/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.

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.