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.
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
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.
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:
- 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.
- 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.
- Event segment: These segments are defined by specific events, such as newsletter sign up, add to cart or purchase.
- 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’.
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.
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.
Below the Technique drop-down menu, is the visualization selector where you can choose between:
- Donut chart
- Line chart
- 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 is shown after all column dimensions.
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.
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.
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.
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.
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.
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.
- About the author: Neal (@northresearch) provides web analytics and CRO consultancy services and has worked in many sectors including financial services, online gaming and retail. He has helped brands such Hastings Direct, Manchester Airport Group Online and Assurant Inc to improve their digital marketing measurement and performance.
- Neal has had articles published on website optimisation on CXL and Usabilla.com. As an ex-market research and insight manager he also had posts published on the GreenBook Blog research website. If you wish to contact us please send an email to firstname.lastname@example.org. You can follow us on Twitter @conversionupl, see Neal’s LinkedIn profile or connect on Facebook.