GA4

Comparing GA3 and GA4 Metrics in Reporting

Google Analytics 4 (GA4) is replacing Universal Analytics (GA3). To prepare everyone for the change, I’ve created a series of blog posts to help users understand the difference between the two versions. Today’s post is a guide to helping you compare your GA3 reporting with your GA4 reporting. You’ll learn how increases and decreases of certain metrics may be more representative of your shift to GA4, rather than optimizations you made as a marketer.

Sessions

Sessions are calculated differently in GA4 than in GA3. Your sessions may be lower in GA4 because:

  1. Session Timeout: While the automatic session duration is defaulted to 30 minutes in both versions of Google Analytics, GA4 allows you to extend that session timeout to 7 hours and 55 minutes. GA3 had a maximum timeout of only 4 hours. That means if you extended your session timeout to GA4’s maximum, someone that stepped away from your website for five hours would be counted as two sessions in GA3, while in GA4 they would be counted as one.
  2. Midnight Restart: In GA3, all sessions break at midnight. In GA4, this isn’t the case. This midnight cutoff is based on the time zone you set within your Google Analytics settings. For example, let’s imagine you’re an ecommerce vendor who gets a lot of traffic from across the U.S. on Thanksgiving. While your fellow East Coast residents have likely gone to bed at midnight, it’s only 9:00 p.m. on the West Coast, and people may still be up shopping. Any session that spans both 8:59 p.m. PST and 9:00 p.m. PST will appear as though you’ve gotten 2 sessions in GA3. In GA4, the session will persist and more accurately reflect the one session. 
  3. New Campaign Parameters: If someone encountered two of your campaigns within a short time frame while browsing the web, each of those campaigns would cause a new session to start in GA3. However, in GA4, the session won’t break. For example, if someone first clicks on a paid ad, goes to your website, immediately goes back to Google and clicks on your Google Business Places profile, GA3 would count that as two sessions. In contrast, GA4 would only count that as one session. 
  4. Automatic Bot Filter: GA4 has a bot filter that removes known bots and spider traffic from your website. Google populated this list based on their own research and the International Spiders and Bots List. GA3 did not have such a filter.

There’s one reason, however that your GA4 numbers may be higher than your GA3 numbers:

  1. Manual Filters: At the time of writing this post, GA4 does not allow for filters beyond IP filters. This lack of filters means that if you were filtering out hostname traffic in your GA3 property settings instead of configuring your Google Analytics tag within Google Tag Manager, your unwanted hostnames will appear in GA4.

Conversions

GA3 groups all website activity within a session. In doing so, Google also deduplicated any conversions within that session. If someone filled out a form 8 times, Google Analytics counted that as one goal completion. Conversely, GA4 isn’t a data model based on sessions. As a result, in GA4, those 8 form fills would be counted as 8 conversions. This difference means you will likely see an uptick in GA4 conversions compared to GA3, whether or not you make any website optimizations.

It’s also important to point out that because of this lack of deduplication, the transition to GA4 is a great time to test the validation on your forms. If your conversion event fires when you click a “submit” button without ever filling out a form, you likely need to tighten up your event logic to more accurately reflect your lead count.

Average Session Duration

To calculate the average time on page metric in GA3, Google would subtract the time you visited page 1 from the time you visited page 2. The inherent flaw was that Google Analytics had no way of measuring the time someone spent on your exit page because there was no subsequent pageview from which to subtract. 

Because Google could never figure out the length of time someone spent on an exit page, GA3 always undercalculated average session duration. Sometimes this was undercounted by mere seconds. Other times, it was under calculated by 10 minutes or more. 

GA4 measures time differently by sending a timestamp with every event. I dive deeper into the comparison in another blog post, but the bottom line remains the same. When you compare your average session duration metrics in GA3 to GA4, GA4 will likely have the higher number.

Bounce Rate

GA3’s bounce rate metric was largely influenced by the amount of event tracking you added to your Google Analytics account. The more events you had, the less bounces you likely had. As a result, there is a good chance anyone with lots of event tracking would naturally have a lower bounce rate in GA3. In contrast, account owners that did not implement any custom event tracking would naturally have a higher bounce rate.

In another blog post, I explain why GA4 bounce rate is a little harder to inflate. With GA4, event tracking will only decrease your bounce rate if you count that event as a conversion. The result is that if you had a super low bounce rate in Universal Analytics due to lots of event tracking, your GA4 bounce rate will likely be higher. Inversely, if your bounce rate was incredibly high in Universal Analytics, don’t be surprised if it lowers in GA4.

Summary

Below is an easy takeaway of what you should expect when you compare your GA3 to your GA4 reporting.

MetricResults in GA4 Reporting
SessionsLikely lower than GA3, but it depends
ConversionsLikely higher than GA3
Average Session DurationLikely higher than GA3
Bounce RateIt depends

Make sure to take this new measurement into consideration when doing analysis so you don’t inadvertently make an optimization decision based on incorrect assumptions. GA4 is continuously rolling out new features. Check back for updates or dive into other comparisons of Universal Analytics and GA4.

Comparing GA3 Behavior Flow and GA4 Path Exploration Reports

Google Analytics 4 (GA4) is replacing Universal Analytics (GA3). To prepare everyone for the change, I’ve created a series of blog posts to help users understand the difference between the two versions.  Today I’m diving into the four main differences between the Behavior Flow report in Universal Analytics and the Path Exploration report in GA4.

Background

Released in 2011 under the name “Visitors Flow,” the Behavior Flow report was one of the few opportunities in Universal Analytics to view a customer’s journey throughout one session on your website. Despite the promise for true customer insights, the lack of customization and clumsy aggregation made analysis with this report often difficult. With the introduction of GA4, the Behavior Flow report got a new name – Path Exploration – and new functionality. Below I’ll talk about the four main differences between the Behavior Flow report and the Path Exploration report.

Difference # 1 – Explore More Than Six Paths 

One major limitation of the Behavior Flow report in GA3 was that you could only get a detailed look at the top six interactions that occurred after a starting point of your choosing. For these top six interactions, you could see the next page path (or event) and continue to follow along to find the next eleven steps in the user journey. Beyond those top six interactions, though, data was limited. For the top seven interactions and beyond, you could only see the percentage of traffic a subsequent page received and that page’s drop off rate. Unlike the top six interactions, you couldn’t get any insight into the next three, four, or eleven steps in the customer journey.  

With GA4 and the Path Exploration report, the limit of events you can explore is much higher. Instead of looking at the top six interactions, you can explore up to nineteen of your top interactions and the steps that followed those interactions.

GA3 Behavior Flow Report
GA4 Path Exploration Report

Difference # 2 – Filtering Out Events

I previously mentioned the lack of configurability within the Behavior Flow report, and filtering out events was no exception to that rule. To provide more insightful analysis, sometimes you needed to exclude events within your customer journey. Even with strategic event tracking, some events only provide thought-provoking data in certain circumstances. For example, if you’re tracking scroll depth, that could be a great way to measure engaged on your blog. However, including this event in the Path Exploration could result in unwanted noise that distracts from a larger goal, such as the key CTAs someone used before submitting a lead form.

With GA4, the pencil icon appears next to each step, allowing you to filter out events you don’t believe are relevant to your particular analysis.

The pencil icon allows you to filter out certain events in the GA4 Behavior Flow report

Difference # 3 – Exploring Paths in Two Different Directions

GA3 limited the Behavior Flow report to one direction – forwards. While you could tell the steps someone took to prior to completing a goal with the aptly named Reserve Goal Path report, this was even more limited than the Behavior Flow report. With the Reserve Goal Path report, you could only see the three previous steps to completing a goal. Furthermore, this was limited to pages that someone visited prior to completing a goal – there was no granular drill down into custom events you may have tracked.

GA4’s Path Exploration report provides greater flexibility, allowing you to analyze different user paths based on either a starting or ending point. This means you can tell the paths someone tool after they visit your landing page, or you can tell what pages or CTAs someone interacted with prior to submitting a form.

GA4 allows you to choose an ending point instead of a starting point within the Behavior Flow report.

Difference # 4 – Exploring Paths Across Sessions

One of the main selling points of GA4 was a new data model that was event based instead of GA3’s session based data model. This meant that any report you viewed would not be constricted by sessions like it was in GA3. Instead, you could see the full customer journey, regardless of how long it took. 

We see this logic applied within the Path Exploration report, which shows the customer journey across sessions. In contrast, GA3’s the Behavior Flow report only included activity within one session.

GA4 allows you to explore user behavior across sessions, as evidenced by the multiple “session_start” events

Summary

The Path Exploration report in GA4 is a huge upgrade from the Behavior Flow report in GA3, with more visible data, no session limitations, filtering, and the ability to analyze events from a starting or ending point. GA4 is continuously rolling out new features. Learn more about GA4 reporting metrics with the blog articles below:

Comparing the Measurement of Time GA3 vs. GA4

Google Analytics 4 (GA4) is replacing Universal Analytics (GA3). To prepare everyone for the change, I’ve created a series of blog posts to help users understand the difference between the two versions.  Today I’m going to walk through how time is measured in GA3 with the average session duration and average time on page metrics and compare that to how time is measured in GA4 with the average session duration and average engagement time per session metrics.

Measuring Time in GA3

With Universal Analytics (GA3), the average time on page and average session duration metrics were wildly inaccurate. This is because time measurement in GA3 was inherently flawed. Instead of collecting timestamps intermittently throughout a session, a timestamp was only recorded with a pageview. To calculate the average time on page metric, Google would subtract the time you visited page 1 from the time you visited page 2. The inherent flaw was that Google Analytics had no way of measuring the time someone spent on your exit page because there was no subsequent pageview from which to subtract.

Now, many marketers are big-picture thinkers. Sure, the number may not have been 100% accurate, but it gave us a starting point, right? Wrong. Numerous blog articles go into this in-depth, providing real-life examples, but it took a metaphor before I truly understood how inaccurate and misleading GA3 time metrics could be. 

The way Google Analytics measured time in GA3 was similar to measuring the distance it takes to get somewhere using stoplights. While most of us encounter stoplights when we drive, think about the exceptions to the rule. For example, this method doesn’t account for highways, which don’t have stoplights. It also doesn’t account for the time it takes you to drive to a business that’s located right off the highway. It also doesn’t account for the fact that sometimes you’re driving across the state (or across several states for my non-Texan readers). Below is a graphic that measures the time to get from Houston to the DFW airport realistically vs. using GA3’s time measurement logic.  

Measuring Time in GA4

Measuring time in GA4 is leaps and bounds more accurate. Not only is a timestamp sent with every pageview, but it’s also sent with each event that occurs. And with GA4’s out-of-the-box events, such as outbound links, file downloads, and form fills, you’re already halfway to a more accurate measure of time.

Even beyond the events you can configure for your GA4 property, Google sends some automatic events. One of these automatic events is a user_engagement event that is dispatched with the page onunload event. This means that Google Analytics understands if a user closes their browser or navigates away from the page, giving us another layer of insight into how users engage with your website that was never available in GA3.

Now let’s compare the two with real numbers. In GA3, if someone opened your webpage, browsed for 1 minute, then spent 30 minutes on another website before coming back to your website and visiting a second page. After 10 minutes on the second page, they exit the website. With GA3, their average session duration and their average time on page would be 31 minutes. In GA4, the session engagement time (the time your website was in the foreground) would be 1 minute and the average session duration would be 11 minutes (1 minute on the first page, plus 10 minutes on the second page).

Below is a comparison of the two time-based metrics in Universal Analytics (GA3) compared to GA4’s time metrics. GA4 released the average session duration metric on November 29, 2022.

Average Time on PageAverage Session DurationAverage Engagement TimeAverage Session Duration
GA3GA3GA4GA4
The timestamp of one pageview, subtracted from the timestamp of the subsequent pageview.
If there is no subsequent pageview, the time on page will be measured as 0.		The average duration (in seconds) of users’ sessions, regardless if your webpage is in the foreground or background.
This excludes the time on page for the last visited webpage.		The average time that your webpage is in the foreground. The average duration (in seconds) of users’ sessions, measured from the initial session start to the unload event.

Based on the differences in measurement, it is highly likely that your average session duration will increase when you switch to GA4 due to the underreporting in GA3. As such, I highly recommend you don’t compare the two numbers.

Summary

Time measurement is another major upgrade you can expect from GA4, giving you more insight into which content actually engages users. GA4 is continuously rolling out new features. Learn more about GA4 reporting metrics with the blog articles below:

Comparing Engagement Rate in GA4 and Bounce Rate in GA3

Google Analytics 4 (GA4) is replacing Universal Analytics (GA3). To prepare everyone for the change, I’ve created a series of blog posts to help users understand the difference between the two versions. Today I’m comparing the difference between engagement rate in GA4 and bounce rate in Universal Analytics.

How Bounce Rate Was Calculated In Universal Analytics

Every time data was sent to Universal Analytics (GA3), it was called a “hit.” There were different types of hits, such as page hits, event hits, and ecommerce hits. Pageviews (page hits) and events (event hits) by default in GA3 were counted as an “interaction” hit. In contrast to a “non-interaction” hit, an interaction hit signifies to Google that the action occurred was of significance. Whenever a user only has one interaction hit in their session, it’s considered a bounce – someone left your website without completing two significant interactions. As a result, bounce rate is the percentage of sessions that did not have a second interaction hit.

Based on this calculation, any Google Analytics account owner who had a large amount of event tracking on their website would naturally have a lower bounce rate. Account owners that did not implement any custom event tracking would naturally have a higher bounce rate.

How Engagement Rate in GA4 Is Different

When GA4 was first released, bounce rate wasn’t even included as a metric. This was done in an effort to put the focus on customers who actively consuming your website and contributing to your bottom line instead of focusing on users who abandoned your website. In short, GA4’s focus was all about engagement.

Unlike bounces, engagements had stricter rules. A session can only be considered engaged if a user had 2+ pageviews, a conversion, or a time threshold of your choosing, ranging from 10 to 60 seconds. This means that you can’t add event tracking in GA4 to lower your bounce rate, unless you count that event as a conversion. 

Engagement rate is the rate of engaged sessions over total sessions. Due to increasing public pressure, Google finally released bounce rate in July 2022. However, the calculation is slightly different from the definition in Universal Analytics. In GA4 bounce rate is now just 1 – engagement rate.

Because bounce rate was so easy to inflate in Universal Analytics, if you had a super low bounce rate in Universal Analytics, don’t be surprised if it gets higher when you make the transition to GA4. Inversely, if your bounce rate was incredibly high in Universal Analytics, don’t be surprised if it lowers in GA4.

Looking Beyond Engagement Rate – Active Users & Session Engagement Time in GA4

Engagement within GA4 didn’t stop at engagement rate. Google also used this concept to help define two new dimensions – active users and session engagement time. The active users dimension describes those users who have had an engaged session at any point. This metric is helpful if you’re trying to separate out bot traffic from actual users.

Session engagement time sounds very similar to average session duration, but it’s important not to confuse the two metrics. Unlike Universal Analytics, GA4 is able to measure when a user closes their tab, navigates to another website, or reloads the current page. More simply, Google knows when you’re actively engaging with a website versus when that website is in the background. GA4 uses this information to give us session engagement time – the time a user actively had your website in the foreground.

Summary

The shift from bounce rate to engagement rate is a big one, but a good one. Lean into the change and begin focusing on users who are driving your bottom line with engagement related metrics. GA4 is continuously rolling out new features. Learn more about GA4 reporting metrics with the blog articles below:

When to Use Funnels vs. When to Use Path Exploration in GA4

Google Analytics 4 (GA4) is replacing Universal Analytics (GA3). To prepare everyone for the change, I’ve created a series of blog posts to help users understand the difference between the two versions. Today’s post is a brief comparison of the Funnel report and the Path Exploration report in GA4. Both reports provide you with insight into a customer’s journey throughout your website; however, there are times when it’s better to use one report over the other.

9 Reasons to Use the Path Explorations Report 

  1. I want to learn more about the customer journey in general, regardless of whether someone completes the action I want them to complete. 
  2. I want to see when someone starts and ends a session throughout their journey on my website. 
  3. I have a specific ending point I want for a user, but not necessarily a specific starting point. 
  4. I want to learn about the wide variety of paths someone took to complete a desired action. 
  5. I have a specific starting point I want for a user, but not necessarily a specific ending point. 
  6. When looking at how a user progresses through the website, I want to easily switch between visualizations of different breakdowns. 
  7. I want to see the count of events, not just active users. If one user completes an event twenty times, that’s okay with me. 
  8. I want to compare how the total users progress through the website and compare it to active users. I realize this may include some bot traffic.
  9. I want to see where someone goes on my website whenever they’re not completing the action I want them to.

9 Reasons to Use the Funnel Report 

  1. I have a specific list of actions I want a user to complete in a specific order. 
  2. I want to quantify drop offs in a multi-step process by pulling metrics such as abandonment rate and completion rate. 
  3. The analytics terminology sometimes loses me – I want to change the name of “Step 3” to “Fill Out Name” in my report. 
  4. I want to understand how drop off in a multi-step process differs between session campaigns. 
  5. I want to understand how drop off in a multi-step process differs between session sources. 
  6. I want to understand how drop off in a multi-step process differs between first acquisition campaigns. 
  7. I want to see the time elapsed between each step in a multi-step process, even though it may extend beyond one session. 
  8. I want to timebox my desired actions – I only care about people that went from Step 1 to Step 2 in a certain about of time. 
  9. I want to see an exact date that drop offs in a multi-step process sharply increased or decreased.

Summary

The Path Exploration report and the Funnel report have a lot of overlapping features. Still, it’s important to know why you default to using one report instead of the other. GA4 is continuously rolling out new features. Check back for updates or dive into other comparisons of Universal Analytics and GA4.