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product-analytics

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Product Analytics

产品分析

Define, track, and interpret product metrics across discovery, growth, and mature product stages.
在产品探索、增长和成熟阶段定义、跟踪并解读产品指标。

When To Use

使用场景

Use this skill for:
  • Metric framework selection (AARRR, North Star, HEART)
  • KPI definition by product stage (pre-PMF, growth, mature)
  • Dashboard design and metric hierarchy
  • Cohort and retention analysis
  • Feature adoption and funnel interpretation
本技能适用于：
  • 指标框架选择（AARRR、North Star、HEART）
  • 按产品阶段定义KPI（Pre-PMF、增长期、成熟期）
  • 仪表盘设计与指标层级搭建
  • 群组与留存分析
  • 功能采用与漏斗解读

Workflow

工作流程

  1. Select metric framework
  • AARRR for growth loops and funnel visibility
  • North Star for cross-functional strategic alignment
  • HEART for UX quality and user experience measurement
  1. Define stage-appropriate KPIs
  • Pre-PMF: activation, early retention, qualitative success
  • Growth: acquisition efficiency, expansion, conversion velocity
  • Mature: retention depth, revenue quality, operational efficiency
  1. Design dashboard layers
  • Executive layer: 5-7 directional metrics
  • Product health layer: acquisition, activation, retention, engagement
  • Feature layer: adoption, depth, repeat usage, outcome correlation
  1. Run cohort + retention analysis
  • Segment by signup cohort or feature exposure cohort
  • Compare retention curves, not single-point snapshots
  • Identify inflection points around onboarding and first value moment
  1. Interpret and act
  • Connect metric movement to product changes and release timeline
  • Distinguish signal from noise using period-over-period context
  • Propose one clear product action per major metric risk/opportunity
  1. 选择指标框架
  • AARRR：适用于增长循环与漏斗可视化
  • North Star：适用于跨职能战略对齐
  • HEART：适用于UX质量与用户体验衡量
  1. 定义适配阶段的KPI
  • Pre-PMF：激活率、早期留存、定性成功指标
  • 增长期：获客效率、用户拓展、转化速度
  • 成熟期：留存深度、营收质量、运营效率
  1. 设计仪表盘层级
  • 管理层级：5-7个方向性指标
  • 产品健康层级：获客、激活、留存、参与度
  • 功能层级：采用率、使用深度、重复使用、结果相关性
  1. 开展群组+留存分析
  • 按注册群组或功能接触群组划分用户
  • 对比留存曲线，而非单一时间点快照
  • 识别新用户引导与首次价值时刻附近的转折点
  1. 解读并采取行动
  • 将指标变化与产品更新及发布时间线关联
  • 利用同期对比区分信号与噪音
  • 针对每个主要指标风险/机会提出一项明确的产品行动

KPI Guidance By Stage

各阶段KPI指南

Pre-PMF

Pre-PMF

  • Activation rate
  • Week-1 retention
  • Time-to-first-value
  • Problem-solution fit interview score
  • 激活率
  • 首周留存率
  • 首次价值达成时间
  • 问题-解决方案匹配度访谈得分

Growth

增长期

  • Funnel conversion by stage
  • Monthly retained users
  • Feature adoption among new cohorts
  • Expansion / upsell proxy metrics
  • 各阶段漏斗转化率
  • 月度留存用户数
  • 新群组的功能采用率
  • 用户拓展/升级代理指标

Mature

成熟期

  • Net revenue retention aligned product metrics
  • Power-user share and depth of use
  • Churn risk indicators by segment
  • Reliability and support-deflection product metrics
  • 与净营收留存对齐的产品指标
  • 核心用户占比与使用深度
  • 按细分群体划分的流失风险指标
  • 可靠性与自助服务产品指标

Dashboard Design Principles

仪表盘设计原则

  • Show trends, not isolated point estimates.
  • Keep one owner per KPI.
  • Pair each KPI with target, threshold, and decision rule.
  • Use cohort and segment filters by default.
  • Prefer comparable time windows (weekly vs weekly, monthly vs monthly).
See:
  • references/metrics-frameworks.md
  • references/dashboard-templates.md
  • 展示趋势，而非孤立的点估计值。
  • 为每个KPI指定唯一负责人。
  • 为每个KPI搭配目标值、阈值和决策规则。
  • 默认启用群组和细分群体筛选器。
  • 优先使用可对比的时间窗口（周同比、月同比）。
参考：
  • references/metrics-frameworks.md
  • references/dashboard-templates.md

Cohort Analysis Method

群组分析方法

  1. Define cohort anchor event (signup, activation, first purchase).
  2. Define retained behavior (active day, key action, repeat session).
  3. Build retention matrix by cohort week/month and age period.
  4. Compare curve shape across cohorts.
  5. Flag early drop points and investigate journey friction.
  1. 定义群组锚定事件（注册、激活、首次购买）。
  2. 定义留存行为（活跃日、关键操作、重复会话）。
  3. 按群组周/月和周期构建留存矩阵。
  4. 对比不同群组的曲线形态。
  5. 标记早期流失点并排查用户旅程中的摩擦。

Retention Curve Interpretation

留存曲线解读

  • Sharp early drop, low plateau: onboarding mismatch or weak initial value.
  • Moderate drop, stable plateau: healthy core audience with predictable churn.
  • Flattening at low level: product used occasionally, revisit value metric.
  • Improving newer cohorts: onboarding or positioning improvements are working.
  • 早期大幅下滑，低平台期：新用户引导不匹配或初始价值薄弱。
  • 适度下滑，稳定平台期：核心受众健康，流失可预测。
  • 在低水平趋于平缓：产品被偶尔使用，需重新审视价值指标。
  • 新群组表现提升：新用户引导或定位优化生效。

Anti-Patterns

反模式

Anti-patternFix
Vanity metrics — tracking pageviews or total signups without activation contextAlways pair acquisition metrics with activation rate and retention
Single-point retention — reporting "30-day retention is 20%"Compare retention curves across cohorts, not isolated snapshots
Dashboard overload — 30+ metrics on one screenExecutive layer: 5-7 metrics. Feature layer: per-feature only
No decision rule — tracking a KPI with no threshold or action planEvery KPI needs: target, threshold, owner, and "if below X, then Y"
Averaging across segments — reporting blended metrics that hide segment differencesAlways segment by cohort, plan tier, channel, or geography
Ignoring seasonality — comparing this week to last week without adjustingUse period-over-period with same-period-last-year context
反模式修复方案
虚荣指标 —— 在无激活上下文的情况下跟踪页面浏览量或总注册数始终将获客指标与激活率和留存率搭配使用
单一时间点留存 —— 仅报告“30天留存率为20%”对比不同群组的留存曲线，而非孤立快照
仪表盘过载 —— 一个屏幕上显示30+个指标管理层级：5-7个指标。功能层级：仅展示对应功能的指标
无决策规则 —— 跟踪KPI但无阈值或行动计划每个KPI都需要：目标值、阈值、负责人，以及“若低于X，则执行Y”的规则
跨群体平均 —— 报告混合指标，掩盖群体差异始终按群组、套餐层级、渠道或地域细分指标
忽略季节性 —— 直接对比本周与上周数据，未做调整使用同期对比（如今年本周与去年本周）

Tooling

工具

scripts/metrics_calculator.py

scripts/metrics_calculator.py

CLI utility for retention, cohort, and funnel analysis from CSV data. Supports text and JSON output.
bash
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一款CLI工具，用于从CSV数据中进行留存、群组和漏斗分析，支持文本和JSON格式输出。
bash
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Retention analysis

留存分析

python3 scripts/metrics_calculator.py retention events.csv python3 scripts/metrics_calculator.py retention events.csv --format json
python3 scripts/metrics_calculator.py retention events.csv python3 scripts/metrics_calculator.py retention events.csv --format json

Cohort matrix

群组矩阵

python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain month python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain week --format json
python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain month python3 scripts/metrics_calculator.py cohort events.csv --cohort-grain week --format json

Funnel conversion

漏斗转化

python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay --format json

**CSV format for retention/cohort:**
```csv
user_id,cohort_date,activity_date
u001,2026-01-01,2026-01-01
u001,2026-01-01,2026-01-03
u002,2026-01-02,2026-01-02
CSV format for funnel:
csv
user_id,stage
u001,visit
u001,signup
u001,activate
u002,visit
u002,signup
python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay python3 scripts/metrics_calculator.py funnel funnel.csv --stages visit,signup,activate,pay --format json

**留存/群组分析的CSV格式：**
```csv
user_id,cohort_date,activity_date
u001,2026-01-01,2026-01-01
u001,2026-01-01,2026-01-03
u002,2026-01-02,2026-01-02
漏斗分析的CSV格式：
csv
user_id,stage
u001,visit
u001,signup
u001,activate
u002,visit
u002,signup

Cross-References

交叉引用

  • Related: 
    product-team/experiment-designer
    — for A/B test planning after identifying metric opportunities
  • Related: 
    product-team/product-manager-toolkit
    — for RICE prioritization of metric-driven features
  • Related: 
    product-team/product-discovery
    — for assumption mapping when metrics reveal unknowns
  • Related: 
    finance/saas-metrics-coach
    — for SaaS-specific metrics (ARR, MRR, churn, LTV)
  • 相关：
    product-team/experiment-designer
    —— 用于识别指标机会后的A/B测试规划
  • 相关：
    product-team/product-manager-toolkit
    —— 用于基于指标驱动的功能RICE优先级排序
  • 相关：
    product-team/product-discovery
    —— 用于指标揭示未知时的假设映射
  • 相关：
    finance/saas-metrics-coach
    —— 用于SaaS特定指标（ARR、MRR、流失率、LTV）