Signup Funnel Audit

This drill produces a complete audit of a self-serve signup funnel: every step instrumented, every drop-off quantified, and every friction point identified. The output is a prioritized list of conversion bottlenecks with baseline metrics.

Input

• The signup flow URL(s): landing page, signup form, email verification, initial setup
• PostHog project with SDK installed on all signup pages
• At least 100 signups in the last 30 days (or best available data)

Steps

1. Define the signup funnel events

Using posthog-custom-events, ensure every step in the signup flow fires a distinct event:

| Event | Trigger | Key Properties | |-------|---------|----------------| | signup_page_viewed | User lands on the signup page | referrer, utm_source, utm_medium, utm_campaign, device_type | | signup_form_focused | User focuses the first form field | field_name, device_type | | signup_form_field_completed | User completes a form field (on blur with value) | field_name, field_position, device_type | | signup_form_field_error | Validation error shown on a field | field_name, error_type (e.g., invalid_email, password_too_short) | | signup_form_submitted | User clicks the submit button | field_count_filled, time_on_form_seconds, device_type | | signup_form_error | Server-side error after submission | error_type (e.g., email_taken, rate_limited, server_error) | | signup_completed | Account successfully created | signup_method (email, google, github), time_to_complete_seconds, device_type | | email_verification_sent | Verification email dispatched | signup_method | | email_verification_completed | User clicks verification link | time_to_verify_seconds | | initial_setup_started | User begins post-signup setup | device_type | | initial_setup_completed | User finishes post-signup setup | steps_completed, time_to_complete_seconds |

Implement field-level tracking with this pattern:

document.querySelectorAll('form input, form select').forEach(field => {
  field.addEventListener('blur', function() {
    if (this.value) {
      posthog.capture('signup_form_field_completed', {
        field_name: this.name,
        field_position: Array.from(this.form.elements).indexOf(this),
        device_type: /Mobi/.test(navigator.userAgent) ? 'mobile' : 'desktop'
      });
    }
  });
});

2. Build the signup funnel in PostHog

Using posthog-funnels, create the primary signup funnel:

POST /api/projects/<id>/insights/
{
  "name": "Signup Funnel - Full",
  "filters": {
    "insight": "FUNNELS",
    "events": [
      {"id": "signup_page_viewed"},
      {"id": "signup_form_focused"},
      {"id": "signup_form_submitted"},
      {"id": "signup_completed"},
      {"id": "email_verification_completed"},
      {"id": "initial_setup_completed"}
    ],
    "funnel_window_days": 7
  }
}

Break down by:

• utm_source — which traffic sources convert best
• device_type — mobile vs desktop conversion
• signup_method — email vs OAuth completion rates

3. Identify the primary bottleneck

Pull the funnel data and find the step with the largest absolute drop-off. Rank all steps by:

1. Absolute drop-off: how many users are lost at this step
2. Relative drop-off: what percentage of users reaching this step fail to proceed

The step with the highest absolute drop-off is the primary bottleneck. This is where optimization effort should focus first.

4. Diagnose friction with session recordings

Using posthog-session-recording, pull recordings of users who dropped off at the primary bottleneck:

GET /api/projects/<id>/session_recordings/?events=[{"id":"signup_form_focused"}]&date_from=-14d

Filter for sessions where signup_form_focused fired but signup_form_submitted did not. Watch 15-20 recordings and categorize the friction:

• Form confusion: user fills fields in wrong order, re-enters data, or hovers over labels
• Validation blocking: user hits errors repeatedly on a specific field
• Distraction: user scrolls away from form, opens other tabs, or leaves
• Mobile friction: form does not render properly, keyboard covers fields, or tapping is imprecise
• Trust concern: user pauses at password or credit card fields, reads privacy policy, then leaves

5. Analyze signup paths

Using posthog-user-path-analysis, examine the paths users take before reaching the signup page and the paths within the signup flow:

• Which pages do converting users visit before signing up?
• Do users who visit the pricing page first convert at a higher rate?
• What is the median number of page views before signup?
• Do users who sign up via OAuth complete at a higher rate than email signups?

6. Establish baseline metrics

Record the following baseline metrics with dates:

| Metric | Definition | Baseline Value | Date | |--------|-----------|---------------|------| | Signup page CVR | signup_completed / signup_page_viewed | | | | Form start rate | signup_form_focused / signup_page_viewed | | | | Form completion rate | signup_form_submitted / signup_form_focused | | | | Form success rate | signup_completed / signup_form_submitted | | | | Email verification rate | email_verification_completed / signup_completed | | | | Setup completion rate | initial_setup_completed / signup_completed | | | | Median time to complete signup | Time from signup_page_viewed to signup_completed | | | | Mobile CVR vs Desktop CVR | Signup CVR broken down by device_type | | |

Store baselines in Attio as a campaign record note.

Output

• Full signup funnel instrumented with field-level tracking
• PostHog funnel showing conversion rates at each step
• Prioritized bottleneck list (ordered by absolute drop-off)
• Session recording playlist of drop-off behavior at the primary bottleneck
• Baseline metric table with values and dates
• Friction diagnosis categorization from session recording review

Triggers

Run once during Smoke test. Re-run whenever the signup flow changes (new fields, new design, new OAuth providers).