Ecommerce reporting is essential for understanding product performance and is an incredibly powerful tool in a business’s analytics belt. Google Analytics does a brilliant job of providing this reporting functionality, but in the step up from Universal Analytics to Google Analytics 4, a few things have changed. This blog will explore what’s new in built-in and custom ecommerce reporting in GA4, and how to best make use of the new functionality. This will be split into three demo use cases:
- Built-in Enhanced Ecommerce Reports
- Machine Learning Insights
- Building Custom Reports with Analysis Hub
Built-in Enhanced Ecommerce Reports
GA4 comes with a suite of built-in Ecommerce reports to provide a top-level view of product performance. These reports can be found in the Monetisation menu:
Within Monetisation, the Overview tab provides a quick look at overall Ecommerce performance. This includes total revenue, the number of both total and first-time purchasers, average purchase revenue per user and more.
The E-Commerce Purchases report provides a deeper look into how individual products are performing. This tab hosts two useful graphs: Item Views over time, and a scatter graph depicting Item Views and Add-to-baskets.
Below these two graphs is a table with further details about each product, including add-to-baskets, basket-to-view rate, purchase-to-view rate and more.
You can change the primary dimension of this table by clicking the Item Name drop-down on the top left of the table, or add a secondary dimension by clicking the blue “+” on the right of the drop-down. Choosing a new primary dimension will also change the primary dimension of the graphs mentioned previously. For example, Item Views by Item Name over time could become Item Views by Item ID over time.
Machine Learning Insights
Machine learning insights in GA4 can be accessed by clicking the insights button on the top right of the page. This tool provides you with suggestions of questions that you may want answered.
Clicking on the insights button will open out the panel below, offering various categories of questions like Basic Performance, Demographics and User Acquisition. In our case, we’re focused on the Ecommerce category at the bottom.
Clicking on the Ecommerce category will expand it and offer a few questions that you may find useful, these include Top products by revenue, Revenue by devices this year and more. Select an insight to view and it will open your insight for you in the same panel.
The Analysis Hub in GA4 hosts all of your visualisations in one place. This was available in Universal Analytics but was specific to events defined by Google. Now in GA4 we can define our own shopping behaviour when doing analysis. There are several different types of data visualisations you can make use of, but for the purposes of this blog we’ll be looking at the funnel analysis.
When opening a new Funnel analysis you’ll be greeted with the analysis interface. When building a funnel analysis, defining steps is a good start. To do this, click the pencil icon in the ‘Steps’ section.
Clicking the icon will open the steps interface, which will allow you to fully customise each step in the funnel. Steps can be a mixture of events, page views, demographics, date and time and more. In the below example, step 1 is defined using the events ‘first_open’ OR ‘first_visit’. Remember to name each step.
When adding another step, we can further customise by deciding whether it has to directly or indirectly follow the previous step, and we have the option to set a time limit on how long after the previous step this one can occur. In the below example, step 2 is defined as an add to cart event within 10 minutes of the first step.
As the funnel steps are updated, on the right-hand side of the screen will be a summary of the users and events captured by the funnel in it’s current configuration. It’s worth paying attention to this as steps are updated.
Once all steps are configured, click ‘Apply’ in the top-right and you’ll be able to see your Funnel with a table beneath, containing metrics like Users, Completion rate, Abandonments and Abandonment rate. This can be broken out further by adding a breakdown dimension. This can be useful to see, for example, how different device categories progress through your funnel.
Once your funnel is fully configured, make sure to name your analysis, and select an appropriate date range. The analysis hub is very powerful and it’s worth using it to fully get to grips with what it’s capable of.
In this blog, we’ve looked at the ecommerce reporting capabilities in GA4, how they compare to the facilities in Universal Analytics, and how best to make use of them. If you have any questions about the topics discussed, please feel free to contact our team of experts at https://www.merkle.com/emea/contact.
For all blogs in our GA4 series, please see below: