---
name: funnel-drop-off-analyzer
description: "Analyzes step-by-step conversion funnel data to locate where users abandon, quantify the revenue or activation impact of each leak, diagnose the likely friction cause, and produce ranked friction points with testable hypotheses and recommended experiments. Use when investigating why a funnel underperforms, prioritizing which drop-off to fix first, or turning raw step conversion numbers into an action plan."
argument-hint: "[funnel-steps] [conversion-data]"
allowed-tools: ["Read"]
---

# Funnel Drop-Off Analyzer

## Purpose
Turn raw step-by-step conversion data into a diagnosis of where and why users are leaking, quantified by impact, and ranked into an ordered list of friction points with testable hypotheses. Solves the problem of staring at a funnel chart knowing something is wrong but not knowing which step to fix first or what to test.

## When to Use
Invoke this skill when the user asks to:
- Figure out why a signup, checkout, onboarding, or activation funnel is underperforming
- Decide which funnel step to prioritize fixing when several are dropping users
- Turn step conversion percentages into a diagnosis and action plan
- Identify the single highest-impact leak in a conversion or activation flow
- Build hypotheses and experiment ideas from funnel analytics data
- Compare drop-off across segments (device, source, plan, cohort) to isolate a cause

## Inputs Required
Before starting, collect:
- **Funnel steps**: The ordered list of steps, named, from entry to conversion
- **Volume or conversion at each step**: Users entering each step, or step-to-step conversion rate
- **Conversion goal**: The final action that defines success (purchase, activation, subscription, etc.)
- **Value per conversion**: Revenue, LTV, or activation value of one completed conversion (for impact sizing)
- **Segment breakdowns** (if available): Drop-off by device, traffic source, plan, new vs. returning, geography
- **Known context**: Recent product/design changes, timeframe of the data, seasonality, or anything unusual
- **Benchmark or target**: Any known industry benchmark or internal target per step

If value per conversion is missing, size impact in recoverable users instead of revenue and note the assumption. If segments aren't provided, analyze the aggregate funnel and flag which segment cuts would sharpen the diagnosis.

## Instructions

### Step 1: Normalize and Compute Step Metrics
Convert whatever was provided into a consistent table before analyzing:
- **Step-to-step conversion rate**: Users entering step N+1 ÷ users entering step N
- **Cumulative conversion**: Users at each step ÷ users at step 1
- **Drop-off count**: Absolute users lost between each step (this is what matters for impact, not just the percentage)
- **Drop-off rate**: Percentage of the step's entering users who did not advance

State any data quality caveats: gaps, tracking that may double-count, steps that overlap, or windows too short to be significant.

### Step 2: Locate the Leaks by Impact, Not by Rate
Rank drop-offs by absolute users lost, then by rate — the two often disagree, and the disagreement is the insight:
- **Biggest absolute leak**: The step losing the most raw users — usually near the top of the funnel where volume is high
- **Highest-rate leak**: The step where the largest share of arriving users abandon — often signals a specific broken or confusing step
- **Below-benchmark leaks**: Any step materially worse than the provided benchmark/target
- Flag the "silent killer" pattern: a mid-funnel step with a modest rate but high volume that quietly costs more than a dramatic late-funnel cliff

### Step 3: Diagnose Likely Cause per Leaking Step
For each significant leak, map it to friction categories and reason from the step's position and behavior:
- **Motivation/relevance friction** (top of funnel): wrong traffic, unclear value prop, expectation mismatch from the referring source
- **Effort/UX friction** (any step): form length, required fields, unclear next action, load speed, mobile layout, dead ends
- **Trust/risk friction** (near commitment steps): price surprise, missing social proof, security concerns, unclear terms
- **Cost/value friction** (decision steps): price, shipping, plan confusion, value not yet demonstrated
- **Technical friction** (any step): errors, broken flows, payment failures, cross-device breakage
- Use segment data to confirm or narrow the cause — e.g., a leak isolated to mobile points to UX/technical, not motivation

### Step 4: Size the Recovery Opportunity
For each leaking step, quantify what fixing it is worth:
- **Recoverable users**: If this step improved to benchmark (or a stated realistic target), how many additional users reach the goal
- **Recoverable value**: Recoverable users × value per conversion (or recoverable users if value unknown)
- Model downstream compounding: a top-funnel fix multiplies through every later step's conversion rate
- This sizing is what drives prioritization — a smaller-rate leak on a high-volume, high-value step can outrank a dramatic late-stage cliff

### Step 5: Rank Friction Points and Attach Hypotheses
Produce an ordered list, each entry containing:
1. **Friction point**: The step and the specific problem
2. **Impact**: Recoverable users/value from Step 4
3. **Hypothesis**: A single falsifiable statement in "We believe [change] will [outcome] because [reasoning]" form
4. **Recommended experiment**: The specific test — what changes, what stays constant, primary metric, and the segment to run it on
5. **Effort estimate**: Low / Medium / High implementation cost
Rank by impact-to-effort, not impact alone. Be explicit about which one to run first.

### Step 6: Recommend the Sharpening Cuts
Name the 2–4 additional data slices that would most improve confidence in the diagnosis (e.g., "break the checkout step down by payment method," "compare drop-off for paid vs. organic traffic"). This prevents acting on a leak whose true cause is masked by aggregation.

## Output Format
- **Structure**: Step metrics table → leak location summary → per-step diagnosis → ranked friction points with hypotheses → recommended data cuts
- **Length**: 900–1,300 words
- **Format**: Metrics as a table; diagnosis as structured bullets; ranked friction points as a numbered list with the 5-field structure per entry
- **Sections**: Normalized Funnel Metrics → Where the Leaks Are → Cause Diagnosis → Recovery Sizing → Ranked Friction Points & Hypotheses → Data Cuts to Sharpen the Diagnosis
- **Tone**: Growth analyst — quantitative, opinionated about priority, no hedging on what to fix first

## Quality Checklist
Before delivering output, verify:
- [ ] Every step has both an absolute drop-off count and a drop-off rate — not just percentages
- [ ] Leaks are ranked by impact (recoverable users/value), and the ranking is distinct from the raw drop-off rate
- [ ] Each significant leak is mapped to a specific friction category with reasoning, not a generic "improve UX"
- [ ] Every ranked friction point has a falsifiable hypothesis in "We believe... will... because..." form
- [ ] Each hypothesis has a recommended experiment naming the primary metric and control conditions
- [ ] A single highest-priority fix is explicitly named — the output does not end in a tie

## Notes
- The biggest-percentage drop-off is rarely the right first fix — high-volume, high-value steps with modest leaks usually recover more. Always size before ranking.
- If the data window is too short for the funnel's typical conversion lag, say so — a "leak" may just be users who haven't converted yet.
- Watch for survivorship bias in late-funnel steps: the users who reach step 5 are already the most motivated, so late-stage rates look artificially healthy.
- A leak that appears only in one segment (a device, a source, a plan) is a diagnostic gift — it points straight at the cause.

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## How to use this skill

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Restart Claude Code or open a new session to register.

**Using in Claude.ai, ChatGPT, Gemini, or other AI tools:**

1. Drag this file into a new chat, or copy and paste its contents
2. Send your task or request as your next message — the AI will apply the skill where it fits

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