The analysis of user activities on websites is the fundamental component of Google Analytics 4 (G4A) . In this post, you’ll learn about the...
The analysis of user activities on websites is the fundamental component of Google Analytics 4 (G4A). In this post, you’ll learn about the structure of the GA4 interface and the most important reports about users and the content they access.
When analyzing websites, you’ll encounter some of the same questions again and again, regardless of whether analyzing a company website, a blog, or a store: What content is accessed, and what actions are performed? When do my users come, and where do they come from? And in general, who are the users that accept my offerings?
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| Unlocking Hidden Insights Website Analysis with GA4 |
The GA4 Home Page
At this point, you’ve set up your Google Analytics property and created data streams, and data is coming in. When you view the home page, the most important general key figures are displayed, and you can access topics and more detailed reports, as shown in below figure.
The contents of the home page are partly predefined, but also partly result from the configuration of your GA4 property and partly from your use of the reports. For this reason, you should not be surprised if tiles or key figures do not always match the examples in the figures.
The menu contains further links in addition to the home page:
- Reports: The reports contain analyses of user activities relating to content, users, and more.
- Explore: For the prepare phase, you can create your own individual reports and analyses.
- Advertising: This page contains analyses of campaigns, user sources in general, and attribution (i.e., the tracking of users over several visits to your website).
- Admin: We’ve already encountered this item in the previous posts. This page is where you configure and set up data collection.
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| The Home Page for Your GA4 Property |
Snapshots in the First Overview
On the right, you’ll see two tiles that provide an overview of trends and show real-time data. For trends, you’ll see the values for four important key figures, namely, Active users, Event count, New users, and Key events, for the last 7 days, as shown in below figure. You can see at a glance how the last few days have developed, where peaks or dips have occurred. The Preceding period is shown as a dotted line for comparison, so you can easily to detect changes against the past.
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| Trend Overview of the Most Important Key Metrics |
Upon the first view, the trend line of users is shown, and by clicking on the figures above the trend line, you can switch the diagram to one of the other datasets. Click the arrow next to the name to set which dataset you want to view. You can choose from a large number of metrics, such as Revenue or New users, to customize the dashboard to meet your needs.
The time period can be changed below the diagram. Last 7 days is the default setting, but with one click, you can access other time periods such as 14 days, but also Last 12 months or This year (January to today). Of course, you can also define a period according to your requirements.
In many cases, Google Analytics adjusts the time period to enable a comparison that makes sense. For example, the 30 days setting adjusts the time periods so that the same weekdays are always compared. Thus, Monday is compared with Monday, Tuesday with Tuesday, and so on. A comparison of Wednesday with Saturday is often not useful because, in many cases, a website/ web 3.0 or app is used differently on the weekends than on weekdays.
Clicking the View reports snapshot link on the right, below the diagram, opens the main page of the Reports menu.
The Realtime tile next to the trend overview shows the user data for the last 30 minutes of your website, as shown in below figure. Google Analytics first processes the incoming data for real-time analyses in which data appears almost immediately. However, the real-time reports do not provide the same depth of analysis as the processed reports available in the menu.
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| Data from the Last 30 Minutes |
Period “Today”
Among the time period options, you’ll find the item Today. This option outputs the values that Google Analytics has processed up to this point. However, this is not the actual time at which you view the report because Google Analytics needs a certain amount of time to store the data and make it available to the reports. So, you must account for a certain time delay between the receipt, the processing, and the provision of the data. This delay is not constant and depends on various factors, but it lasts at least several hours. Thus, a report covering the period Today should always be treated with caution; it can only give an initial indication of the day’s performance.
The data in the real-time view, on the other hand, shows everything that happens on the website but only for the last 30 minutes
Your Activity History
Below the first row of tiles, you’ll find a list of the most recently accessed pages in your Google Analytics property, as shown in below figure. These pages may have been reports or, for example, admin settings.
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| Your Most Recently Used Reports and Settings |
Below this list, you’ll find the Suggested for you section, which directly shows the most important tiles from various reports that you view frequently.
Insights & Recommendations: Conspicuous Developments
At the bottom of the home page, another section provides insights based on your statistics. In this section, Google Analytics shows you specific insights and developments from your users’ data on individual tiles, as shown in below figure.
Click the icons below the tiles to jump further into the data analysis. The link above the tiles, View all insights, takes you to the admin section of Analytics Intelligence, which provides analyses for generating insights. You can also store your own conditions and checks there, which are then displayed in the insights section.
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| Insights Tiles Showing Anomalies in Your User Data |
Insights: Answers to Frequently Asked Questions
With its Insights function, Google Analytics provides alternative access to user data. Instead of clicking through to the correct report in the menu, you can select from a list of questions. Some questions are about the general development of the offering, while others cover more specific topics such as the distribution of operating systems.
You can display the sidebar for insights by clicking on the icon at the top right of the home page, as shown in below figure. The icon is available on the home page and in all other GA4 reports.
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| Consulting Analytics Intelligence for Insights |
After clicking on a question, you’ll be shown a short table with data from the last few days, which you can rate and share as a link or report. Below this first insight, GA4 references additional insights on the selected topic.
If you want to answer a specific question with Google Analytics data but don’t know which report to go to, Analytics Intelligence can provide further guidance.
The Search Function in GA4
Above the reports, you’ll see a large field for entering search terms. Analytics searches menus, reports, user data, administration, and documentation for your input similar to searching google.com.
A search for “key events” directly returns the measured value for the last week as well as links to help articles from the Google documentation, as shown in below figure.
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| Searching Beyond Reports |
A search for “data stream” returns the measurement ID of this property, including the option to copy the ID into the clipboard. This feature saves you having to go through the admin section and all the corresponding clicks. In addition, links to the settings in admin and other help articles are provided, as shown in below figure.
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| Search for “data stream” Showing All Measurement IDs |
With search results and recommended insights, Google Analytics allows you to access the desired user data, the necessary configurations, and other information in general more easily and quickly.
While using Google Analytics is still not a piece of cake, hopefully, you’ll find the right report or settings a little more quickly.
Reports: Analyzing Users
Under Reports, you’ll first reach an overview page with an additional menu and several tiles, similar to the home page. You’ll see several reports listed on the left, depending on the selections you made when creating the property.
You’ll either find the Business objectives and Users sections or Life cycle and User, as shown in below figure. These categories contain links to various reports.
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| Navigation in GA4 |
The Business objectives and Life cycle menus are similar in terms of content, with minor differences in the reports they each contain, as outlined in below table. In our examples, you’ll see the Life cycle menu throughout, which has been the available model for some time. However, our discussion applies to the other menu without any major problems.
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| Comparison of Business Objectives and Life Cycle Menus |
You can set the menus to be displayed in the library.
The first group contains analyses of usage and user actions in your offering:
- Acquisition is all about the source of the visits: Which website, campaign, or link did users come from?
- Engagement shows the accessed content and measured events about your users.
- Monetization provides reports on all revenue sources on your site. These sources can include e-commerce or in-app purchases or revenue from advertisements displayed on your website.
- Finally, the Retention report is available individually in the menu and shows the number of new and returning users.
The next group is the User group, which contains reports on Demographics (e.g., age, gender, and more) and Technical data (i.e., the browser and operating system).
Important Basic Terms
On the Report snapshot page, in addition to the trend overview and the real-time view, you’ll discover a number of other tiles that display information about the sources, origins, and content accessed by your users. A link often leads to a detailed report on the topic displayed. Some dimensions and measured values appear repeatedly in the various lists. The most important values are listed in below table.
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| Important Terms in GA4 Reports |
The home page already provides some early insights into users’ responses to your offerings. The tiles and the reports behind them can tell you a lot about the users themselves.
General User Data
Let’s return to the trend overview of total users on the home page, as shown in below figure. Through this tile, you’ll see the proportion of new users for whom activities have been measured.
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| Trends in General User Data |
New users are visitors who, according to Google Analytics, have not previously visited your website. Technically speaking, these users have not yet saved a Google Analytics cookie from a previous visit in their browser. If a user has deleted their cookies after a previous visit or is using a different browser or computer, they will be considered a new user again. To mark a user, the first_visit event is saved in GA4.
The counterpoint to a new user is a returning user, that a user who has already visited your website, as evidenced by the corresponding cookie already in their browser.
Tracking Only with User Consent
As the operator of a website, you’re obliged to obtain a user’s consent before tracking their activities. In the corresponding consent query, users can both consent to and deny tracking. Users who deny tracking will not appear in your reports.
Users who are presented with a consent request when they first visit your website and then leave the page again (or close the browser) won’t be counted either.
Consent rates vary depending on the audience, the topic of the website, and the design of the consent query. Most consent managers keep statistics on consent and rejection decisions. You should review those figures to estimate the total number of users.
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| Consent: Accept Versus Denial in Usercentrics |
As shown in above figure, an acceptance rate of around 52% is reported. Analytics measured 1,822 users over a certain period of time. You can now estimate the 48% of users who have not given their consent to tracking using a rule of three:
1,822 / 52 × 100 = 3,504
In fact, there were (probably) 3,504 users on the website. However, estimating what they have done is impossible. In addition, the approval rate can fluctuate greatly depending on how these users came across the offer.
The average engagement time is the time between the first and last measured user event on your website. The first engagement is usually a first page view; the last engagement can be either a page view or another event.
If a user stays on your website without carrying out any further (counted) actions, they become invisible to Google Analytics. Let’s say, for example, a user has lingered on an offer to read a longer text. If no action takes place, this time will no longer be tracked. However, GA4 attempts to minimize this proportion of unrecorded time through automatic events, such as scrolling or clicks.
Note: In UA, the average session time refers to the period between the first action and the last action.
Sessions for which multiple actions on the offer are counted are totaled by GA4 as engagement sessions. During such a session, either two page views were made, or it lasted at least 10 seconds and included a conversion event.
You can view engagement sessions as a counterpart to the bounces previously used in Google Analytics. Bounces were defined as sessions in which no further page or event was tracked after the first page view.
The Lost Meaning of Bounces
However, with the introduction of consent banners to request consent, bounces have lost their significance. A bounce used to be regarded as an indication that a user did not want to go on to your offer and had clicked through. Now, users turn around at the consent query and therefore do not appear in your analytics data at all. Without that first page view, there is no bounce, and the insight from this bounce as a metric is no longer given.
GA4 focuses more on engagements compared to the bounces in UA. You’ll see at least one column with engagement values in almost all reports. However, the bounce rate is still available as a dimension for custom reports.
The engagement rate is the proportion of sessions with engagements out of all sessions. This rate is reciprocal value of the bounce rate. In other words, engagement rate indicates the proportion of users who have seen more than just the first landing page.
If a user views only a single page of an offering, only a single action will be measured for them. In this case, the engagement time for the user’s entire session is 0 seconds. If a high number of such sessions without engagement are measured for your offer, the average engagement time will be correspondingly low.
Tracking User Activity in Real Time
A special GA4 report is available via the Realtime menu item. Some user data that has been collected in the preceding 30 minutes is displayed, as shown in below figure. A measured action on the website should appear in the report a few seconds later. In the other Google Analytics reports, however, a certain amount of time is required for the data to be processed and displayed.
This report shows the total number of current users and their distribution by device category. The world map on which active sessions are currently lit up is rather prominent. Below the map, you’ll find some tiles that show you the active users in more detail, such as the following information:
- Users by source (or campaign, channel, and more)
- Users by audience
- Views by page title and screen
- Event count by event name
- Conversion by event name
- Users by user status
Compared to the previous overviews, these tiles are somewhat limited in their display. You’ll only ever see six lines and must scroll to get more results. Unfortunately, the tiles cannot be customized.
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| Tracking Users Live on Your Website |
Real-Time Report for Pages
The Page title and screen name tile only shows the title, but not the URL. For a better overview of the pages accessed, you must select the separate Realtime pages report item in the navigation. As shown in below figure, this step takes you to a live list of the URLs visited by users on your website.
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| Live View of Realtime Pages Report |
Unfortunately, the report does not provide the same options as the overview: No comparisons can be applied, and the primary dimension of the report cannot be changed. The table is always more useful than the real-time overview for quick feedback on the content currently being used.
Two tiles of the overview provide added value for a quick analysis of your users: Audience and Key events. You can define the classification of users into a specific group yourself, just as you can mark certain events as key events. As a result, you only see what you have defined as interesting in the tiles.
As with any report, you can add filters and comparisons to the entire report, allowing you to focus your analysis on specific areas or groups.
By clicking on a location on the map, you can create a comparison of these users with all users of the offer. You’ll see the value of these two groups for each tile, as shown in below figure.
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| Comparison of Events for Two Groups of Users |
On top of the maps, you can click the View user snapshot button to access the ongoing sessions of randomly selected users, as shown in below figure. Then, you can look “over the shoulders” of your users.
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| Tracking the Actions of a User |
For instance, you can see which events were fired by a user at what time, which user properties they had, and more. The problem with this view is the selection of the sessions shown: You cannot predefine them, for example, by using filters on the overview map. Instead, a few randomly selected users are always shown. While this view makes for a nice visualization, explicit analysis of specific users is not provided.
The structure and functionality of the user overview are similar to the DebugView, which you can use to track sessions you have started and marked. Thus, events and properties of the tracking can be tested live within GA4.
Demographics
The User section of the menu contains reports on the Demographics and Technologies of visitors to your website. There are two entries for both areas in the menu: an overview with tiles for various data (as you’re already familiar with from the home page) and a detailed report in which you can switch between the various dimensions. As mentioned earlier, no separate menu item exists for each dimension as before; in GA4, each path leads to the relevant overview page instead.
Location
One demographic feature that you can evaluate more precisely is the location of your users. In this context, a distinction is made between country, region, and city; you’ll find a separate tile for Country in the overview, as shown in below figure.
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| Location Data of Your Users by Country |
The location is determined using the user’s IP address. Google has databases in which the assigned countries and if possible cities are listed. According to Google, this breakdown already takes place within the European Union (EU), so that their data privacy requirements for the processing of IP addresses are met. For all other analyses, IP addresses are either not used at all or only in abbreviated form. You can be completely safe by using a server-side Google Tag Manager, which allows you to explicitly prevent IP addresses from being passed on to Google.
In the Demographic details report, you can analyze properties, such as the regional origins of your users, as shown in below figure.
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| Details View of Demographic Data |
At the beginning of the table, just below the search field, you can switch between different dimensions, as shown in below figure. The metrics in the table always remain the same when switching. You’ll find this menu in almost every report that contains a data table. The dimensions that are included depend on the selected report.
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| Switching Between Dimensions in the Table |
The first dimensions (Country, Region, and City) are all derived from the user’s location. Regions are smaller units within a country, such as states, provinces, or administrative areas.
Accuracy of Geodata
If Google Analytics does not recognize the regional origin, these users are summarized in the report under the (not set) item. At the Region and City level, the significance of the data may vary depending on the audience of your offering. If you offer services or information for companies, for example, you’ll increasingly have business users on your content who access the internet from their office and therefore their corporate network. The location is then more likely to be the company location. For large corporations with multiple branches, this network location may differ from the actual office location.
For private users, depending on their internet connection, the next largest city may be selected as the location. For this reason, you must consider how reliable this data is when analyzing audience location data on a small scale.
Language
Every browser transmits to a website its primary display language. Google Analytics uses this information for the Language report. The user’s current location has no effect on this value. For example, a user in Germany may have configured their browser with English as the default language.
For languages, you’ll find a tile in the overview, as shown in below figure, and a table in the data view. As the language setting might be independent of a user’s location, interesting combinations often arise, especially in multilingual countries like Switzerland.
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| Languages Set in Your Users’ Browsers |
You can easily and quickly combine two dimensions within a report by clicking on the plus icon next to the dimension selection.
This icon opens a menu with a list of dimensions that you can use to further break down this report. In the country report, you can select the Language item from the menu under Platform/Device. This selection adds a second dimension column to the table, and the data will now be broken down by this additional field.
In the example shown in below figure, English is the most frequently used language for United States, India, and Canada. But in all countries, some users have set English as their primary browser language. However, the website in this example does not have any English content—these users are not necessarily purely English-speaking; perhaps they simply have an English-language browser installed as the company default.
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| Primary and Secondary Dimension in a Table |
In the second example, shown in below figure, the users for Canada are subdivided. This type of analysis allows you to estimate which languages might be worthwhile for your own content. In such a case, you should also check the use of Google Translate or other translation services, which (partially) leave their own traces in the data.
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| Different Browser Languages in Canada |
Demographic User Data
If your GA4 property is linked to a Google Ads account, you’ll see tiles for the Age, Gender, and Interests in the demographic details overview. This data comes from the Google advertising network, in which this information is collected for the possible control of campaigns. If the tiles remain empty and only display the No data available message, most likely no Google Ads account is currently linked to your property.
The gender of your users is divided into Male and Female, as shown in Figure 3.23. Note that the two groups and the resulting percentage distribution only refer to users for whom data is available in the advertising network! Check out the later information box on the reliability of age, gender, and interest data.
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| Users Divided by Gender |
The age distribution of your users is roughly divided into groups of 10 years, starting at 18. A distinction is made between several dozen categories of interests, such as Movie Lovers or Frequently Eats Dinner Out. In combination with the content that is accessed, for example, both data points can help you determine whether the website provides the right offers for your users.
Reliability of Age, Gender, and Interest Data
This information about your users originates from a Google Ads account. If the link between Google Analytics and Google Ads is set up correctly, user data from your website is compared with data in the advertising network. Of course, this feature only works if you have data about your users stored in the advertising network. The distribution of the individual categories is represented by bars in the overview tiles, as shown in below figure.
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| Age Distribution of Your Users |
However, if you click in the corresponding data table, the data looks slightly different, as shown in below figure.
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| Age Distribution as a Table |
In this excerpt, the largest group by far is the one referred to as unknown, as shown in above figure. This label refers to users for whom no data was found in the advertising network which is almost 80% of all users. Unfortunately, the diagram in the report and in the overview only represents 20% of your users! You’ll see the same distribution in the reports on gender and interests. The significance of this data should therefore be treated with caution.
The rate at which your users are recognized varies from website to website and can be both higher and lower. Be aware that only some of your users are represented. Unfortunately, determining this rate in GA4 is more complicated than in UA (where a percentage was displayed).
Viewing Technical Data
The reports on the Technology (abbreviated as Tech in the menu) can also be found in the User area. These reports provide insight into which devices and software systems users are using on your website. As with the demographic details, two items are listed: an overview page and a detailed view for the various dimensions and metrics.
A note at the beginning: The maps and reports on apps, app releases and so on only provide data if you have set up data streams for apps.
Platforms and Devices
In GA4, the Platform report describes the type of data stream on which the users were measured. You can create data streams for measuring web offerings as well as iOS and Android apps.
If you’ve implemented comprehensive user recognition for your offers using the user ID, you can see which users visit multiple platforms based on the intersections in the graphic, as shown in below figure.
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| Distribution and Overlap of Web and App Users |
In the report overview, you can also see the distribution of platforms for real-time users that were measured on your offer in the last 30 minutes.
This tile is only useful if you combine data from an app and a website in the property. If the data comes exclusively from a website, the charts will always show 100% web users. Users of the website via smartphones are also listed as web users in this presentation.
In this case, a breakdown of users into the Device category is more informative. A distinction is made here between Desktop, Mobile, and Tablet, as shown in below figure. In this way, you can see the distribution between users who work on a large screen (Desktop) and those who use a mobile device. Apps are always counted as Mobile in this view.
The combination of the two dimensions is represented by the Platform/device category, as shown in Figure 3.28. In this case, too, a distinction is made between Web, iOS, and Android but this time combined with the device categories Desktop, Mobile, and Tablet. (There is even a Smart TV category.) Using both web and app data will result in a number of combinations.
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| The Most Frequently Used Device Categories of Your Users |
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| Platform and Device Categories Combined |
Browser and Operating System
The Browser and Operating system dimensions enable you to find out more about the software installed on your users’ devices, as shown in below figure. All common browsers are differentiated for the analysis (e.g., Chrome, Firefox, Safari, and others). The use of a browser module within an app is also recognized and listed as a Webview.
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| Browsers Used by Your Users |
Where Is the Browser Version?
In contrast to UA, the browser version is no longer one of the default dimensions in the report, and you cannot view the browser along with its version. However, the browser version is still recorded as a separate dimension, so that you can either include it in a report under Explorations or in your own report. Interestingly, GA4 also provides the version as a selection option for the Windows operating systems.
The breakdown by operating system allows you to draw conclusions about the system and hardware capabilities of the end device. The same applies to screen resolution data, which you should consider when developing new content or website designs. For mobile websites in particular, screen resolution can make a noticeable difference.
On the desktop, the differences between screens are now more in the “very high” or “ultra-high resolution” categories, and thus, most users do not notice any differences in the display. Your website should be built in such a way that it is cleanly displayed and usable on all end devices and systems.
The detailed view, with both total figures and metrics for engagements and events, can indicate issues with the website when you look at the different dimensions, as shown in below Figure.
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| Differences in Engagement via Desktop and Mobile Devices |
Even if the distribution of browsers and systems is different, the usage data should be similar. (Chrome is often the most frequently used browser, but certain environments favor high usage numbers for Safari.) Thus, a Chrome user should normally navigate through your website in a way similar to a Firefox user. If instead you see major differences in duration, engagement rate, and so on, investigating the differences could be worthwhile: Outliers or particularly deviant figures can indicate problems, for example, links that are not displayed correctly.
Performing such an analysis every day is unnecessary, but it is worth looking at these reports, especially after major changes to the website and at regular intervals. However, remember to differentiate between desktop and mobile device users in these analyses, as these categories are expected to behave differently.
Content: Events and Pages
In the Engagement menu item, Google Analytics collects reports on the content accessed and the measured actions of your users. Engagement is the starting point for knowing what is happening with your offer.
The Engagement Overview
The first page of Engagement is an overview of the data, as you’re already familiar with from other areas. You’ll find tiles on the trend of engagements and the engagement time as well as on the general development of views and events, as shown in below figure.
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| Trend Overview of Views and Events |
The Event count shows the total quantity of all measured events in the period, while the Views only refer to special events for measuring pages of a website (page_view event) or screens in an app (screen_view event) and are analyzed in more detail in a separate report. An additional tile displays the top entries for both the events and the pages/screens that have been viewed.
As on the home page and the report snapshot, a tile displays real-time data with the measured users of the last 30 minutes. You’ll see the pages these users have viewed.
User Actions in the Event Report
You can access the Events table by clicking the tile in the overview or by selecting the item in the menu. The report lists all events GA4 has measured for your offerings, including entries for automatically recorded events, optimized analyses, custom events, and events created within GA4 by the system or via configuration settings.
Above the table, you’ll see the daily progression of the top events as a line chart, as shown in below figure. To see individual lines more clearly, you must hover over the items in the legend. The top 5 lines and the total of all events are always displayed as a dotted line. Use the diagram as a quick overview and check for unforeseen developments.
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| Development of Various Events over Time |
You can adjust the items displayed in the data table below the diagram. First, deselect unwanted lines and then select the checkboxes of the desired events. When you first view the report, five entries are usually already selected, which causes Google Analytics to gray out the remaining boxes so you must first deselect them. Then, click on Plot rows, as shown in below Figure.
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| Selecting Rows for the Chart Display |
A maximum of five rows plus the total can be selected. You can switch the total number on or off via the Total checkbox (the unlabeled column of checkboxes all the way on the left).
You can change the time scale of the lines above the diagram. The data output per day is the default view at first. However, you can also change the scale to weekly or monthly. For longer periods of time, this option can be more clear: Compare the charts shown in Figure 3.34 and Figure 3.35. In the latter chart, based on weeks, strong and weak weeks are easier to recognize.
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| One Year Per Day View |
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| One Year Per Week View |
In the table, the Event name is followed by columns for Event count, Total users, Event count per active user, and Total revenue.
The count shows the actual views of this event, regardless of whether it was fired once or multiple times by users. In contrast, the value for users shows how often this event was fired at least once by users. As the event must be logged at least once for each user, the event count is always higher than or equal to the number of users.
The Event count per active user column tells you how often an event was fired per user on average. According to the definition above, the value must be at least 1, and the higher the value, the more often the event was fired by users.
The Event count per active user shows how many events users have accessed on average. For some events, such as first_visit, the value is always 1 by definition because a user can only have a first visit once. For the page_view, the value shows how many pages users have viewed on average. Important average values can also be found in the overview screens as separate metrics, for example, First visit or Views per active user.
Calculating the Average
The Event count per active user is an ordinary average value, so you cannot distinguish whether all users have fired an event a few times or whether a few users have fired an event very often.
In other words, the value of 5 events per user can result from either:
- 10 users who fire an event 5 times each
- 2 users who fire an event 25 times
You can visualize this distribution in a scatter plot.
The Total revenue is calculated from the events for which revenue values are provided, for instance, e-commerce sales, advertising revenue and revenue from subscriptions. These values give you an insight into the significance of these events for your offerings and help with the analysis, since a high number of events does not automatically result in high revenue numbers.
Total Revenue versus Page Value
The Total revenue column only provides added value in the event list if you have sent values for various events. If you only track revenue for sales (with the purchase event), your total revenue will be assigned to this event since this table only assigns the revenue to the transferred event. The revenue is not allocated to all events involved in the purchase. This approach also applies to the page report, which means that the total revenue works in a different way than the page value in UA where the revenue was distributed evenly across all pages (events) viewed by the buyer.
Event Details
We introduced you to several event names earlier. For instance, the page_view, session_start, user_engagement, and first_visit events are automatically tracked when the Google tag (gtag) is installed on your website. These events serve as the basis for other reports and metrics. For example, the views of first_visit correspond to the New users metric of the home page.
The scroll and click events were fired by optimized analyses and transfer the scroll depth or the click on an outgoing link. The other entries (potential_applicants, content_cta_navigation, webinar_interest, and webinar_register) are individual events coming out of your Google Tag Manager or from code on your website, as shown in below figure.
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| Events Collected in Various Ways |
Clicking an event takes you to the detailed view of the data, including the usage history for the selected event with its own trend graph. You’ll also see the measured values from the overview as well as some tiles with further demographic data about the users who fired the event.
A useful special feature is the tile displaying the real-time data of the last 30 minutes, as shown in below figure.
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| Parameters Also Visible in the Realtime View |
You can select all the parameters of the events for which data has been collected in the last few minutes from a menu, as shown in below Figure.
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| Transmitted Parameters Found in the Realtime View |
These parameters can be subsequently analyzed in a separate report (e.g., page_location, from which the page report is created).
However, you’ll also see parameters in the real-time view that are transmitted but not saved because no dimension or measured value is provided for them. This data expires 30 minutes after the incoming event. To save these values for later analysis, you must set up a custom dimension or metric by this name in the configuration.
Content Used in the Page Report
Page views in your website are logged using the page_view event. To analyze the content viewed, GA4 provides the Pages and screens report, as shown in below figure. The term screens refers to the different views in an app.
When you view this report, you’ll see the familiar structure: a line chart at the top showing the total number of hits and the top pages. Below this chart is the table displaying entries for each page.
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| Page Report Showing Content in Demand |
Page Path and Page Title
The Page path and screen class dimension is selected upon the first view and shows the URLs of the pages viewed. The URL is something like a unique identifier for a page. In addition to the content, the URL also reflects the content’s hierarchy in the website, through directories, which is also important, for example, when comparing campaign data or during search engine optimization (SEO).
If you previously used UA, note the following difference in GA4: In UA, the default value for the page report was the Page path and query string dimension. Different parameters in a URL generated several entries in the listing. In GA4, the default dimension is the page path without URL parameters!
How Does Google Analytics View a URL?
The page path corresponds to the part of the URL between the domain name and any existing URL parameters. A URL can be split into multiple components, as shown in below figure. GA4 tracks the host name, page path, and page path and query string components as separate dimensions for its reports. The protocol, the stand-alone query string, and fragments are not automatically stored by GA4.
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| A URL and Its Components |
If capturing those fragments is important for your website, you can “retrofit” tracking via Google Tag Manager or through JavaScript programming.
You can use the dimension menu above the first column in the table to customize these displays. Click Page title and screen name to display the known page titles in a table, as shown in Figure 3.42.
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| HTML Title as a Dimension in the Page Report |
The goal of GA4 is to make identifying the content of the pages easy. The page title should correspond to the HTML title of a page, that is, the text found in the source code of the page, as shown in below figure. Sometimes this gives better insight into the content of a page than its path.
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| A Page Title in the HTML Source Code |
At first glance, whether you look at the page title or the page path may not make much difference. Every page has a title, and every page has a URL but these two things are not always congruent. If the query string is added, the differences become even more probable.
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| Selecting Page Path Option to Show URLs without Parameters |
The total number of entries in the list is displayed above the page report table. Now, switch between Page title and Page path. For the website from the previous examples, we get the following results:
The number of entries shows that this website has more different page titles than paths (325 > 285). This means there must be several titles for some paths—how can that happen? A closer examination of the page titles reveals language variants that do not exist on the website: English is first, but other languages are also included.
These entries are created when Google Translate is used. Let’s assume a user translates the page with this Google service within the Chrome browser. The HTML title in the source code is also translated. Since the gtag always transmits the current title of the page currently being viewed, the page title is also in the new language variant.
This example illustrates another feature of page titles in GA4: The title is entered anew each time the page is viewed. If the title of a page changes, it creates a new entry and is treated as a new page. So, if you often change the titles of your pages, for example, for SEO optimization, you’ll create a new entry in the Page title list each time.
As mentioned earlier, experience has shown that page path is the more permanent dimension to consider. If your website uses a large number of URL parameters, consider switching to Page path and query string. This option also shows the parameters that are appended after the question mark (?) in a URL.
Key Figures of the Data Table
The data table of the page report has nine columns of metrics, some of which we’ve seen in other reports, as shown in below figure. These values are totaled according to the primary dimension that you have selected, such as Page title or Page path.
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| Metrics in the Page Report |
The top row of the table shows the total for the specific metric. Thus, you can always compare the individual entries. The percentage share of the total value is displayed below the total. In a filtered report, this value indicates the proportion of the total entries found.
The Views measure every time a page gets loaded in the user’s browser. Loading a page multiple times for example, when a user clicks the Reload page icon in a browser produces multiple views. Remember that an automatic reload of a page by a script is also measured as another view. Depending on the programming of your page, this reloading can happen, for example, when the window size of a browser changes. Even on mobile websites, closing and then reopening the mobile browser can cause a page to reload.
The Active users column shows how many users have loaded this page one or more times. For some pages, this value is more meaningful; for others, whether they are accessed multiple times during a visit doesn’t matter. Multiple page views can occur due to user guidance or for technical reasons, but multiple users don’t. The number of users thus tends to show how many contacts a page or content has had.
The number of users for the total amount refers to all users of your website. The entries in the table, on the other hand, are exclusive; in other words, each page is counted individually. A user who was on page A, but also views pages B and C, is shown once in each row of the table. The total of the number of users in all rows is therefore higher than the total in the column header.
The Views per active user entry shows how often a page has been viewed multiple times by users. A value of 1 means that each user has only loaded this page once. If 200 hits were measured for a page by 125 users, the value is 200 / 125 = 1.6
We touched upon the Average engagement time per active user entry earlier part of the post. However, we want to highlight a special feature in this page report: how much time users have spent on average on a specific page. In contrast, the engagement time in other reports, for example, the report by country, refers to the total time users have spent on the website.
The Event count entry shows how many events were measured on this page. A higher number of events represents more user engagement with this page. Under the column name, via the All events menu, you can select the value for an event from all measured events, as shown in below figure. For example, if you have set up an event for a contact form, you can find all the pages on which the event was triggered.
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| Selecting Individual Events for Event Count in the Page Report |
After selecting an event, we recommend sorting the table by this value by clicking on the column name. The pages with entries appear at the top of the list.
The Key events column works in the same way as Event count, but in this case, you only have key events to choose from, as shown in below figure. Again, use the menu to select specific key events under the column header.
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| Viewing Any Key Event in the Page Report |
Finally, the Total revenue shows the pages on which you have generated revenue from e-commerce, app sales, or advertising.
Content Groups
If your website comprises many pages, view individual areas is not always easy. Content groups enable you to combine multiple pages of your offering and thus obtain a total of views and users for different content areas, rather than each individual page or all pages at once. Select the Content group dimension in the menu above the page report, as shown in figure below.
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| Content Group Dimension in the Page Report |
Therefore, content areas with different page counts can also be compared. You’ll see the number of users across all pages for each area, regardless of whether 5 pages or 500 pages make up the content area. Such a direct comparison would otherwise require extra work and be implemented via segments.
For content group entries, GA4 analyzes the content_group field of a page_view event. You can enter this field as a parameter either in Tag Manager or in the gtag code, if you implemented the Google tag directly.
Alternatively, you can use the modification of events in the Admin section. For this task, you must create a new entry under Change events.
For the matching conditions, you can use the entire URL or a part of the URL that describes the area for page_location. As shown in figure below, we’ll use the following matching conditions:
event_name is equal to page_view
page_location contains /blog/
If the conditions match, the content_group parameter will be filled with a descriptive name. This parameter will later appear as an entry in the content group report. You need an entry in the list of changes for each content group.
To apply a rule, all conditions must always be fulfilled. In other words, the conditions are linked with a logical AND operator. If you want to apply different conditions for the definition of a content group (e.g., “if page X or page Y, then group B”), you must create several rule entries, as shown in Figure 3.49. The adjustment conditions are different for these rules, but the changed parameter is the same.
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| Multiple Event Changes for Content Groups |
Unfortunately, the matching conditions for the event changes we’ve described so far are not as powerful as in UA; for example, you can’t use regular expressions for comparison. You also cannot use URL parts as the name for a content group. If you want to perform more complex comparisons, you should switch to Google Tag Manager, which provides more options with regard to search tables and regular expressions.
The Average engagement time per active user is not calculated for content groups. In the list of groups, the value for all entries remains at 0 seconds.
Analyzing User Action with Events
Through optimized analyses, GA4 collects by default many user actions on a website; however, no reports are visible in the menu. To analyze this data, you must use custom reports. In this section, we’ll explore which reports are possible and which values you need for them.
Outbound Links
GA4 refers to links that lead away from the current domain of your website as outbound links. If the corresponding optimized analysis is activated, a click event is fired for each clicked link. This event will capture and transfer information like the URL, the domain, and CSS information.
A custom report with the Link URL dimension and the number of users shows you where your visitors have clicked, as shown in figure below.
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| Links Clicked from Your Offering to Other Websites |
In GA4, the definition of what an outbound link is is quite extensive. Not only links to other websites are considered as outbound but also links to files on other websites as well as mailto and tel links to email addresses or phone numbers. Pretty much everything that does not refer to the same domain as the current page or that is not stored as a domain in the configuration is counted in this context.
You can also use the click event and its associated parameters to collect further clicks via scripts or Google Tag Manager. The basic setting of the optimized analysis already covers many cases, but as always, you may need to address individual additional requirements.
Downloads
If you have activated the optimized analysis and your pages contain links to files, clicking on such a link is collected with the file_download event. No ready-made report is available to analyze this event; you must create your own analysis, as shown in figure below.
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| Downloaded Files |
Possible dimensions include File name and File extension as well as Link URL and the Link text. The optimized analysis uses the file extension in the link to determine whether the link is a download.
The domain on which this file is located is not relevant for the count. The click will also be counted if it leads away from the website. In this case, a click event is also fired for an outbound link.
Download Not Counted Automatically
You need individual tags via scripts or Google Tag Manager if the file extension is not automatically recognized or does not yet appear in the linked URL. Some websites initially link to a coded URL such as:
https://foo.bar/dl/?file=xxx...
From there, the user is redirected, and the actual download is started via script. In such a case, you fire the file_download event yourself and fill the parameters with the values of the final file.
Internal Search
The optimized analysis for website searches tracks the entries in the Search term dimension. The search terms are recognized if they are appended as parameters in the URL of the search results page, as shown in figure below.
But what if your search does not use URL parameters to transfer the input? Or what do you do if you use a more dynamic search feature on your website that, for example, displays results as suggestions within the search field? Figure 3.53 shows such an example from https://apple.com. A user can use quick links to jump to the target page before submitting, without opening a results page.
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| Search Term Passed as a Parameter in a URL |
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| Typeahead Search on apple.com |
In these cases, you can send the event for the search entries to GA4 yourself. For this setup, you must fire an event as soon as the search results appear as a preselection, as shown below.
[gtag('event', 'search', { 'searchterm': 'macbook' }); ]
The searchterm parameter is given the value of the search input. You can also pass up to ten additional parameters, which are saved as search categories, as shown below.
[gtag('event', 'search', {'searchterm': 'iphone','q_cat1': 'mobile','q_cat2': 'ios'}); ]
You can implement this feature via JavaScript in the browser using the gtag as shown earlier, or you can use Google Tag Manager.
Sending Forms
Forms are used on many websites. Users can get into contact using a form, sign up for a newsletter, or register for an account. These often important actions can tell you a lot about the success of a website and should therefore be recorded in Google Analytics. Let’s suppose you want to see a separate event in GA4 for a successfully submitted form.
Google Analytics uses the optimized analysis for engagement with forms to record how users use your forms. The form_start and form_submit events are used for this purpose.
Semi-Automatic Tagging of Forms
The optimized analysis for forms also transfers the following two parameters with the two events:
form_id contains the CSS ID of the form.
form_destination contains the destination URL, to which the form sends the data.
However, these two parameters lack corresponding dimensions in GA4, which is why you must create them yourself. Doing so also helps you better identify which form was submitted if this information cannot be determined from the page containing the event.
In the end, you should have the same entries in your list of custom dimensions.
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| Custom Dimensions for Forms |
Depending on how a form is programmed, this automatic tracking may not work reliably or may not work at all, and you may need to program a solution yourself or configure the dimensions in Tag Manager.
We can distinguish among several approaches to detection, including the following:
They use the form recognition feature from optimized analysis, and the programming allows reliable counting.
After successfully submitting the form, you’ll be redirected to a confirmation or thank you page. This page is automatically recorded because it contains the Analytics tag. In this case, you can use the Create event function to trigger an additional event in the data stream, as shown below.
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| contact_thankyou Event Triggered When the Thank You Page Is Viewed |
Of course, this variant only works reliably if users land directly on the thank you page. If loading speeds are long and the user cancels the session before landing on the final page, the event will not fire. If the thank you page is not explicit or can be viewed again later, you might have a problem with too many hits instead of too few.
When a form is sent, certain messages are generated in the browser to which a JavaScript or Google Tag Manager can respond. In Google Tag Manager, you can use the Send form trigger to wait for the corresponding message, as shown in below figure. Google Tag Manager records further information about the form sent so that you can distinguish between a contact form and a newsletter form, for example. The trigger allows you to fire a GA4 event and assign a descriptive name.
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| Using Google Tag Manager to Count When a Form Is Submitted |
This variant of recording becomes problematic if the form redirects the user to another page because then a timing problem arises in the browser. The user should be redirected as quickly as possible, but the tracking data must be sent beforehand; otherwise, this process is interrupted as soon as a new page is loaded. In this case, you can select the Wait for Tags option, which attempts to “pause” the browser until all analytics tags have been fired.
Programming Forms
Modern website frameworks send form content as scripts. Therefore, these forms lack a unique destination URL as a property by which you can recognize a specific form. Thus, distinguishing between multiple forms on one page or finding one form on multiple pages can be difficult without additional markers.
Each form should have a unique HTML ID or CSS class so that you can clearly differentiate between multiple instances in Google Tag Manager, which can query these and react accordingly.
If the first two variants are problematic for your offerings, you can try to capture the click on the send button of the form. For this, the click trigger in Google Tag Manager is used. However, this approach has the same timing problem as in point 2 if the form redirects to another page.
Note also that some forms perform an input check after the submit click, as shown below, but before the actual submission, for example, to ensure that all required fields have been completed. In this case, the trigger fires before the input check has been completed. Then, you also count a click for incorrect entries, which has not yet led to a submission. If the check is already running during input (i.e., before the click), this problem won’t arise.
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| Input Check After Clicking the Submit Button |
If the form does not send the user to a destination page, then integrating an explicit call in the programming is a more secure variant than either variants 2 and 3. When you use Google Tag Manager, it can respond to a dataLayer call, which is recommended.
If you use the gtag code directly, you can fire a call after successfully submitting the form with the following code:
[gtag("event", "generate_lead", {currency: "EUR",value: 10}); ]
This option provides control over when exactly the form is considered successfully completed. However, programming must be adapted, which is not always possible for cost and/or time reasons.
Google Analytics has two recommendations for event names:
- generate_lead for a customer contact
- sign_up for registration to a service or website
The advantage of using these names is that they are automatically marked as conversions in your GA4 property. However, you can also use custom event names that you find meaningful and that facilitates analysis later.
Regardless of the name, you can use two parameters:
- value is registered as the value of a conversion and is included in the sales figures. You can enter this value directly in the event or assign it to an event subsequently using the Edit event function in the data stream.
- currency must be transferred together with value so that GA4 knows how to calculate the value.
The use of these parameters is optional since events and conversions count even without them.
Videos
GA4 can record video playback and general engagements with a video player integrated on your website. For this feature, the player must send the necessary JavaScript messages to the browser and thus to the Analytics tag.
If you integrate the YouTube player on your website, activate the necessary configuration by adding the enablejsapi=1 parameter to the URL of the video, for example:
https://www.youtube.com/embed/iuYlGRnC7J8?enablejsapi=1
This addition allows Google Analytics to record a user’s engagements with the player. The start of the video, its progress, and the end of a video are recognized and transmitted as separate events, together with the following parameters: the title of the video, its URL, its progress, and the video provider (like YouTube), as shown in figure below. The page on which the video player is integrated is also recorded with the event.
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| Automatically Triggered Video Player Events |
To analyze the video views, you must create a custom report in GA4 under Explorations again.
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| Data Analysis Showing the Video Player Actions for Each Title |
Even YouTube Videos Don’t Always Work
The optimized analysis in GA4 is a good starting point for the Google tag and creates a framework for reporting. However, some limitations like being dependent on the use of the JavaScript application programming interface (API) in the player and only firing at certain points in a video.
In Google Tag Manager, you’ll also find a trigger for YouTube videos that provides some additional options, as shown below. The trigger automatically adds the necessary parameters to videos that have already been integrated and lets you define percentages and time values for progress. You can also limit the tag to specific pages, videos, or other criteria.
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| Video Tracking in Google Tag Manager |
In the GA4 tag to be triggered, you should use the same names and parameters as the optimized analysis in GA4: video_start, video_progress, and video_complete. Now, user data flows into the dimensions and metrics that have already been created, and you do not need to create any custom entries.
Scrolling
In the page report, the automatic scroll tracking data is integrated into the report table. This information enables you to see on which pages users have scrolled down 90%.
This output can be used in the page report, and you can use the same event in your own tracking. For this task, trigger an event named scroll with the percent_scrolled parameter. Enter the desired value for the parameter. For the gtag code, a view for 50% scrolling on a page would look like the following example:
[gtag('event', 'scroll', { 'scroll': '50' }); ]
Scrolling in Google Tag Manager
The Tag Manager has its own trigger that you can use; below figure shows its corresponding configuration. The points at which triggering is supposed to take place are stored in the scrolling trigger: 25%, 50%, and 75%. The value of the Scroll Depth Threshold parameter is automatically filled at these points with the values that you have specified in the associated trigger.
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| Scroll Event in Google Tag Manager |
The value that was transferred for the event is saved in GA4 in the Page scrolled (%) dimension, which can be analyzed in a custom report in the Explorations section, as shown.
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| Analyzing Scroll Events with a Data Analysis |
Searching, Filtering, and Comparing
GA4’s reports list important data for you in tables. You can thus see at a glance which pages have been viewed frequently or which browser is currently being used by many users. However, sometimes you only want to see data for specific users. GA4 provides three options in the user interface for restricting or segmenting data in reports, namely, the following:
- Searching within a data table
- Filtering an existing report
- Comparing multiple user groups in one report
These features are available for many or even all reports.
Searching Within a Data Table
GA4 reports with data tables contain a search function above each data table. This search field means you can restrict the rows displayed in the table. The text you enter is interpreted as an is contained in query. You can therefore search for the entire contents of a dimension or just a part of it. If you have shown a secondary dimension column using the plus sign, both columns are searched equally.
In our example shown below, searching for the text “country” matches the entries Switzerland, Netherlands, and Poland.
If you’re looking for a specific page, you can copy the URL directly from the browser and paste it into the search field. However, remove the host name.
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| Table Row Must Contain Search Text "land" |
If you search for specific directories in the page report, you must add the / character before and after the directory name. Thus, you must search for the /product/ directory and not just for product, as this text also appears in a page name, such as productlist.html, for example.
The search function provides a quick method to narrow down large tables. Unfortunately, it does not allow more complex queries and is therefore somewhat limited.
Limiting a Report with Filters According to Specific Criteria
Filters provide more options for limiting the contents of a filter. These filters are displayed directly under the heading in data reports, or you’re provided with the option to add them, as shown below. Filters are only offered in table reports.
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| Add Filter Button Directly Below the Heading |
You cannot apply any general filters to overviews, but you can edit each tile individually.
By clicking on the button, you can display a sidebar where you can set restrictions for the report being viewed. The options are the same for all reports.
Select an entry from a long list of different Dimensions, such as Pages viewed, Event names, Source, or Country. In the second step, select the Match Type: Either your input should be contained in the dimension or matches should be excluded. You can also select more complex matches and use regular expressions. If you decide on the exact match, GA4 provides a selection list. For other types, you must enter the search text in the text field.
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| Filtering Based on the Session Source |
Once you click on Apply, the conditions will be applied to the report. As shown above, only users who have google / organic as their source/medium (i.e., those who came to the website via a Google search) are included. You can thus use filters to restrict the report according to dimensions that you do not even see in the report itself.
Furthermore, multiple filters can be combined: You can specify up to five conditions in a filter, and thus, more complex issues can be mapped.
As soon as you exit a report, the filter you have just created will disappear as well. However, filters can be permanently applied to reports and then saved.
Comparing Data from Different User Groups
A comparison allows you to view the data of multiple user groups in a GA4 report. At the top of each report (including overviews), you’ll find the Add comparison button, as shown below.
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| Add Comparison Button Above Each Report |
Click this button to open a list of predefined models. The All Users item is preselected, but you can add up to three more models. If you confirm the application by clicking the Apply button, the report will be reloaded. Then, the values for both selected user groups are displayed in the diagram and in the data table.
Depending on the report, you’ll see multiple lines or pairs of bars in the diagram. The values for the various groups are shown in each row in the table. In this way, you can view values directly in relation to each other, as shown below.
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| Two Comparisons Applied to One Report |
By default, a GA4 property contains models for the containment of the following information:
User source
Here you must distinguish between paid, organic, and other channels.
User devices
You can compare users who access with a PC/Mac, a tablet such as an iPad, or with smartphones.
You can also create and use your own models. For this purpose, click the Create new model button in the header of the overview list. The subsequent window, as shown below, you’ve already seen in the context of filters. Now, you have the same dimensions and match types as with the filters. Click the Apply button to add the new model to the report.
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| Filtering Dimensions for Your Own Comparison |
If you have sufficient rights in the GA4 property, you can also save the model for later use. Users do not need any special rights to use comparisons. As an administrator, you can therefore create models for co-workers who can then run them independently.
Applied comparison models are always visible above the current report. The slide switch allows you to quickly show and hide the additional data groups, as shown below. If the report is scrolled to the top or you move the mouse over it, a model can be removed by clicking the X icon. This process also works for the standard All Users model, which is particularly useful if you want to compare directly opposing groups, such as desktop users and smartphone users.
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| Comparisons Added in the Event Report |
In contrast to filters, once you have selected a comparison, it is retained even if you change the report. Your selected models remain visible in the header. However, the All Users model is always selected when pages are switched, and the other models are deactivated. You can reactivate these models using the slide switch.
Thus, you can select comparisons once and then click through different reports to compare data. The comparisons are removed when you close your browser. Comparisons cannot be permanently applied to a report, as is permitted by filters.
In this post, you learned about the GA4 user interface and how to navigate through overviews and data tables. In addition to the important metrics such as users, sessions, or engagement rates, you now know which reports you can use to find out more about your users and the content they access.
You can use events to analyze specific actions by your users, such as the downloading of files or playing videos. In addition, clicks on links and the sending of forms can be tracked.
We’ve now laid the foundation for using reports and functions of Google Analytics. In the following posts, you’ll learn more about analyzing campaigns and sources through which users find your offering.
