Know Further About GA4 Analytics

How user behavior is tracked and reported is one such change that has had a significant impact on owners, managers, and marketers of websites and applications.

The “canned” reports from UA have all but disappeared. They have been replaced by a robust and completely customizable editor for producing practically endless user data reports.

But as Spider-Man sort of said, great power also comes with great learning.

As a result, we’re going to cover everything in GA4 Explorations in this installment of our guides to using GA4 to its full potential.

Take a guided tour of all the essential information you need to know, dive in to find out what the heck that means, and you’ll emerge from it with your very first exploration.

How Do GA4 Explorations Work?

If you’ve recently had a chance to play around in Google Analytics, you probably already know that GA4 still offers what it refers to as “Standard reports,” which let website owners report on users, acquisition, engagement, monetization, and more.

But those aren’t the topics of our discussion today.

In this manual, we’ll discuss GA4 Explorations, a brand-new and distinctive topic.

Explorations are robust charts that can be easily created from scratch and customized to view detailed visitor data (web and app) and unearth useful insights about consumer behavior.

Just to be clear, Google Analytics didn’t upend everything for no reason; reporting (along with many other capabilities) had to change as a result of Google Analytics’ new data model, which prioritizes events over UA’s reliance on factors like sessions and pageviews.

Why You Need to Learn GA4 Analytics

Flexibility is the main advantage of Explorations in GA4.

While it is possible to customize the pre-written reports offered by UA, personalized explorations are on an entirely different level.

Exploration fields make it simple to compare custom audience segments, cross-reference any data, analyze behavior over a full user lifetime, uncover and study undiscovered user journeys, and almost anything else you can think of. They also come with several helpful graph templates to get you started.

In addition to being exceptional at dissecting data, GA4 Explorations is also very helpful at visualizing and making all that data interactive so that you can actually comprehend what it is showing you.

The speed of GA4 Explorations is yet another awesome aspect. With just a click, you can create robust reports on-the-fly, add and remove filters, share, or export exploration reports, and instantly see your charts update.

In addition to all of these advantages, if you’re using Google Analytics, explorations are the new way to delve into the data. Therefore, mastery is no longer optional if you want to use the platform to continue understanding and reporting on the users of your web and application.

Let’s learn more about GA4 Explorations’ features and workings with that in mind.

The Essentials of GA4 Investigations

Unsurprisingly, GA4 Explorations contains a lot of moving parts. We’ll go over the key elements you need to understand to use the feature in this section.

To make this feature walkthrough as helpful as possible, we strongly advise opening your GA4 panel and following along.

Investigations Home Page

Select the account, property, or app for which you want to create an exploration once you are signed into the most recent instance of Google Analytics. Use the dropdown menu next to the Analytics logo in the top left corner of the screen (on desktop) to do this.

You can enter by choosing Explore from the sidebar menu.

Here, you can decide whether to begin with a “technique” for your investigation or a blank template. (To view templates made for particular use cases, like acquisition, and industries, like e-commerce and gaming, click “template gallery”).

There are different kinds of visualizations. At the time of writing, GA4 offers the following methods:

Free-Form Exploration

Free-form is somewhat less intimidating than beginning an exploration from scratch. Start by laying out your data in a crosstab layout, and then add additional visualization components of your choice, such as geo maps, scatter plots, line charts, bar charts, and more.

Cohort Exploration

With a cohort exploration, look at user groups that have similar characteristics to learn more about their patterns of behavior.

Funnel Exploration

The process of visualizing the steps users take to complete particular conversions is known as funnel exploration. Knowing this helps you anticipate and concentrate on the crucial actions that result in conversions that generate income.

Segment Overlap

Segment overlap is self-explanatory; this investigation shows the similarities between different segments (up to three at this time). Utilize this to identify fresh, very specific audiences.

User Explorer

With user explorer, you can examine both user groups and individual users, such as low- or high-spenders, to learn what motivates them and how you can replicate their behavior.

Path Exploration

Create a thorough tree graph using path exploration to show how users navigate your website or app.

User Lifetime

The user lifetime exploration can reveal unique user behavior and value over the course of their relationship with you as a customer. This exploration offers cues about the kinds of sources, campaigns, demographics, and behaviors that may lead to high lifetime value, much like user explorer does for users.

Once you’ve decided on a template or technique, you’ll enter the exploration editor, or “canvas,” as Google refers to it.

Canvas

You’ll see a larger area in the canvas on the right side of the screen that will display your data using the technique you’ve selected. Several different techniques may be employed during a single exploration. To create a new one, simply open a new tab near the top of your canvas (ten tabs are currently the maximum allowed).

Your configuration will be completed in the two columns labeled Variables and Tab Settings on the left side of the screen.

Variables

Your variables are located in the first column from the left. These three primary variable sections are currently present in every exploration:

  • Segments

Subsets of data known as segments include users from a particular country, users who have recently been active, users who haven’t made a purchase, users who have made a purchase, and others. There are numerous ways to slice the data in this case. While Google Analytics offers segment suggestions, you can also come up with your own or use their predictive functionality to build audiences based on the behavior it has seen on your website or mobile app.

  • Dimensions

Events, products, users, and other types of data points all have attributes that are called dimensions. For instance, a product’s dimensions could include its name, category, cost, SKU, and so forth. Over 100 predefined dimensions can be customized in Explorations.

  • Metrics

Measuring variables are called metrics. Metrics include things like the quantity of users who are actively using the service, the quantity of new customers making purchases, the quantity of checkouts, etc. Google offers more than 100 options, and you can also make your own.

Use the plus button for each of these variables to reveal a menu that allows you to view, save, and import both predetermined and custom variables. Each exploration can have up to 20 dimensions and 20 metrics applied.

To create custom dimensions and metrics, click the Admin gear icon in the lower left corner of your screen, ensure that you are in the account and property of your choice, and then choose “Custom definitions.”

You can also name your investigation and specify a time frame in this column.

You must record any segments, metrics, or dimensions you want to see in your tab-based exploration here in Variables. The input section for all the analytics data your investigation will use is called variables.

Tab Settings

Let’s now turn our focus to the Tab Settings column in the following column.

While variables specify the subsets, metrics, and dimensions of data you want to examine, Tab Settings determines how the data is presented in accordance with the technique. By using rows, columns, filters, comparisons, and various other configurations, Tab Settings gives data shape and context. In other words, you can modify how your exploration appears by using the Tab Settings.

The Tab Settings that let you personalize your exploration differ greatly depending on the technique.

For instance, you can use your chosen dimensions to set up the rows and columns in your exploration chart when creating a free-form report. Instead, when using the path technique, you configure nodes (which are data points along the path). Additionally, the settings for the funnel technique allow you to select the subsequent action from your dimensions.

Drag and drop variables into the dotted box to apply them to your tab settings, or click the box to bring up a dropdown menu of choices.

Every technique’s Tab Settings always have a dropdown menu at the top where you can select your technique.

We’ll provide these quick links to Google’s guides to each of the GA4 techniques currently available to provide the details on how precisely to configure each Tab Setting:

How to Create Your GA4 Analytics

In order to further explore, gain insights from, and target with your marketing efforts hyper-specific segments within your existing audience can be found using the segment overlap technique, which is pretty unique.

In order to create and share a GA4 exploration based on the segment overlap technique, let’s go through the entire process:

  • At this point, you are probably familiar with the procedure: sign into Google Analytics, go to the GA4 property or app where you want to create the exploration, select the Explore tab from the left sidebar, and then click the Explorations landing page.
  • To open the canvas, select the segment overlap template.
  • Specify the date range you want to use for this exploration and give it a name in the Variables column.
  • Using the plus buttons in Variables, add the segments, dimensions, and metrics that characterize the data by which you want to partition your investigation.
  • You can select up to three segments to compare in the Tab Settings column under Segment Comparisons.
  • Add dimensions to Breakdowns, which are attributes that give your segments additional context, such as country, device type, etc. Your rows will be made up of these.
  • Add dimensions to Breakdowns, which are attributes that give your segments additional context, such as country, device type, etc. Your rows will be made up of these.
  • Decide how many rows to display and the first row in your table.
  • Select your metrics, which are qualitative measurements like the number of ad clicks, sessions, etc., in the Values section. Your columns will be guided by these.
  • Add dimensions or metrics to the Filters section to further refine your results. You can use this field to be extremely specific about the types of devices, regions, and other factors through which you can view your exploration.
  • A table and chart should start to take shape in your canvas. The final graph you see is interactive! To discover more about specific niches within the larger audience you serve, mouse over overlapping and various segments. The data that served as the foundation for your new diagram can be seen in the table below.

This example from Google shows what it might look like if you created a segment overlap exploration to see where new users, mobile traffic, and converters user segments converge:

  • You can now share and/or export your exploration, if you’d like. There is what appears to be a download button in the top right corner of your canvas. You will then have a variety of options for exporting your data.

A humanoid icon that allows sharing will also be visible. This button allows you to share the investigation with every other user who has already signed up for this Google Analytics domain. Shared explorations can only be read. People who receive your share must access their primary Explorations page (step 1 above), locate the exploration you shared (it’s just below the templates), and duplicate it using the dotted menu. They can use and modify the new version they create as they see fit.

The explorations in GA4 go much deeper than the Universal Analytics reports that many of us have grown accustomed to. It will obviously take some time and effort to set up and get used to, even though it means great things for your depth of understanding of your audience and your digital properties.

If you don’t currently have that much time or energy available, think about enlisting the aid of HostForLIFE’s marketing experts.

George W
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