One of the requests we get from some of our clients is “just tell me the answer quickly”. Zuko has been designed to deliver the world’s most in-depth behavioural data and insights for online forms. This is great if you have the time and inclination to run through everything and come to your own conclusions.
Not everyone has that luxury, however, which is why we have created the first iteration of our insights report. It has been designed to bring together in one place some of the metrics that pinpoint where the UX issues are in a form.
As field level data is often why our customers use Zuko in the first place - to identify their problem fields and fix them - we have started with metrics that help you do just that. Ongoing, we intend to iterate our insights report to summarise deeper findings from across your form.
We are wary, though, of a “smoke and mirrors” approach. We don’t want to create “friction scores” based on spurious algorithms. Instead we pull the pertinent data into one place so you can draw conclusions yourself. Initially, we have focused on four of the most important metrics that may indicate that a particular field or question is causing visitor issues. These are outlined below along with thoughts on how to interpret them:
The field that a visitor last interacted with before abandoning their session is always a good place to start when it comes to investigating where the friction is in your form. This first graph shows you the top 5 fields with the biggest number of abandonments on them, showing you the biggest loss of potential customers by volume alone. It helps you prioritise the fields that will give you the biggest return if you improve their conversion rate.
Whilst looking at total abandonment numbers is a good place to start, it is not enough on its own. Many forms have conditional pathways meaning that not all visitors are shown the same questions. Moreover, the start of the form will generally tend to have higher abandonment volumes than the later ones as visitors who drop out at the start of the form cannot, by definition, drop out near the end.
This is where abandonment rate comes in. It looks at only visitors who have interacted with a field and measures the proportion of them who drop out on that field. This reveals fields that may have a low total abandonment figure (because they are not being shown to all visitors) but who have a high dropout rate of the visitors who actually interact with it. Again the graph shows the top 5 for this metric.
Some of the biggest UX insights you can discover are based on observing the difference in behaviour between those who successfully complete a form and those who abandon it. This graph shows the 5 fields with the biggest (statistically significant) difference in return (correction) rate between these two groups. This shows the fields where the need to return to a field (possibly prompted by an error message) is linked to the ultimate abandonment of the form. By understanding this, you can take steps to reduce the need to return to the field (by changing validation, error messages or microcopy for example) to improve your UX and increase the likelihood of form completion.
Another quick way of identifying problem fields is to see what happens after a visitor unsuccessfully tries to submit your form. The fields they jump back to are the ones they are most likely having a problem with. This graph shows the most common fields that visitors head to after a failed submission but then ultimately abandon. Again, this reveals the biggest areas of friction on the form that you should be looking to improve the UX for.
Note that this metric will only be relevant if you have a well defined ‘Submit’ button on your form.
A field that appears on all of these lists is likelier to be a bigger issue than a field that only appears on one. This is where the ‘Highlight Recurring Fields’ function comes in. It indicates via a colour key the fields that appear on multiple lists.
As noted earlier, this is just the first iteration of the Insights report. We will be working to improve it further and would like feedback to help us do that. If you have any thoughts on new metrics / graphs you would like to see, or any suggested improvements for the existing ones then please let us know at email@example.com