There is no way to create a pie chart that includes new and returning user data using just the GA4 data source in Looker Studio. The problem is that, unlike Universal Analytics, there isn’t a single dimension which encompasses values for both new and returning users, and a Looker Studio pie chart is based on a single dimension.
But the good news is, it can be done! It’s kind of weird and a bit complicated, but it works! We’ll use a combination of custom fields, blends, and the mysterious coalesce function to get all the data into one table, in a format that works to create a pie chart.
View the video to see all the specifics of how the data is transformed and the report is built, and jump below the video for the steps used to create the chart.
A blend is a method of joining two data sources, based on one or more common dimensions. For the purpose of reporting on new vs. returning users, we join a GA4 data source to the same GA4 data source. Returning users is a calculated value made up of all users minus new users.
- In Looker Studio, go to Resource > Manage blends > Add a blend.
- Select GA4 as the data source for Table 1. (If you don’t already have a GA4 data source in your dashboard, you’ll need to add one first.)
- Click ‘Join another table’ to add Table 2. Select GA4 as the data source for this table as well.
- Both tables will probably have defaulted to include the dimension Event name. Remove these, then click Add dimension > CREATE FIELD.
- Name the field “User type” and set the formula to “New” on Table 1 and “Returning” on Table 2.
- Add New users as a metric to Table 1.
- In Table 2, select Add metric > CREATE FIELD and name the field “Returning users” and set the formula to Total users – New users.
- Lastly, click Configure join, and select Full outer join. This will return the values from Table 1 and Table 2, in spite of the fact that the values for User type don’t match.
- Name the blend something you’ll remember, like “GA4 new vs returning users” and save.
We are almost ready to create our pie chart, but there’s still one problem. A Looker Studio full outer join creates a dataset with holes in it. If you could look at the raw data in your blended data source (wouldn’t that be nice?), it would look like this:
To create the pie chart, we need it to look like this:
This is where the magical coalesce function comes in. Looker Studio’s coalesce function returns the first non-missing value found in a list of fields.
We use this function to take the two columns for user type and combine them into a single column. Because the User type dimension from the left table never has values when the User type from the right table does, and vice versa, this function effectively combines the data into one column. We’ll use the same trick to combine New users and Returning users into a single column and voilá, we have a pie chart!
- Insert a pie chart into your dashboard and select the blend you just created as the data source.
- Remove the default dimension and select CREATE FIELD. Name your custom dimension “User type” and give it the following formula:
- Then remove the default metric and select CREATE FIELD. Name your custom metric “Users” and give it the following formula:
COALESCE(New users, Returning users)
Note that the inputs used in your formula will be based on the names you’ve set in previous steps, so if you used different names, adjust your coalesce functions accordingly.
I’ve tried to cover all of the important steps in this post, but I haven’t described every mouse click. If you need more detail, I recommend watching the video.
Limitations of New vs. Returning Users in GA4
When it comes to user metrics in Google Analytics, there are some factors to keep in mind.
For one, the primary means of identifying a user is based on cookies. This means that if you visit a website on multiple devices or with different browsers, you will likely appear as several different users. Additionally, if you clear cookies or use an ad blocker, GA will treat you as a new user. Both of these result in over-counting users and incorrectly categorizing them as new, even if they’ve been to the website before.
GA4 is actually designed to identify users from more than just the cookie that identifies them for Google Analytics, through a feature called “reporting identity.” It uses multiple types of identity data, including Google signals, device ID, and modeling based on similar users. But in practice, at least as of now, this doesn’t appear to affect new vs. returning user data much.
In general, the proliferation of internet-connected devices and the strengthening of browser privacy features means that users are getting harder to track. Think of any user-based metric as directionally useful, but take it with a grain of salt.
Second, a high percentage of new users isn’t inherently good or bad. It depends on the question you’re trying to answer. If you’re trying to acquire new customers through advertising, then a high percentage of new users could be beneficial. However, returning users may indicate that you are attracting interested prospects. It’s rarely a clear-cut decision, and the context of the situation matters.
What’s the difference between new and returning users in GA4?
New users are visiting your site for the first time. Returning users have visited your site before. GA4 is designed to do a better job than Universal Analytics in identifying users across multiple sessions and devices through Reporting Identity features, so theoretically it should have more accurate returning user data. In practice we haven’t seen much improvement, and changes in user behavior and privacy concerns may offset any gains.
How do I check new or returning users in GA4?
Find new and returning user data in the Retention report in the Reports section of GA4. Unfortunately, the returning users metric shown in this report doesn’t exist in Explorations or the Looker Studio GA4 connector.
Learn more about GA4 reporting in Looker Studio in our tutorials, including how to report on individual GA4 conversions in Looker Studio and how to create a GA4 scroll tracking report in Looker Studio.
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