metrics-review

name	metrics-review
description	Run a regular metrics review — pull data, spot patterns, translate numbers into narrative. Use for weekly or biweekly product health checks.

Metrics Review & Data Storytelling

Turn your weekly metrics review from a 3-hour dashboard-clicking session into a 30-minute focused analysis. Claude pulls the patterns, flags anomalies, and drafts the narrative. You interpret the results, decide what matters, and communicate it.

When to Use

• Weekly or biweekly product health check
• Preparing a metrics update for team standup or leadership review
• You noticed something in a dashboard and need to investigate quickly
• Pulling together a data story for a specific audience
• Quarterly business review prep

When NOT to Use

• Churn is increasing and you need root cause analysis — use Growth & Retention Diagnostics
• You're setting up metrics for the first time — use KPI definition in CLAUDE.md
• You're analyzing a specific experiment — use the Experiment Brief template

The AI-Native Approach

Step	Time	Claude Does	You Do
Pull and structure data	10 min	Organize metrics, calculate trends, flag anomalies	Feed the data, confirm accuracy
Identify patterns	10 min	Surface what's moving, what's flat, what's surprising	Apply context Claude doesn't have
Build the narrative	10 min	Draft the data story for your audience	Add judgment, implications, and next steps

Process

Step 1: Pull and Structure (10 minutes)

Here's this [week's/month's] metrics data:
[Paste from your analytics tool, spreadsheet, or dashboard export]

Structure a metrics review:
- For each key metric: current value, previous period, trend (improving/flat/declining), vs. target
- Flag anything that moved more than [10%/your threshold] in either direction
- Flag anything that's been flat for 3+ periods when you'd expect movement
- Calculate rates of change — is the trend accelerating or decelerating?

Step 2: Identify Patterns (10 minutes)

Based on the structured metrics:
- What's the headline? (The single most important thing in this data)
- What's improving and why might that be?
- What's declining and what hypotheses explain it?
- What's surprisingly flat — should we be concerned?
- Are there correlations between metrics? (e.g., activation up but retention flat)
- What happened this period that might explain changes? (releases, marketing, seasonal, external)

Add your context: What shipped this period? What's happening in the market? What do you know from user conversations that explains a number?

Step 3: Build the Narrative (10 minutes)

Different audiences need different data stories.

For your team:

Draft a team metrics update:
- Headline metric and trend
- What's working (keep doing)
- What needs attention (investigate or act)
- Specific actions for this sprint based on the data
Keep it to 5-7 bullet points max.

For leadership:

Draft an executive metrics summary:
- Lead with the business metric they care about (revenue, growth, retention)
- Show trajectory vs. target — are we on pace?
- One risk to flag with a mitigation plan
- One bright spot worth highlighting
Keep it to one paragraph or 3-5 bullets.

For a quarterly review:

Structure a quarterly business review section:
- Quarter-over-quarter trends for key metrics
- Progress against OKRs (actual vs. target)
- Cohort comparison: are newer cohorts healthier?
- Top 3 wins and top 3 concerns with evidence
- Outlook: what the data predicts for next quarter

Output

• Structured metrics dashboard with trends and anomaly flags
• Pattern analysis with hypotheses
• Audience-appropriate data narrative (team, leadership, or quarterly review)

Common Pitfalls

1. Reporting numbers without interpretation. "MRR grew 3%" is data. "MRR grew 3%, driven by expansion in the enterprise segment, which validates our upsell investment" is a story.
2. Cherry-picking good metrics. Include the bad numbers. Credibility comes from honesty. If churn is up, say so and say what you're doing about it.
3. Too many metrics. A weekly review should cover 5-8 key metrics, not 30. If everything is a priority, nothing is.
4. No action items. A metrics review that doesn't lead to "so what do we do?" is a waste of time. Every review should end with at least one action.
5. Trusting AI-generated insights without validation. Claude spots patterns in the data you provide. It doesn't know about the pricing change you announced, the competitor that launched, or the bug that affected 5% of users. Add your context.

Related Skills

• Growth & Retention Diagnostics — when a metric needs deep investigation
• OKR & Goal Setting — metrics review feeds OKR progress tracking
• Reference: FRAMEWORKS.md — SaaS Metrics definitions and benchmarks
• Reference: AGENTIC-WORKFLOWS.md — User Analytics Agent for automated monitoring