Analyze metrics into product decisions
data-analysis-standardskillsetup L1★327
mohitagw15856/pm-claude-skills ↗What it does
Convert raw numbers into product decisions with root-cause analysis
Best for
Explaining unexpected metric movements before escalating to leadership; answers 'what changed, why, so what, now what' for any metric problem.
Inputs
- · Metric or question being investigated
- · Time period and current vs. baseline values
- · Available segments (platform, cohort, channel, geography)
Outputs
- · Root-cause analysis with evidence (not just hypothesis)
- · Confidence level and recommended actions with owners and timelines
Preconditions
- · Question clearly stated (not vague like 'why is revenue down')
- · Baseline or benchmark available for comparison
- · Access to segmented data (can't analyze by platform if data is aggregated)
Failure modes
- · Confuses correlation with causation (names pattern but not cause)
- · Over-confident conclusion without stating what data is missing
- · Recommends action without owner or timeline
- · Analyzes aggregated data — masks important segment-level shifts
Trust signals
- · Four-question framework required: what changed, why, so what, now what
- · Root-cause hypothesis with evidence (data point, not speculation)
- · Confidence level stated and justified
- · Explicitly names what the data cannot tell you
- · Recommended action includes owner and timeline