A Step-By-Step Guide to Create an Exploration Report in Google Analytics 4

Google Analytics 4 offers users a powerful suit of insight tools in the form of the Analysis Hub. I have previously covered how to create a funnel report in Google Analytics 4, and this should be one of your go to reports for optimising your site or app. In this post, I will take you through how to create an exploration report in Google Analytics 4.

The exploration report in Google Analytics 4 displays your data in a dynamic table format and provides advanced functionality which is not available in Universal Analytics (UA). This includes the ability to apply multiple custom segments and filters to uncover new insights to help you optimise your digital experience. The exploration report replaces custom reports in the UA version of Google Analytics.

The Analysis Hub in Google Analytics 4 makes it worthwhile upgrading to GA4 and complements what you already get from Universal Analytics. If you want to know more about how GA4 compares to Universal Analytics checkout my post on how to upgrade to Google Analytics 4 with GTM.

1. Create Events for GA4:

Before you can create an exploration report in Google Analytics 4 you will need to configure key events in GA4 and use GTM to set up custom events. You can read my post on how to track events in Google Analytics 4 with Google Tag Manager.

If you want to implement enhanced ecommerce in GA4, then check out Simo’s blog post here.

2. The Analysis Hub:

Go to your GA4 property and go to Analysis > Analysis Hub and click into the Exploration report.

3. Exploration Report in Google Analytics 4:

This will open up the Exploration interface which containers three tabs, Variables, Settings and the output tab. The first two allow you to tailor and configure the exploration report to your specific needs. However, this requires some planning and preparation to ensure you have access to the correct segments, dimensions and metrics for your report.

4. Variables:

The variables tab is where you configure the data you want to use in your exploration report in Google Analytics 4. This requires some planning as you can only access dimensions and metrics that are being sent to your GA4 property. This tab covers:

• Report name
• Date range
• Segments
• Dimensions
• Metrics

4.1 Report Name:

Give your report name a descriptive title and bear in mind that in the Analysis Hub listing it won’t display the type of report (e.g., funnel or exploration) it is.

4.2 Date Range:

Set the period you want the report to cover, but consider when you first created your GA4 events, as this may limit the date range you can use. You can use a pre-set date range (e.g., Last week) or set a custom date range and compare data to the previous period.

4.3 Segments:

The segments allow you to compare your exploration report results against different cohorts of interest. The default segments may not be relevant and are often incomplete. Delete the segments that you won’t need and create new segments that you are interested in. There are four types:

1. User segment: Visitors who meet a set criterion. For example, visitors who click on a paid advert in Google (Paid traffic) or are from an individual country.
2. Session segment: When sessions meet defined criteria. For example, all sessions where users were acquired from a set campaign or are in a set age group.
3. Event segment: These segments are defined by specific events, such as newsletter sign up, add to cart or purchase.
4. Suggested segments: These include three types of segments:

• General segments such as recently active users and non-purchasers.
• Templates cover such segments demographics (e.g., age or gender), acquisition and technology.
• Predictive segments allow you to build predictive audiences based on purchasing and churning events. To be eligible to use predictive segments your site or app will need to meet these criteria:

Over a seven-day period at least 1,000 returning users who meet the predictive criteria (i.e., purchase or churn) over the previous 28 days.

Over a seven-day period at least 1,000 users who did not purchase or churn over the previous 28 days.

This must be sustained over a seven-day period, and the eligible data must be sent to the property as the purchase or app_purchase events.

Predictive metrics are then generated for each active user once per day. This data can then be used to build an audience for remarketing to these users via Google Ads.

Once you have decided which segments are important to your digital experience, go about creating these by using the appropriate type. For example, I wanted to compare mobile traffic to desktop users. Here I created the segment for desktop traffic by searching for Device category and selecting contains or equal to ‘desktop’.

4.4 Dimensions:

You will also see a list of default dimensions. Just like with segments, you can create new dimensions by clicking on the ‘+’ button and delete dimensions that are not relevant. Dimensions can be used to create filters to exclude users who can never convert and to breakdown your exploration table for more detailed analysis.

You can have up to five dimensions in rows and two in columns for your GA4 exploration report.

4.5 Metrics:

Metrics define the nature of the analysis by transforming your report into actionable data. You may want to add metrics such as transactions, conversions, and user engagement to go along side more generic metrics like active users.

5. Tab settings:

This column allows you to create your exploration report and configure what it looks like. We are concentrating on the exploration report, but you can change this in the Technique drop-down menu where you can select other reports such as Cohort analysis, path analysis and Funnel Analysis.

5.1 Visualization:

Below the Technique drop-down menu, is the visualization selector where you can choose between:

• Table
• Donut chart
• Line chart
• Scatterplot
• Bar chart
• Geo map

Depending upon which type of visualization you choose, you will have different choices in terms of customisation of the report. I will concentrate on the table here as this gets most use and is where you may begin to identify new insights.

5.2 Line Chart:

However, the line chart is also worth briefly mentioning because this is useful to see if metrics are changing over time as a result of your optimisation activity.

The line chart allows you to set different levels of granularity to reflect the volume of data and how frequently you want to measure your key metrics.

The line graph also has an anomaly detector enabled by default. This allows GA to automatically indicate when something is causing a problem and so can save you valuable time during the analysis process. The training period determines how much data is collected to calculate the expected value. This means the longer the training period the more likely any anomaly will be real and so set the higher value here.

Sensitivity determines the sizes of the anomaly required to trigger the indicator. The higher the setting, the narrower the area of the expected value will be able to signify an anomaly.

5.3 Table Visualisation:

Going back to the table visualization, the next setting to consider is the segment Comparison. You can include up to 4 segments in your data table to compare your performance. Double click each segment or click and drag into the Segment Comparison widget.

The exploration report example here has the following configuration:

• Two segments – purchasers and non-purchasers
• Browser dimension in the row section
• Device category in the column section
• Active user as the chosen metric

5.4 Pivot Table Options:

The exploration reports gives you four pivot table options to choose from. The default option is ‘First column’, which means the segments is shown in the first column. This is what is looks like.

The next option is ‘First row’. This means the segment appears first in every row and looks like this:

The third option is ‘Last row’. This means the segment is displayed last in every row and it looks like this.

Finally, ‘Last column. This means the segment shows after all column dimensions.

5.5 Rows:

This is where you select the dimension for the rows in your report. Here I have selected browser for my table, and so each row displays a different browser in the table.

You can have multiple dimensions in rows in your report. Here I have added browser version to go alongside browser. You can also decide from which row to start and set a maximum number of rows to display.

However, if you want to have more than a single row you may want to toggle to nested so that the second row is grouped according to the first row. Here the second row is the browser version and so all Safari browser versions are shown first before other browsers are displayed.

5.6 Columns:

This allows you to add dimensions as columns. Click and drag dimensions into the row section. Here I have added Device category as the column. Again, you can decide from which column to begin displaying data and the maximum number of columns per single dimension.

5.7 Values:

Simply click and drag the metrics that you want to display in your table into the Values section. You can add up to 10 metrics in a single exploration report. The cell type allows you to choose how to display the metric cell based on its value and the ratio to other rows in the same column. The options here are:

Bar chart – this will display horizontal bar charts in every metric cell.

Plan text – no visual enhancement is shown if this is selected.

Heat map – the colour of cells are darker if their value is higher compared to other rows of the same dimension column.

5.8 Filters:

This allows you to exclude certain users or events to reduce noise from the analysis. For example, here I use City to exclude users from outside London as the website only delivers within the boundaries of Greater London. You might want to exclude users who are logged in or visitors from certain countries, depending upon what kind of analysis you wish to undertake.

6. The Exploration Report:

Now that you have configured your exploration report in Google Analytics 4, you can now duplicate it to change the type of technique (e.g. make it a line graph) or add a new tab to begin creating a totally new exploration report. You can also download the report into various formats, including Google Sheets and a PDF.

By default, all your exploration reports are only visible to you. This means that you will need to share the report with other users of the GA4 account if you want other users to be able to access the report. If other users wish to edit the report, including changing the date range, they will have to duplicate the report so that they become the report owner.

6.1 Right-Click:

If you right-click a cell in your report this give you the following options:

• Include section
• Exclude section
• Create segment from selection
• View users

Include only selection. This adds an include filter to the table based upon the cell you clicked and so will enable you to narrow down the report accordingly.

Exclude selection. Adds an exclude filter to the table based upon the cell you clicked into.

Create segment selection. This will automatically open the segment creation interface with some conditions prefilled based upon the cell you selected.

View users. This will open an explorer report with users who make up the same selection.

7. Summary:

The exploration report in Google Analytics 4 offers an extensive range of features to deep dive into your data. However, preparation is the key to success. Ensure you have created the events needed for your analysis using the GA4 interface and GTM.

Similarly, consider what segments you want to compare against in your report. If you have the required volume of purchasers you could create predictive segments and use these for remarketing. Next, set up the dimensions you want to breakdown your exploration report by as you will need these to be in the variables tab to add them to your report.

Ensure you also have your metrics defined as you can add up to ten to your exploration report. You can then decide which segments, dimensions and metrics to create your report. Remember to set appropriate filters to reduce noise and narrow your audience as required.

Google Analytics 4 not only offers advanced web analytics, but its predictive audiences can be used to identify which users are most likely to convert or churn. This allows marketers to create more effective campaigns in Google Ads for both remarketing and re-engagement.

GA4 could also allow marketers to make higher bids for keywords if they are triggered by a predictive audience that has a higher propensity to convert. If you haven’t already upgraded to Google Analytics 4 this may be a reason not to delay anymore. Predictive audiences deliver a valuable tool for conversion rate optimisation to help grow the business and improve conversions.

1. What are predictive audiences?

GA4 automatically enriches data using machine-learning to predict the future behaviour of your visitors. GA4 currently has three predictive metrics for building predictive audiences.

Purchase probability: The propensity that a user who has been active in the last 28 days will convert in the next 7 days. This is only for purchase and in_app_purchase.

Churn probability: The propensity that a user who has been active within the last 7 days will not be active in the next 7 days.

Revenue prediction: The estimated revenue from all purchase conversions in the next 28 days from a user who has been active in the past 28 days.

2. Prerequisites:

Given predictive metrics rely on machine-learning, GA4 requires a certain number of conversions to activate these metrics. To successfully train the predictive models, over a 7-day period GA4 needs a minimum of 1,000 returning users who had previously converted (purchases or churned users) in the past 28 days, and also 1,000 non-converters over the same period.

These volumes of conversions must be over a period of time for a GA4 property to be eligible for predictive metrics. If your property meets the criteria, the predictive metrics will be generated for each active user once per day.

3. Create predictive audiences:

To build predictive audiences go to your GA4 property and navigate to ‘Audiences’ and click on ‘New audience’.

This takes you to the ‘Build a new audience’ interface where you will have the option to create an audience from scratch or use ‘Suggested audiences’. Select the ‘PREDICTIVE’ tab on the Suggested audiences section.

Depending upon the volumes of data from your GA4 property, you will get an eligible or not eligible to use message below each audience.

There are currently 5 suggestions for predictive audiences:

• Likely 7-day purchases. Users with a high propensity to purchase in the next 7 days.
• Predicted 28-day top spenders. Users who are generating the most revenue in the next 28 days.
• Likely 7-day churning users. Active users who are also unlikely to visit your site or app in the next 7-days.

You cannot modify the predictive condition for each audience, but you can add new non-predictive conditions. Such as Device category equals mobile.

4. How to use predictive audiences?

When you create predictive audiences, make sure you use a Google account that has permissions for your Google Ads account. Predictive audiences will then be automatically shared with all Google Ads accounts linked to your GA4 property. Google suggests two ways of using predictive audiences in remarketing campaigns and re-engagement campaigns.

4.1 Remarketing audiences:

Predictive audiences are ideal for remarketing campaigns because GA4 uses machine learning to identify deep patterns of behaviour that are unique to your site or app which indicate the user is likely to convert. The ‘Likely 7-day purchasers’ are the ideal audience for a remarketing campaign. A persuasive follow-up message delivered to these users could also be the trigger they need to complete a purchase.

4.2 Re-engagement campaigns:

Re-engagement campaigns can help maintain engagement with your business among users who are showing waning interest in your products or services. The ‘Likely 7-day churning users’ are a cohort of users who need to be re-engaged and would benefit from a strategic message or special offer to reverse a decline in engagement.

4.3 Strategic bid adjustments:

You could also use predictive audiences that are likely to purchase to trigger keyword bids. This should allow you to place higher bids on keywords because these users will have a higher propensity than normal to purchase.

4.4 New customer campaigns:

Predictive audiences can also grow your customer base. Try using the ‘Likely first-time 7-day purchasers’ audience as a means of attracting new customers across the Google Display Network, Gmail, YouTube and the Search Network.

Conclusion:

Predictive audiences offer marketers an opportunity to improve targeting and conversions in the digital space. Testing of predictive audiences require building up the data to prove their effectiveness. But as the machine learning processes more data it should become more accurate at predicting outcomes.

Featured image by Fancy Crave on Pixabay

The funnel analysis report in Google Analytics 4 is an awesome report which anyone involved in conversion rate optimisation will find invaluable. The report can help you instantly identify areas with the greatest potential for optimisation. It allows you to automate and compare completion rates for each step in a journey by key customer segments and breakdown the report by important dimensions, such as device category. The funnel analysis report is an exponential improvement on existing goal funnel reports provided in Google Analytics 3 (Universal Analytics).

Google Analytics 4 offers you a powerful new funnel visualisation tool that was previously only available in GA360. This allows you to create a funnel visualisation with any kind of event, including impressions, clicks and pageviews. In addition, you can build a funnel analysis report in Google Analytics 4 to include:

• A comparison of completion rates by up to four customer segments (e.g. mobile, desktop traffic and tablet users),
• Breakdown each step in your funnel by dimensions (e.g. source of traffic),
• Elapsed time between each step of the funnel,
• Identify the next event immediately after each step in the funnel,
• Set a filter for the funnel to exclude users who could never convert (e.g. use country or region).

This allows you to immediately identify any differences in drop off rates at each step in the funnel for these important dimensions and segments. It also allows you to use filters to exclude traffic which will never convert or to create separate funnels for different markets or geographic locations.

In this blog post, I will show you how to create and fully configure a funnel analysis report in Google Analytics 4. This will ensure you fully benefit from the functionality of the Google Analytics 4 funnel analysis report.

Watch my video of how to plan and create a funnel exploration report or read the instructions below:

1. Planning Events for your Funnel Analysis Report in Google Analytics 4:

Firstly, to create a funnel visualisation in Google Analytics 4 you will have first need to configure GA4 events for each step in the user journey. Check out my post how to track events in GA4 using Google Tag Manager. This explains how to create most of the events you will need, including click and element visibility tags.

GA4 automatically records all pageviews under a single event name. For this reason, you may need to configure events for individual pages before creating your funnel analysis report. This can be done by setting up events in the GA4 interface as explained here.

It is also worth consider which dimensions and segments you will want to analyse your funnel report by. This is one of the most powerful elements of the funnel analysis and will save you from having to undertake custom analysis.

2. Create a New Funnel:

Secondly, in GA4 go to ‘Analysis’ > ‘Analysis hub’ and select ‘Create a new funnel’.

3. Funnel Console:

You will be taken to the funnel console which has a Variables, Tab Settings and Exploration tab.

Variables tab: This is where you enter your funnel name, set the time-period, and configure segments and dimensions for use in your GA4 funnel.

Tab Settings: Is where you define the nature of the analysis, including the type of visualisation, segment comparisons and funnel breakdown. It is also where you configure the individual steps in the funnel.

Exploration tab. Here you will see the funnel visualisation and the corresponding data table of each step of the funnel you created. What you see here is determined by your settings in Tab Settings. But you can also create other analysis here using all the features of the Analysis hub.

4. Name Your Funnel:

Choose a short, but descriptive name for your funnel as this is what is displayed when someone goes to the Analysis hub.

5. Date Range:

Now set a suitable date range and if you have only recently created events in GA4 you may have to use the custom range. This allows you to set a suitable date range based upon when you first started collecting data on the funnel steps.

5. Default Funnel:

You will now be able to see a default funnel which is automatically set up when you first create a funnel in Google Analytics 4.

6. Type of Funnel Visualisation:

Google Analytics gives you two types of funnel visualisation to choose from. You can select a standard funnel which gives you a snapshot in time or a trended funnel.

The later allows you see how the funnel is changing over time which can be very useful when you make changes to the user journey. We will select the Standard funnel for this example, but you can switch between the two if you so wish.

 7. Delete Default Funnel Steps:

To create a new funnel Google Analytics 4, you first need to delete the default steps in the console. Remove each of the existing steps until there are no steps remaining.

8. Create First Step in GA4 Funnel:

Select the edit icon and this will open the first step in your funnel. Give the step a short, but descriptive name as this will appear in the funnel visualisation. Click on ‘Add new condition’ and begin typing the event name. Select the event name you wish to use.

You have the option to add a parameter if you need to set conditions based upon events or dimension that are configured in GA4. You can for example set the page path as ‘/’ OR ‘about-us’ to allow users to land on different pages before proceeding to the next step in the funnel. There is also an ‘AND’ condition if you want users to comply with two conditions.

On the right-hand side of the console there is a display which shows how many users comply with any conditions you set for your funnel. Click ‘Add step’ to create a second step.

9. Create Second Step in GA Funnel:

Here I want to create a step which records if the site can deliver to the customer once they have entered details of their location. Select the event name by clicking on ‘Add new condition’ and search for the name.

I now need to set a parameter value which indicates the customer’s location is valid. Click ‘ADD PARAMETER’ and search for the parameter’s name which is related to the event being used. In this case it’s ‘delivery_response’.

Now select the parameter condition, which in this case is ‘exactly matches (=)’.

Finally, select the parameter value, which in this case is ‘accept’. This was set by getting a developer to add a data layer push to the site to record if the user’s address was valid for delivery.

GA4 allows you to set conditions so that only users who proceed directly from the previous step are shown in your funnel. This often occurs for secure, logged pages which limit navigation options.

Select ‘is indirectly followed by’ and this will give you the option to choose ‘is directly followed by’ to set this condition in your funnel.

You can also set a time limit for the period between a previous step. This may be useful if your site has strict time-out conditions for users to complete certain tasks. This is common for ticket purchases on event sites. Check the box shown below.

This will allow you to choose the appropriate time-period from seconds, minutes, hours, or days. I would avoid using days because increasingly browsers are deleting cookies after 24 hours and so it is unlikely to be a reliable metric.

If you forget a step or get the order of events wrong, click on the three dots on the right-hand side of each step. This allows you to copy, remove or add a step (above or below).

10. Continue with Remaining Steps:

The GA4 analysis funnel allows you to have up to ten steps and so you may need to plan your funnel to ensure you don’t run out of steps. Let’s hope Google increases this as ten steps is quite limiting for many sites.

Once you have created your final step in the funnel, remember to ‘Apply’ as this will save your report. If you don’t ‘Apply’ you will lose the steps you have created.

11. Set Dimensions to Refine the Funnel:

Dimensions are useful because they allow you to set filters, next steps or to breakdown your funnel by important categories. The default dimensions in the funnel analysis report are limited and so you will probably need to remove some default dimensions and add more useful ones to the report.

Click on the plus sign and search for relevant dimensions. Here I want to use city because the site only delivers to certain locations in the UK. Select the dimension and click ‘Apply’.

Continue with this process until you have added all the dimensions you need for funnel breakdown, next steps, and filters.

12. Create a Filter:

Consider using a filter to remove visitors who could never convert. In this example, I will use ‘City’ because the site won’t deliver to users outside of a certain geographic location. You may want to use country or region depending upon the nature of the site. Click and drag the City dimension into the Filter box.

Select the match type, in this case ‘exactly matches’, and then search for the expression.

Select the expression you are looking for, I’ve chosen ‘London’ for the example here and ‘Apply’. The filter is now set, and it should avoid you including users who have no chance of converting.

13. Open or Closed Funnel:

This tab setting allows you to choose between a closed and open funnel. An open funnel shows users who enter the funnel at any point rather than only following the sequential approach of a closed funnel. A closed funnel won’t include users who join the funnel after the first step, and so may give a false impression of your conversion rate if users can join it on any step. The default setting is closed.

14. Segment Comparisons:

The Funnel analysis allows you to compare up to four segments that are shown in the Segments section of the Variables tab. You can drag and drop or just double click the segments.

You may need to add the relevant segments before you can do this. Click on the ‘+’ to open the Segments interface. You will be presented with a choice between creating a custom segment or using a suggested one.

There are three types of custom segments:

User segment: Where all users meet certain criteria. For example, desktop visitors or users from specific countries.

Session segment: Where all sessions meet set criteria. For example, all sessions where users originated from an individual campaign or visited a certain page.

Event segment: Where you only include certain events. For example, all visitors who have previously made a purchase or added to basket.

In this example, I want to compare mobile users to desktop visitors. As desktop traffic is not a default segment in the Variables Tab it is necessary to create this first. Give the segment a suitable name and add a description.

Search for the condition, select the match type and the expression (desktop). You also have the option to check ‘At any point’ to include users who meet the matching condition at any point in the user journey. Now click ‘Save and Apply’ to add the segment to your funnel.

In the report, each segment is shown as a separate bar chart above the funnel and you can hover over it to display the raw data. Your segments are also shown as the second column in the funnel table and so you will see segment A followed by each parameter for your breakout dimension.

15. Breakdown:

This allows you to breakdown your funnel by a single dimension (e.g. Device category). Where the dimension has more than a few possible values you can specify the number of rows per dimension. The default is 5. Click and drag the dimension you want to breakdown the funnel by as shown below.

16. Elapsed Time:

This displays the average time between each step in the user journey. It’s great for understanding how long it takes users to proceed through a user journey and often highlights how users on different devices behave.

17. Next Action:

To see what event occurs immediately after each step in the funnel you can add ‘Event name’ or ‘Screen name’ for apps to the ‘Next Action’ box. Scroll over the bar chart to see the top 5 next events.

18. Share Your Funnel:

By default, your funnel report is only available to you and so you need to share it with other users of the property if you want other people to be able to view it. Click on the icon shown below and click ‘Share’ to give read only access to other users of the GA4 property.

19. Download the Funnel:

You can download your funnel analysis report in:

• Google Sheets
• TSV
• CSV
• PDF
• PDF (all tabs)

20. Funnel Visualisation:

You should now have a fully configured analysis funnel. This will allow you to instantly investigate each step of the user journey by defined segments and breakdown by individual dimensions. This is a huge improvement on goal funnels you may have used in Universal Analytics.

Spend time analysing each step and summarising insights that you identify. The funnel analysis report should save you from having to create many custom reports and time manipulating data to compare completion rates by important segments and dimensions.

21. Replicate Funnel Analysis Report:

You may want to replicate the funnel for different journeys or users with different needs. This can be easily done by coming out of the report and navigating to the Analysis hub. Click on the three dots as shown below and select ‘Duplicate’.

22. Don’t Navigate to Another Property When in a Funnel Report:

When you are in a funnel report, avoid changing to another property. If you navigate directly from the Analysis funnel report to another GA4 property, the funnel report will move to the second GA4 property. This is a bug that may be fixed by Google, but at present it can cause you problems. If this happens to you, navigate directly back to the original GA4 property.

Summary of how to create a funnel Visualisation in Google Analytics 4:

The funnel analysis report in GA4 report is one area without doubt where GA4 outshines Universal Analytics. However, it does require some planning and preparation to optimise your funnel report.

Create events in GTM, and if necessary, configure pageviews for individual pages in the GA4 interface. Consider which segments and dimensions you will need to analysis your funnel by and which might allow you to exclude visitors who could never convert.

Decide what type of funnel you want, either a standard funnel or a trended funnel. Set a time range which relates to when you began collecting your events in GA4.

The funnel analysis report allows you to create up to ten individual steps for a user journey. Remember to use parameters when you need to restrict the nature of the funnel, such as how users respond to a question. Use the indirectly or directly followed settings as needed.

If you have secure pages and strict time-out periods, you can consider setting time limits between steps. Once your steps have been saved, consider setting a filter to exclude users who could not possibly convert on the site.

Decide whether to use a closed or open funnel. If users can join the funnel at almost any step, I would strongly recommend using an open funnel.

Use the dimensions you have configured to apply up to four segments for comparison. Set a single dimension, such as device category to breakdown the funnel.

Enable the ‘Elapsed time’ to display how long it takes users to go from one step to another.

Set the ‘Next Action’ to show the event which occurs immediately after each step in the journey.

Finally, remember to share your funnel analysis with other users of the GA4 property.