What is form analytics? Find the field where visitors give up

What form analytics is

Form analytics measures what happens inside a form, field by field. A page report might say 1,000 people reached the checkout and 300 submitted. Form analytics adds how many started the form, which field each person was on when they left and how long they spent in it. If 200 people start a form and 50 submit it, the completion rate is 25% and the abandonment rate is 75%.

The metrics that matter

Six numbers cover most of what a form report can tell you.

• Views, starts and submissions: how many people saw the form, how many touched a field and how many sent it. Losing people between view and start is a different problem from losing them between start and submit.
• Abandonment rate: abandoned forms divided by started forms. A form is abandoned when someone interacts with it and leaves without submitting.
• Drop-off by field: of all the abandoned forms, the share that ended on each field. The field with the biggest share is where to look first.
• Hesitation time: the pause between clicking into a field and typing the first character. A long pause means the person is reading, unsure or looking for information.
• Refills: the share of people who went back and changed a field after completing it. A high number usually means the format was unclear or an error message appeared.
• Ignored fields: fields that people who used the form skipped. These are often optional fields that look required, or required ones that look optional.

How to read a problem field

Combine the metrics, because each one alone can mislead. Three patterns come up often.

• High drop-off with long hesitation points to confusion. People tried to answer and could not, because the label is vague, the format is strict or the question needs information they do not have to hand.
• High drop-off with short hesitation points to reluctance. People read the field, decided not to answer and left. Phone number, date of birth and company size fields often behave this way when the form does not say why they are needed.
• High refills with low drop-off mean people get through but fight the field. A postcode or phone field that rejects a valid format is the classic case.

Check the number against the rest of the form before acting. A field that ends 30% of abandoned forms matters more than one that ends 3%. If almost nobody starts the form in the first place, the fix is on the page above it.

Why field-level data beats a conversion rate

A form’s conversion rate says that something is wrong. Field-level data says where. Two forms can both convert at 20%, one losing people at the first field because it asks for too much too soon, the other at the last because the payment step fails. The fixes have nothing in common.

The same logic applies to a conversion funnel. A funnel shows which step loses the most people, and form analytics zooms into the step that is a form. Use the funnel to pick the form and the form report to pick the field.

Turning the numbers into a fix

Start with the field that ends the most abandoned forms and ask what a visitor would need to get past it. Common fixes are short.

• Remove the field, or move it after the submit step, if the form works without it.
• Say why you are asking. A line such as “Only used for delivery updates” under a phone field answers the question the visitor is silently asking.
• Accept more formats. Allow spaces and dashes in phone numbers and postcodes, and show the expected format before people type, not after an error.
• Show errors next to the field, in plain words, while the person is still on it.

Then test the change instead of assuming it worked. How long an A/B test takes depends on how many people reach the form, so a busy checkout can answer in a few weeks while a low-traffic enquiry form may need longer.

Form analytics in Cuttl

In Journey Insights, forms are tracked automatically once the Cuttl Tag is on the page, with no setup per form. Each form shows how many people viewed it, started it and submitted it, the average time to complete, and for every field the average time, hesitation, interaction time, refill percentage, ignored percentage and drop-off percentage. You can filter by segment, such as mobile visitors or one traffic source, to see whether a field is a problem for everyone or only for some.

Form analytics in Cuttl measures timing and drop-off, never what people enter. Form drop-off also feeds the automatic issues list, so a form that is losing people shows up there with the sessions it affects. The session recordings from those sessions usually show the cause, such as an error message or a field that did not respond. Once you have a fix, Web Experiments can test it on real traffic.

Common questions