Funnel Analysis

Funnel Analysis

What You'll Need

| Input | Required? | Example | |-------|-----------|---------| | Funnel stage names and conversion data | ✅ Required | Sign-up → Activation → First Purchase (1000 → 420 → 180 users) | | Time period for the analysis | ✅ Required | Q4 2025, last 30 days | | Product / feature being analyzed | ✅ Required | Onboarding flow for B2B SaaS | | Historical baseline or benchmark | 🟡 Recommended | Industry average activation rate ~40% | | Qualitative user feedback about drop-off points | ⚪ Optional | Session recordings, support tickets |

> Don't have everything? Start anyway — the skill will work with what you provide and flag where richer input would improve the output.

Purpose

Systematically analyze a conversion funnel to find the highest-impact drop-off points, generate hypotheses for why users are leaving, and recommend concrete experiments to improve flow.

Domain Context

Funnel analysis answers the question: "Where are we losing users, and why?" The most valuable insight isn't just where the biggest drop occurs — it's why it happens and what to test next. This skill combines quantitative analysis (conversion rates, leakage volume) with structured hypothesis generation to turn data into action.

Key principle: Fix the biggest leaks first, but consider absolute volume, not just percentage drop. A 5% drop at 100,000 users/month is more impactful than a 30% drop at 1,000 users/month.

Instructions

You are analyzing a conversion funnel for $ARGUMENTS.

Work through each section systematically.

Phase 0: Context Confirmation

Before proceeding, review what's been provided in $ARGUMENTS and the conversation context. Only ask about what's missing — don't re-ask what's already clear.

1. Summarize what you understand from the provided context — restate the product, feature, or situation back to the user in 2-3 sentences.
2. Identify gaps — if any of the following are unclear, ask:

• What conversion funnel are we analyzing?
• What are the funnel stages from entry to completion?
• What time period should we examine?

1. Confirm: "Here's my understanding: [summary]. I plan to [brief description of what the skill will produce]. Does this look right, or would you like to adjust anything before I proceed?"

If the user provides additional context, incorporate it. If the user confirms, proceed.

Step 1: Map the Funnel

For each stage provided, calculate:

• Users entering: count or percentage
• Users exiting: count or percentage
• Conversion rate: users who proceeded / users who entered × 100
• Drop-off rate: 100 - conversion rate
• Drop-off volume: absolute number of users lost at this stage

Present as a table:

| Stage | Users In | Users Out | Conversion Rate | Drop-off | Drop-off Volume | |-------|----------|-----------|----------------|----------|----------------| | [Stage 1] | [N] | [N] | [%] | [%] | [N] | | ... | | | | | |

Overall funnel conversion: [first stage users → last stage users] = [%]

Step 2: Identify the Biggest Leaks

Rank stages by drop-off volume (not just percentage). Highlight the top 2-3 stages where the most users are lost.

Step 3: Generate Leakage Hypotheses

For each major drop-off stage, generate 3-5 hypotheses for why users are leaving:

Hypothesis format: "Users are dropping at [stage] because [reason]. Evidence: [what would confirm this]. Effort: [Low/Medium/High]."

Draw from common funnel failure patterns:

• Friction: Too many steps, confusing UI, slow load time
• Value not clear: Users don't understand what they get if they continue
• Trust gap: Missing social proof, unclear privacy, unfamiliar brand
• Wrong audience: Users who arrived don't match the product's target persona
• Technical failure: Errors, broken flows, mobile incompatibility
• Timing mismatch: Asking for commitment (payment, sign-up) too early

Step 4: Prioritize Experiments

For the top 2-3 drop-off stages, recommend experiments using an ICE score (Impact × Confidence × Ease):

| Experiment | Stage | Hypothesis Tested | Expected Impact | Confidence | Ease | ICE Score | Recommended? | |-----------|-------|-------------------|----------------|-----------|------|-----------|-------------| | [Experiment name] | [Stage] | [Hypothesis #] | [H/M/L] | [H/M/L] | [H/M/L] | [1-10] | [Y/N] |

Step 5: Generate Output Report

## Funnel Analysis: [Product / Flow Name]

**Period**: [date range]
**Overall conversion**: [X]%

### Stage-by-Stage Breakdown
[Table from Step 1]

### Biggest Leaks (ranked by volume)
1. [Stage]: [N] users lost ([%] drop) — [brief reason]
2. [Stage]: [N] users lost ([%] drop) — [brief reason]

### Leakage Hypotheses
[Step 3 hypotheses for top stages]

### Recommended Experiments
[Step 4 table]

### Quick Wins (low effort, moderate+ impact)
- [Experiment]: [Expected lift] — [1-sentence rationale]

### Benchmarks
[Industry benchmarks for this funnel type if available]

### Next Steps
- Run [Experiment 1] first — highest ICE score
- Instrument [stage] to gather qualitative data on drop-off reason
- Review with [team] to validate hypotheses before building

Notes

• Funnel data can be misleading without segmentation — the same funnel often has very different conversion rates by acquisition channel, device type, or user persona
• Always validate hypotheses with qualitative data (session recordings, user interviews) before committing to a fix
• Benchmark against industry norms, but your product's context matters more — a 2% free-to-paid conversion in B2B SaaS is excellent; in consumer apps it may be poor

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: tarunccet
• Source: tarunccet/pm-skills
• License: MIT

Install and usage instructions live in the source repository linked above.