Author: Brad Gerick

GA4 Audiences: Content, News Site Guide

Google Analytics 4

What Are Audiences in GA4?

Google Analytics 4 audiences are groups of users that you can create based on dimensions, metrics and/or events. You can use audiences to segment your users and target them with specific content, offers or ads.

You can also create custom reports based on these audiences. While I’ll get into more specific details below, you can base these on behavior, location, specific referral sources and more.

Table of Contents
  • I. Audience Types
  • II. What is the difference between UA and GA4 Audiences?
  • III. Create Audience in GA4
  • IV. GA4 Audience Examples
  • V. Audiences in Looker (Data Studio)
  • V. Predictive Audiences

    • I. Audience Types

    Prebuilt audiences are automatically created by GA4 based on your data. For example, GA4 will create a “Purchasers” audience for you that includes all users who have made a purchase on your website.

    Custom audiences are created by you based on your specific criteria. For example, you could create a “New visitors” audience that includes all users who have visited your website for the first time in the past 30 days.

    You can use audiences in GA4 for a variety of purposes, including:

    Segmenting Users

    You can use audiences to segment your users into different groups based on their behavior. This can help you to better understand your users and to target them with more relevant content and offers.

    Targeting Ads

    You can use audiences to target your ads to specific groups of users. This can help you to improve the performance of your ad campaigns.

    The best way to do this is to connect your Google Analytics 4 property to your Google Ads (or other compatible ads) account.

    Measuring Effectiveness of Marketing Campaigns

    You could track how many users in your “Purchasers” audience have made a second purchase after seeing your ad campaign.

    You could also analyze the behavior of specific groups of users you’re targeting based on your email newsletter or specific social media posts. One great way to do this would probably be with UTM codes that pass information to GA4, which you can sort out in your reports.

    How Many Audiences Can You Have?

    You can have up to 100 audiences per property in GA4.

    Audiences vs. Segments

    Audiences and segments are both ways to group users in Google Analytics 4, but they serve different purposes.

    You may be used to the Universal Analytics dashboard where you could create segments directly in a reports dashboard.

    This image has an empty alt attribute; its file name is Google-Analytics-Universal-Behavior-Dashboard-with-Segment-1024x560.png

    In Google Analytics 4, you do not have that option. Segments are only something you create and use in the Explore section of GA4.

    The good news? Any segment you create using Explore can be turned into an audience, too.

    Now that understand some of the differences between segments in UA vs. GA4, let’s shift our attention completely back to GA4.

    As we talked about above, audiences are groups of users that you can create based on dimensions, metrics and/or events. You can use audiences to segment your users and target them with specific content, offers, or ads.

    Segments, however, are subsets of your data that you can use to analyze your users’ behavior. You can use them to compare different groups of users, identify trends and measure the effectiveness of your marketing campaigns.

    Here are some of the key differences between audiences and segments:

    1. Audiences are used to target users, while segments are used to analyze data. You can use audiences to target your ads to specific groups of users, or to show them different content on your website. You can use segments to compare the behavior of different groups of users, or to identify trends in your data.
    2. Audiences are created based on dimensions, metrics and events, while segments are created based on conditions. You can create an audience based on any combination of dimensions, metrics and events. You can create a segment based on any combination of conditions, such as “users who have visited the website in the past 30 days” or “users who have added an item to their cart but not purchased it.”
    3. For me, this is a big one: Audiences are not retroactive, while segments are. Audiences will only start accumulating users once you create them. Segments will include all users who meet the criteria, even if they met the criteria before you created the segment.

    II. What is the difference between UA and GA4 Audiences?

    The main difference between UA and GA4 audiences is that GA4 audiences are based on events, while UA audiences are based on sessions.

    In UA, a session is a group of user interactions that happen within a certain period of time. For example, if a user visits your website and then leaves, that would be considered one session. If the user then comes back to your website later that day, that would be considered a new session.

    In GA4, an event is any interaction that a user has with your website or app. For example, a user viewing a page, clicking on a link or signing up for your newsletter are all examples of possible events.

    Since GA4 audiences are based on events, they can be more granular than UA audiences. For example, you could create a GA4 audience of users who have viewed a certain page, or who have added an item to their cart. You couldn’t create an audience like that in UA, because UA audiences are based on sessions, not events.

    Another difference between UA and GA4 audiences is that GA4 audiences are updated in real time, while UA audiences are updated on a daily basis. This means that you can use GA4 audiences to target your ads to users as soon as they take an action on your website or app. This also means any reports that are based on audiences will be more up-to-date.

    III. Create Audience in GA4

    Google Analytics 4 includes two standard or “predefined audiences” in each property: All users and purchasers.

    All users is exactly what it sounds like – every user who’s ever visited your website or opened your app.

    Purchasers, while automatically included, is something you need to set up. That is, you have to define for Google who a purchaser is.

    While helpful to be able to analyze these two default audiences, you probably want (and need) more granularity.

    So, how do you create audiences in Google Analytics 4? With custom audiences.

    Custom Audiences

    To create a custom audience in GA4, follow these steps:

    1. In the left navigation, click Admin.

    2. Under the Property column, click Audiences.

    3. Click the blue New Audience button.

    4. Click Create Custom Audience (or use one of the suggested audiences, which will also count as custom)

    Once you have created an audience, you can use it to segment your users, target your ads and measure the effectiveness of your marketing campaigns.

    I like to use these audiences to analyze data within the Reports and Explore tabs, respectively.

    The primary benefit is being able to analyze or target specific groups of users who visit your site or app instantly.

    IV. GA4 Audience Examples

    Abandoned Carts

    This audience would include users who have added items to their carts but have not completed their purchases. You could target this audience with ads for the items they left in their carts, or you could offer them a discount to encourage them to complete their purchases.

    This is useful whether you sell products, services or subscriptions, such as to a news website or a paid newsletter.

    Multiple Website Visits

    This audience would include users who have visited your website more than once.

    I like to set up audiences for the following buckets:

    • 1 session/30 days: These users are essentially coming once per month
    • 2 sessions/30 days. These users visit your platform twice per month
    • 3–5 sessions/30 days: These users visit your platform about once per week
    • 6–14 sessions/30 days: These users visit your platform multiple times per week
    • 15–29 sessions/30 days: These users visit your platform at least every other day
    • 30+ sessions/30 days: These users visit your platform at least daily

    Engaged Video Watchers

    You could also track users who watch at least 50% of a video on your website. (This is especially easy if you’re embedding a YouTube video, since Google owns YouTube and has some built-in features for that platform in GA4.)

    By Geography

    Is your target audience worldwide? Then you’ll probably want to group users by country to get a better idea of behavior, at a high level, around the globe.

    What if you only care about people reading content or or purchasing your products domestically? Then creating audiences by your most important regions (i.e. states in the U.S.) would be useful.

    Perhaps, though, you’re a hyperlocal news website. Then you would want an audience for the specific city you target.

    Can you target audiences by zip code?

    No, Google Analytics does not allow targeting by zip code. The best way around that, in my opinion, is to get a list of all the cities within the specific zip code you want to target. Then create an audience that includes all those cities.

    To ensure you’re not pulling in irrelevant user data from other same-named cities in different states, include an AND condition in your audience for your state (that is, region). 

    By Device or Technology

    You can get really creative here. GA4 allows you to create broader audiences such as mobile, tablet and desktop.

    You can get more specific, though, and target by device type (iPhone vs. Android).

    It doesn’t stop there, though. You can even target by the operating system version on a particular device.

    You can also group users by screen dimension. And the list goes on.

    Why would you do this? You might notice that bounce rate is high for a particular device or operating system. This could be a clue to do some tests on that specific device and figure out if there’s something incompatible with your website or app.

    Or you may notice that while you don’t have any UX issues, Apple purchases have a higher affinity toward your content than Android users.

    Be careful not to draw strong conclusions from limited data sets. With the right audiences and analysis, though, this is yet another way you can learn which type of user likes which type of content or products that you offer.

    By Engagement

    Are there ways to interact with your site? Perhaps by commenting. Or liking a post, contributing to a forum, creating an account, downloading your app and more.

    Whatever is a valuable action that users can take on your site, you’ll want to know how your most valuable visitors are interacting with your content and products.

    By Source, Medium or Channel

    One favorite example here would be to create an audience based on Google Organic search traffic. While there are other search engines, Google is king. So this gives you a good idea of how your SEO strategy is performing.

    Taking the same approach, you could target based on a particular social network, like Facebook or Twitter.

    If you want to go even broader, you could target by channel. That is, Organic Search, Paid Search (how are your ads performing?), Email (your newsletters?), etc.

    V. Audiences in Looker (Data Studio)

    Audiences are a useful way to segment users in Looker Studio. This can be helpful if you want to analyze the behavior of different groups of users. (Take any example from the previous section to get an idea.)

    There are two ways (though I’m not saying their the only ones) to structure reports in Looker Studio (formerly known as Google Data Studio). The first is to have an entire report based on one “topic”, or in this case, one audience.

    So you might have single robust report all about organic search.

    The second way you could do it is to create an all-encompassing report that features all of your audiences, or whatever other analyses you want to do.

    Benefits of Breaking Your Audience Analysis into Separate Reports

    • Faster load time: The fewer pages there are on your report, the faster they will load
    • Fewer tokens used: Tokens are what Looker users to determine how much data you can load in a given time frame (hour, day) or property
    • Easier sharing segmentation: Not everyone on your team needs to see all your data. This way, you can manage individual access to specific reports

    Benefits of Keeping Doing All Your Audience Analysis in the Same Report

    • Streamlined Sharing: If you don’t care who has access to what data – probably the case at a smaller organization – this is a much more efficient way to do things. Just share one single Looker report and let users navigate based on your table of contents.
    • More Efficient Analysis: Instead of having to jump between different reports and load multiple pages to make comparisons, you’ll have everything you need in the same place.

    Looker Studio, in my opinion, is the best way to analyze Google Analytics 4 data, so long as you don’t have problems with the aforementioned API limit.

    VI. Predictive Audiences (GA4)

    They are audiences that are created based on machine learning models that predict future user behavior. For example, you could create a predictive audience of users who are likely to churn, or who are likely to make a purchase.

    Predictive audiences can be a valuable tool for marketers, as they can help you to identify users who are likely to take certain actions in the future. This information can then be used to target those users with relevant content or offers, or to exclude them from certain campaigns.

    If you’re unfamiliar with this kind of segmented marketing, it’s worth hiring someone to make sure your content strategy is closely aligned with user intent. And even if you’re not running a complex marketing campaign, predictive audiences can give you ideas about different content buckets you could create to target specific groups of users.

    To create a predictive audience in GA4, you will need to have a sufficient amount of data for the machine learning model to train on. You will also need to specify the type of action that you want to predict, such as churn or purchase. Once you have done this, GA4 will create the audience and start updating it on a regular basis.

    If you want to check whether you have access to predictive audiences in GA4:

    1. Go to Admin
    2. Click Audiences in the Property column
    3. Click the blue New Audience button
    4. In the Suggested audiences box (seen below), click the PREDICTIVE tab. If Google Analytics 4 doesn’t yet have enough data to create a predictive audience for you, you’ll see something like this:

    The different predictive audiences are as follows:

    • Likely 7-day purchasers
    • Likely 7-day churning users
    • Predicted 28-day top spenders
    • Likely first-time 7-day purchasers
    • Likely 7-day churning purchases

    Here are the prerequisites, according to Google, for each of these:

    1. A minimum number of positive and negative examples of purchasers and churned users. In the last 28 days, over a seven-day period, at least 1,000 returning users must have triggered the relevant predictive condition (purchase or churn) and at least 1,000 returning users must not.
    2. Model quality must be sustained over a period of time to be eligible. (Learn more about what actions you can take to make sure your property has the best chance possible of being eligible for predictive metrics.)
    3. To be eligible for both the purchase probability and predicted revenue metrics, a property has to send the purchase (recommended for collection) and/or in_app_purchase (collected automatically) events. When you collect the purchase event, you need to also collect the value and currency parameters for that event.

    Just because you meet the thresholds on a given day doesn’t mean they’ll continue to be available. as soon as you drop below the required minimums, your predictive audiences will go away.

    If you do have access to predictive audiences, you can use them for:

    • Targeting ads to users who are likely to take a certain action. For example, you could target ads for a new product to users who are likely to make a purchase.
    • Excluding users from certain campaigns. For example, you could exclude users who are likely to churn from a campaign designed to retain customers.
    • Segmenting users for analysis. For example, you could segment users into groups based on their likelihood of churning and then analyze the behavior of each group to see what factors are associated with churn.

    You can also use the User lifetime technique in the Explore tab to use Purchase probability and Churn probability.

    Predictive audiences can be a powerful tool for marketers, but it is important to use them carefully. It is important to understand the limitations of machine learning models, and to not rely on them too heavily. You should also make sure that you are collecting the right data to train the models, and that you are using the audiences in a way that is ethical and compliant with all applicable laws and regulations.

    Audiences are a powerful tool in Google Analytics 4. While they don’t function the same way you may have been used to in Universal Analytics, I would argue that they’re even better.

    Do you have other use cases for audiences I haven’t covered? Let me know in the comments.

    Google Analytics 4 Data Streams Replace UA Views

    Google Analytics 4

    There are many differences between Google Analytics 4 and Universal Analytics.

    You may have noticed, for example, that there are no “views” in GA4.

    They were roughly replaced by “data streams.” Google defines these as “a flow of data from a customer touchpoint (e.g., app, website) to Analytics.”

    While Google cautions in its official documentation that Views and Data Streams are not the same thing, they are a close parallel.

    Here’s everything you need to know about how web and app data is now tracked within the same Google Analytics 4 property.

    Content

    1. Google Analytics 4 Data Streams vs. Universal Analytics Views
    2. How To Create a GA4 Data Stream
      1. Add Web Data Stream
      2. Add iOS App Data Stream
      3. Add Android App Data Stream
    3. Filter Reports by Data Stream GA4
      1. In the Reports Tab
      2. In the Explore Tab
    4. GA4 Data Streams Limit
    5. Data Stream Permissions
    6. Delete a Data Stream
      1. What happens if you delete a data stream?

    Google Analytics 4 Data Streams vs. Universal Analytics Views

    The main difference between Google Analytics 4 data streams and Universal Analytics views are their names.

    Having multiple data streams in the same GA4 property allows you to holistically analyze your audience across platforms.

    You no longer have to switch back and forth between properties to see data for your website and your app(s), respectively.

    Here’s a comparison of GA4 account structure vs. Universal Analytics account structure:

    Google Analytics 4 Account Property View Data Stream Structure

    The Google Analytics 4 structure is Organization* > Account > Property > Data Stream

    (Notice the empty “Views” column for GA4 properties.)

    Google Analytics 4 Account Structure

    The Universal Analytics structure is Organization* > Account > Property > Views

    Universal Analytics Account Structure

    *The organization level is optional.

    Google says treating Data Streams like views to separate data “limits your ability to tie users across data streams, since each stream is a separate collection source of data.” In other words, the same user may be counted more than once, depending on how your site’s set up.

    How To Create a GA4 Data Stream

    1. Go to Admin

    2. Click Data Streams in the Property Column

    3. Click the blue “Add stream” button

    4. Select your stream type: iOS app, Android app or Web

    From here, things fork off depending on the kind of stream you’re setting up.

    Add Web Data Stream

    5. Enter your Website URL

    6. Enter your Stream name

    7. Enhanced measurement events are turned on by default. Leave them that way unless you have an exceptional reason to turn them off.

    8. Click “Create stream”

    In order for your web data stream to be functional, you must have the universal site tag on your site or publish a GA4 Configuration tag in your GTM container.

    Add iOS App Data Stream

    Starting in Google Analytics 4

    (Continued from steps 1–4 above)

    5. Register your app by filling in your iOS bundle ID, app name and app store ID and click Next

    6. Wait while Google configures your settings in the next step. When finished, you’ll see four green checkmarks next to each of the options. Then click Next

    7. Download the GoogleService-Info.plist file and move it into the root of your Xcode project and add it to all targets and click Next

    8. Add the Firebase SDK and click Next

    9. Add the initialization code Google provides to your main AppDelegate class and click Next

    10. Run your app to verify installation. Once verified, click Finish

    You should then see a screen that looks like this:

    Google Analytics 4 iOS App Stream Details

    Starting in Firebase

    1. Open your Firebase project
    2. Go to Project settings and click Integrations
    3. Click “Link” on the Google Analytics card
    4. Create a new Analytics account, or select an existing one

    Add Android App Data Stream

    Starting in Google Analytics 4

    5. Enter your package name from applicationId in your app-level build.gradle file and enter your app name, then click Register app

    6. Wait while Google configures your settings in the next step. When finished, you’ll see four green checkmarks next to each of the options. Then click Next

    7. Download the google-services.json file and move it into your Android app module root directory. Then click Next

    8. Add the Firebase SDK provided by Google. When finished, press “Sync now” in the IDE. Then click Next

    9. Run your app to verify installation. Once verified, click Finish

    Starting in Firebase

    Same as above for iOS

    Filter Reports by Data Stream in GA4

    If you have multiple data streams in GA4, you may want to analyze them separate from each other, or even side by side.

    (If you only have one data stream, you don’t need to worry about this.)

    In the Reports Tab

    To analyze data for a single data stream, use the “Add comparison” option in any of the reports.

    1. Click the Add comparison button

    2. Select Stream ID under the Device category in the first dropdown menu

    3. Check off the dimension(s) you want to analyze

    Google Analytics 4 Filter by Data Stream

    If you want to analyze your web data stream by itself, click the web option and click OK. Then X out the “All Users” comparison at the top of the page to restrict the chart and rows to data related to your web stream.

    If you want to analyze your iOS and Android app data together – not side by side – check them both off.

    Google Analytics 4 Analyze App Data Streams Together

    In the Explore Tab

    1. Add “Stream name”* as a Dimensions option
    You can also add “Stream ID,” but I prefer Stream name

    2. Add your desired metric(s) options

    3. In Tab Settings, add the Stream name dimension to Rows or Columns

    4. Add your desired metric(s) to Values in Tab settings

    Google Analytics 4 by Data Stream Explore

    This will show you data for all data streams.

    To see data for a particular data stream, use the filter at the bottom to include or exclude particular sources.

    Google Analytics 4 by Data Stream Explore Filter

    GA4 Data Streams Limit

    Each Google Analytics 4 property can have up to 50 data streams.

    There’s no limit to web streams, but only 30 of those can be app streams.

    Data Stream Permissions

    You cannot give permissions on a per-data-stream basis.

    Since data streams are contained in properties, every user who has permission to a particular property has access to every data stream within that property.

    You can also give access at the organization or account level. The user will then have access to every level below their access.

    For example, if you give a user permissions for a particular account, they will have access to that account as well as all the properties and data streams within the account. They will not, however, have access to the parent organization.

    Delete a Data Stream

    1. Go to Admin

    2. Click Data Streams in the Property column

    3. Click the data stream you want to delete

    4. Click the three vertical dots in the top-right portion of the window that popped out

    Google Analytics 4 Delete Data Stream

    5. Click “Delete stream”

    What happens if you delete a data stream?

    Google will continue to store its historical data. That data will no longer be processed, though.

    It will no longer be available to use in report filters, either.

    Also see:

    Google Analytics 4 Traffic by City, State

    Google Analytics 4

    Google Analytics 4 provides information on the city and state from which users are visiting your website.

    Let’s say you run a hyperlocal news website in Santa Monica, Calif., that sells subscriptions. You’ll probably want to focus on readers who live in or near your coverage area. Even if you get visits from other parts of the country/world, they’re not as likely to pay for your news.

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    Or perhaps you’re an online store that also has a brick-and-mortar in Santa Monica. If sales tend to be higher in person than online, you’re going to want to reach users who are closest to your store.

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    But before doing a deep analysis, you need to know how many users are visiting your site from Santa Monica, and other relevant nearby areas. Then you can drill down even further to figure out how to draw them to your website and store to make purchases.

    Here’s how to analyze traffic by city and region/state in Google Analytics 4.

    Contents

    1. Google Analytics 4 Traffic by City, State: Summary

    2. Google Analytics 4 Traffic by City, State: Detailed Explanation

    Google Analytics 4 Traffic by City, State: Summary

    Go to Reports > Users > Demographics overview and click on “View countries.”

    Add a comparison specifying the city and/or state by which you want to filter your data.

    Add a secondary dimension in the table to further filter and organize your data.

    Also Read: How To Install Google Analytics 4

    Google Analytics 4 Traffic by City, State: Detailed Explanation

    Go to Reports > Users > Demographics overview.

    By default, the first widget you will see on this dashboard sorts traffic by country. This can be useful if you’re an international publication or have clients all over the world. Otherwise, you’ll want a more detailed look at your readers’ locations.

    Google Analytics 4 Demographics overview dashboard

    Click on “View countries,” and you’ll be brought to a Demographic details report page.

    Note: Google Analytics 4 provides user location data based on the location of the user’s IP address on their particular device. This does not necessarily show you where people live – GA4 doesn’t provide that data – but rather where they were when they accessed your site.

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    GA4 Traffic by State

    1. Click Add comparison, which will initiate the Build comparison pop-out you see on the right in the screenshot above.

    2. Under “Include,” select region, which is the equivalent of “State” in the United States, as well as most other countries.

    3. Click the Dimension values dropdown, where you will see a list of all the states (regions) from which your site has received traffic in the specified time range. If there are a lot of options, you may be better off typing the state name and selecting it instead of scrolling through the entire list.

    You can select multiple states if you wish.

    4. Once you have selected the desired state, click OK, then the Apply button, which will now turn blue, at the bottom right.

    You will now see a side-by-site comparison of All Users and California (or whatever state(s) you selected) users, side-by-side.

    Google Analytics 4 Demographics Traffic Comparison All Users California

    Again, if you don’t have a lot of international readers or customers, you may not be interested in analyzing traffic from “All Users.” Simply click the X next to the All Users comparison at the top, and you’ll be left with only the users in the states you selected.

    Google Analytics 4 Demographics Traffic Comparison California

    GA4 Traffic by City

    Now that you have filtered traffic by a single (or few) state(s), you’re ready to drill down by city.

    If you’re interested in analyzing traffic from just one particular city, or a small number of cities, you could use the same process as above for states, except you would include “City” in the first dropdown instead of “Region.”

    But perhaps you want to see a list of all the cities in a specific region from which users and sessions come to your website. In that case, we’re going to pick up where we left off, filtering traffic only by California users.

    Scroll down to the table, and you’ll see that there’s only one row of data for United States users. Click the dropdown where it says “Country” and change to “Region.”

    We’ll still only see readers from California, but now it’s more clear instead of saying “United States.”

    If you’re only filtering traffic by one region, this step isn’t necessary and you can change the dropdown to “City” instead of “Region.” I’m including this step here in case you want to filter by multiple states, and therefore see which cities correspond to which states.

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    Click the + symbol next to Region in the dropdown menu you just changed, and you’ll see a similar dropdown. Select “City.” I also like to expand the visible rows to more than 10, which allows you to see a large list of data without having to click through the list 10 at a time.

    Here’s what that would look like if you added a second state, as I have now with Arizona, to make it easier to know which city corresponds to which state:

    Now you can see just how many users and sessions come to your website from a particular city and state.

    Is there another way you would like to analyze your data geographically? Let me know in the comments.

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    Also read: How To Create a Google Analytics 4 Landing Page Report

    Google Analytics 4 Site Search Report (GA4)

    Google Analytics 4

    The Google Analytics 4 site search report can help you track what users are trying to find on your website.

    This information can be found in the view_search_results event in GA4. This is an enhanced measurement event, which means it’s already set up for you upon launch. (So long as you don’t turn it off.)

    If your Google Analytics site search settings are properly configured, too, then you’re ready to get started.

    Content

    1. Google Analytics 4 Site Search Event

    2. GA4 view_search_results Parameters

    3. GA4 Report: Track Site Searches

    • Site Search Report in Reports
    • Site Search Report in Explore

    4. What are the benefits of having site search on my website?

    5. Why aren’t I getting more searches on my site?

    Google Analytics 4 Site Search Event

    The official GA4 event for site search is view_search_results.

    This is activated by default in your enhanced measurement events.

    GA4 enhanced measurement event settings

    As you can see, there are some advanced settings for site search. This is where you ensure that Google is capturing your site search query and additional query parameters from your results-page URL.

    GA4 view_search_results Parameters

    The search_term parameter is included in the view_search_results event by default.

    You can add additional parameters with additional query parameters in the advanced settings of your Site search event. (See previous screenshot.)

    This will allow you to see data concerning the view_search_results event, the search_term parameter and any other custom parameters you add.

    GA4 Report: Track Site Searches

    There are two ways to look at a site search report in Google Analytics 4: In the Reports tab and in the Explore section.

    Site Search Report in Reports

    In your Google Analytics 4 dashboard, click on reports on the left sidebar.

    Then click Engagement under the Life cycle menu. From there, click on Events.

    When you scroll down, this will show the 10 most popular events based on how many times they have been triggered.

    Scroll down to view_search_results. If you don’t see it in the first 10, you can either add more rows to the report by changing the number in the dropdown next to “Rows per page,” or you can type “view_search_results” into the “Search…” bar at the top-left portion of the table. (Not the main GA4 search bar you see in the following screenshot.)

    Google Analytics 4 Report view_search_results

    This will tell you (from left to right) how many times the view_search_results event has been triggered, the total number of users who triggered it, how many times per user the event was triggered and the total revenue generated by this event.

    All these numbers will be based on the time frame you have selected for the report.

    You can change this at the top-right portion of your dashboard screen. The numbers should then change as well.

    Now click the “view_search_results” event in the table. You will be taken to another page with data limited to that event.

    Google Analytics 4 view_search_results event page in Reports

    Scroll down slightly and you’ll see a widget that allows you to look at data for parameters associated with this event. When you click the dropdown, you should see a “search_term” option.

    This will show you exactly what people searched for on your site. But there’s a catch: this only shows data for the last 30 minutes.

    Google Analytics 4 view_search_results event page with search_term parameter in Reports

    What if you want to see the search terms used on your site for the entire date range you selected?

    The best way to do that is in the Explore section.

    Site Search Report in Explore

    Here’s how to see the exact search terms users are using within your site. (Not to be confused with the keywords people use to reach your site from search engines like Google.)

    Click the Explore tab on the left sidebar menu in your GA4 dashboard.

    Start a new “Blank” exploration by clicking the “Blank” option.

    Google Analytics 4 Explore New Blank Exploration

    Now follow these steps:

    1. In the Variables column, select your desired date range

    2. Click the + sign next to DIMENSIONS

    3. Search for “Event name” (found under “Event”), check the box next to it

    4. Search for “Search term” (found under “General”), check the box next to it and click “Import”

    5. Click the + sign next to DIMENSIONS

    6. Search for “Event count” (found under “Event”), check the box next to it and click “Import”

    7. Double-click “Event name,” “Search term” and “Event count”

    These should now have populated the Tab Settings column. So long as there have been site searches within your selected date range, you should see data on the right.

    Tip: Instead of double-clicking dimensions and metrics from the left, you can also drag them. In this case, double-clicking is fine because by default, the DIMENSIONS go into the ROWS section and the METRICS go into the VALUES section.

    The DIMENSIONS can also go into the COLUMNS section, but we’re not going to do that for this report.

    One more thing: The order in which the dimensions are placed (from top to bottom) in the ROWS or COLUMNS section is the order in which they will appear (from left to right) in the data visualization window.

    Also see:
    Creating Custom Dimensions in GA4
    GA4 Custom Dimensions Limit

    8. Scroll to the bottom of the Tab Settings column and click the box below where it says “FILTERS.” Now select “Event name.”

    9. Then choose the “exactly matches” filter and select “view_search_results” as the event name.

    Google Analytics 4 Tab Settings Filters Event Name

    10. If you have a high volume of searches on your site, change the number in the “Show rows” number in the dropdown menu to as high as 500.

    Your dashboard should now look something like this:

    Google Analytics 4 Explore report view_search_results and search_term

    Not big enough? To see your data in an even larger window, minimize the Variables and Tab Settings columns by clicking the small horizontal lines highlighted in the previous screenshot.

    Google Analytics 4 Explore report view_search_results and search_term enlarged

    What are the benefits of having site search on my website?

    The Google Analytics 4 site search report can tell you a lot about what user’s want to see from you.

    Here are some benefits of having site search on your website:

    1. Easier for users to find content

    Depending on how your site is structured, it may be easier for someone to find content by searching than by navigating.

    2. Reinforces existing content topics

    If people are searching for things that are already on your website, that’s confirmation that you’re providing information on relevant topics.

    3. Gives you new content ideas

    If people are searching for things that are NOT on your website, these could be hints as to what topics you should create content for next.

    For example, let’s say you have a flowers website. Imagine that your “roses” content performs well.

    While checking the report you just set up, you notice that people frequently search your site for content related to “tulips.” The problem? You don’t have any content about tulips.

    If the volume is high enough, this could be a hint to start creating some high-quality tulips content.

    Why aren’t I getting more searches on my site?

    If you’re not getting a lot of searches on your site, here are two possible explanations:

    1. Your website doesn’t actually have a search bar

    If this is the case, check within your CMS to see if it’s possible to add one. You can also ask one of your site developers about adding one.

    2. Your site search bar may be hidden

    Most site search bars are in the page’s header, so that’s probably where most users would expect to find it.

    Here’s what the search bar looks like on Amazon:

    Amazon search bar example

    If your site search bar is located somewhere else, your users may be less likely to utilize it.

    Integrate Firebase with Google Analytics 4 (GA4)

    Google Analytics 4

    Firebase is a great way to connect your Apple (iOS) and Android apps to Google Analytics 4.

    That’s because Firebase and GA4 are both Google products, making them highly compatible.

    You can integrate Firebase with Google Analytics 4 by starting with either of the two platforms. I’m going to show you how, explain some of the benefits of doing so, and share other useful information about Firebase, such as the analytics they offer. (Spoiler: They’re not as robust as GA4’s full dashboard.)

    Content

    I. Introduction to Firebase

    II. Advantages of Connecting Firebase to GA4 Property

    III. How To Use Firebase with Google Analytics 4

    • Planning Firebase GA4 Integration Setup
    • Firebase Integration with GA4: Starting in Firebase
    • Firebase Integration with GA4: Starting in GA4
      • Add Firebase to your Apple (iOS) App
      • Add Firebase to your Android App

    IV. Firebase Analytics

    V. Firebase Pricing

    I. Introduction to Firebase

    Firebase is an app development platform that easily integrates with Google Analytics 4. It was founded in 2011.

    Google acquired Firebase on Oct. 21, 2014. It had more than 100,000 registered developers on its platform when it was purchased.

    Today, Firebase can be used for both Android and iOS (Apple) apps. If you already have your apps on Firebase, integrating them with GA4 is easy.

    Google Firebase logo
    Firebase logo

    II. Advantages of Connecting Firebase to GA4 Property

    1. The biggest and most obvious benefit is that both Firebase and Google Analytics 4 are Google products, which means they play well together.

    2. GA4 properties are a Web + App model. (In Universal Analytics, every website and app was a separate property.)

    This means that if you have a website for [yourcompany].com as well as a [Your Company] iOS and Android app, you will be able to see aggregated data for all three in the same GA4 dashboard.

    (Don’t worry – within your dashboard you can also break out reports by particular data streams.)

    3. Google Analytics 4 has reporting dashboards that aren’t available in Firebase Analytics.

    Read More: Google Analytics 4 FAQs

    III. How To Use Firebase with Google Analytics 4

    The are two ways to complete GA4 Integration with Firebase. The first is in firebase itself, and the other is via the GA4 dashboard.

    Here are the steps to set up the GA4 Firebase integration for both options.

    Planning Firebase GA4 Integration Setup

    How you connect Firebase to Google Analytics 4 largely depends on whether you already have your apps in Firebase. If so, connect your project to GA4 from there.

    If you don’t already have your apps in Firebase, it may be easier to follow the second process below.

    Firebase Integration with GA4: Starting in Firebase

    Here’s are the steps to integrate an existing Firebase project with GA4.

    1. Click the settings cog at the top left of the page and click Project settings

    Google Firebase Project Overview. Select project settings.

    2. Click the integrations tab at the top of the page

    Google Firebase Project settings Integrations tab selected.

    3. On the box where you see Google Analytics – it should be the first one on the top left – click “Link” at the bottom-right portion of the box.

    4. Select and connect your existing Google Analytics 4 property.

    Your page should now look like this if you successfully linked your property:

    Google Firebase Project Settings Integrations. GA4 property connected with iOS and Android apps.

    Firebase Integration with GA4: Starting in GA4

    1. Go to Admin

    2. In the Property column, click Data Streams

    Add Firebase to your Apple (iOS) App

    Click the “iOS app” button and follow the six steps to set up your mobile app stream through Firebase.

    Google Analytics 4 Data Streams. Add stream for iOS or Android.

    Example Stream Name: Acme Newspaper iOS

    Add Firebase to your Android App

    Click the “Android app” button and follow the five steps to set up your mobile app stream through Firebase.

    Example Stream Name: Acme Newspaper Android

    IV. Firebase Analytics

    Firebase Analytics aren’t nearly as robust as Google Analytics 4. There is some data you can track, though.

    Dashboard

    This is a high-level summary of user activity on your apps for the selected date range.

    Many widgets are included by default, such as events, conversions, cohorts, retention and app stability.

    Realtime

    A look at the last 30 minutes of user activity on your apps.

    This section includes a world map, first user source (how a user first reached your app), the name of the pages and screens they have visited, their events and conversions, and more.

    Events

    A list of your Events, along with the total “count” of how many times they have been triggered, and how many “users” have triggered them.

    You can also turn any of your Events into Conversions here.

    This is basically the same as the Events dashboard in GA4.

    Conversions

    A list of your Events, along with the total “count” of how many times they have been triggered, and the “value” of each event.

    This is basically the same as the Conversions dashboard in GA4.

    Audiences

    A list of your audiences, which are groups of users placed together based on certain behaviors or demographics.

    For example, users who purchase something on your app are put into the “Purchasers” audience.

    You can have up to 100 audiences per GA4 property.

    This dashboard is basically the same as the Audience dashboard in GA4.

    Custom Definitions

    This is where you can add custom dimensions and custom metrics to your dashboard.

    Dimensions are “word-based” and include things like City and Page location.

    Metrics are “number-based” – they can be calculated – and include things like Views and First opens.

    This dashboard is basically the same as the Custom Dimensions dashboard in GA4.

    Latest Release

    This dashboard is unique to Firebase Analytics.

    Google Firebase Analytics Latest Release Dashboard.

    It includes the name of your apps, the release number, release status, percentage of active users and percentage of crash-free users.

    This is where you go to find out how your latest release is performing.

    DebugView

    The DebugView is designed to be used in conjunction with Google Tag Manager.

    This is where you can see whether new events and parameters you have added. You should use this dashboard to make sure everything is performing as expected before publishing new events and parameters.

    For a robust analysis of activity on your apps, I recommend using the full GA4 dashboard.

    Firebase Analytics API

    Data on your short Dynamic Links can be accessed using the REST API.

    The linkStats endpoint can be used to retrieve individual Dynamic Link data.

    V. Firebase Pricing

    Your Firebase costs will vary depending on your app usage. Below you can see a list of the various factors that affect pricing.

    If you’re just getting started and mostly need basics like analytics, cloud messaging and crash reporting, you probably won’t have to pay anything.

    But if your app setup is more complicated and you already have a high volume of usage, you can calculate your costs with the blaze plan.

    Firebase has a no-cost “spark” plan and a pay-as-you-go “blaze” plan.

    The following products are free and unlimited as part of the spark plan:

    • Analytics
    • Cloud Messaging
    • Notifications composer
    • Remote Config
    • App Indexing
    • Dynamic Links
    • Crash Reporting

    The spark plan is included in the blaze plan, meaning that all these products are free there as well.

    You can calculate your potential costs of the blaze plan on the Firebase website.

    Projects are billed on a monthly basis, and are based on usage of the following products:

    • Realtime Database
    • Cloud Firestore
    • Authentication
    • Cloud Storage
    • Cloud Functions
    • Hosting
    • Test Lab
    • Firebase ML

    Google Analytics 4 Session: What is it?

    Google Analytics 4

    A Google Analytics 4 session is counted every time someone visits your site or app. This is how sessions are initiated:

    • App: A user opens your app on their phone in the foreground
    • Website: A user views a page on your website

    Let’s take a look at how the GA4 session timer stops and starts, how you can change it, and the difference between a regular session and an engaged session.

    Content

    1. How are GA4 sessions counted?
      1. ga_session_id Parameter
      2. ga_session_number Parameter
    2. When does the Google Analytics 4 session timer reset?
    3. How To Change Google Analytics 4 Session Timeout Limit
      1. Change in App Stream
      2. Change in Web Stream
    4. What is an engaged session in GA4?
      1. Bounce Rate vs. Engaged Sessions in GA4
      2. How do you calculate bounce rate in Google Analytics 4?

    How are GA4 sessions counted?

    When a user comes to your site, they are assigned a session ID and a session number. These are parameters that go with the session_start event, which is automatically included in your GA4 dashboard.

    (In order to access these this information in your Explore reports, you must create a custom dimension for each one.)

    ga_session_id Parameter

    A session ID, identified by the ga_session_id parameter, is generated each time a user comes to your site. If the same user comes to your site, leaves, and comes back to initiate a new session, two different session IDs are generated.

    The same user can have multiple session IDs, because each new session generates a new ID, regardless of the user.

    ga_session_number Parameter

    The session number, identified by the ga_session_number parameter, counts the different number of sessions generated by each user.

    Here’s a simplified example of how the session_start event and its ga_session_id and ga_session_number parameters are structured:

    Google Analytics 4 session_start event and ga_session_id and ga_session_number parameters visual example
    Note: The ga_session_id numbers here are made up to simplify this graphic.

    When does the Google Analytics 4 session timer reset?

    Google’s default timer ends sessions after 30 minutes of inactivity.

    How to change your GA4 session timer

    Let’s say someone opens your app on their phone so that it is in the foreground. That person has initiated a session. (This example applies to visitors to your website, too.)

    They spend one minute on your app (or website) before checking their email, opening a social media app, sending a WhatsApp message, and setting their phone down to charge.

    An hour later, they pick up their phone and access your app (or website) again. Is this considered a new session in Google Analytics 4?

    Yes, even though your app (or app) was running in the background. After 30 minutes of your app (website) not being in the foreground, the user’s initial session ended.

    But just because there’s a 30-minute inactivity limit before a new session is initiated, it does not mean that the user’s initial session was 31 minutes.

    Since the user was inactive for 30 minutes, their active session timer will have been paused at the beginning of those 30 minutes.

    Google Analytics 4 session timer example
    An example of how Google Analytics 4 session timers work.

    How To Change Google Analytics 4 Session Timeout Limit

    If you prefer a different timeout limit than 30 minutes, here’s how to change it:

    Change in App Stream

    Use the setSessionTimeoutDuration method and specify your desired limit down to the millisecond*

    *In fact, the default is not actually 30 minutes, but rather 1,800,000 milliseconds, which is equal to 30 minutes.

    Change in Web Stream

    1. Go to Admin
    2. Click Data Streams
    3. Click your web data stream
    4. Scroll down and click More Tagging Settings, which is the last option under Additional Settings
    5. Click Adjust session timeout
    6. Choose how long your want your session timeouts to last in minutes and hours
      (You can also change your timer limit for engaged sessions)
    Google Analytics 4 changes session timeout length

    What is an engaged session in GA4?

    Wondering what the difference is between a regular session and an engaged session in Google Analytics 4? An engaged session must meet one of three requirements:

    1. The session must last at least 10 seconds (or 10,000 milliseconds) AND/OR
    2. One or more conversion event was completed AND/OR
    3. The user viewed two or more screens (app) or pages (web)
    Google Analytics 4 engagement dashboard featuring session stats

    You can change the minimum session duration to activate an engaged session. Follow the steps in the previous section to do so.

    Bounce Rate vs. Engaged Sessions in GA4

    In GA4, bounce rate is not calculated the same way as it was in UA. Bounce rate and engagement rate are now inversions of each other.

    In most cases, your goal should be to have a low bounce rate and a high percentage of engaged sessions.

    How do you calculate bounce rate in Google Analytics 4?

    Google Analytics 4 bounce rate was added July 11, 2022. It’s the inverse of engagement rate.

    That means your bounce rate in GA4 is 100 minus your engaged sessions percentage.

    e.g. If your engaged sessions percentage is 50%: 100 – 50 = 50% bounce rate.

    Non-engaged sessions are any GA4 sessions that last less than 10 seconds and don’t include a conversion event nor multiple pages/screens.

    Also read:

    What’s a Bad Bounce Rate in Google Analytics 4 (GA4)?

    Google Analytics 4

    A “bad bounce rate” in Google Analytics 4 is subjective, so take any numbers someone gives you with a big grain of salt. (I’m talking Mediterranean sea salt flakes.)

    As a reminder, GA4 bounce rate is the percentage of users who visit your site or app and do NOT complete any of the following three actions:

    • Spend more than 10 seconds on the site/app
    • Visit a second page/screen
    • Complete a conversion event

    Read more: What is a conversion in GA4?

    Because every website is different, a “bad” bounce rate for you may be “great” for someone else, and vice versa.

    Content

    1. How To Find Your Bounce Rate in GA4

    2. What’s a Bad Bounce Rate for My Site?

    How To Find Your Bounce Rate in GA4

    There are two ways to see your bounce rate in Google Analytics 4:

    1. Add bounce rate to your Reports dashboard (this is the option we’re going to use)

    2. Add bounce rate to an Exploration

    Now that you know where to find bounce rate, we can determine what’s a “bad” bounce rate for your site.

    What’s a Bad Bounce Rate for My Site?

    Here’s a process I recommend to determine a “bad” bounce rate for your website.

    1. Go to the Reports section of Google Analytics 4.

    2. Go to Engagement > Pages and screens

    GA4 Reports Select Pages and screens

    3. Select the Last 28 days in your date range. (You can increase or decrease this depending on your site’s size and traffic, but I think 28 is a good balance between being recent enough, but not too cumbersome.)

    GA4 Report Select Date Range 28 Days

    4. Click the pencil in the top-right portion of the screen:

    Google Analytics 4 customize reports

    5. Click the > across from Metrics

    GA4 Customize Report Metrics

    6. Click the + Add metric button and scroll down to select bounce rate. (In the following screenshot, I have already added it. You probably won’t see it in your list if you haven’t added it yet)

    GA4 custom reports add metric

    7. Drag the different metrics from top to bottom to order them. The ones up top will appear the furthest to the left in your report

    8. Click the blue Apply button

    9. Click the blue Save button and choose one of the two options: saving changes to a current report, or as a new report.

    GA4 Save Custom Report

    You should now see bounce rate in the report you customized. Remember, you can add this (and other custom metrics) to almost any of your Reports in Google Analytics 4.

    10. Change “Rows per page:” to the maximum of 5,000.

    GA4 Report Change Rows Per Page to 5000

    11. Scroll through your list of pages and see which is the last page with at least 28 views*.

    *I chose 28 because it means that page is averaging at least one user per day. Depending on how large or small your site is, you can increase or decrease this number.

    GA4 Report Pages with at Least 28 Views

    In this case, the first 375 pages in my date range average at least one user per day.

    12. Export the file as a CSV.

    GA4 Report Download File

    13. Scroll down to around row 5,000 in the spreadsheet.

    For some reason, the GA4 export does this weird thing where all the columns in the dashboard are not displayed at the top of the spreadsheet. Since we have Views and Bounce rate in our first two columns, we see it as the second “section” of data here, beginning with row 5013.

    14. Scroll down to the last row with at least 28 views and delete all the rows below it in this section. 

    GA4 Exported CSV File Data

    16. Since bounce rate is expressed as a percentage, highlight the entire bounce rate column (column E in our case) and change the cell format to %.

    GA4 Export Change Bounce Rate to Percentage

    17. Highlight all three columns in this section only – in this case A5015:C5389 – and sort the Bounce Rate column (C) from Largest to Smallest (highest to lowest).

    GA4 Bounce Rate Analysis Sorted

    Now we have all the pages on our site that average at least 1 visit per day in the last 28 days sorted from those with the highest bounce rate to the lowest.

    18. However many rows (pages) you have in this report, take 50 percent. In our case we have 375 pages. Ten percent of 374 is 187.5. I’m going to round down to 187.

    That is going to be our marker for a “bad” bounce rate. All pages from 1 to 187 in the list are a “bad” bounce rate for our site. That means anything at 86.84 percent and above.

    GA4 Bounce Rate Analysis Sorted

    We now have a “bad” bounce rate for our site using Google Analytics 4 data.

    Remember, this is how I suggest analyzing bounce rate. You can adjust multiple parts of this process to your preferences, though:

    • Change your date range (from 28 days)
    • Change your “minimum views” cutoff (from 28)
    • Change your cutoff percentage (from 50)

    Another way to refine your list is to eliminate pages you don’t want to analyze. e.g. Your homepage, section pages, bio pages, about pages and more.

    Next Read: How To Lower My Bounce Rate in GA4

    Google Analytics 4 Bounce Rate (GA4)

    Google Analytics 4

    After months of complaining from frustrated users, you can now find bounce rate in Google Analytics 4.

    Bounce rate did not exist in Google Analytics 4 until July 2022.

    It was a useful stat in UA because it told you the percentage of users who left your site after viewing a single page and not performing any other activity. This was especially helpful when analyzing inbound search traffic.

    Google Universal Analytics Bounce Rate Example
    How bounce rate appears in Google Universal Analytics.

    Bounce rate, however, has a different definition in Google Analytics 4.

    Content

    1. GA4 Bounce Rate Definition

    • What is an engaged session in GA4?

    2. Bounce Rate Report in GA4

    • Bounce Rate in Customized Reports
    • Bounce Rate in Explore

    3. Typical Google Analytics 4 Bounce Rate

    4. Why is My Bounce Rate High?

    • Bounce Rate Factors: Any Site or App
    • Bounce Rate Factors: News Publishers
    • Bounce Rate Factors: Shopping

    GA4 Bounce Rate Definition

    What does bounce rate indicate in Google Analytics 4?

    “Bounce rate is the percentage of sessions that were not engaged sessions.”

    This means that it’s the inverse of engagement rate. (I recently discussed this in more detail in my newsletter.)

    Also see: How To Lower My Bounce Rate in GA4

    What is an engaged session in GA4?

    A Google Analytics 4 engaged session is one in which a user completes one or more of the following actions:

    1. Spends at least 10 seconds on the site/app
    2. Views two or more screens (app) or pages (web)
    3. Completes one or more conversion event

    Therefore if a user did NOT complete one of these actions, they are considered to have “bounced” from your website or app.

    Also See: Change Your Engaged Sessions Timer to Something Other Than 10 Seconds

    Your bounce rate and your engagement rate should always add up to 100.

    For example, if your bounce rate is 45 percent, your engagement rate should be 55 percent. Google calculates this for your automatically, so no action is required.

    Bounce Rate Report in GA4

    You can find Google Analytics 4 bounce rate data in two places: customized reports and the Explore section.

    Bounce Rate in Customized Reports

    While in the Reports tab (the first one below the “Home” house on the left sidebar), you can customize most reports. Here’s how:

    1. Click the pencil in the top-right portion of the screen

    Google Analytics 4 customize reports

    2. Click the > across from Metrics

    GA4 Customize Report Metrics

    3. Click the + Add metric button and scroll down to select bounce rate. (In the following screenshot, I have already added it. You probably won’t see if in your list if you haven’t added it yet)

    GA4 custom reports add metric

    4. Drag the different metrics from top to bottom to order them. The ones up top will appear the furthest to the left in your report

    5. Click the blue Apply button

    6. Click the blue Save button and choose one of the two options

    GA4 Save Custom Report

    You should now see bounce rate in the report you customized. Remember, you can add this (and other custom metrics) to almost any of your Reports in Google Analytics 4.

    Bounce Rate in Explore

    To add bounce rate to an Exploration, click the + next to METRICS.

    Type “bounce rate” into the search bar at the top, check off the box next to it, and click “Import.”

    Add Bounce Rate to GA4 Explore Report

    You should now be able to use it in your Exploration by adding it from the METRICS section.

    Also Read: What is a Bad Bounce Rate Google Analytics 4?

    Why is my bounce rate so high?

    As discussed, bounce rate is relative to each industry and niche. A high bounce rate for you could be a pipe dream for a competitor.

    If you believe your bounce rate is high across many pages on your site, it’s worth addressing.

    Remember, a user bouncing in GA4 means they left your site/app in less than 10 seconds without completing a conversion event or going to a second page/screen.

    Do You Want a High or Low Bounce Rate?

    It’s usually better to have a low bounce rate, but that’s not always the case.

    Is a High Bounce Rate Always Bad?

    No. It’s possible to have pages with high bounce rates, yet nothing be wrong.

    When is a High Bounce Rate Good?

    A high bounce rate in Google Analytics can be considered “good” if a user is able to quickly find what they needed on your site or app.

    Let’s look at an example.

    The USDA website has very little content for its “What is bacon?” page. At the same time, it probably tells most people everything they need to know about the pork product.

    A high bounce rate from this page may not necessarily be a problem.

    But what if someone searches “best way to cook bacon” because they’re looking for detailed instructions? A high bounce rate here might indicate that your recipe isn’t very helpful.

    Here are some reasons why users might be likely to bounce from your site.

    Bounce Rate Factors: Any Site or App

    You’re probably not the only website or app in the world providing your service or information.

    That means users are more impatient than ever to find what they want.

    Avoid these pitfalls to decrease your bounce rate:

    • Slow load times
      The longer your page/screen takes to load, the more likely a user is to bounce
    • Too many ads
      Bonus (negative) points if it’s difficult to distinguish the ads from the content
    • Too many popups
      Cookie preference permissions are typical these days. But if someone has to close multiple elements before they can access your content, you’re begging them to leave
    • Autoplay video (with sound!)
      This is one of the worst ways you can “welcome” a user

    Bounce Rate Factors: News Publishers

    Not only are you competing with other news sites, but also social media, email and real-life interactions.

    If a user has come to your site, it’s probably because they want to go deeper on a particular topic. The headline in the push alert wasn’t enough. They’re hungry for quality journalism.

    To keep them around – and coming back for more – don’t do these things:

    • Newsletter sign-up popups
      This might be their first time on your site. How do they know they want to subscribe to your newsletter before they even see what you offer?
      Set this to occur after multiple visits, or at least keep it from triggering until they scroll down the page a bit
    • Any other pre-content popup
      Again, aside from legal obligations, like having users accept/reject cookies, don’t put anything between the user and their news
    • Burying the lede
      This isn’t just journalism 101, it’s also bounce rate 101.
      An enticing, draw-you-in opener for a feature story is fine. But if there’s been a big election or a tragic event, don’t leave people guessing about the details. Invert the pyramid and go from there
    • Unappealing layout
      Don’t let your choice of fonts, colors and design distract from the content. Be as minimal as possible, and think of how a user is going to experience the story on a phone, not merely desktop

    Also review the list in the previous section, as many news sites are guilty of those errors.

    Bounce Rate Factors: Shopping

    If you run an ecommerce site, you already know that not every visitor is ready to buy. But that doesn’t mean their bounce rate has to be high, too.

    Here are some ways to scare off potential customers:

    • The price was too high
    • The product was out of stock
    • The product wasn’t what the user expected
    • The product page took too long to load
    • You didn’t have their desired size/make/model
    • You didn’t have their desired color(s)

    Also See: Experiment with the GA4 Demo Account

    How To Lower My Bounce Rate Google Analytics 4 (GA4)

    Google Analytics 4

    If you want to reduce your bounce rate in Google Analytics 4, you have come to the right place.

    We’re going to explore what bounce rate is, what contributes to a high and low bounce rate and how to encourage users to stay on and engage with your website, blog or app.

    I’ll also take you through a step-by-step process to identify which pages on your site are most vulnerable to bounces before going over how to protect against this.

    Content

    1. What is Bounce Rate in Google Analytics 4?

    • Most Important Statistics for Bounce Rate in GA4

    2. How Bounce Rate Works in GA4

    3. Anti-Bounce Moats

    4. Bounce-Rate Analysis Warning

    • ‘Empty’ Engaged Sessions
    • Data Distortion
    • Comparing with Other Sites

    5. How To Lower My Bounce Rate Google Analytics 4 (GA4)

    6. Conclusion

    —

    What is Bounce Rate in Google Analytics 4?

    Before we try to decrease the bounce rate of your website, app or blog, let’s define it.

    GA4 bounce rate is the inverse of engagement rate (the percentage of sessions that are “engaged”).

    An engaged session means a user performed at least one of the following three actions:

    • Spent longer than 10 seconds on the page (web) or screen (app)
    • Navigated to a second page/screen
    • Completed a conversion event

    A user is considered to have “bounced,” then, if they 1) left your website/app in 10 seconds or less 2) without navigating to a second page 3) or completing a conversion event.

    Personally, I like this better than the Universal Analytics bounce rate definition:

    “Bounce rate is single-page sessions divided by all sessions, or the percentage of all sessions on your site in which users viewed only a single page and triggered only a single request to the Analytics server.”

    The GA4 definition is more specific and gives us a clearer path to improve it.

    Most Important Statistics for Bounce Rate in GA4

    In addition to bounce rate, understanding the three statistics that determine bounce rate is key to our plan to lower your bounce rate.

    Average Engagement Time

    Engagement time is counted whenever a user has your website in the foreground of their screen.

    If your website is open in the background of someone’s comptuer, the engagement timer is paused until they bring it back to the foreground or the session is ended.

    Multiple Page Views

    A simple way to determine whether someone has changed pages on your website is if the URL changes. (Don’t worry – Google keeps track of this for you.)

    Typically, the more pages a user visits in a given session, the better.

    An exception would be if they erratically navigate from one page to another, unable to find what they seek, before leaving the site.

    Conversions

    Google Analytics 4 conversions are events that you have marked as conversions. Since this is different for every website, the best way to see this for yourself is to go to Configure > Conversions in your GA4 dashboard.

    You can mark any existing event as a conversion by going to Configure > Events and toggling the button at the end of the row to the right so that it turns blue.

    Also see: Google Analytics 4 Conversions Explained

    How Bounce Rate Works in GA4

    To understand bounce rate, you have to understand the factors that determine whether a user “bounced” from your site.

    You may be asking: If we can just look at pages with the highest bounce rate, why analyze other stats?

    In other words, bounce rate itself is a statistic, so what’s the point in looking at 1) engaged time, 2) page views and 3) conversions if they’re “baked in” to overall bounce rate?

    To illustrate the importance or paying attention to all three factors – not just bounce rate itself – let’s look at four scenarios of different sessions on your site as tracked by GA4:

    At first glance, you may think, “Wow, three engaged sessions out of four. Not bad.”

    Let’s dig deeper, though.

    Session A

    This is just about the worst-case scenario. The user left your site/app in four seconds without visiting a second page/screen or converting.

    This is also the only one of the four sessions we’re analyzing that would be considered “bounced.”

    Session B

    A little more time on the site, but they still only visited a single page. And didn’t convert.

    Nonetheless, it’s an “engaged session” by definition.

    Session C

    Yes, it’s possible to visit three pages in nine seconds. But for a user to do so and leave your site is almost certainly a bad sign if it wasn’t accompanied by a conversion.

    Even so, this also counts as an engaged session.

    Session D

    If I’m running a website, this is the only scenario of the four that gets me excited. Even if it didn’t have a conversion, I would still be encouraged.

    Anytime a user spends more than a minute on your site while visiting multiple pages, it’s a positive sign. In internet time, 1 minute and 24 seconds is an eternity.

    The overall engagement rate of these four sessions is 75 percent, meaning the bounce rate is 25 percent. Again, this sounds great in a tweet, but until I get more sessions like scenario D, I’m not satisfied.

    If session B had spent 17 seconds less on the site, it would have been a bounce.

    If session C had left after seeing one page instead of three, it would have been a bounce.

    Session D, however, has a nice “anti-bounce moat.”

    Anti-Bounce Moats

    I don’t just want to help you lower your bounce rate by a little bit. I want to help you create what I call “anti-bounce moats (a term I made up – take it or leave it).

    Any session where two of a page’s three key statistics meet the following criteria has an “anti-bounce moat”:

    • 33+ seconds engagement time
      (11 * 3, since 11 seconds is the minimum engaged time to trigger an engaged session)
    • An internal referral to another page in the same session
    • A conversion on the page
    GA4 Anti-Bounce Moat Factors

    Further down on this page, I’m going to show you how to identify vulnerable and under-performing pages, and how to boost their engagement rate and lower their bounce rate.

    Bounce-Rate Analysis Warnings

    Important: We’re not trying to lower bounce rate just for the sake of it. Here are some things to be aware of as we go through this process.

    ‘Empty’ Engaged Sessions

    Bounce rate is a statistic that helps us identify pages where little time is spent and little action is taken. But be careful!

    Just because all your users spend 5 minutes on each page and read 10 pieces of content per session doesn’t mean they’re having an impact on your business.

    Imagine a brick and mortar toy store where visitors strolled the aisles for an average of two hours and looked at 100 items each, but no one ever bought anything.

    Toy store without sales

    That business’s “bounce rate” would be very low, but it would soon be out of business.

    In other words, what’s important is that your site/app visitors convert. That they take actions that earn you money, directly or indirectly. (If earning money is your goal.)

    If part of your revenue comes from display ads, more time on site and more views certainly help. But many websites and apps don’t use display ads as their primary source of income.

    Data Distortion

    A small data sample can distort your analysis.

    For example, imagine these two scenarios on an app:

    1. A screen (equivalent to a page on a website) that only three people have seen in the last 30 days with an average engagement time of 55 seconds.

    2. A different screen on your app that 1,000 people have seen in the last 30 days with an average engagement time of 40 seconds.

    The first page might seem more engaging, but the sample size is too small.

    So far, three people have seen screen 1 for a total of 165 seconds (an average of 55 each). If two more people view that screen for five seconds each, we’re suddenly looking at an average engagement time of 35 seconds.

    Don’t get excited about a page with super high averages when a small number of users have seen it, nor frustrated about a page with super low averages in the same situation.

    Comparing with Other Sites, Blogs, Apps

    All three of the factors that determine whether a site is engaged or “bounced” on Google Analytics 4 can be manipulated.

    I’m going to explain why, and hopefully convince you not to put stock in what others say their bounce rate is compared to yours.

    Public vs. Private Data

    People sometimes share data publicly. It could be at a conference. In a tweet. Or a simple text message.

    But unless someone also shares full-screen screenshots of their GA4 dashboard along with all their property settings, don’t believe them.

    You can’t see a GA4 dashboard unless you’re given access, which means you can’t confirm it, either. So don’t be concerned when someone shares a statistic that makes yours pale in comparison.

    They might be lying. Or worse, they might not even understand the data their quoting.

    Aside from those factors, here are some more specific ways people can manipulate their dashboard.

    10-Second Cutoff

    By default, a GA4 session must last more than 10 seconds to be considered “engaged.” But you can change that.

    And so can your competitors.

    Read More: How To Change Google Analytics 4 Engaged Session Timer

    What if someone running content strategy at an industry peer is told by their boss that bounce rate needs to go down 10 percent?

    They could do the hard work (like you) of trying to lower user bounce rate. Or they could simply set the engagement time to 2 seconds, assuring that nearly all their visitors would be considered “engaged.”

    Comparing yourself to this competitor would probably drive you crazy.

    Multiple Page (or Screen) Views

    You could manipulate the number of views on your site in a couple ways.

    One is with redirects where Google counts each page as a separate view. This could happen if someone goes to your site from a search engine and is redirected to a second page after a few seconds.

    Another scenario would be with infinite scroll. While there’s nothing wrong with implementing infinite scroll on your site, it often triggers additional page views when a user reaches the end of a page or article without realizing it, and the following page is accessed.

    Weak Conversions

    Conversions on your site are determined by you. As mentioned before, conversion events should have a direct – or at least closely correlated – affect on your bottom line.

    Google marks the “purchase” event a conversion by default in GA4, and that can’t be changed. (And rightfully so.)

    What about “add_to_cart”, though? Someone could add an item to their cart without purchasing. Yet you still might consider this important enough to mark as a conversion. (Most people do).

    That’s fine, so long as you’re aware that this will likely increase your conversion rate.

    Marking an event like “page_view” as a conversion, though would be ridiculous. Nearly every user would “convert,” and your conversion rate would be through the roof.

    So when someone gives you a conversion rate for their website that’s much higher than yours, don’t fret.

    Even if they have the same exact conversion events in place, it’s still not likely a fair comparison.

    A website that sells $80 shoes is likely to have more conversions than one that sells $15,000 espresso machines. If you’re the espresso seller, you probably have far fewer conversions than your shoe-slinging friend.

    To be clear, I’m not recommending you do any of these things to lower your bounce rate. That would only dilute your data.

    My goal is to make you aware of ways others can inflate their numbers, giving you undue reason for concern.

    Determine what makes for a low (and high) bounce rate for your site, and let that be your measuring stick.

    As we go through the different data points, we’re going to compile a list of “worst” pages. I recommend recording them in a spreadsheet along with their corresponding statistics.

    None of these tactics can be applied blindly to any platform. You, as the expert of your domain, are the key to this analysis.

    For the sake of simplicity, from here on I’ll talk in terms of pages/websites, but know that this strategy can also be applied to screens/apps in almost every case.

    How To Lower My Bounce Rate Google Analytics 4 (GA4)

    We’re going to use a two-step process to lower your bounce rate:

    1. Determine the lowest performing pages, not just in terms of bounce rate, but also engagement time, internal referrals and conversions
    2. Deploy tactics on the pages with the highest bounce rates to increase engagement time, internal referrals and conversions

    Step 1: Create a Master List of Pages with Highest Bounce Rate etc.

    Your master spreadsheet is going to have six columns. Here they are in order from left to right:

    1. Page type
    2. Page title
    3. Bounce rate
    4. Averaged Engaged Time
    5. Internal Referrals
    6. Conversion Rate

    While you can certainly sort your pages by the worst bounce rates and work on them first, filtering by the other three columns and following the tactics were about to go over will allow for hte most targeted, effective progress.

    Step 1A: Determine Page Types

    Your bounce rates will likely vary by page type. Before beginning your analysis, determine the different types of pages on your site, and which ones you want to analyze.

    News website example

    • Homepage
    • Section pages (i.e. Local News, Sports, Business, Government, A&E)
    • Article pages
    • Video pages
    • Photo galleries
    • Miscellaneous (subscriptions, signups, bios)

    Business website example

    • Homepage
    • Blog posts
    • Product pages
    • Cart
    • Checkout page

    Step 1B: Choose Date Range

    The more content your site has, the shorter the date range you can choose here.

    I would recommend analyzing at least 28 days’ worth of data. If you don’t get a lot of traffic, though, consider analyzing everything within the last year.

    Maintain the same date range throughout all steps of the analysis.

    Step 1C: Determine Analysis Size

    The number of pages you want to analyze for bounce rate depends on two things: the size of your site and the section(s)/page types (see step 1A) you’re analyzing.

    I like to start with a maximum sample size of 100 pages for each of the four statistics we’re going to look at (bounce rate plus the three statistics that determine bounce rate).

    If you want to do more or less, that’s fine. For the rest of this exercise, I’ll be operating under the asumption of analyzing 100 pages in each category.

    If this doesn’t completely make sense, it will soon.

    Another option is to select the most important pages on your website and perform that following process only on those pages, even if they not amount your highest “bouncers”.

    Step 1D: Create Bounce-Rate Report in Explore

    1. Go to the Explore tab on the left sidebar of your Google Analytics 4 dashboard. Open a new Blank report.

    2. In a new tab, add the Page Title dimension to ROWS and add the following four metrics to VALUES: Bounce rate, Average engagement time per session, User conversion rate and Views.

    3. In the Tab settings column, change “Show rows” to 500

    4. Add the following filter:

    • Views >99*
      *By only looking at pages with 100 or more views in the selected time period, we eliminate outliers.

    Adjust this number as necessary for your report to surface more or less pages.

    Ideally, your report should have less than 500 rows so that you only have to sort and export one time, as in the following steps.

    5. Sort the Bounce Rate column from highest to lowest. (The arrow to the left of Bounce rate should be pointing down)

    6. Export the report to a CSV or other spreadsheet format

    If you have less than 500 rows in your report, skip to step 8.

    7. Repeat steps 5 and 6 for the engagement rate and user conversion rate columns, respectively.

    If you don’t have any conversions set up, or if they’re new, you can skip the conversion rate analysis.

    You should now have two or three exported spreadsheets.. We’ll get back to them in a moment.

    8. Open a second tab in the same Exploration

    9. Add Page referrer and Page Title to ROWS and change “Show rows” to 500 again

    10. Add Stream name to COLUMNS
    The stream name dimension itself isn’t that important. What we’re trying to accomplish is minimize the number of columns, since there’s a 20-column limit on the report.

    This allows us to ensure we’re capturing the total number of referrals from each page to other pages.

    The only way this wouldn’t work is if you have more than 20 data streams. (The limit is 50.) In that case, consider using a dimension like languages or something else that doesn’t generate more than 20 columns.

    11. Add Views to VALUES

    12. Add the Views >99 filter again

    13. Add another filter where Page referrer contains “yourwebsite.com”
    This will ensure that we’re only looking at internal referrals in the report

    14. Add one more filter where Page referrer does not exactly match “https://yourhomepage.com/”

    Since your homepage will likely be responsible for an outsize number of referrals, we’re going to eliminate it from this report.

    You can do this for any other pages on your site – like section home pages – that may skew your data.

    You can also use the “contains” function to only include certain types of posts, like articles. To do this, you need to have something in your URL that allows you to distinguish those pages from the rest.

    15. Sort the data by ordering the totals column from lowest to highest

    16. Export the report to a CSV or other spreadsheet format

    You should now have two exported spreadsheets (unless you had more than 500 rows in your reports, in which case you’ll have exported four separate ones).

    17. As you look through the page titles, you can eliminate that don’t make sense to analyze such as “Page Not Found” or any other pages you’re not concerned about optimizing.

    18. Optional: Combine the spreadsheets into one using the VLOOKUP function.

    All set? Now it’s time to do the fun work of creating anti-bounce moats on a page-by-page basis.

    The first column in your spreadsheet – bounce rate – is mostly for tracking purposes. Save the master spreadsheet so that you can look back on your progress for the specific pages where you’ll be making the following changes.

    Step 2: How To Increase Average Engaged Time GA4

    The best way to increase average engaged time on your site is to give users a reason to spend more time there. The foundation of that is original, quality content.

    User the following tactics on the pages that have the lowest average engagement time in your master spreadsheet. Pay special attention to those with 10 seconds or fewer of average engaged time, and work up from there.

    Start by sorting the pages in your data from lowest engagement time to highest. These will be listed in seconds, so your prime targets are any pages that average 10 or less, moving up from there.

    1. Deliver on Promises
    2. Go Deep with Content
    3. Be Original

    Deliver on Promises

    Think about what a user might expect when coming to your site, depending on how they got there.

    Social Media

    When you share a link on social media, does your post accurately preview/tease what the user will find on your site?

    This can be improved by making sure the accompanying text you put on your post matches the content. The link preview that social networks generate also plays a role.

    You can usually see this before publishing your post, but the headline and description, which are pulled from your site, should match the content.

    Search Results

    What about when your link appears in search results, does a user find what they expect when clicking through?

    Newsletters

    If someone has opted in to receiving your emails, you’re already in a strong position. Here’s a high-level view of the steps they go through to get to your site.

    1. Subject: Is it engaging enough to entice users to open the email?

    2. Top of the Email: This could be an image and/or intro text. Just like with the subject line, you want to provide enough useful and intriguing information for the reader to continue reading/scrolling.

    3. Body/Primary Email Content: If a user has come this far – opened your email and scrolled down to see more – you’re doing great.

    If you’re able to get them to click through, does your site’s content deliver on what’s promised in the email? If so, they’re likely to spend more time on your site.

    Go Deep with Content

    When I say “go deep,” I’m talking about quality, not quantity. Providing a lot of quality content, however, will in turn boost your quantity.

    Here are some ways to go deeper on your website:

    • Interview people: Don’t just share your opinion and experience on a topic. Share others’, too.
    • Be Visual: Use quality, original screenshots and photos. Not only will they break up blocks of text, but they also make it easier for others to retain information.
    • Give examples: Like we’re doing here. Don’t just tell people they need to increase average engaged time to lower bounce rate. Give them specific examples of how to increase engaged time.

    Be Original

    Think of it this way: If you’re providing the same information on a topic as 100 other websites, there’s a 1 percent chance users will come to you and not a competitor.

    That percentage goes down significantly if you don’t have as much credibility or authority as your competitors.

    But what if you’re the only one providing the information on your website? You have just wiped out your competition simply by providing useful content that can’t be found anywhere else.

    That doesn’t mean users are guaranteed to flock to your site in droves. You still have to put in the work of promoting your content, building links and more. But it does mean that they’re more likely to stay once they do find you.

    Step 3: How To Increase Internal Referrals GA4

    There’s some overlap here with increasing engaged time. That’s because the more high-quality and original your content, the more likely it is that users will want more of it.

    When users want more of your content, they’re more likely to navigate to additional pages.

    What I want to talk about here, though, is facilitating user navigation in practical ways. This is primarily done with internal linking.

    When applying these tips, start with the pages that have the fewest outgoing internal referrals. Make it as easy and attractive as possible for people to get to additional pages on your site.

    Start with those that refer the fewest users to additional pages and work your way down your list.

    1. Quality Anchor Text
    2. Connected Content

    Quality Anchor Text

    Anchor text is the clickable text that takes you to the page you’re clicking on. For example, the anchor text in the previous sentence is “Anchor text”.

    Not only is anchor text an SEO-ranking factor, but it also gives users an idea what they’re about to click on.

    Try to use concise phrases that people would also search for on Google to find that content.

    Since the headline of this article is “How To Lower My Bounce Rate Google Analytics 4 (GA4)”, that would be the ideal anchor text for any other page that links to this one. (That goes for both internal as well as external links.)

    But even things like “lower bounce rate GA4” or “how do i improve my bounce rate?” would be great anchor texts to link to this page.

    Connected Content

    This website has a lot of Google Analytics 4 content. It’s natural, therefore, to connect lots of information on this page to other pages on the site.

    This article is about how to practically lower your bounce rate. But I also wrote something explaining what bounce rate is, exactly. So that’s a natural piece of content I’m going to link to here in case someone wants to review the fundamentals of bounce rate before putting these tactics into practice.

    For example, if you run a gardening website and you write about “How To Plant Vegatable Seeds”, you would also want to link to your article about “Where To Buy Vegatable Seeds.”

    Don’t link to other pages just for the sake of it – think about what would be most useful to the reader.

    In addition to linking within the body of your article, use “Read More”-type callouts. Not only do they stand out, but they break up the text, making it easier on your readers. Here’s an example:

    Read More: Google Analytics Bounce Rate (GA4)

    Step 4: How To Increase Conversions GA4

    Finally, we come to the third and final element of increasing bounce rate/lowering bounce rate: conversions.

    These should be the most important and valuable events on your site. The ones that have a direct – or indirect but strong – affect on your bottom line.

    When applying the following tactics, start with the pages from your master spreadsheet list that have zero conversions. Then work your way up to pages with single-digit conversions, and so on.

    1. Increase Conversion Opportunities
    2. Make Conversions More Accessible
    3. Follow Up with Users who Fall Off

    Increase Conversion Opportunities

    As mentioned above, we don’t want to create conversion events just for the sake of getting more conversions.

    If you don’t have any conversion events set up yet, that’s OK. But start thinking about ones you could create. If users can purchase products on your site, some of conversion events should be obvious.

    If not, consider newsletter signups, account signups and blog subscriptions.

    In fact, having fewer conversion events – even just one – can be an advantage just starting out. You’ll know exactly what your No. 1 priority is for your users.

    Make Conversions More Accessible

    Perhaps you do have conversion events on your site, but they’re not easy to find.

    While it may not make sense to put newsletter signups on every single page of your site, you don’t want to relegate them to a single page, either.

    If you have some kind of sign-up or download button, consider adding them to one or more of the following areas of your site:

    • The header
    • Strategically place the button – with a compelling callout – in the body of blog posts and other content
    • The footer

    Put examples in between bullet points, with captions citing website/source

    Follow Up With Users Who Falloff

    Have you ever begun filling out a form or began a purchase on a site, only to leave it without finishing? There’s a good chance your email was captured anyway, and you received an automated message a day or two later.

    Hi Fred, we noticed you didn’t complete your purchase of our Google Analytics 4 course.

    If you’re having second thoughts or would like to ask us a question, please let us know.

    In the meantime, here’s a link to everything that’s included in the course: [LINK]

    This can be effective because you’re targeting people who already showed a strong interest in your product.

    Conclusion

    Lowering bounce rate (and by definition, increasing your engagement rate) is hard work, but there’s a huge payoff.

    The more time a user spends on your site, the more valuable your display ads and the more likely people are to take actions that help your business.

    While a 0 percent bounce rate for a website with any amount of traffic is practically impossible, you’ll never run out of opportunities to improve. Especially as your content volume increases.

    Periodically run through the practical exercise we did together to decrease your bounce rate.

    If you have other tips to increase engagement and lower bounce rate, I would love to hear about them in the comments.