form-analyzer

Form Analyzer

Field-by-field form analysis — which fields cause abandonment, where validation errors hit, how long each field takes, and where users get stuck.

When to Use

• "Which field on the signup form has the highest abandonment?"
• "Why are users dropping off at the password field?"
• "How long does it take users to complete the checkout form?"
• "Which form fields trigger the most validation errors?"
• "Compare form completion on mobile vs desktop"
• "Did the new inline validation reduce form abandonment?"

Mental Model

A form is a sequence of fields users must fill. Each field is a micro-funnel: enter → validate → proceed. The goal is to find which fields cause users to stall, error, or abandon.

Forms have unique failure modes:

• Confusion: Users don't know what to enter (e.g., "Username" vs "Email")
• Validation friction: Strict rules reject valid input ("Password must contain a special character")
• Technical failure: Field doesn't respond, autofill breaks, input type mismatch
• Trust barrier: Asking for too much info too early ("Why do you need my phone number?")

Workflow

Step 1: Identify the form

Clarify which form and what success looks like:

• Form page: /signup, /checkout, /contact
• Success event: page navigation to /signup/confirmation, custom event form_submitted, or purchase completion
• Fields of interest: all fields, or specific ones the user suspects

Step 2: Build form metrics

Start with the overall funnel:

fullstory:build_metric(
  query="users who visited /signup, then completed signup",
  output_type="funnel"
)

Then build field-level metrics. For each field, build a metric for users who interacted with the field but abandoned before completing the form:

fullstory:build_metric(
  query="users who focused or typed in the password field on /signup but did not reach /signup/confirmation",
  output_type="single_number"
)

Step 3: Detect friction per field

For the highest-abandonment fields, investigate: