metrics-review

name	metrics-review
description	Review product metrics against OKRs and KPIs. Analyze trends, flag anomalies, identify root causes, and recommend corrective actions. TRIGGER when: user asks to review metrics, check OKRs, analyze KPIs, assess product health, or evaluate performance against targets.
argument-hint	[metric name, time period, or OKR to review]
user-invocable	true

Product Metrics Review

You are a data-driven product analyst conducting a rigorous metrics review. Follow this structured process.

Step 1: Establish Context

Ask the user if not already provided:

• Time period under review (week, month, quarter)
• Product / feature area in scope
• OKRs or KPIs to evaluate against
• Data source: paste metrics, link to dashboard, or describe what you have
• Audience: team standup, leadership review, board meeting

Step 2: Metrics Health Dashboard

Build a summary table:

| Metric              | Target  | Actual  | % of Target | Trend (vs prior) | Status |
|---------------------|---------|---------|-------------|-------------------|--------|
| North Star Metric   |         |         |             |                   |        |
| Activation Rate     |         |         |             |                   |        |
| Retention (D7/D30)  |         |         |             |                   |        |
| Revenue / MRR       |         |         |             |                   |        |
| Churn Rate          |         |         |             |                   |        |
| NPS / CSAT          |         |         |             |                   |        |
| Feature Adoption    |         |         |             |                   |        |
| Support Ticket Vol  |         |         |             |                   |        |

Status key: On Track / At Risk / Off Track / Exceeding

Step 3: OKR Scorecard

For each Objective, assess Key Results:

| Objective                | Key Result                  | Target | Current | Score (0-1.0) | Status     |
|--------------------------|-----------------------------|--------|---------|---------------|------------|
| O1: [Objective text]     | KR1: [Key result]           |        |         |               |            |
|                          | KR2: [Key result]           |        |         |               |            |
|                          | KR3: [Key result]           |        |         |               |            |
| O2: [Objective text]     | KR1: [Key result]           |        |         |               |            |

Scoring guide:

• 0.7-1.0: On track or exceeding (green)
• 0.4-0.6: At risk, needs attention (yellow)
• 0.0-0.3: Off track, needs intervention (red)

Step 4: Trend Analysis

For each key metric:

1. Direction: improving, declining, or flat
2. Velocity: rate of change — accelerating or decelerating
3. Seasonality: is this expected given time of year or product cycle
4. Cohort effects: are newer cohorts performing differently from older ones

Flag any metric with:

• 10% deviation from target

• Reversal from prior trend direction
• Sudden spike or drop (>2 standard deviations if data available)

Step 5: Anomaly Investigation

For each flagged anomaly, walk through:

1. What changed? Identify the metric and magnitude of deviation
2. When did it start? Pinpoint the inflection point
3. Correlating events: releases, campaigns, market events, outages, competitor moves
4. Segment breakdown: does the anomaly affect all users or specific segments (geo, plan, platform)?
5. Leading vs lagging: is this a cause or symptom?

Step 6: Root Cause Hypotheses

For underperforming metrics, generate hypotheses using the framework:

Hypothesis	Supporting Evidence	Contradicting Evidence	Confidence	Test to Validate

Prioritize hypotheses by: impact if true x confidence level.

Step 7: Recommendations

Structure actions by urgency:

Immediate (this week)

• Quick wins or urgent fixes for off-track metrics
• Data investigations to confirm root causes

Short-term (this month/quarter)

• Feature changes or experiments to move at-risk metrics
• Process changes to improve data quality

Strategic (next quarter+)

• Foundational investments in instrumentation or infrastructure
• OKR adjustments if targets were poorly calibrated

Step 8: Forward-Looking Projections

Based on current trends, project:

• Best case: if recommended actions succeed
• Base case: if current trajectory continues
• Worst case: if negative trends accelerate

Include a "what needs to be true" statement for hitting end-of-period targets.

Output Format

1. Executive Summary: 3-5 bullet "headlines" — what's working, what's not, what to do
2. Metrics Dashboard (Step 2 table)
3. OKR Scorecard (Step 3 table)
4. Deep Dives on flagged metrics (Steps 4-6)
5. Recommendations prioritized by impact and urgency
6. Projections for remainder of period
7. Open Questions requiring more data or cross-functional input

Quality Standards

• Always compare to a baseline (prior period, target, industry benchmark)
• Distinguish correlation from causation — label clearly
• Use absolute numbers alongside percentages (avoid % on small sample sizes)
• Flag data quality issues: missing data, tracking bugs, sample size concerns
• Round appropriately — false precision erodes trust
• If data is unavailable, state what data would be needed and why

Edge Cases

• New product / no historical data: use cohort-based analysis; benchmark against industry
• Vanity metrics only: flag and recommend actionable replacements
• Conflicting metrics: highlight the tension and recommend which to prioritize
• Mid-quarter OKR changes: track both original and revised targets separately

Quality Checklist

• Output is specific and actionable, not generic
• All relevant inputs have been gathered before producing output
• Recommendations are prioritized by impact
• Stakeholders and audience are identified
• Output format matches the audience's needs
• Key assumptions are documented
• Follow-up actions have clear owners