Use with the Google Analytics Source

Using Google Analytics data within Google Sheets presents virtually endless possibilities for analysis and reporting. Here are a few example sheets you might create:

• Daily Performance Dashboard: A sheet that tracks daily website traffic, engagement metrics, and conversion rates. By setting up charts and pivot tables, you can visualize trends over time.
• Audience Demographics Report: Create a detailed report on your website's audience including age, gender, location, device, and browser. This can help you tailor your content and marketing strategies more effectively.
• Campaign Analysis Sheet: Compare the performance of various marketing campaigns by pulling data such as campaign ID, impressions, clicks, and conversions directly from Google Analytics into Google Sheets. This can help in determining ROI and planning future marketing strategies.
• Content Analysis Tool: Evaluate which pages or blog posts are performing the best in terms of views, time on page, and bounce rate. Use this data to guide your content creation efforts.

By leveraging the power of both Google Analytics and Google Sheets, marketers can create custom dashboards and reports that fit their specific needs, making data-driven decisions faster and more accurately.

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