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From pm-data-analytics
Analyze a conversion funnel — identify drop-off points, calculate stage-by-stage conversion rates, generate leakage hypotheses, and recommend improvement experiments. Use when diagnosing funnel performance, prioritizing optimization work, or designing experiments to improve conversion.
npx claudepluginhub tarunccet/pm-skills --plugin pm-data-analyticsHow this skill is triggered — by the user, by Claude, or both
Slash command
/pm-data-analytics:funnel-analysisThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
| Input | Required? | Example |
Use this skill when the user asks to "analyze my funnel", "where are users dropping off", "funnel analysis", "conversion analysis", "activation funnel", "onboarding funnel", "purchase funnel", "what's my conversion rate", or has funnel data showing step-by-step drop-off rates and wants to diagnose where to focus.
Analyzes product funnels to identify drop-offs, diagnose low activation rates, design metrics frameworks, set up OKRs, and evaluate feature performance.
Audits marketing conversion funnels to identify drop-offs, benchmark rates, analyze causes, model revenue impacts, and prioritize fixes. Use for bottlenecks and gaps.
Share bugs, ideas, or general feedback.
| 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.
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.
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.
You are analyzing a conversion funnel for $ARGUMENTS.
Work through each section systematically.
For each stage provided, calculate:
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] = [%]
Rank stages by drop-off volume (not just percentage). Highlight the top 2-3 stages where the most users are lost.
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:
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] |
## 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