What is a form error nuisance rate (FENR)?
The % of sessions which abandon a form after triggering an error. If 100 sessions trigger an error on a form, and 20 of them don’t complete the form = 20% form error nuisance rate.
July 9, 2020 by Adrian Durow . Analytics
The % of sessions which abandon a form after triggering an error. If 100 sessions trigger an error on a form, and 20 of them don’t complete the form = 20% form error nuisance rate.
Form errors happen. Maybe due to a user deliberately omitting data on a required field. Maybe due to a question not being easy to understand. There’s many reasons for them, but errors happen.
The fewer errors generated, the more likely forms are to be completed. The better UX-handling of those errors which are generated, then the less problematic they will be.
To see how problematic these errors are, you need to measure the % of session which abandon after triggering an error.
Ultimately though, this metric helps with conversion optimisation at the end of a user journey. If users are clicking CTAs, starting to complete forms, but not converting, then improving/reducing form error nuisance rates could be critical to getting more conversions.
Some errors may be more of a nuisance than others. So it’s important to monitor fields/questions/errors individual error nuisance rates.
In the below example, whilst many more users are triggering an error on ‘How did you find us?’, the phone number field is more problematic. More of a nuisance, because it’s more likely to cause an abandonment, and is causing more abandons:
#1 Get errors tagged via Google Tag Manager
You’ll need to write some custom JavaScript to listen for:
And create tags in GTM to send this data into GA as events.
#2 Create a conditional segment in GA
This is a segment to track either the number of sessions which trigger an error generally, or the number of sessions which trigger a specific error if you are looking to measure an individual error nuisance rate.
#3 Create another conditional segment in GA
This time, to record the error generation, AND the abandonment.
Again, either for all errors and abandons, or for specific errors and subsequent abandons.
The example to the left portrays an ecommerce form abandonment (transactions = 0)
#4 Create a report for the metric
We use Google Data Studio for this.
Import the 2 segments, and create a new metric by dividing 1 by the other.
The same can be done for individual form error nuisance rates.