When operating an online business, it is important to understand the Behaviour of customers who visit the website to browse merchandise and add items to their shopping carts. One key metric of interest in this context is the number of customers who abandon their shopping carts before making a purchase. To gain insight into user Behaviour on websites and apps, the use of a conversion funnel can be beneficial in this regard.
A typical ecommerce conversion funnel consists of four distinct stages. These stages represent the decision-making process that a consumer goes through as they engage with the brand, product, and competition. The stages are as follows:
By analyzing data at each stage of the conversion funnel, businesses can gain a deeper understanding of how customers engage with the brand, product, and competition. This information can then be used to identify areas for improvement and optimize the customer journey for maximum conversions.
When it comes to funnel visualisation in Analytics, there are different ways available to visualise the available data.
In UA, there are two types to visualise the data in the funnel Analytics.
In your analytics property, you can navigate to Conversions> Goals> Funnel Visualization. We can create the goals depending on the business models of the various clients which can differ from one company to another.
In your Analytics property, you can navigate to Conversions> Ecommerce> Shopping Behaviour/Checkout Behaviour.
Here you can see the Shopping Behaviour Funnel where you can see the data mentioned below:
This funnel shows you the steps of the checkout process which has been set to view the data in Analytics.
Universal Analytics (UA) provides the capability to automatically calculate the funnel phases for both the shopping funnel and checkout funnel based on the labelling provided by the user. However, it is important to note that for this functionality to work correctly, it is crucial to ensure that the necessary e-commerce data types are being sent. Even for experienced developers, it may require several iterations of creation and testing to ensure that the tags are providing the correct data.
Despite the convenience of automatic funnel calculation, there are limitations to this approach.
UA automatically calculates the funnel phases for both reports based on your label. This makes sending the necessary e-commerce data types crucial. Even for a professional developer, it may take several iterations of creation and testing to ensure that the tags provide the correct data.
However, there are limitations here in the both types of funnels are:
You would observe a session to /url1, a session to /url2, and an exit from /url2 to /url1 in the example.
As we can see in the screenshot up top, each funnel we establish in UA displays how many sessions it takes the user to complete it. Additionally, we can view the user's entrance and exit points at each stage. For example, from the /homepage, the user reaches the cart page before leaving the funnel to access other pages, such as the /signin page.
Now, in GA4 everything is event based (pageviews (i.e., URL visits), session starts, video plays, etc.) and Google has streamlined reporting to make it easier for you to track the user experience from beginning to end from platform to platform as well as swiftly acquire marketing insights linked to your chosen funnel stage.
The funnel exploration feature of GA4 gives you a comprehensive picture of how customers interact with your company throughout the customer lifecycle, from acquisition and conversion through monetization and retention.
The funnel exploration helps identify the path users take to complete a desired journey which could lead to purchase or form submission on the website. This indicates that not all website visitors proceed through the processes outlined in your funnel. There will inevitably be a drop off to some extent.
Because GA4 works for both websites and mobile apps, you may build bespoke funnels based on events or page urls/screens, and there are no restrictions on the path or events that can be used to create a desired funnel. You can navigate to the ‘Explore’ section and there will be various option available to choose and create the custom reports.
One of the options available is, ‘Funnel exploration’ or if you are already in one of the custom report you will get a dropdown to choose the reports from:
Now we will check the funnel by adding each feature available in GA4 one by one to understand the data in detail and how we can use the features available in the reporting to make better decisions.
Now let’s take a look at a funnel created in GA4 based on the checkout process for e-commerce so that would be relatively easy to understand.
Let’s consider below steps which are the events triggered at checkout steps for any website in GA4:
We can create a funnel in GA4 of upto 10 steps, if you have more than that we can help you out creating the custom funnel in Data Studio.
Now as we all know that these are all event names set by GA4 to pass based on the website flow to get the checkout process. Below we have created a funnel for steps mentioned above:
The funnel displayed above illustrates the journey of active users on the website or app. Active users are defined as those who have engaged on the site over a specified time period, with Google Analytics 4 examining active users in terms of one day, seven days, and 30 days.
The data presented in the funnel represents the number of active users who have progressed through the funnel, from adding items to the cart to making a final purchase. The bars on the website or app indicate the total number of active users who have completed the journey. For example, the data may show that 2.9K active users were counted, with the percentages shown based on the active users who have moved on to the next step in the journey or have dropped off from the funnel.
The percentages displayed above the bars indicate the percentage of total users who have progressed to the next step in the journey. For example, 37.8% of users may have proceeded to the "begin_checkout" step. Similarly, in Universal Analytics, the data is presented in a similar manner, with the red arrow indicating the number of active users and percentage who have dropped off from the first step of the journey, which has been set in the funnel.
Additionally, the table below presents the completion rate, abandonments, and abandonment rate of each step separately. For example, it may be shown that 1.8K active users, or 62.2% of the total (2889), have dropped off from the first step. The completion rate for the third step of adding shipping information is 51.1%, with an abandonment rate of 48.9%.
Here, we have considered funnel based events mentioned below for each step:
Here in GA4, we have functionality of adding custom segments based on users and events data which is being collected in the GA4 property. We can roll out a different blog on custom segment creation.
Now that we are aware of how many users there are at each stage, we want to know which types of devices or channels these users are using. Consequently, we can add the dimension needed to fully comprehend each user base and we have breakdown the funnel based on the First User Default Channel Grouping and it will be applied to each and every step in the funnel. Also, the data for completion rate and abandonment rate will be divided based on the channels.
For example: the total users were 850 on the 3rd step of add shipping information, out of it 405 were from Paid Search, 233 from Organic search and so on.
The funnel with elapsed time, you can notice the average time which has elapsed based on the user engagement duration between each step of the funnel. For example: 6h 23 m is the average time taken by the users to move to the next step i.e. Begin checkout.
Finally, you can see the top five actions people do after finishing a certain phase using the new funnel exploration tool. Select "Event name" as the dimension and then select "Next Action." The top five actions that users do after a certain funnel phase are then displayed to you by GA4 when you click on that step.
For example: as you can observe in the funnel, after completing the add to cart event users are likely to perform the events shown on the pop-up of step-1 in the funnel like pageview, header clicks and then again add to cart.
Now in the funnel if you would like to view the data for specific medium the data is coming from like the Organic users on the website, then we can add a filter with the value to see the required changes in the data.
We can see that the GA4 comes with a variety of new features in the funnel which can be used in a better way to meet the business goals.
To take advantage of GA4's features like make Google Analytics 4 your cross-platform Analytics solution, cross domain tracking and many more.
Migrate now to GA4, as Google has stated that it will terminate its Universal Analytics (UA) platform and switch to a completely redesigned GA4 platform starting in June 2023 to satisfy all of your tracking requirements. In case you haven't migrated, reach out to us and we will help you with the easy migration process.
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