Metrics Review

1---2name: metrics-review3description: 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.4argument-hint: "<time period or metric focus>"5---6 7# Metrics Review8 9> If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](../../CONNECTORS.md).10 11Review and analyze product metrics, identify trends, and surface actionable insights.12 13## Usage14 15```16/metrics-review $ARGUMENTS17```18 19## Workflow20 21### 1. Gather Metrics Data22 23If **~~product analytics** is connected:24- Pull key product metrics for the relevant time period25- Get comparison data (previous period, same period last year, targets)26- Pull segment breakdowns if available27 28If no analytics tool is connected, ask the user to provide:29- The metrics and their values (paste a table, screenshot, or describe)30- Comparison data (previous period, targets)31- Any context on recent changes (launches, incidents, seasonality)32 33Ask the user:34- What time period to review? (last week, last month, last quarter)35- What metrics to focus on? Or should we review the full product metrics suite?36- Are there specific targets or goals to compare against?37- Any known events that might explain changes (launches, outages, marketing campaigns, seasonality)?38 39### 2. Organize the Metrics40 41Structure 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.42 43If the user has not defined their metrics hierarchy, help them identify their North Star and key L1 metrics before proceeding.44 45### 3. Analyze Trends46 47For each key metric:48- **Current value**: What is the metric today?49- **Trend**: Up, down, or flat compared to previous period? Over what timeframe?50- **vs Target**: How does it compare to the goal or target?51- **Rate of change**: Is the trend accelerating or decelerating?52- **Anomalies**: Any sudden changes, spikes, or drops?53 54Identify correlations:55- Do changes in one metric correlate with changes in another?56- Are there leading indicators that predict lagging metric changes?57- Do segment breakdowns reveal that an aggregate trend is driven by a specific cohort?58 59### 4. Generate the Review60 61#### Summary622-3 sentences: overall product health, most notable changes, key callout.63 64#### Metric Scorecard65Table format for quick scanning:66 67| Metric | Current | Previous | Change | Target | Status |68|--------|---------|----------|--------|--------|--------|69| [Metric] | [Value] | [Value] | [+/- %] | [Target] | [On track / At risk / Miss] |70 71#### Trend Analysis72For each metric worth discussing:73- What happened and how significant is the change74- Why it likely happened (attribution based on known events, correlated metrics, segment analysis)75- Whether this is a one-time event or a sustained trend76 77#### Bright Spots78What is going well:79- Metrics beating targets80- Positive trends to sustain81- Segments or features showing strong performance82 83#### Areas of Concern84What needs attention:85- Metrics missing targets or trending negatively86- Early warning signals before they become problems87- Metrics where we lack visibility or understanding88 89#### Recommended Actions90Specific next steps based on the analysis:91- Investigations to run (dig deeper into a concerning trend)92- Experiments to launch (test hypotheses about what could improve a metric)93- Investments to make (double down on what is working)94- Alerts to set (monitor a metric more closely)95 96#### Context and Caveats97- Known data quality issues98- Events that affect comparability (outages, holidays, launches)99- Metrics we should be tracking but are not yet100 101### 5. Follow Up102 103After generating the review:104- Ask if any metric needs deeper investigation105- Offer to create a dashboard spec for ongoing monitoring106- Offer to draft experiment proposals for areas of concern107- Offer to set up a metrics review template for recurring use108 109## Product Metrics Hierarchy110 111### North Star Metric112The single metric that best captures the core value your product delivers to users. It should be:113 114- **Value-aligned**: Moves when users get more value from the product115- **Leading**: Predicts long-term business success (revenue, retention)116- **Actionable**: The product team can influence it through their work117- **Understandable**: Everyone in the company can understand what it means and why it matters118 119**Examples by product type**:120- Collaboration tool: Weekly active teams with 3+ members contributing121- Marketplace: Weekly transactions completed122- SaaS platform: Weekly active users completing core workflow123- Content platform: Weekly engaged reading/viewing time124- Developer tool: Weekly deployments using the tool125 126### L1 Metrics (Health Indicators)127The 5-7 metrics that together paint a complete picture of product health. These map to the key stages of the user lifecycle:128 129**Acquisition**: Are new users finding the product?130- New signups or trial starts (volume and trend)131- Signup conversion rate (visitors to signups)132- Channel mix (where are new users coming from)133- Cost per acquisition (for paid channels)134 135**Activation**: Are new users reaching the value moment?136- Activation rate: % of new users who complete the key action that predicts retention137- Time to activate: how long from signup to activation138- Setup completion rate: % who complete onboarding steps139- First value moment: when users first experience the core product value140 141**Engagement**: Are active users getting value?142- DAU / WAU / MAU: active users at different timeframes143- DAU/MAU ratio (stickiness): what fraction of monthly users come back daily144- Core action frequency: how often users do the thing that matters most145- Session depth: how much users do per session146- Feature adoption: % of users using key features147 148**Retention**: Are users coming back?149- D1, D7, D30 retention: % of users who return after 1 day, 7 days, 30 days150- Cohort retention curves: how retention evolves for each signup cohort151- Churn rate: % of users or revenue lost per period152- Resurrection rate: % of churned users who come back153 154**Monetization**: Is value translating to revenue?155- Conversion rate: free to paid (for freemium)156- MRR / ARR: monthly or annual recurring revenue157- ARPU / ARPA: average revenue per user or account158- Expansion revenue: revenue growth from existing customers159- Net revenue retention: revenue retention including expansion and contraction160 161**Satisfaction**: How do users feel about the product?162- NPS: Net Promoter Score163- CSAT: Customer Satisfaction Score164- Support ticket volume and resolution time165- App store ratings and review sentiment166 167### L2 Metrics (Diagnostic)168Detailed metrics used to investigate changes in L1 metrics:169 170- Funnel conversion at each step171- Feature-level usage and adoption172- Segment-specific breakdowns (by plan, company size, geography, user role)173- Performance metrics (page load time, error rate, API latency)174- Content-specific engagement (which features, pages, or content types drive engagement)175 176## Common Product Metrics177 178### DAU / WAU / MAU179**What they measure**: Unique users who perform a qualifying action in a day, week, or month.180 181**Key decisions**:182- What counts as "active"? A login? A page view? A core action? Define this carefully — different definitions tell different stories.183- 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).184 185**How to use them**:186- DAU/MAU ratio (stickiness): values above 0.5 indicate a daily habit. Below 0.2 suggests infrequent usage.187- Trend matters more than absolute number. Is active usage growing, flat, or declining?188- Segment by user type. Power users and casual users behave very differently.189 190### Retention191**What it measures**: Of users who started in period X, what % are still active in period Y?192 193**Common retention timeframes**:194- D1 (next day): Was the first experience good enough to come back?195- D7 (one week): Did the user establish a habit?196- D30 (one month): Is the user retained long-term?197- D90 (three months): Is this a durable user?198 199**How to use retention**:200- 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).201- Compare cohorts over time. Are newer cohorts retaining better than older ones? That means product improvements are working.202- Segment retention by activation behavior. Users who completed onboarding vs those who did not. Users who used feature X vs those who did not.203 204### Conversion205**What it measures**: % of users who move from one stage to the next.206 207**Common conversion funnels**:208- Visitor to signup209- Signup to activation (key value moment)210- Free to paid (trial conversion)211- Trial to paid subscription212- Monthly to annual plan213 214**How to use conversion**:215- Map the full funnel and measure conversion at each step216- Identify the biggest drop-off points — these are your highest-leverage improvement opportunities217- Segment conversion by source, plan, user type. Different segments convert very differently.218- Track conversion over time. Is it improving as you iterate on the experience?219 220### Activation221**What it measures**: % of new users who reach the moment where they first experience the product's core value.222 223**Defining activation**:224- Look at retained users vs churned users. What actions did retained users take that churned users did not?225- The activation event should be strongly predictive of long-term retention226- It should be achievable within the first session or first few days227- Examples: created first project, invited a teammate, completed first workflow, connected an integration228 229**How to use activation**:230- Track activation rate for every signup cohort231- Measure time to activate — faster is almost always better232- Build onboarding flows that guide users to the activation moment233- A/B test activation flows and measure impact on retention, not just activation rate234 235## Goal Setting Frameworks236 237### OKRs (Objectives and Key Results)238 239**Objectives**: Qualitative, aspirational goals that describe what you want to achieve.240- Inspiring and memorable241- Time-bound (quarterly or annually)242- Directional, not metric-specific243 244**Key Results**: Quantitative measures that tell you if you achieved the objective.245- Specific and measurable246- Time-bound with a clear target247- Outcome-based, not output-based248- 2-4 Key Results per Objective249 250**Example**:251```252Objective: Make our product indispensable for daily workflows253 254Key Results:255- Increase DAU/MAU ratio from 0.35 to 0.50256- Increase D30 retention for new users from 40% to 55%257- 3 core workflows with >80% task completion rate258```259 260### OKR Best Practices261- Set OKRs that are ambitious but achievable. 70% completion is the target for stretch OKRs.262- Key Results should measure outcomes (user behavior, business results), not outputs (features shipped, tasks completed).263- Do not have too many OKRs. 2-3 objectives with 2-4 KRs each is plenty.264- OKRs should be uncomfortable. If you are confident you will hit all of them, they are not ambitious enough.265- Review OKRs at mid-period. Adjust effort allocation if some KRs are clearly off track.266- Grade OKRs honestly at end of period. 0.0-0.3 = missed, 0.4-0.6 = progress, 0.7-1.0 = achieved.267 268### Setting Metric Targets269- **Baseline**: What is the current value? You need a reliable baseline before setting a target.270- **Benchmark**: What do comparable products achieve? Industry benchmarks provide context.271- **Trajectory**: What is the current trend? If the metric is already improving at 5% per month, a 6% target is not ambitious.272- **Effort**: How much investment are you putting behind this? Bigger bets warrant more ambitious targets.273- **Confidence**: How confident are you in hitting the target? Set a "commit" (high confidence) and a "stretch" (ambitious).274 275## Metric Review Cadences276 277### Weekly Metrics Check278**Purpose**: Catch issues quickly, monitor experiments, stay in touch with product health.279**Duration**: 15-30 minutes.280**Attendees**: Product manager, maybe engineering lead.281 282**What to review**:283- North Star metric: current value, week-over-week change284- Key L1 metrics: any notable movements285- Active experiments: results and statistical significance286- Anomalies: any unexpected spikes or drops287- Alerts: anything that triggered a monitoring alert288 289**Action**: If something looks off, investigate. Otherwise, note it and move on.290 291### Monthly Metrics Review292**Purpose**: Deeper analysis of trends, progress against goals, strategic implications.293**Duration**: 30-60 minutes.294**Attendees**: Product team, key stakeholders.295 296**What to review**:297- Full L1 metric scorecard with month-over-month trends298- Progress against quarterly OKR targets299- Cohort analysis: are newer cohorts performing better?300- Feature adoption: how are recent launches performing?301- Segment analysis: any divergence between user segments?302 303**Action**: Identify 1-3 areas to investigate or invest in. Update priorities if metrics reveal new information.304 305### Quarterly Business Review306**Purpose**: Strategic assessment of product performance, goal-setting for next quarter.307**Duration**: 60-90 minutes.308**Attendees**: Product, engineering, design, leadership.309 310**What to review**:311- OKR scoring for the quarter312- Trend analysis for all L1 metrics over the quarter313- Year-over-year comparisons314- Competitive context: market changes and competitor movements315- What worked and what did not316 317**Action**: Set OKRs for next quarter. Adjust product strategy based on what the data shows.318 319## Dashboard Design Principles320 321### Effective Product Dashboards322A good dashboard answers the question "How is the product doing?" at a glance.323 324**Principles**:325 3261. **Start with the question, not the data**. What decisions does this dashboard support? Design backwards from the decision.327 3282. **Hierarchy of information**. The most important metric should be the most visually prominent. North Star at the top, L1 metrics next, L2 metrics available on drill-down.329 3303. **Context over numbers**. A number without context is meaningless. Always show: current value, comparison (previous period, target, benchmark), trend direction.331 3324. **Fewer metrics, more insight**. A dashboard with 50 metrics helps no one. Focus on 5-10 that matter. Put everything else in a detailed report.333 3345. **Consistent time periods**. Use the same time period for all metrics on a dashboard. Mixing daily and monthly metrics creates confusion.335 3366. **Visual status indicators**. Use color to indicate health at a glance:337   - Green: on track or improving338   - Yellow: needs attention or flat339   - Red: off track or declining340 3417. **Actionability**. Every metric on the dashboard should be something the team can influence. If you cannot act on it, it does not belong on the product dashboard.342 343### Dashboard Layout344 345**Top row**: North Star metric with trend line and target.346 347**Second row**: L1 metrics scorecard — current value, change, target, status for each key metric.348 349**Third row**: Key funnels or conversion metrics — visual funnel showing drop-off at each stage.350 351**Fourth row**: Recent experiments and launches — active A/B tests, recent feature launches with early metrics.352 353**Bottom / drill-down**: L2 metrics, segment breakdowns, and detailed time series for investigation.354 355### Dashboard Anti-Patterns356- **Vanity metrics**: Metrics that always go up but do not indicate health (total signups ever, total page views)357- **Too many metrics**: Dashboards that require scrolling to see. If it does not fit on one screen, cut metrics.358- **No comparison**: Raw numbers without context (current value with no previous period or target)359- **Stale dashboards**: Metrics that have not been updated or reviewed in months360- **Output dashboards**: Measuring team activity (tickets closed, PRs merged) instead of user and business outcomes361- **One dashboard for all audiences**: Executives, PMs, and engineers need different views. One size does not fit all.362 363### Alerting364Set alerts for metrics that require immediate attention:365 366- **Threshold alerts**: Metric drops below or rises above a critical threshold (error rate > 1%, conversion < 5%)367- **Trend alerts**: Metric shows sustained decline over multiple days/weeks368- **Anomaly alerts**: Metric deviates significantly from expected range369 370**Alert hygiene**:371- Every alert should be actionable. If you cannot do anything about it, do not alert on it.372- Review and tune alerts regularly. Too many false positives and people ignore all alerts.373- Define an owner for each alert. Who responds when it fires?374- Set appropriate severity levels. Not everything is P0.375 376## Output Format377 378Use tables for the scorecard. Use clear status indicators. Keep the summary tight — the reader should get the essential story in 30 seconds.379 380## Tips381 382- Start with the "so what" — what is the most important thing in this metrics review? Lead with that.383- Absolute numbers without context are useless. Always show comparisons (vs previous period, vs target, vs benchmark).384- Be careful about attribution. Correlation is not causation. If a metric moved, acknowledge uncertainty about why.385- Segment analysis often reveals that an aggregate metric masks important differences. A flat overall number might hide one segment growing and another shrinking.386- Not all metric movements matter. Small fluctuations are noise. Focus attention on meaningful changes.387- If a metric is missing its target, do not just report the miss — recommend what to do about it.388- Metrics reviews should drive decisions. If the review does not lead to at least one action, it was not useful.