Google switched to Google Analytics 4 (GA4) to provide users with a more comprehensive and flexible platform for web analytics. GA4 offers a range of new features and improvements over the previous versions of Google Analytics, including enhanced machine learning capabilities, improved privacy, and support for cross-device tracking.
The switch to GA4 reflects Google's broader focus on privacy, as GA4 includes new features such as improved data retention policies and support for the Privacy Sandbox initiative. This allows users to collect and analyze data more securely and privacy-consciously while still providing insights into their website traffic and user behavior.
There are some concerns, however, on how privacy-friendly GA4 really is. Multiple EU member states (Italy, France, Austria, Denmark) essentially banned the use of Google Analytics entirely as it violated privacy laws. (Update: litigation over Google Analytics is currently on halt but might start again, as the issues over data transfers and GDPR are not entirely solved yet).
All this aside, let's dig a bit deeper into how GA4 works and answer the most relevant question, hereby focusing on a few different metrics!
- What are the biggest changes from Universal Analytics?
- How are Google Analytics 4 metrics different?
- Bounce rate in GA4
- What is engagement time in GA4?
- What is an audience trigger in Google Analytics 4?
- What is a Predictive Audience in Google Analytics 4?
- What is an attribution model, and what attribution model does Google Analytics 4 use?
- Final Thoughts
Let’s dive in!
What are the biggest changes from Universal Analytics?
There are several key differences between Google Analytics 4 (GA4) and Universal Analytics. Some of the most significant differences include the following:
- GA4 uses User-ID tracking. This feature allows you to track users across multiple devices and identify them as individual customers.
- Enhanced data privacy and security measures: GA4 takes a more stringent approach to data privacy and security, with improved measures in place for user authentication and data encryption.
- Greater control over data analysis and reporting: You can use Google Analytics 4 to gain deeper insights into customer behaviors and create targeted campaigns based on these insights. Additionally, you can use its machine-learning capabilities to uncover previously hidden trends in your customers’ behavior.
- New data collection methods: GA4 utilizes a variety of new data collection methods, including event-based tracking, advertising ID integration, and more. This allows for a more precise measurement of user behaviors.
- A more straightforward implementation of custom tags: You can easily customize the tracking code for a website or app events with GA4’s simplified tag library.
Some marketers are unhappy about the switch to Google Analytics 4 (GA4) because it represents a significant change from the previous versions of Google Analytics. This change can be challenging for users who are already familiar with Universal Analytics, as they will need to learn how to use the new features and interface of GA4.
In addition, some marketers are concerned that GA4’s new privacy features may impact their insights. The new data retention controls may limit the amount of data available for analysis, which in turn could affect the accuracy and usefulness of the insights provided by GA4.
How are Google Analytics 4 metrics different?
Google Analytics 4 uses a different data model and tracking code than Universal Analytics, which results in different metrics from Universal Analytics.
Some of the metrics that are different in GA4 include:
- Unique Events: GA4 has no metrics for tracking unique events. Instead, you should use Event Count to track the total number of events that occur.
- Goal Completions: GA4 does not have any goal completion metrics; instead, it uses Conversion Events to track conversions.
- Social Interactions: This metric is unavailable in GA4 since social interactions are tracked through event tracking.
In addition, Google Analytics 4 introduces new metrics, such as “User Engagement” and “Lifetime Value,” previously unavailable in Universal Analytics.
Understanding the metrics of Google Analytics 4 is key to properly analyzing your data and getting the most out of your website analytics. We’ll touch upon these in more depth.
Bounce rate in GA4
As I mentioned earlier, the metrics in GA4 differ from those in Universal Analytics because GA4 uses a new data model designed to provide a complete picture of how users interact with your website or app. These differences also include bounce rate, a key metric in web analytics
- In Universal Analytics, a bounce was counted when a user visitsed a single page on a website and then left without triggering any other events or interactions. This means that if a user visits a page on your website, spends some time on it, and then leaves, it will not be counted as a bounce.
- In GA4, bounce rate is a more complex metric that accounts for how many sessions are not "engaged sessions". A session is considered engaged when the user stays for 10 or less seconds, views no other screen, and does not trigger a conversion event.
Long story short, the new bounce rate of GA4 does not count 10+ seconds sessions as a bounce. This substantially deflates bounce rate compared to Universal Analytics.
What is engagement time in GA4?
In Universal Analytics, engagement time and time on page refer to the same thing: the amount of time a user spends on a page on your website. This metric is calculated by subtracting the time the user entered the page from the time that the user left the page.
In GA4, engagement time and time on page are two separate metrics. Engagement time is a new metric that measures the total amount of time a user spends actively interacting with your website or app. This can include time spent on page, but it can also include time spent watching videos, filling out forms, or engaging with other content on your website.
On the other hand, time on page is a legacy metric similar to how engagement time is calculated in Universal Analytics. It measures the amount of time a user spends on a page on your website, but it does not include time spent on other pages or interacting with other content on your website.
What is an audience trigger in Google Analytics 4?
In GA4, an audience trigger is a condition or set of conditions that can be used to define an audience. Users will be added to the audience when they satisfy the conditions of an audience trigger.
Audicence triggers can be based on various factors,such as the user's location, the device they are using, and the pages they have visited on your website. In addition, they can be used to create custom segments of users, such as people who have purchased from you in the past.
Using audience triggers is a powerful way to gain insights into how your website is being used and what types of visitors are most likely to convert. This can help you create more effective marketing campaigns and tailor content to different customer segments.
Audience triggers also allow you to segment users by various conditions, tracking user engagement over time or comparing the performance of different customer segments. With these insights, businesses can better understand their customer base and make more informed decisions about their digital marketing strategy.
What is a Predictive Audience in Google Analytics 4?
A Predictive Audience in Google Analytics 4 is a group of users predicted to be more likely to take specific action on your website, such as making a purchase or subscribing to a newsletter. This likelihood is calculated through machine learning algorithms that analyze user behavior and demographics to identify patterns. You can use Predictive Audiences to guide personalized marketing efforts and improve the effectiveness of your online campaigns.
What is an attribution model, and what attribution model does Google Analytics 4 use?
Attribution models are used to track how customers interact with your business across multiple channels, such as organic search, paid search, social media, or email. They measure the effectiveness of each channel and assign a value to each touchpoint in the customer journey toward conversion.
Google Analytics 4 uses an advanced attribution model called data-driven attribution (DDA). This is an algorithm that analyzes customer journeys and assigns values to each touchpoint based on its impact on the conversion. DDA uses machine learning to analyze data from your website, such as pageviews, time spent on a page, and more. It then uses this data to assign a “credit” to each touchpoint in the journey leading up to a conversion. This design helps you understand which channels are most effective at driving conversions and how much value they contribute.
Final Thoughts
The deprecation of Google Analytics 4 brings challenges for marketers that have been using Universal Analytics for quite a long time.
The decision to sunset Universal Analytics is a response to an ever-growing need for privacy- but Google Analytics isn't really doing any better privacy-wise.
Given that Google Analytics 4 is hard to master and privacy-invasive, marketers and business owners might question whether they still want to continue using Google Analytics at all.
In an ever-growing competitive landscape, more and more web analytics tools are presenting themselves as an alternative to Google Analytics. The privacy flaws in Google Analytics are also why we built Simple Analytics. A privacy-friendly Google Analytics alternative that is simple to use (hey, what's in the name, right?) and comes with a handy AI assistant to find exactly the data you need!
Feel free to check our live dashboard to see whether you like it. If this resonates with you, feel free to give us a try!