pm-claude-skills/skills/data-analysis-standard/SKILL.md

name, description

name	description
data-analysis-standard	Structures product data analysis, metric deep-dives, funnel analysis, and cohort studies. Use when asked to analyse product metrics, investigate a drop in conversion, build a dashboard spec, or explain data to stakeholders. Triggers on "analyse metrics", "funnel analysis", "cohort analysis", "data deep dive", "why did X drop".

Data Analysis Standard Skill

Turn raw numbers into product decisions. Structure every analysis with a clear question, methodology, finding, and recommended action.

Analysis Framework: The 4-Question Method

Every analysis starts here:

1. What changed? (describe the metric and its movement)
2. Why did it change? (root cause — segment, funnel step, cohort, channel)
3. So what? (business or product impact)
4. Now what? (recommended action with confidence level)

Never deliver data without answering all four. A chart with no narrative is not an analysis.

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Metric Triage Template

Use when a metric has moved unexpectedly:

METRIC: [Name]
MOVEMENT: [X% change over Y period]
BASELINE: [What was normal]

SEGMENTATION CHECK:
- By platform (iOS / Android / Web)?
- By user cohort (new / returning / power users)?
- By acquisition channel?
- By geography?
- By plan/tier?

ROOT CAUSE HYPOTHESIS:
1. [Most likely explanation] — Evidence: [data point]
2. [Alternative explanation] — Evidence: [data point]
3. [Ruling out] — Eliminated because: [reason]

CONCLUSION: [Single sentence answer to "why did this change?"]
CONFIDENCE: [High / Medium / Low] — based on [data available]

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Funnel Analysis Structure

Stage	Metric	Current	Benchmark/Target	Drop-off %	Notes
[Top of funnel]	[Users]	[N]	[N]	—
[Step 2]	[Users]	[N]	[N]	[X%]
[Step 3]	[Users]	[N]	[N]	[X%]
[Conversion]	[Users]	[N]	[N]	[X%]

Biggest drop-off: [Step X → Step Y] — Hypothesis: [reason] Recommended investigation: [specific query or test]

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Cohort Analysis Guidelines

Always define:

• Cohort definition: [What groups users — signup week, first action, plan type]
• Retention metric: [What counts as retained — login, core action, revenue]
• Retention window: [D1, D7, D30, W4, M3, etc.]

Output a cohort retention table and annotate:

• Baseline retention for each cohort
• Cohorts that over/underperform and why (feature launch? campaign? seasonal?)
• Trend direction across cohorts (improving / declining / stable)

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Stakeholder Analysis Output Format

[Analysis Title] — [Date]

Question being answered: [Specific question in plain English] Time period: [Date range] Data source: [Where data comes from]

Finding:

[1–2 sentence plain-English summary of what the data shows]

Key chart / table: [Include or describe]

Root cause: [Best explanation with evidence]

Confidence level: [High / Medium / Low] — [reason]

Recommended action:

1. [Immediate action — owner, timeline]
2. [Investigation needed — what to check next]
3. [Monitoring — what metric to watch and at what cadence]

What this analysis does NOT tell us: [Important caveat — what data is missing or what can't be concluded]

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Guidelines

• Always state what the data cannot tell you — never oversell confidence
• Correlations are not causation — flag this every time
• If the user has no baseline, recommend establishing one before drawing conclusions
• Recommend the simplest chart for each finding: bar for comparison, line for trends, scatter for correlation, table for detailed breakdowns
• Always specify the time window — "conversion dropped" is meaningless without "from X to Y over Z period"