/metrics-review

name: metrics-review
description: Review and analyze product metrics with trend analysis and actionable insights. Use when running a weekly, monthly, or quarterly metrics review, investigating a sudden spike or drop, comparing performance against targets, or turning raw numbers into a scorecard with recommended actions.
argument-hint: "<time period or metric focus>"

Metrics Review

> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md).

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

Usage

/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 a metrics hierarchy: North Star metric at the top, L1 health indicators (acquisition, activation, engagement, retention, revenue, satisfaction), and L2 diagnostic metrics for drill-down. See **Product Metrics Hierarchy** below for full definitions.

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

Product Metrics Hierarchy

North Star Metric

The single metric that best captures the core value your product delivers to users. It should be:

• **Value-aligned**: Moves when users get more value from the product
• **Leading**: Predicts long-term business success (revenue, retention)
• **Actionable**: The product team can influence it through their work
• **Understandable**: Everyone in the company can understand what it means and why it matters

**Examples by product type**:

• Collaboration tool: Weekly active teams with 3+ members contributing
• Marketplace: Weekly transactions completed
• SaaS platform: Weekly active users completing core workflow
• Content platform: Weekly engaged reading/viewing time
• Developer tool: Weekly deployments using the tool

L1 Metrics (Health Indicators)

The 5-7 metrics that together paint a complete picture of product health. These map to the key stages of the user lifecycle:

**Acquisition**: Are new users finding the product?

• New signups or trial starts (volume and trend)
• Signup conversion rate (visitors to signups)
• Channel mix (where are new users coming from)
• Cost per acquisition (for paid channels)

**Activation**: Are new users reaching the value moment?

• Activation rate: % of new users who complete the key action that predicts retention
• Time to activate: how long from signup to activation
• Setup completion rate: % who complete onboarding steps
• First value moment: when users first experience the core product value

**Engagement**: Are active users getting value?

• DAU / WAU / MAU: active users at different timeframes
• DAU/MAU ratio (stickiness): what fraction of monthly users come back daily
• Core action frequency: how often users do the thing that matters most
• Session depth: