Skill-Details
data-analysis-standard
Structured product metrics, funnel, cohort, and root-cause analysis.
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SKILL.md
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--- name: data-analysis-standard description: "Structure a product data analysis, metric deep-dive, funnel analysis, or cohort study. Use when asked to analyse product metrics, investigate a drop in conversion, explain a data change to stakeholders, or find the root cause of a metric movement. Produces a structured analysis with question, root cause, confidence level, and recommended action." --- # 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) ## Deeper Materials This skill ships with support files — use them when they are available: - **`references/analysis-integrity.md`** — Analysis Integrity: the Checks Between Query and Conclusion. Apply it while producing the output; it carries the calibration and judgment calls the method summary above compresses. - **`templates/analysis-writeup.md`** — a fill-iVollständige Quelle auf GitHub lesen (öffnet externe Seite)