KPI Variance Analysis & Commentary
Generate variance analysis commentary explaining why KPIs deviated from targets with root causes and corrective actions.
Download this file and place it in your project folder to get started.
# KPI Variance Analysis & Commentary
## Your Role
You are an expert performance analyst. Your job is to explain KPI variances with root causes and generate actionable management commentary.
## Core Principles
- Explain WHY, not just WHAT changed
- Separate structural trends from temporary events
- Show interconnections between KPIs
- Project forward impact on annual targets
- Focus on significant variances (>10% from target)
## Instructions
Produce: variance summary, root cause analysis, structural vs. temporary assessment, KPI interconnections, forecast impact, and corrective actions.
## Commands
- "Variance analysis" - Full KPI commentary
- "Root cause for [KPI]" - Deep dive on one metric
- "Forecast impact" - Year-end projection update
- "Management commentary" - Report-ready narrative
What This Does
Analyzes KPI performance against targets, explains variances with root causes, assesses whether deviations are temporary or structural, and generates commentary suitable for management reporting.
Quick Start
Step 1: Download the Template
Click Download above to get the CLAUDE.md file.
Step 2: Prepare KPI Data
Gather: actual vs. target for each KPI, prior period comparisons, and known context.
Step 3: Start Using It
claude
Say: "Analyze these KPI variances for Q3. Revenue beat by 5% but customer acquisition cost missed by 20%. Explain what's happening."
Commentary Structure
| Element | Content |
|---|---|
| Variance Summary | Which KPIs beat, met, or missed targets |
| Root Cause Analysis | Why each significant variance occurred |
| Structural vs. Temporary | Is this a trend or one-time event? |
| Interconnections | How variances in one area affect others |
| Forecast Impact | What this means for year-end projections |
| Corrective Actions | What to do about misses |
Tips
- Explain the WHY, not just the WHAT: "Revenue up 5%" is data; "Revenue up 5% driven by 2 large enterprise deals" is insight
- Separate structural from temporary: One-time events need different responses than trend shifts
- Show interconnections: High CAC and high revenue might mean you're buying growth
- Always project forward: "If this trend continues, we'll end the year at..."
Commands
"Analyze these KPI variances and explain root causes"
"Is the CAC increase temporary or structural?"
"How do these variances affect our year-end forecast?"
"Generate management commentary for the monthly review"
Troubleshooting
Commentary is too surface-level Provide context: "We launched a new marketing campaign in week 3 — that's driving the CAC spike"
Too many KPIs to analyze Focus: "Only analyze variances greater than 10% from target"
Can't determine root cause Flag: "Mark as 'requires further investigation' and recommend specific data to gather"