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

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"