data-analysis-standard
The data-analysis-standard skill structures product metric investigations using a four-question framework that identifies what changed, why it changed, business impact, and recommended actions. Use it when analyzing product performance drops, investigating metric movements, conducting funnel or cohort analyses, or explaining data changes to stakeholders. It produces outputs with segmentation checks, root cause hypotheses, confidence levels, and actionable recommendations.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/data-analysis-standard && cp -r /tmp/data-analysis-standard/plugins/pm-analytics/skills/data-analysis-standard ~/.claude/skills/data-analysis-standardSKILL.md
# 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. --- ## 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] ``` --- ## 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] --- ## 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) --- ## 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] --- ## Required Inputs Ask the user for these if not provided: - **Metric or question** being investigated - **Time period** (what changed, from when to when) - **Data available** (which segments, sources, or queries you have access to) - **Business context** (what decision this analysis informs) - **Audience** (who will read this — exec / team / data team) ## Quality Checks - [ ] Analysis answers all 4 questions: what changed, why, so what, now what - [ ] Root cause has evidence (not just hypothesis) - [ ] Confidence level is stated and justified - [ ] What the data cannot tell us is explicitly named - [ ] Recommended action includes an owner and timeline ## Anti-Patterns - [ ] Do not present correlations as causation — always state the distinction explicitly - [ ] Do not report a metric movement without stating the time window and comparison baseline - [ ] Do not skip the "so what" — raw observations without recommended actions are incomplete analysis - [ ] Do not overstate confidence — label hypotheses clearly and note what data would be needed to confirm them - [ ] Do not ignore segment breakdowns — aggregate metrics can mask opposing trends in sub-segments ## 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"
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