Metrics Review

# 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 acquisit