grailautomation/metrics-review

Metrics Review

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Review and analyze product metrics, identify trends, and surface actionable insights.

Usage

/product-management:metrics-review $ARGUMENTS

Workflow

1. Gather Metrics Data

If ~~product analytics is connected:

• Pull key product metrics for the relevant time period
• Get comparison data (previous period, same period last year, targets)
• Pull segment breakdowns if available

If no analytics tool is connected, ask the user to provide:

• The metrics and their values (paste a table, screenshot, or describe)
• Comparison data (previous period, targets)
• Any context on recent changes (launches, incidents, seasonality)

Ask the user:

• What time period to review? (last week, last month, last quarter)
• What metrics to focus on? Or should we review the full product metrics suite?
• Are there specific targets or goals to compare against?
• Any known events that might explain changes (launches, outages, marketing campaigns, seasonality)?

2. Organize the Metrics

Structure the review using the metrics hierarchy from the metrics-tracking skill: North Star metric at the top, L1 health indicators (acquisition, activation, engagement, retention, revenue, satisfaction), and L2 diagnostic metrics for drill-down.

If the user has not defined their metrics hierarchy, help them identify their North Star and key L1 metrics before proceeding.

3. Analyze Trends

For each key metric:

• Current value: What is the metric today?
• Trend: Up, down, or flat compared to previous period? Over what timeframe?
• vs Target: How does it compare to the goal or target?
• Rate of change: Is the trend accelerating or decelerating?
• Anomalies: Any sudden changes, spikes, or drops?

Identify correlations:

• Do changes in one metric correlate with changes in another?
• Are there leading indicators that predict lagging metric changes?
• Do segment breakdowns reveal that an aggregate trend is driven by a specific cohort?

4. Generate the Review

Summary

2-3 sentences: overall product health, most notable changes, key callout.

Metric Scorecard

Table format for quick scanning:

| Metric | Current | Previous | Change | Target | Status | |--------|---------|----------|--------|--------|--------| | [Metric] | [Value] | [Value] | [+/- %] | [Target] | [On track / At risk / Miss] |

Trend Analysis

For each metric worth discussing:

• What happened and how significant is the change
• Why it likely happened (attribution based on known events, correlated metrics, segment analysis)
• Whether this is a one-time event or a sustained trend

Bright Spots

What is going well:

• Metrics beating targets
• Positive trends to sustain
• Segments or features showing strong performance

Areas of Concern

What needs attention:

• Metrics missing targets or trending negatively
• Early warning signals before they become problems
• Metrics where we lack visibility or understanding

Recommended Actions

Specific next steps based on the analysis:

• Investigations to run (dig deeper into a concerning trend)
• Experiments to launch (test hypotheses about what could improve a metric)
• Investments to make (double down on what is working)
• Alerts to set (monitor a metric more closely)

Context and Caveats

• Known data quality issues
• Events that affect comparability (outages, holidays, launches)
• Metrics we should be tracking but are not yet

5. Follow Up

After generating the review:

• Ask if any metric needs deeper investigation
• Offer to create a dashboard spec for ongoing monitoring
• Offer to draft experiment proposals for areas of concern
• Offer to set up a metrics review template for recurring use

Output Format

Use tables for the scorecard. Use clear status indicators. Keep the summary tight — the reader should get the essential story in 30 seconds.

Tips

• Start with the "so what" — what is the most important thing in this metrics review? Lead with that.
• Absolute numbers without context are useless. Always show comparisons (vs previous period, vs target, vs benchmark).
• Be careful about attribution. Correlation is not causation. If a metric moved, acknowledge uncertainty about why.
• Segment analysis often reveals that an aggregate metric masks important differences. A flat overall number might hide one segment growing and another shrinking.
• Not all metric movements matter. Small fluctuations are noise. Focus attention on meaningful changes.
• If a metric is missing its target, do not just report the miss — recommend what to do about it.
• Metrics reviews should drive decisions. If the review does not lead to at least one action, it was not useful.