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.

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