metrics-tracking

What this skill does

# Metrics Tracking Skill

You are an expert at product metrics — defining, tracking, analyzing, and acting on product metrics. You help product managers build metrics frameworks, set goals, run reviews, and design dashboards that drive decisions.

## 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 retained long-term?
- D90 (three months): Is this a durable user?

**How to use retention**:
- Plot retention curves by cohort. Look for: initial drop-off (activation problem), steady decline (engagement problem), or flattening (good — you have a stable retained base).
- Compare cohorts over time. Are newer cohorts retaining better than older ones? That means product improvements are working.
- Segment retention by activation behavior. Users who completed onboarding vs those who did not. Users who used feature X vs those who did not.

### Conversion
**What it measures**: % of users who move from one stage to the next.

**Common conversion funnels**:
- Visitor to signup
- Signup to activation (key value moment)
- Free to paid (trial conversion)
- Trial to paid subscription
- Monthly to annual plan

**How to use conversion**:
- Map the full funnel and measure conversion at each step
- Identify the biggest drop-off points — these are your highest-leverage improvement opportunities
- Segment conversion by source, plan, user type. Different segments convert very differently.
- Track conversion over time. Is it improving as you iterate on the experience?

### Activation
**What it measures**: % of new users who reach the moment where they first experience the product's core value.

**Defining activation**:
- Look at retained users vs churned users. What actions did retained users take that churned users did not?
- The activation event should be strongly predictive of long-term retention
- It should be achievable within the first session or first few days
- Examples: created first project, invited a teammate, completed first workflow, connected an integration

**How to use activation**:
- Track activation rate for every signup cohort
- Measure time to activate — faster is almost always better
- Build onboarding flows that guide users to the activation moment
- A/B test activation flows and measure impact on retention, not just activation rate

## Goal Setting Frameworks

### OKRs (Objectives and Key Results)

**Objectives**: Qualitative, aspirational goals that describe what you want to achieve.
- Inspiring and memorable
- Time-bound (quarterly or annually)
- Directional, not metric-specific

**Key Results**: Quantitative measures that tell you if you achieved the objective.
- Specific and measurable
- Time-bound with a clear target
- Outcome-based, not output-based
- 2-4 Key Results per Objective

**Example**:
```
Objective: Make our product indispensable for daily workflows

Key Results:
- Increase DAU/MAU ratio from 0.35 to 0.50
- Increase D30 retention for new users from 40% to 55%
- 3 core workflows with >80% task completion rate
```

### OKR Best Practices
- Set OKRs that are ambitious but achievable. 70% completion is the target for stretch OKRs.
- Key Results should measure outcomes (user behavior, business results), not outputs (features shipped, tasks completed).
- Do not have too many OKRs. 2-3 objectives with 2-4 KRs each is plenty.
- OKRs should be uncomfortable. If you are confident you will hit all of them, they are not ambitious enough.
- Review OKRs at mid-period. Adjust effort allocation if some KRs are clearly off track.
- Grade OKRs honestly at end of period. 0.0-0.3 = missed, 0.4-0.6 = progress, 0.7-1.0 = achieved.

### Setting Metric Targets
- **Baseline**: What is the current value? You need a reliable baseline before setting a target.
- **Benchmark**: What do comparable products achieve? Industry benchmarks provide context.
- **Trajectory**: What is the current trend? If the metric is already improving at 5% per month,