metrics-review

What this skill does

# 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: how much users do per session
- Feature adoption: % of users using key features

**Retention**: Are users coming back?
- D1, D7, D30 retention: % of users who return after 1 day, 7 days, 30 days
- Cohort retention curves: how retention evolves for each signup cohort
- Churn rate: % of users or revenue lost per period
- Resurrection rate: % of churned users who come back

**Monetization**: Is value translating to revenue?
- Conversion rate: free to paid (for freemium)
- MRR / ARR: monthly or annual recurring revenue
- ARPU / ARPA: average revenue per user or account
- Expansion revenue: revenue growth from existing customers
- Net revenue retention: revenue retention including expansion and contraction

**Satisfaction**: How do users feel about the product?
- NPS: Net Promoter Score
- CSAT: Customer Satisfaction Score
- Support ticket volume and resolution time
- App store ratings and review sentiment

### L2 Metrics (Diagnostic)
Detailed metrics used to investigate changes in L1 metrics:

- Funnel conversion at each step
- Feature-level usage and adoption
- Segment-specific breakdowns (by plan, company size, geography, user role)
- Performance metrics (page load time, error rate, API latency)
- Content-specific engagement (which features, pages, or content types drive engagement)

## Common Product Metrics

### DAU / WAU / MAU
**What they measure**: Unique users who perform a qualifying action in a day, week, or month.

**Key decisions**:
- What counts as "active"? A login? A page view? A core action? Define this carefully — different definitions tell different stories.
- Which timeframe matters most? DAU for daily-use products (messaging, email). WAU for weekly-use products (project management). MAU for less frequent products (tax software, travel booking).

**How to use them**:
- DAU/MAU ratio (stickiness): values above 0.5 indicate a daily habit. Below 0.2 suggests infrequent usage.
- Trend matters more than absolute number. Is active usage growing, flat, or declining?
- Segment by user type. Power users and casual users behave very differently.

### Retention
**What it measures**: Of users who started in period X, what % are still active in period Y?

**Common retention timeframes**:
- D1 (next day): Was the first experience good enough to come back?
- D7 (one week): Did the user establish a habit?
- D30 (one month): Is the user r