reatlat/fullstory-claude-plugin

Funnel Doctor

Diagnose conversion funnels — find the leakiest step, quantify the loss, and show you the sessions that explain why users leave.

When to Use

• "Where are users dropping off in our checkout flow?"
• "What's the conversion rate from signup to first purchase?"
• "Compare the old onboarding flow vs the new one — did we improve completion?"
• "Show me sessions where users abandoned at the payment step"
• "What's the biggest drop-off in our app? Quantify the lost revenue."

Mental Model

A funnel is a sequence of steps users must complete. The goal is to find the step where the most users leave — not just by absolute count, but by the percentage drop from the previous step.

Example: 10,000 land on /checkout → 8,000 enter shipping → 6,000 reach payment → 2,000 complete purchase. The biggest absolute drop is payment (6,000 → 2,000 = 4,000 lost). The biggest percentage drop is also payment (67% drop). Both matter — present both.

Workflow

Step 1: Define the funnel steps

Clarify what steps the user cares about. Typical funnels:

• E-commerce: product page → add to cart → checkout → shipping → payment → confirmation
• SaaS signup: landing → signup form → email verification → onboarding → first action
• App flow: home → search → results → detail → booking

If the user doesn't specify steps, ask for the start and end point. You can infer the middle steps from page navigation data.

Step 2: Build a funnel metric

Call fullstory:build_metric with:

• query describing the funnel steps in order (e.g., "users who viewed /checkout, then reached shipping, then reached payment, then completed purchase")
• output_type="funnel" — this tells the builder to create a sequential funnel, not a simple count

If a funnel metric already exists for this flow, search first: fullstory:get_metric(regex="checkout funnel"). For funnels built with build_funnel, use fullstory:get_funnel(id) — it returns name, description, rendered definition, and correct num_steps (needed for the get_funnel_sessions workflow).

Step 3: Compute and analyze

Call fullstory:compute_metric(metric_id) — or fullstory:compute_funnel(funnel_id) for funnel-specific options.

Period comparison — add compare_to to see prior-period deltas:

fullstory:compute_funnel(funnel_id, compare_to="previous_period")

Each step gets a previous object with the prior-period count and conversion rate. Use this for "did the new design help?" questions instead of building two separate funnels.

Dimension breakdown — add dimension to split by a property:

fullstory:compute_funnel(funnel_id, dimension="device_type")

Each step gets a groups array broken down by that dimension (browser, country, device, custom var, etc.). Replaces manual segment-per-group workflows.

Present results as a step-by-step breakdown:

Checkout Funnel (last 30 days)
1. /checkout            10,000 users  (100%)
2. Shipping form         8,000 users  (80% — lost 2,000)
3. Payment page          6,000 users  (75% of remaining — lost 2,000)
4. Confirmation          2,000 users  (33% of remaining — lost 4,000) ← BIGGEST DROP
Overall conversion: 20%

With compare_to, append deltas:

4. Confirmation          2,000 users  (33% of remaining — lost 4,000)
   Previous period:      1,400 users  (28% of remaining)
   Delta:               +600 users    (+5pp conversion)  ↑

With dimension, show per-group conversion at each step.

Call out the step with the biggest absolute drop AND the biggest percentage drop — they're often the same, but not always.

Step 4: Scope with segments

If the user asks about a specific cohort (mobile users, enterprise, new vs returning), build a segment and attach it:

fullstory:build_segment("mobile users") → segment_id
fullstory:update_metric(metric_id, segment_id)
fullstory:compute_metric(metric_id)

For A/B comparisons of funnels (old flow vs new, mobile vs desktop), use the comparisons skill to pick the right mechanism (dimensionality vs segments).

Step 5: Investigate the worst step

Once you've identified the leakiest step, find out why:

1. Call fullstory:get_sessions(metric_id=metric_id) filtered to sessions that reached but didn't complete the problem step
2. Load 3-5 sessions through the session-context agent with focused tasks:
   • "What did the user do on the payment page before leaving? Did they encounter an error?"
   • "Was the form validation blocking them? What fields were highlighted?"
   • "Did they rage-click or dead-click anything before abandoning?"

Step 6: Report

Synthesize everything:

• The funnel numbers (table format)
• Which step is the biggest problem (with both absolute and percentage loss)
• What the session evidence shows about why users leave at that step
• If applicable, estimated revenue impact (user count × average order value)

Guidelines

• Always show both absolute drop (user count) and percentage drop at each step.
• Surface the metric_url so the user can verify in the Fullstory UI.
• If a funnel step has a very high drop-off, prioritize investigating that step — don't spend equal time on every step.
• For revenue impact, ask the user for average order value. Don't guess.
• If the user asks "did the new design help?", build two funnel metrics (before/after deploy date) and present side by side.