Transform Your Data Insights: Custom Reports in Google Analytics 4

Google Analytics 4 (GA4) is equipped with a number of reports by default. Compared to previous versions of Google Analytics, however, the s...

Google Analytics 4 (GA4) is equipped with a number of reports by default. Compared to previous versions of Google Analytics, however, the selection is manageable. You can change and extend GA4’s menus and user interface yourself, and you can create detailed analyses using individual reports.

Custom Reports in Google Analytics 4

In previous posts, we showed you various menus and reports in GA4. Compared to Universal Analytics (UA), the selection of reports initially seems quite basic, which is because not all possible report combinations have been given their own entry in the menu. Some reports can only be accessed via links presented in the overviews, while others cannot be found at all through the standard user interface.

Therefore, the Reports menu in GA4 does not cover everything the tool can do, such as the following:

• You can change and expand the menu and reports shown in the user interface.
• Under Explorations, you’ll find a powerful reporting function that not only allows you to create custom reports but actually analyze the data in more depth.

The initial selection of reports is smaller in GA4, but you can customize GA4 according to your own needs which was not possible at any time in UA.

Customizing the Navigation in the Library

In GA4’s Reports section, you’ll find the Library item at the bottom of the menu bar. With this item, you can customize the menu and the items listed under Reports, as shown below.

Customizing the Navigation and Reports in the Library

The first thing that catches your eye in the library is certainly the Collections section. This section features some tiles that contain folders. Each collection is displayed as a section in the navigation menu of your GA4 property.

A collection is the term for a whole block of entries. You can open and close a collection in the menu. If you have a newly created property in front of you, that property contains the Life cycle and User collections. The App developer collection can be added via an app data stream. On the library home page, you’ll see the current property collections represented as a series of tiles, as shown below.

Various Collections in the Library

A collection can contain one or more topics, which is a group for organizing your actual reports. The collection must be published for these reports to appear in the navigation.

If you set up a link between your GA4 property and the Google Search Console, a new Search Console collection will appear. Why don’t you also see this collection in the menu navigation? Simply put, a new collection is not automatically published. You must click on the collection menu (the three dots icon in the top right) and select Publish. Now, the collection is visible in the menu.

If a collection is visible but you want to hide it from the navigation, you can select Unpublish from the menu again. The collection remains available in the library but won’t be displayed in the menu.

If you change the publication status of the collection, a temporary bug arises in GA4: After the change, you’ll have two collections with the same name: one published, one unpublished. You can delete the variant that is no longer required.

You can customize each collection by changing or adding further menu items. For this task, either click Edit collection under the tile or select Edit from the tile menu. From the tile menu, you can also create a copy, rename a collection, or delete the collection.

Clicking a button or menu item takes you to the settings view, as shown below. On the left, you’ll see a list of current entries in this collection. The first item that appears on this list is the link to a template (if applicable), followed by the topics and reports.

A Collection with Navigation Items and Reports

A topic refers to a set of reports grouped together in the navigation. The name of the group corresponds to a heading in the Reports menu. Unfortunately, you can no longer change the name of existing groups, which means you must delete the group, create a new group with the desired name, and reassign reports.

Collection Templates

The collections contained by default in GA4 are linked to a template. You can recognize the link by the small symbol and the name of the template next to the publication status or when you’re editing a collection.

Thus, these menus can be changed and customized by Google when the GA4 system is updated. If this occurs and the collection is linked to a template, your customizations will be overwritten. However, the collections themselves have not been changed since the release of GA4, so it is questionable whether this is a concern for the near future. To ensure that your menus remain as they are, you can safely remove the link.

Each group can include reports in two sections, as shown below. One or both sections can remain empty:

• You can file a maximum of one overview report.
• You can have any number of detail reports.

Assigning Reports to a Topic

On the right, you’ll see a list of all the overview reports and detail reports available in GA4. You can use the search function to filter for specific reports; however, the list does not start with many entries, so you won’t necessarily need to search.

You can drag and drop reports from the list on the right to a collection on the left, as shown below. Only one overview report can exist per group. If you drag an overview entry onto a group with an existing overview, then that overview will be replaced. Since a group can comprise multiple reports, a dragged report entry will be added to the list.

Next, click Create new collection, and on the selection screen, you’ll find a list of all the templates available. Note that the Reports on games (or gaming) template chooses a slightly different breakdown than the Life cycle collection. If you do not need any of these templates, you can start an empty collection.

Dragging and Dropping Reports to a Topic

Almost all of the reports in the list are already included in the Life cycle, User, and Search Console collections, and thus, there’s not really much new to discover yet.

Creating and Editing Reports

The library contains a list of reports in which all individual reports from your current GA4 property are listed, as shown below. The two report types, the overview report and the detail report, are parts of collections. The Creator and Last modified columns are empty when they are called for the first time. Next is the Template column, which refers to the default GA4 templates, as is the case with the collections.

The Collection column shows where the report is located in the GA4 navigation. Finally, the Description column contains a brief explanation for each report.

List of all Reports Contained in the Property

You can edit and change any report on this list, which a big difference from UA, where the default reports from the navigation were unchangeable. Let’s revise the Pages and screens report: Clicking the entry opens the edit mode. This report is a detail report with a data table.

Customizing a Report Directly from the Frontend

You can access the same edit mode from any report in GA4. In the top-right corner, you’ll find a number of icons, as shown below. The last icon stands for Customize report. Clicking this icon navigates you to the settings.

Customization Icon in Each Report

You’ll then see the familiar page report including the current data for the property. You can navigate as usual within the report, turn pages in the table, or filter the table. To the right of the report, you’ll find the Customize report sidebar, with a range of options, as shown below. This sidebar allows you to customize the presentation and the contents of the report.

At the top of the list, there are two fields for Metrics and Dimensions.

Report Configuration Options in the Sidebar on the Right

Adding Dimensions to the Report

The dimension marked as Default is the dimension that has been preselected when you call the report. This default setting can save you a couple of clicks in your daily work: For example, if you prefer to use the page path instead of the page title in the report, you create a new default setting using the menu in the entry. Then, confirm by clicking the Apply button and save these changes to the report by clicking the Save button in the title bar.

The report can be expanded by adding dimensions, as shown in below figure. Clicking the button at the end of the dimension list shows you all the available entries you can include. Not every entry seems to make sense. For example, why should you add the City or Device model to the selection in the page report? But some dimensions are useful when they are filled with data on your website. In the page report, these entries include the following:

• File name, if you record downloads via optimized analyses
• Hostname, if your property combines data from multiple websites
• Landing page, if you want to see where users enter your offerings
• Page path and query string, if you also want to see attached URL parameters for the page viewed

Defined Dimensions (Left) and Dimension Menu in the Report (Right)

If you use GA4 only to evaluate a website, you can do without the Page title and screen name dimension and remove it, as it represents the same data as the Page title and screen class.

Customizing Columns and Metrics of the Data Table

In the Metrics menu, you can specify which columns the report table should contain. A long list of entries to choose from is available and includes e-commerce data, average values, and time periods. Your individual offering will determine which values make the most sense for your website.

If you worked previously with UA, you may miss the bounce rate value in your reports when you switch over to GA4. You can add this value back to your report under the Metrics list.

You can add up to twelve metrics to a table, as shown below. Clicking a metric defines it as the primary value: The rows in the report table are sorted in ascending or descending order.

A Maximum of Twelve Measured Values in a Table

Filtering the Table

You can use the Report Filter to restrict the data in the table as soon as it is called, as shown below. Not only can the dimensions from a table be filtered, but also most of the properties of the users and views.

Using a Report Filter to Restrict the Data

In this way, you can narrow down the data to a specific host name or data stream and also filter by country or by user source. You can set whether users that fulfill some condition should be included or excluded in these reports.

You can apply a total of five conditions to a report and, for each condition, define whether a condition leads to inclusion or exclusion. The individual conditions are linked with a logical AND operator; in other words, the entries that end up in your data table must fulfill all criteria, as shown. Unfortunately, you cannot save a filter for further use, which means you must enter the settings anew for each report.

Filtering for Up to Five Dimensions

Once a filter has been applied, it is always displayed below the heading, as shown.

Active Filter Displayed Under the Report Title

Visualization with Diagrams

Two entries are displayed in the Diagrams section that the two charts above the data table in the report. The display can be switched on and off by clicking the eye icons. If you deactivate one of the diagrams, the other one will be extended to cover the entire length, as shown.

Line Chart at Full Width Above the Table

You can choose from three types for both diagrams: Bar chart, Scatter chart, and Line chart. The data displayed by the diagram is defined by the other settings, in the following ways:

• All charts use the dimension defined as the default for the display.
• Both the line chart and bar chart use the default metric for the values. Both show the top 5 entries in the data table.
• The scatter chart uses the default metric and another value from the data table, usually the first or second column, depending on what you’ve defined as the default.

In addition, the diagrams do not allow for any further customization and are directly linked to the table displayed. If you’re looking for other display formats or want to further customize the diagrams (e.g., with colors), Google Looker Studio provides more options.

Report Templates Are Updated by Google

The section on the Report template has a similar meaning to the collection templates: Google maintains some standard reports as a template for new GA4 properties. If a report is linked to such a template, it is also updated when the template is updated. If you remove the link, a system-wide change to the reports will not affect you.

Summary Cards for Overview Reports

In the final section, you can create summary cards for use in an overview report. These cards are the data tiles you’ve already seen in the various overview screens and snapshots. The template-based reports already have cards that you can customize. If your report does not yet have a card or you want to add another one, click the Create new card button.

The selection fields in the editor should look familiar to you; you can make similar settings for a card as for the entire report, as shown below. You can select dimensions and metrics for the card. The entries you have set up in the overall table are available for selection as well. You can store multiple entries for both dimensions and metrics so that you can switch between them in the card via a small menu.

Customizing the Cards for Overview Reports

The following four formats are available for visualization:

1. Bar chart
• The first lines of the data table are displayed.
2. Pie chart
• You should use the pie chart only if the table contains only a few rows in the table, for example, for the device category. The pie chart also represents the first entries in the data table. If your table contains more entries, confusion can arise.
3. Line chart
• Shows the top entries in the table over time.
4. Table
• Lists the top entries with the metric. Only one metric is displayed at a time. If you have added more than one metric to the map, you can switch between them.

Finally, you can add a filter for the map, either adopting a filter from the report or creating your own new filter. In this way, you can have slightly different values displayed on the card than in the actual report. A report can contain multiple cards that can be used for overviews.

Creating a New Report

Up to this point, we’ve customized an existing report or used it as a template for a copy. You can of course also create a detail report from scratch. For this task, click Create detail report above the list of reports. Then, select a blank report or a template, as shown below. The templates contain some preselected dimensions and values that you can customize according to your requirements. If the template is empty, you can set everything from scratch.

GA4 Provides Several Templates for New Reports

Whether you decide to copy an existing report or create a new one is ultimately irrelevant to GA4: No decisive difference exists between the two methods.

Customizing or Creating an Overview Report

You probably know overview reports from every topic area of the GA4 menu: For example, the topics of Acquisition, Engagement and Monetization each have their own overview which in turn consists of several cards. You can also edit the existing overview reports or create a new report from scratch. Click on any overview report from the table. Again, you’ll see the report itself and the sidebar for configuration. This time, however, you’ll only see the Add cards to this report option, as shown below. Note that a report can contain a maximum of 16 cards.

Adding Cards to an Overview

Once you start adding, a screen for selecting a card is displayed. The listed cards are divided into two tabs: Summary Cards and Other Cards. The first tab lists the cards of reports from all collections that are currently published in your property, as shown.

Selecting Cards from the Current Collections

These cards are stored in a detail report. If you add a card to such a report, it will appear in this list.

The second tab, Other Cards, contains generic cards that are always available in GA4, as shown below. These cards include, for example, Active users by country, Product revenue by category, or Conversions by campaign. To add tiles to the overview, you must select the checkbox. After that, the card will be accepted.

General Cards Independent of Collections

Once you have selected all the cards you want, you can change the order in the list and thus also in the overview.

Locating the Cards from Your Newly Created Report

Let’s say you’ve created a detail report and want to include the summary cards in an overview. Are your cards not included in the selection under Add? Then, you must first add the newly created report to a collection and publish it (if not published yet). Only then will the card appear as a selection so that you can use it, as shown.

Only Cards in Published Collections Are Listed

Google also maintains templates for overview reports. As with collections and detail reports, you can remove the link to the template. When saving a report, you can either change the existing report or create the settings in a new report.

Suggestions for Customizing and New Reports

With the library options for customizing and creating reports, you have a powerful tool to customize GA4 to your needs. In the following sections, we’ll provide some suggestions for new reports and customizations that will simplify your daily work with GA4.

Google Ads Campaign Report

GA4 can be linked with Google Ads, and the two systems can then exchange their data. However, you can only access the standard report for analysis via the Overview option in the Acquisition menu. The general menu does not contain a corresponding entry. To add an entry into the navigation, you must proceed with the following steps:

1. Link your property to your Google Ads account.
2. On the Acquisition overview page, click the View Google Ads campaigns link.
3. This step takes you to the Google Ads campaigns report. Click on the icon to edit the report.
4. Save the report directly by clicking the Save button. Which option you choose doesn’t matter. In any case, this step creates an entry for the Google Ads report in the report list.
5. If you wish, you can further customize the data table, for example, with return on advertising spend (ROAS).
6. In the library, you can edit the collection for the Life cycle (or create your own) to which you add the newly saved Google Ads report, as shown below.
   
7. Finally, you need to save the collection. You now have your own menu item to quickly access your Google Ads campaigns, as shown.

Google Ads Report Now Directly Accessible in the Navigation

Landing Pages with Bounce Rate

If you have created your GA4 property recently or in the last few months, you’ll find a report on Landing pages in the Engagement menu, as shown below.

Creating a Report for Landing Pages

However, if your property is older, this report may still be missing. You can still create this report yourself by following these steps:

1. Start in the library and edit the Pages and screens report. (Alternatively, click on the report in the navigation under Engagement and start editing using the icon.)
2. In the Dimensions menu, add Landingpage as a new dimension and mark this dimension as the default. If you wish, you can delete some or all of the remaining menu items.
3. Add Sessions and Bounce rate to the Metrics item and position them higher in the order.
4. If you see the value (not set) in your data table: Add a filter that excludes all landing pages with the (not set) value.
5. Customize the Summary card so that it uses Sessions and Bounce rate as the Landingpage dimension and metric.
6. Save everything as a new report and assign the name “Landingpages.”
7. Add the report to the Life cycle collection under the Engagement topic.

Customizing a Page Report

In the page report, you can remove some dimensions if your property is only used for a website and not an app.

Set the page path as the default dimension if you prefer to work with the URL rather than the page title.

Use the Page path and query string dimension to analyze parameters in the URLs.

Report on File Downloads

Do you have downloads on your website that you want to track with the optimized analyses features of GA4? You can look up the calls of the file_download event relatively easily in the event report. However, what you cannot see is what has actually been downloaded.

For a more detailed report on downloaded files, follow these steps:

1. Create a new report in the library. You can either start with the event report as a template (or with a blank report, the effort is roughly the same).
2. Select Filename as the dimensions. If other dimensions appear, delete them.
3. Select Sessions and User as metrics. Delete the other metrics.
4. You can set both diagrams to be invisible.
5. Remove the link to the report template.
6. Create a summary card, for example, a table if you wish.
7. Save everything as a new report named “Document Downloads” or something similar.
8. Maintain the report in a collection.

The result should resemble the report shown below. The first line is a little confusing: The empty value stands for all events for which no file name was recorded, that is, for all events that were not of the file_download type, such as page_view, session_start, and others. Because of this “empty” line, you cannot use the diagrams, or the diagrams simply don’t make any sense. The problem results from the way the data tables and filters work. Consider the following rules:

• The table always shows 100% of all calls or users for the filename parameter. Empty values are not automatically excluded.
• The filters you can apply to reports lack the option of making a suitable restriction. You can neither filter on a specific event (file_download) nor on a file name.

Report on Downloaded Documents

Despite these limitations, such a report is the quickest way to make downloads visible in the overview report and to make them accessible as a separate menu item. With a custom report under Explorations, you can apply better filters, but these reports cannot be included in the menu.

Outbound Links from Your Website

Links that lead away from your website can be analyzed using a report that’s similar to the report for downloads. Instead of Filename, select Link URL or Link text as the dimension. If you save everything as a new report, you can create your own menu item.

Unfortunately, not all dimensions provided by GA4 can be used in the reports or filtered as you would like. So you’re not (yet?) completely free in the designs of your reports. Compared to UA, however, GA4 is definitely much more flexible, and you can adapt the interface to your needs. Even if the existing reports and menus provide a good basis, you can simplify and speed up your daily work with your own reports.

Creating Exploratory Data Analyses

The Explorations menu item takes you to the area for individual evaluations in GA4. Google refers to the reports you create in this area as exploratory data analyses, as shown in below. At first glance, these reports look like beefed-up versions of custom reports. However, the goal is somewhat different: These analyses are all about working with data quickly and easily: You can quickly change dimensions and metrics, customize displays, or apply filters.

Data Analyses to Enable Individual Reports

These features are great for conducting analyses. However, these reports are not well suited for the preparation of data, especially for beginners or sporadic users. The library or dashboard options in Looker Studio are better suited for these tasks

Your First Data Analysis

When you start a new exploratory data analysis, you’re greeted with a blank report and a lot of elements and options in the sidebars, as shown in below. In the sidebar on the left, under Variables, you must first assign a name and set the time period for which you want to view data.

Then, you can prepare the actual report. The configuration is divided into two parts for data analysis: First, you’ll define which Segments, Dimensions, and Metrics could appear in the report. At this stage, we’re not asking what data should actually appear in the presentation, but what data you want to work with in the report.

You must select at least one dimension and one metric before you can continue working. Segments are optional, so you can deal with them later.

Data Analysis Working Area

Click on the plus sign icon next to Dimensions to open a list of all dimensions that are available in the GA4 property, as shown below. These dimensions include Event names and Pages, but also more specific data such as Date of last purchase or Last audience name. A total of 159 predefined dimensions are available from which you can choose.

List of Selectable Dimensions

These dimensions are grouped by topic to help you find your way around, as shown.

Groups in the Data Analysis Dimension List

In addition, the custom dimensions you’ve created appear under the Configure navigation item. Since you can create 50 dimensions at the event level and another 25 at the user level in a GA4 property, you could reasonably end up with more than 200 entries in this list.

Some entries are underlined. Hover your cursor over this entry, and a box will appear with a description and sometimes with further links, as shown.

Underlined Entries with Additional Information

You can see all the entries in the list. You can switch between the predefined and custom dimensions using the tabs above the list. Alternatively, you can search for entries by entering them.

Now, select the dimensions you want to use in your report. Searching is the quickest option if you know what you need. Search for the entry, select it, and start a new search. The selection remains, even if the entry is no longer visible after a new entry. In this way, you can find all your desired dimensions in one step.

Not every dimension must be displayed in the report; even if you want to filter for certain values, the dimension must be selected. If in doubt, better to include more dimension with the import, even if you’re not yet sure whether you’ll use it.

Compatible Lines

When looking through the dimensions or metrics, you may have noticed some grayed-out entries that you cannot select. This limitation is due to the different scopes of dimensions and metrics. In short, some activities are tracked per event or per user. For the combination of dimension and metric to return meaningful results, the scopes must match. In the background, GA4 checks which metrics can be combined with the dimensions you have selected and removes the others from the range accordingly. This consistency check works in both directions, that is, regardless of whether you first select dimensions or first select metrics.

In both windows of the selection lists, you’ll see the Expand all compatibles link in the top right. Click this link, and only the topic groups from which you can select entries will open.

For an initial sample report, try selecting the Page path and screen class, Device category, and Month. Then, click Import to import the dimensions into your report.

Next, you’ll need metrics for these dimensions. An extensive range of metrics is on offer: You have 150 predefined and up to 50 custom metrics at your disposal. Below summarizes groups of available metrics.

Groups in the Metrics List of a Data Analysis

As a basis for many analyses, you can start with the metrics for Users, Sessions, and Views. After adding some metric, the sidebar on the left should now resemble the sidebar shown.

Selected Dimensions and Metrics

Now, you can start filling in the analysis; that is, you’re now working in the second sidebar. When calling a new data analysis, the Free format method and the Table display options are preselected, which you can leave as is. Now, drag and drop the Page path dimension to the free Drop or select dimension field. Alternatively, you can click on the field and select an entry from the available dimensions.

Then, scroll down a little and drag the Views metric to the free field under Values. You have now made the necessary basic settings, and a table appears in the work area on the right, as shown below.

Now, you can add other metrics to the table by dragging them to the free field under Values. Make sure you hit the free field because if a metric lands on an existing entry, it will replace it. A total of ten columns can be used for metrics.

If you add further dimensions, the entries will be further subdivided accordingly, as you already know from the Secondary dimension option in the standard reports. However, not only can you add an additional dimension to a data analysis, but this report can hold up to five columns for dimensions.

Your First Data Analysis Table

More Data and Customizing the Display

Up to this point, the structure and presentation of this report are still similar a standard report that you can customize in the library. However, the data analysis features can do much more.

In the Rows area, you can further customize the table according to your wishes. For example, you can define the start row and the number of rows to be displayed (up to 500). The start row is important since no navigation option is available in the report table. Thus, you cannot simply switch from the top 10 pages to entries 11 through 20 with a mouse click as you could do in a regular report. To see this later section, you must enter the value “11” in the Start row field.

Using the Nested rows option only makes sense if you have multiple dimensions in your table. In this case, Google Analytics lists the combination of the individual dimensions as a row. If you combine Page and Device category, for example, you’ll obtain the rows listed...

Basic Page and Device Categories

If you set Nested rows to Yes, the rows will be grouped according to the first dimension. In our example, the page views on desktops and mobile devices are combined but still displayed separately.

Page and Device Categories with Nested Rows

You can choose from three cell types to display the Values, described below.

Cell Types for Values

Visualizations are helpful when the values in the table are at a certain distance from each other. If this distance between the first and subsequent cells is large, differentiability is lost.

Pivot Table with Dimensions as Columns

A special feature of data analysis is the representation of dimensions in columns. Values of one dimension are entered in the first column, the values of a second dimension form the other columns, and the table cells contain the metrics for the respective combination of the two. (In Microsoft Excel, this representation is called a pivot table.) For this task, you must drag the second dimension onto the free field under Columns.

Such a list is useful if you combine a dimension with many different entries with a dimension that has few entries that occur frequently. Below, for example, shows the Pages in rows, combined with the Device category in columns.

Dimensions in Rows and Columns as a Pivot Table

The number of column groups and the start group can be set. However, if you include a large number of columns in the display, the table quickly becomes confusing.

Filtering the Data

The final section of the sidebar is the Filter section. In this field, you can enter a dimension or a metric, and then, an input screen with further options opens. For a dimension, you must first select the type of comparison. The following options are available:

• exactly matches
• contains
• begins with
• ends with
• matches the regex
• does not match exactly
• does not contain
• does not begin with
• does not end with
• does not match the regular expression

You thus now have significantly more options than the normal report search. (GA4 only offers a search by content and therefore significantly fewer options than in UA.) Select a suitable comparison type for your purposes.

If you need to filter dimensions frequently, you should familiarize yourself with regular expressions. With regular expressions, you can create many other types of comparisons as well as craft explicit search queries. We’ll cover this topic in more detail in a later information box.

Now, you need to enter the expression, that is, tell Analytics what you’re looking for. Even before you start typing, GA4 suggests entries from which you can choose, as shown below. If you want to filter by a specific entry (e.g., desktop), click with the mouse. Alternatively, you can enter a text freely, which makes sense and is usually necessary in combination with the contains filter or a regular expression.

Google Analytics Suggested Entries for Filtering

Once you have finished entering everything you need, confirm by clicking the Apply button, and the filter will be applied to the data. If you want to edit the filter, simply click on the entry again, and the input fields will reappear.

Applying Filters to Individual Data Points with a Click

In the data table and the other chart forms, further options are available for quickly applying simple filters to the data. In the data table, right-click on a row that you want to use as a filter to include or exclude that value. A menu then opens with the Include only selection and Exclude selection items, as shown.

Creating a Filter via the Context Menu

Clicking on one of the two menu items creates a filter with the value of the dimension as the filter text and the matches exactly or does not match exactly operator. This context menu also works on bars or segments in the diagram displays.

If you drag a metric onto Filter, the comparison options differ. You’re now comparing numerical values, in contrast to the text comparison for dimensions. The following are the available comparison types:

Available Comparison Types

You can therefore define filters such as all pages that have more than 100 views or users who have purchased more than 3 products in the order. As mentioned earlier, in this way, you can filter by dimensions and metrics aren’t displayed in the table or other visualization.

Note that, in a data analysis, you’ll no longer receive an additional indicator in the table that you’re looking at filtered data. You can only recognize this status by the entry in the sidebar, under Filter.

Describing Complex Search Patterns with Regular Expressions

You can use regular expressions to define complex character strings for searches or filters. In contrast to simple search queries along the lines of “find everything that contains <<term>>,” you can use wildcards, character lists, and groups in a regular expression. explains the use of common special characters.

Special Characters for the Definition of Regular Expressions

Different Ways of Visualizing Data

Up to now, your report of the Free format type has been displayed as a table. However, listing data as text is not always the most ideal way. Graphics sometimes make comparisons easier or illustrate relationships more quickly. The data analysis provides a total of five additional diagrams. You have already seen all the visualizations in the standard reports.

Donut Charts

A donut chart is well suited for displaying a few different values of a dimension. It can only process one dimension and one value.

Line Charts

A line chart is used to break down values over time. Like the donut chart, it can only contain one dimension and one metric. However, a line chart offers two special features:

• With the Level of detail option, you can select whether the data is displayed per hour, day, week, or month. Each of the first 10 lines of the dimension is displayed as a line is shown in the diagram.
• If you activate Anomaly detection, GA4 flags anomalies in your report data, as shown. Analytics calculates an expected value for the metrics and compares this expected value with the actual value that was tracked.

Anomaly Detection in Line Charts

Using the two sliders, you can set the Training period (the time period GA4 should use for comparisons) and the Sensitivity (how much deviation should be tolerated). Anomalies are displayed as a dot in the diagram. If mouse over this dot, a window with additional information appears.

Changing Dimensions or Values in Visualizations

If you change the dimension or measure in a chart visualization, the chart might stutter. Its axes and proportions may no longer match the data, as shown below. In such a case, we recommend resetting the visualization once to Table and then return to the diagram.

Chart Stuttering When Switching

Scatter Charts

We’ve already encountered this visualization in standard reports, as shown. In the data analysis, you can now freely define which dimension and which metrics should be placed on the X and Y axes.

Scatter Chart Allowing Two Dimensions per Entry

Bar Charts

A bar chart displays values in the shape of bars. In a table, you also have the option of displaying the values as bars, but without the restriction to one dimension and one metric. This presentation therefore does not provide any obvious added value.

Maps

Use this visualization to display the values of a metric as distributed on a map, as shown below. You can group the data by city, region, country, subcontinent, and continent. The entire world or a specific region can be selected as a section of the map.

As soon as you select the map visualization, new dimensions are automatically added to your list for city, region, country, subcontinent, and continent. This data is required to display the map and is therefore mandatory. If you subsequently switch back to a different visualization, the geographical dimensions remain in the list.

User Views Displayed on a Map

Adding Tabs to a Data Analysis

Above the report, you’ll find some controls. First, click the plus sign icon next to a tab to add more tabs to the report. Each tab corresponds to a separate report configuration, as shown. You can change the name of a tab by simply clicking on the name.

Various Representations, Distributed on Tabs

You can select a different format, a different visualization, and a different setting in each tab, as well as view and filter different dimensions and metrics. For example, the same data can be viewed as a chart and as a table. A useful feature is that you can copy existing tabs to adjust the settings. What remains the same for all tabs in the report are the imported dimensions, metrics, segments, and the time period.

Additional Controls

More icons appear to the left of the menu bar, as shown. Hovering over them briefly with your cursor will reveal their meaning. The first icons, Undo and Redo, work in the same way as you’re probably familiar with standard applications like Microsoft Word or Microsoft Excel: The last change is canceled, or a canceled change is restored.

Toolbar with Additional Functions

When switching between the different visualizations, you may have noticed another difference to the reports interface in the library: no Save button in the user interface for data analysis. All changes you make are saved immediately so that the entry in the menu always corresponds to your last configuration. The data analyses are designed to work with the dimensions and metrics, to break them down, filter, or resort them to gain insights about your users in this process. Not all the steps you take will ultimately result in added value. These two buttons help you backtrack along these paths.

If you’ve found a meaningful table or diagram in a report that you want to keep, in the form you want, but still you want to continue working with the data, we recommend duplicating the tab.

Exporting and Releasing Data

Next, we come to the Export data icon. The data contained in the table (or the data on which a diagram is based) can be exported in various formats, such as the following:

• You can export values directly as a Google spreadsheet file and edit them further using Google Sheets. You cannot perform this export in the standard reports, so recreating a report as a data analysis so that it can be used in Google Sheets might make sense.
• With the comma-separated values (CSV) and tab-separated value (TSV) formats, you can download data in a structured text file that can be easily processed in Microsoft Excel, for example.
• The export as PDF is more or less a printout of the screen as a PDF file. You can export either one tab or all tabs. This option can be useful for commenting on an analysis and for passing a report on to colleagues or customers who do not have access to GA4 reports themselves.

An alternative to exporting is sharing the data analysis with other users. This option allows colleagues to see which settings and filters you have used in the GA4 user interface. Click on the last icon in the row to initiate exactly this feature: After confirmation, the analysis will be activated for all users in read-only mode on the property and will also appear in their report lists under Explore.

You can recognize split analyses in the report list by the changed icon in the Type column inline image and the changed icon in the data analysis itself inline image.

A data analysis can only be edited by the user who created it. You can view the analyses released for you, but you cannot change them. In analyses for which you have read access, you can create a copy by clicking the icon in the top row. After copying the analysis to your own account, you can change its settings there as you wish.

Breaking Down Data Using Segments

We encountered a special form of segment, namely, audiences. Both terms describe a group of users based on certain criteria or actions.

Segments in GA4 and UA

UA had segments with similar properties to GA4. You could convert these into audiences and use them in Google Ads. The segments could be set up anywhere in the Universal Data view and applied to most reports.

Although the term segment is retained in GA4, the scope of application is different: Segments only exist as part of a data analysis. A segment is therefore only ever valid for the data analysis in which it was created. You cannot copy or share segments between analyses.

However, audiences can now be applied to the reports in GA4 within a comparison, although they must be configured beforehand.

When you create a segment, a window appears reminding you to set up an audience, as shown.

Defining the Type of User Segment

You can choose from a range of suggestions or create a new segment and choose from three types that differ in their respective scopes (i.e., scopes of consideration). Below describes the three segment types.

Types of Segments

We showed you how to create user segments in the description of target groups in earlier post. The structure of the configuration window is identical for all three segment types, albeit with minor differences. The condition scoping menu, for example, is different, as shown below. If a user segment still contains three entries, two are for sessions, and a single entry is for events. Otherwise, all segments are built in the same way.

Condition Scoping Varying by Segment Type

Creating an Audience from a Segment

When creating a segment, you have the option of creating an audience at the same time. You can then apply these settings to your regular reports in the property or use them for Google Ads. But something that may seem strange to you: You can also create an audience in session segments and event segments, which was not possible in the Audiences and Configure menu. There’s a simple reason for this capability.

If you have specified the properties and click the Build audience option in a session/event segment, GA4 initially accepts this information. If you then hover your cursor over the filter definitions again, the criteria line changes. Thus, inline image suddenly becomes inline image.

As soon as you want to transfer the segment to an audience, GA4 converts your configuration into a user segment. As a result, you cannot define audiences for sessions or events in this way either. Audiences are always user groups.

Applying Segments

Segments initially appear in a list in the sidebar. From the sidebar, you can drag them into your report, just as you did for dimensions or metrics. If you apply a single segment to a report, it will be filtered.

Data for the Segment as Separate Columns

Above shows data filtered by the Blog readers segment in the first column. The second column shows the totals. These values refer to the total number of users from all segments, not the total number of users on the website. This distinction becomes clearer as soon as you add a second segment, as shown below. Since the user groups of the two segments overlap and users can belong to both segments, the total number is smaller than the sum of the individual columns.

Two Segments Distinguished by Columns and Colors

You can select segments in all data analyses. The number of segments you can apply differs from report to report. Up to four segments can be applied in a Free format, whereas only a single segment can be applied in a Funnel exploration. The Segment overlap requires at least two segments to provide data and allows a maximum of three. The Cohort exploration shows two cohorts with segments laid out one below the other, as shown.

Cohort Exploration with Applied Segments

Creating Segments from Analyses

A simple method of creating a segment for specific users is to use the context menu in reports. Right-click on a cell in a table or an element in a diagram, and a menu appears that contains (among other things) the Create segment from selection item, as shown.

Creating a Segment via the Context Menu

Clicking on this menu item takes you to the segment setup window, in which the properties and parameters for the selected group are already filled in. You can save this segment directly, customize it further beforehand, or make a note of the settings for your own segments.

Other Analysis Formats

So far, we’ve only used the free format in data analysis. The data is displayed either as a table or as (comparatively) simple diagrams. At the same time, this format provides the highest degree of flexibility in the combination of dimensions and metrics.

Analytics also provides specialized formats that prepare and better present specific questions about your users’ activities.

Cohort Exploration

The cohort exploration format shows you if and when users have returned to your website after a certain event. The term cohort refers to a group of users who have been on your website for a fixed period of time.

You define users by which event applies as a criterion for inclusion in a cohort, and you can also define which event applies when they return to be counted again. Below shows cohort exploration for the last 28 days. This format considers all user actions on your website and divides them into a weekly view.

Cohort Exploration Comparing Groups Based on Time Periods

Let’s look at this example more closely:

• In the first week (March 16 to March 22), 10,052 users viewed your offerings.
• One week later (March 23 to March 29), 422 users from this first group came back to your offer. At the same time, 10,107 new users visited your website.
• One week later (March 30 to April 5), there were still 179 users from the first group, 321 from the second group, and 9,737 new users.

Cohort exploration thus shows you how users who have been on the site in a certain period of time have continued to act. This information is useful, for example, if you have acquired users via a time-limited campaign, a special offer, or a push message.

By default, the basis for the cohort assignment is the first measured event of a user. Alternatively, you can select any measured event as the criterion for the first contact. You could take add_to_wishlist, for example, to view users only after they have added a product to their wish list. This event must then be set under Included in the cohort exploration. For returning users, any measured event also applies initially, but you can also select more specific Return criteria here.

For a time-based breakdown, a daily or monthly view can be selected in addition to the weekly view for the Granularity of the cohort exploration.

The output of the user numbers can be customized with the calculation and the type of metric. Ultimately, personal taste dictates which presentation you prefer individual or cumulative but the meaning of the data will not change.

Funnel Exploration

In Google Analytics, a funnel describes the sequence of steps users go through on your website. These steps can be specific events or pages that are viewed by users one after the other, as shown below. However, steps can also be defined by other properties, such as the source or the products.

Funnel Running from Left to Right

You can define the steps in the same way as a sequence in a segment. Assign a name to each step and define which dimension and parameters must be checked, as shown.

Defining Events as Steps in the Funnel

The relationship between the steps can be defined more precisely in two ways:

• The steps can follow each other directly or indirectly.
• The steps can take place within a period of x minutes.

The funnel exploration format shows the sequence of these steps from left to right. At each step, users can leave the funnel, which is displayed as Drop-offs. You can use the Use open funnel option to specify whether a user can enter the funnel at any point or must enter at the first step.

The data is also displayed as a table below the funnel. By dropping a dimension under Breakdown, you can break this table down by another dimension.

In the Next action field, you can store event names or a page dimension such as Page title or Page path. This information shows you, when you mouse over the step, where the users who have dropped off have left the funnel, as shown below. As with most analysis formats, you can apply filters and segments to the data.

The Next Event After the Current Step

Funnels for goals and e-commerce processes were already possible in UA. These funnels were set up in goal definitions and e-commerce administration features. GA4 is much more flexible in its definition features and not bound to predefined processes. Thus, your funnel can include not only the checkout process, but also several independent events.

Segment Overlap

With segment overlap, GA4 finds intersections between two or more segments. For this purpose, you first must define the segments and then store them accordingly in the report. Active users are set as a value and cannot be removed, but you can include other values as additional columns in the view, as shown below. In addition, you can apply additional column groupings and filters to data tables as in the free format.

Segment Overlaps Between Desktop and App Users

The sample diagram above looks at desktop and app users. This diagram works in this property because the same users can be recognized on different devices based on the user IDs that were used.

Path Exploration

The path exploration format shows how users move through your offering after a certain start event. The starting point of the path can be an event, a page title, or a screen in an app.

When creating a path exploration, the event name is first used as the dimension for the individual steps of the path of GA4. You can change this representation for the steps in the menu. If instead of selecting an event as the starting point, you want to use a specific page, restart the exploration by clicking the link at the top right, next to the control icons.

Configured to Page titles, the path exploration shows how users click from one page (in this case, blog articles) to the next, as shown below. The Edit icon next to Step +1, Step +2, and so on can be used to set which rows are displayed in this step and what is summarized under the More item.

How Users Move Through an Offering

The device category was used as the Breakdown in the example. When you mouse over this node, you’ll see the individual values of the broken-down dimension.

Path exploration can help you to discover and check certain paths in your offerings that users take again and again. Unfortunately, the analysis type is only available for the following node types: Event name, Page title, and Screen name/class. Other content dimensions such as content groups cannot be considered in this way.

User Explorer

The user explorer lists all users who have visited your website in the selected time period. Each line corresponds to a user. In addition, you can set several metrics, such as Sessions, Views, or Event count, as shown below. You can narrow down the selection of users via filters and segments and thus, for example, only view users who have come to you via Google.

List of Individual Users

You can also call the user explorer for a specific user group from within many reports. For this task, right-click on a cell or an element of a diagram. In the context menu that appears, select View user. This step causes GA4 to create a segment with these parameters and to store it in your list. Then a user explorers report is opened as a new tab, in which the newly created segment is automatically applied.

Clicking on an item in the user explorer opens a new tab with the User activities overview. In this view, all measured activities on your website are shown for the selected user, as shown. This information includes all events that have been logged, as well as revenue and transactions for e-commerce or the total time spent on the website.

Events of a User in the Activity Overview

The Activity overview enables you to track the individual steps of a user on your website. This feature can also provide insights for smaller groups. From a certain group size, however, other report formats can better visualize data correlations.

User Lifetime Value

The analysis of the user lifetime value (LTV) is intended for special consideration. For your users, it shows the values for transactions, engagement, and revenue over the entire time (the lifetime) that GA4 knows this user, as shown below.

This report only allows certain dimensions and measurement values, which it adds to your lists when you create them. In addition to Active users and Total users, you can also select the LTV, the Lifetime engagement duration, and the Lifetime transactions as metrics.

The values can be broken down according to the dimensions Campaign, Source or Medium of the first user visit as well as according to the Last audience and the Date of the first visit.

Looking at these values over the entire “lifetime” of a user is a generally good approach to analytics. However, you should proceed with caution when evaluating this analysis because of the increased risk that some users won’t be correctly recognized due to technical limitations.

Lifetime Values per Channel

Templates and Examples for Data Analysis

The first time you call the Explore navigation point, you’ll see templates that are provided as a starting point alongside a blank analysis. Above these entries, you’ll find the link to the template gallery, where you can discover some further examples and analyses for customization.

The first group, Techniques, lists the analysis types that you can also select within a report, as shown. These choices are followed by templates for specific use cases, but unfortunately none of them really work for websites because they were originally developed for apps.

Templates That Are Unfortunately Not Very Helpful

In this post, we showed you how to customize and extend the GA4 user interface to suit your requirements. You can edit existing reports or create new ones from scratch. The navigation menus can be modified and supplemented for your daily work.

With explorations, you can now also delve deeper into the evaluation of user activities and compile analyses from hundreds of dimensions and metrics. Finally, segments allow user groups to be viewed according to a wide range of criteria and in a high level of detail.

In the next post, you’ll see how BigQuery and Looker Studio enable even more flexible analytics reporting.