---
id: daily-metrics-review
name: "metrics-review"
url: https://skills.yangsir.net/skill/daily-metrics-review
author: anthropics
domain: data-analysis
tags: ["data-analysis", "product-management", "metrics", "business-intelligence", "data-visualization"]
install_count: 2900
rating: 4.40 (20 reviews)
github: https://github.com/anthropics/knowledge-work-plugins
---

# metrics-review

> 分析和审查产品指标，识别趋势和异常，提供可操作的洞察以优化产品策略

**Stats**: 2,900 installs · 4.4/5 (20 reviews)

## Before / After 对比

### 指标分析效率

**Before**:

手动在多个仪表板间切换查找指标，用表格工具制作图表和计算同比环比。每周分析报告需要 3-4 小时，难以发现隐藏的异常和关联，问题暴露时已造成损失。

**After**:

自动聚合多源数据并识别趋势和异常，智能关联指标变化与产品事件。15 分钟生成洞察报告，突出需要关注的 KPI 和推荐行动，提前预警潜在问题。

| Metric | Before | After | Change |
|---|---|---|---|
| 周报分析时间 | 210分钟 | 15分钟 | -93% |
| 异常发现时效 | 72小时 | 2小时 | -97% |

## Readme

# metrics-review

# Metrics Review

If you see unfamiliar placeholders or need to check which tools are connected, see [CONNECTORS.md](https://github.com/anthropics/knowledge-work-plugins/blob/HEAD/product-management/skills/metrics-review/../../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 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, a 6% target is not ambitious.

- **Effort**: How much investment are you putting behind this? Bigger bets warrant more ambitious targets.

- **Confidence**: How confident are you in hitting the target? Set a "commit" (high confidence) and a "stretch" (ambitious).

## Metric Review Cadences

### Weekly Metrics Check

**Purpose**: Catch issues quickly, monitor experiments, stay in touch with product health.
**Duration**: 15-30 minutes.
**Attendees**: Product manager, maybe engineering lead.

**What to review**:

- North Star metric: current value, week-over-week change

- Key L1 metrics: any notable movements

- Active experiments: results and statistical significance

- Anomalies: any unexpected spikes or drops

- Alerts: anything that triggered a monitoring alert

**Action**: If something looks off, investigate. Otherwise, note it and move on.

### Monthly Metrics Review

**Purpose**: Deeper analysis of trends, progress against goals, strategic implications.
**Duration**: 30-60 minutes.
**Attendees**: Product team, key stakeholders.

**What to review**:

- Full L1 metric scorecard with month-over-month trends

- Progress against quarterly OKR targets

- Cohort analysis: are newer cohorts performing better?

- Feature adoption: how are recent launches performing?

- Segment analysis: any divergence between user segments?

**Action**: Identify 1-3 areas to investigate or invest in. Update priorities if metrics reveal new information.

### Quarterly Business Review

**Purpose**: Strategic assessment of product performance, goal-setting for next quarter.
**Duration**: 60-90 minutes.
**Attendees**: Product, engineering, design, leadership.

**What to review**:

- OKR scoring for the quarter

- Trend analysis for all L1 metrics over the quarter

- Year-over-year comparisons

- Competitive context: market changes and competitor movements

- What worked and what did not

**Action**: Set OKRs for next quarter. Adjust product strategy based on what the data shows.

## Dashboard Design Principles

### Effective Product Dashboards

A good dashboard answers the question "How is the product doing?" at a glance.

**Principles**:

- 

**Start with the question, not the data**. What decisions does this dashboard support? Design backwards from the decision.

- 

**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.

- 

**Context over numbers**. A number without context is meaningless. Always show: current value, comparison (previous period, target, benchmark), trend direction.

- 

**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.

- 

**Consistent time periods**. Use the same time period for all metrics on a dashboard. Mixing daily and monthly metrics creates confusion.

- 

**Visual status indicators**. Use color to indicate health at a glance:

Green: on track or improving

- Yellow: needs attention or flat

- Red: off track or declining

- 

**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.

### Dashboard Layout

**Top row**: North Star metric with trend line and target.

**Second row**: L1 metrics scorecard — current value, change, target, status for each key metric.

**Third row**: Key funnels or conversion metrics — visual funnel showing drop-off at each stage.

**Fourth row**: Recent experiments and launches — active A/B tests, recent feature launches with early metrics.

**Bottom / drill-down**: L2 metrics, segment breakdowns, and detailed time series for investigation.

### Dashboard Anti-Patterns

- **Vanity metrics**: Metrics that always go up but do not indicate health (total signups ever, total page views)

- **Too many metrics**: Dashboards that require scrolling to see. If it does not fit on one screen, cut metrics.

- **No comparison**: Raw numbers without context (current value with no previous period or target)

- **Stale dashboards**: Metrics that have not been updated or reviewed in months

- **Output dashboards**: Measuring team activity (tickets closed, PRs merged) instead of user and business outcomes

- **One dashboard for all audiences**: Executives, PMs, and engineers need different views. One size does not fit all.

### Alerting

Set alerts for metrics that require immediate attention:

- **Threshold alerts**: Metric drops below or rises above a critical threshold (error rate > 1%, conversion < 5%)

- **Trend alerts**: Metric shows sustained decline over multiple days/weeks

- **Anomaly alerts**: Metric deviates significantly from expected range

**Alert hygiene**:

- Every alert should be actionable. If you cannot do anything about it, do not alert on it.

- Review and tune alerts regularly. Too many false positives and people ignore all alerts.

- Define an owner for each alert. Who responds when it fires?

- Set appropriate severity levels. Not everything is P0.

## Output Format

Use tables for the scorecard. Use clear status indicators. Keep the summary tight — the reader should get the essential story in 30 seconds.

## Tips

- Start with the "so what" — what is the most important thing in this metrics review? Lead with that.

- Absolute numbers without context are useless. Always show comparisons (vs previous period, vs target, vs benchmark).

- Be careful about attribution. Correlation is not causation. If a metric moved, acknowledge uncertainty about why.

- Segment analysis often reveals that an aggregate metric masks important differences. A flat overall number might hide one segment growing and another shrinking.

- Not all metric movements matter. Small fluctuations are noise. Focus attention on meaningful changes.

- If a metric is missing its target, do not just report the miss — recommend what to do about it.

- Metrics reviews should drive decisions. If the review does not lead to at least one action, it was not useful.

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---
*Source: https://skills.yangsir.net/skill/daily-metrics-review*
*Markdown mirror: https://skills.yangsir.net/api/skill/daily-metrics-review/markdown*