The Analyst's Guide to Google Analytics 4

Google released Google Analytics 4 (GA4) in October 2020 in an effort to improve upon the previous generation called Universal Analytics (UA).

The update to GA4 provides some new analysis features like the ability to find advertising work tools and the ability to group users into cohorts for monitoring. I'll discuss some of these new features in greater detail later in the post.

If your organization is switching to GA4, you might wonder how this will impact your data. In addition, you might think of how this will change your reporting and how you can prepare for this switch.

Below, I'll answer these questions. But first, we need to answer the question: what is GA4?

What is Google Analytics 4?

gaSimply put, GA4 is Google's platform for analyzing the traffic to your website.

You can use GA4 on both your website and app, unlike UA, which you can use on your website only. This feature means you can analyze how both your website and app are performing through one platform. 

Some of the metrics you can measure are:

  •  customer churn, 
  • customer segmentation, 
  • spend analysis, 
  • conversion rates, 
  • user events, 
  • and device category.

What makes GA4 exciting is that it incorporates machine learning (ML) and artificial intelligence (AI)

ML and AI will help you gain better insights into your customers' behavior, which will help you make strategic decisions to improve customer stickiness on your website and app. Some of these decisions include how to improve customer conversion rate, marketing content, etc.

In addition to the incorporation of ML and AI, the model of GA4 is event-driven. 

Being event-driven allows GA4 to view customer behavior as a series of events and provides analytics based on these events.

The event-driven model of Google Analytics 4

The event-driven model is not a new feature in the software engineering and computing space. For GA4, it works like so:

  • Actions of a user, such as a mouse click and keypress, get tagged as events.
  • The events are separated and attached to parameters (e.g., page view or purchase).
  • Determine customer workflow based on these parameters.
  • Perform analytics based on the workflow.

Now that we understand what GA4 is, we will look at some of its features.

Google Analytics 4 features

Most of GA4's features improve upon UA's features, but some are entirely new features.

Let's take a look at a few of the things you can do with GA4.

Advertising workspace

Advertising workspace is a feature advertisers will find very useful. 

It's a dashboard that lets you see an advertising snapshot for your website and app. It also allows you to view resources such as model comparison and conversion paths featured under the "Attribution" setting.

Model comparison

Model comparison lets you add filters based on 5 conditions. These conditions include visitors' age & gender distribution and the audience name (user or purchaser).

After adding the conditions, you can customize the reports to determine your preferred view. 

There are 2 types of views: the interaction time and conversion time of the website visitors. 

  • The interaction time is the event that occurs during your selected time frame. 
  • The conversion time is the event that leads to customer conversion for the selected time frame.

Conversion paths

The conversion paths feature lets you understand the visitor's journey, which leads to conversion. 

It shows you the total purchase revenue made from either the website or the app and shows the number of days users interacted with the ad before the conversion. 

It also shows the number of ad interactions it took for users to convert.

Cohort exploration

The cohort exploration feature lets you group website users into cohorts by selecting users that satisfy specific criteria and monitoring how their behavior changes over time.

When users get grouped into cohorts, their behavior can be analyzed using three types of cohort calculations. The three types of cohort calculations are standard, rolling, and cumulative.

  • Standard calculations measure behavior based on a specific period without regard for other periods.
  • Rolling calculations measure behavior from a selected period and other periods.
  • Cumulative calculations measure behavior that meets the return criteria in any period.

Predictive metrics

As the name implies, this feature lets you predict measures such as:

  • potential buyers,
  • likely customer churn,
  • potential spenders,
  • and possible revenue.

To use this feature, you need to create your audience and add the metrics for comparison. The metrics for comparison that can be added are similar to the conditions available in the model comparison feature.

With predictive metrics, you can view the analytics for your audience by platform, device category, and app version. GA4 has more features, but I'll only discuss these 3 in this post.

Next, let's see how the switch to GA4 might impact your data.

How switching to GA4 might impact your data

There are significant ways in which switching to GA4 will likely impact your data. In this section, I'll discuss some of these ways and focus on the use of AI and ML in GA4.

To begin with, ML will help you gain insights into likely customer behavior. You can use these insights to improve customer experience across your platforms and consider the devices of users.

In addition, data analysts can receive alerts on important trends that can provide insights that will help you make better decisions about your platforms. The decision-making can be about products, service offerings, and general website or app content.

Furthermore, with predictive measures and customer behavior analytics, data analysts can predict likely revenue for your organization within a specific period.

Considering these predictions, the advertising team can focus on directing their ads toward the cohort of customers likely to help generate this revenue. Managers can decide on incentives to give this customer cohort, while the marketing and sales team can focus on converting their prospects and leads into buyers.

With all this in mind, it is safe to say that GA4 will help improve overall workforce productivity.

This productivity will happen as different units can focus on the work that applies to them. They can also work to improve customer experience on the various platforms that belong to the organization.

Now that we have seen how GA4 will likely impact your data, it's time to look at how your reporting might change.

How are reporting insights from GA4 different from UA?

All reports in GA4 can get viewed from the "Reports Snapshot" menu.

Unlike UA, which has more than 4 report segments, GA4 only has 3. These segments are real-time, life cycle, and user and they each have sub-segments.

  • Real-time: This segment helps you view real-time insights on your platform. You can see insights like:
    • users in the last 30 minutes,
    • users per minute,
    • device category,
    • users by source,
    • views by page title and screen name,
    • and event count by event name, among others.
  • Life cycle: Under this segment, you will find "engagement," "acquisition," "monetization," and "retention" as sub-segments. Except for "retention," each sub-segment has additional sub-segments. These sub-segments are like the reports in UA. For instance, "user acquisition" and "traffic acquisition" are found in GA4 and UA. Under the "engagement segment," you will find "conversions," which measure customer behavior by events like login, purchase, search, etc. In UA, events are under the behavior segment.
  • User: This segment is where you will find the report on user demographics and tech. Reports under tech include "operating system" and "device category," which are both available on UA.

As you can see, UA and GA4 have some differences and similarities concerning the names, functions, and position of menu items on the dashboards.

Now that we have covered how your insights reporting will change: let's discuss what you need to consider when switching from UA to GA4.

What to consider when switching to Google Analytics 4?

Before switching to GA4, you might want to consider the following:

  • Do we need GA4 if we don't have an app platform?
  • What are the e-commerce goals of my organization?
  • What are the advertising needs of my organization?
  • Who are the people that need to know about the conversion rates, and how can I present the reports to them?
  • What is the learning curve for the new GA4 platform?
  • What predictions are useful for my organization, and how can they be converted into predictive measures?

Answering these questions will help you determine if you are ready to make the switch to GA4 or if you should continue using the UA platform for a while.

GA4 comes with a learning curve but Panoply can help

Based on all I have discussed above, here are the main points you can take away about GA4:

  • To begin with, GA4 runs on the event-driven model.
  • Next, GA4 has fewer features on its report menu, but more metrics are available under these menus.
  • Furthermore, some GA4 functions are just like UA; you'll need to poke around first to understand what each menu and submenu or subsegment does.
  • Last, GA4 allows you to explore predictive analytics for your website and app using ML and AI.

It might take a while for your team to get used to switching over to GA4 either as data analysts or users of insights and reports.

The users of your insights may struggle with your new reporting mode, and that's where Panoply comes in handy.

Panoply provides a cloud repository for you to store your data, and it allows data analysts to access datasets that are readily available for analysis with limited coding. It also provides the stability that both you, as a data analyst, and users of data need within your organization without impacting your reporting and insights.

You can request a personalized demo of Panoply to get started or try the 14-day free trial.

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