To add the Google Analytics 4 demo accounts to your GA account, first make sure you’re signed in to the Google account with which you’ll want to access them. If you’re not sure to which account you’re signed in (or if you’re signed in at all), simply go to Gmail or YouTube and see which account comes up.
Once you’re sure you’re signed in to the right account, all you have to do is click the following links:
If you’re not sure which one to add, I recommend both. Since there are differences between the two, there may be some actions you can only do in one of the two accounts.
To be clear, you do not need to create a demo account in Google Analytics 4. It already exists for you to access and experiment.
In case you’re wondering, yes, there’s also a Universal Analytics demo account (with web data only). But since it will be obsolete by the second half of 2023, we’re not going to talk about it here.
Benefits of Google Analytics 4 Demo Account
Here’s why I like having the Google Merchandise Store analytics demo account, along with the Flood-it! account demo account:
You can analyze real data without making any changes to your website or app
Take the real-time dashboard for a test drive
Many people have asked me whether GA4 is adequate for real-time analysis. This is especially important for newsrooms and other content platforms that are used to tools like Chartbeat and Parsely.
While the data Google’s demo provides may have nothing to do with your platform, you’ll get a good idea of the data available in the 30-minute report.
Experiment with comparisons
Comparisons in GA4 are the equivalent of segments in Universal Analytics. This is an effective way to compare multiple audience segments in the same dashboard. Especially if you have audiences set up on your account.
Newer users will probably find the Explore section of Google Analytics 4 the most intimidating. What I like about this in the demo account is that not only can you create your own reports, but other users have made theirs public, too.
That means you can take a look at, and experiment with, reports they have already set up. From what I can tell, this is only available in the Merchandise Store and not Flood-It!
Can’t Find the GA4 Demo Account
Are you having problems accessing the GA4 Demo Account? You’re not alone.
Sometime in the spring of 2022, they disappeared from many users’ dropdown menus in their GA4 accounts. They’re probably still there, though, so long as you added them to your account at some point.
Assuming you already followed the instructions at the beginning of this post to add the demo properties, here’s how to find them:
1. At the top of your GA4 dashboard, click the part where it says All accounts > [Account Name]
2. In the dropdown menu, type “demo” into the search bar.
3. Click on the Demo account you want to access – Flood-It! or Google Merchandise Store.
It’s that simple. You’re now ready to continue using the GA4 demo accounts.
For all the benefits of the GA4 demo account, it does have its drawbacks.
Since it’s accessible to anyone who has Google Analytics – that is, millions of people – it’s not 100 percent customizable.
You can’t create or edit events or parameters
While you can analyze existing events, and learn how some of them were created, you can’t change them.
Since events are the foundation of GA4, it’s important you understand them. You’ll have to use your own dashboard for this.
You can’t create audiences
This is something else you’ll have to learn with your own dashboard.
You can access the existing audiences, but not much more.
You can’t edit the Reports dashboard
It makes sense that we can’t add or edit events, audiences or reports. Between the property quota limits the fact that data is shared, it’s for the best. Otherwise it would be a custom-dashboard free-for-all.
Whatever data is available would be a moving target, at the mercy of the latest user’s edits.
You can’t use DebugView
Yes, you can access the DebugView page. But you can’t actually test Google Tag Manager tags and triggers. For that, you would need access to the Demo Account’s GTM container, which you can’t.
Granting millions of users access to the same container would create a similar nightmare to that of shared events and audiences.
As with any tool, the best way to learn Google Analytics 4 is to experiment with it on a regular basis.
Use the demo account to build confidence, then make permanent changes to your own dashboard.
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:
The Google Analytics 4 structure is Organization* > Account > Property > Data Stream
(Notice the empty “Views” column for GA4 properties.)
The Universal Analytics structure is Organization* > Account > Property > Views
*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:
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
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.
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
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.)
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.
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.
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.
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.
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.)
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.
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:
When does the Google Analytics 4 session timer reset?
Google’s default timer ends sessions after 30 minutes of inactivity.
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
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.)
4. Click the pencil in the top-right portion of the screen:
5. Click the > across from 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)
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.
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.
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.
In this case, the first 375 pages in my date range average at least one user per day.
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.
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 %.
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).
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.
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.
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.
Bounce rate, however, has a different definition in Google Analytics 4.
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:
“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.
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.
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.
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.”
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.
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.
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.”
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
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.
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.
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.
By default, a GA4 session must last more than 10 seconds to be considered “engaged.” But you can change that.
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.
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:
Determine the lowest performing pages, not just in terms of bounce rate, but also engagement time, internal referrals and conversions
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:
Averaged Engaged Time
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
Section pages (i.e. Local News, Sports, Business, Government, A&E)
Miscellaneous (subscriptions, signups, bios)
Business website example
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
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.
Deliver on Promises
Go Deep with Content
Deliver on Promises
Think about what a user might expect when coming to your site, depending on how they got there.
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.
What about when your link appears in search results, does a user find what they expect when clicking through?
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.
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.
Quality Anchor Text
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.
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:
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.
Increase Conversion Opportunities
Make Conversions More Accessible
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:
Strategically place the button – with a compelling callout – in the body of blog posts and other content
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.
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.
In the Google Analytics 4 site search advanced settings, we see that there are five possible search term query parameters: q,s,search,query,keyword.
We also see that “Only the first matching parameter will be used.”
In the case of URLs for searches on my site, there’s only one parameter, and it’s “s”. That means Google should capture the data.
Same for the Times, which uses “query.”
If I use DebugView while performing a search on bradgerick.com, I can confirm that the search_term parameter is “custom dimensions” – the same term for which I searched – within the view_search_results event.
This means that the way the site search event is set up by default captures everything I need for my site.
There is no need to make any changes.
What if my site search parameter isn’t included by default in GA4?
If you perform a search on your site, and you see a different parameter in the URL for some reason, you should tell Google what this is in your advanced settings.
For example, imagine that the URL parameter for searches on my site was abcxyz like in this screenshot where I deliberately changed the URL:
If this really were the search parameter for my site, I would need to change my Search Term Query Parameter to abcxyz.
Just like above, to do this I would go to Admin > Data Streams > Enhanced Measurement Settings > Site search event advanced settings.
Then I would replace q,s,search,query,keyword with abcxyz. It would look like this:
After saving the setting, this would begin to collect site search data.
Site Search Additional Query Parameters GA4
The second field in GA4 site search advanced settings is for additional query parameters. As you can see, you’re allowed to have up to 10.
But what is an additional query parameter for a website search?
If I search for shoes on Zara and I click the men’s section, this is what the URL looks like.
The highlighted portion at the end is an additional search parameter: section=MAN.
This means that they would want to put “section” in the second field of advanced search settings.
But is Zara ready to go now? Take one more look at the link. Did you notice the part in yellow?
Their site’s search parameter query is searchTerm, which is not included by default in GA4.
So just like we changed it to abcxyz in my made-up example above, Zara would have to change their GA4 site search query parameter setting.
Along with the additional query parameter change, this is what Zara’s advanced Site search settings should look like:
Remember, you can have up to 10 additional query parameters, so be sure to include them all. Also note that additional query parameters in Google Analytics 4 a case-sensitive.