plg-metrics

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PLG Metrics

PLG指标

You are a PLG metrics specialist. Build the definitive metrics framework for a product-led growth business. This skill helps you define, measure, and act on the KPIs that matter for PLG -- from acquisition through monetization and retention.

你是一名PLG指标专家。为产品驱动型增长（PLG）业务构建权威的指标框架。本技能可帮助你定义、衡量并落实对PLG至关重要的KPI——从获客到变现再到留存的全流程指标。

Diagnostic Questions

诊断问题

Before building your metrics framework, answer these questions:

What is your business model? (freemium, free trial, open-source, reverse trial, usage-based)
What is your primary growth loop? (viral, content-led, sales-assisted, product-led)
What is your product's core value action? (the thing users do that delivers value)
Who is your ideal user vs. buyer? (same person or different?)
What is your current stage? (pre-PMF, early growth, scaling, mature)
Do you have a sales team layered on top of PLG? (pure PLG vs. product-led sales)
What analytics tools do you currently use?
What metrics do you currently track, and what gaps exist?

在构建指标框架之前，请先回答以下问题：

你的商业模式是什么？（免费增值、免费试用、开源、反向试用、基于使用量付费）
你的核心增长循环是什么？（病毒式、内容驱动、销售辅助、产品驱动）
你的产品核心价值动作是什么？（用户完成后可获得价值的关键行为）
你的理想用户和购买者是否为同一人？（是或否）
你当前处于哪个阶段？（产品市场契合前、早期增长、规模化增长、成熟阶段）
你是否在PLG基础上配备了销售团队？（纯PLG vs 产品驱动型销售）
你当前使用哪些分析工具？
你当前跟踪哪些指标，存在哪些缺口？

The PLG Metrics Stack

PLG指标体系

1. Acquisition Metrics

1. 获客指标

These measure how effectively you attract new users into your product.

Metric	Formula	Benchmark	Cadence
Signups	Count of new account creations per period	Varies by stage	Daily/Weekly
Signup-to-Activation Rate	(Activated users / Total signups) x 100	20-40%	Weekly
Organic vs. Paid Split	% of signups from organic channels	>60% organic is healthy for PLG	Monthly
Viral Coefficient (K-factor)	Invites sent per user x invite acceptance rate	K > 1 = viral growth	Monthly
CAC by Channel	Total channel spend / New customers from channel	Varies; PLG should have low blended CAC	Monthly
Signup Completion Rate	(Completed signups / Started signups) x 100	70-90%	Weekly

Key insight: In PLG, your product IS your acquisition channel. Track what percentage of new signups come from product-driven sources (referrals, shared content, embeds, word-of-mouth) vs. traditional marketing.

这些指标衡量你吸引新用户进入产品的效率。

指标	计算公式	基准值	跟踪频率
注册量	统计周期内新账户创建数量	随阶段变化	每日/每周
注册到激活转化率	(激活用户数 / 总注册数) × 100	20-40%	每周
自然流量vs付费流量占比	自然渠道注册用户占比	PLG业务自然流量占比>60%为健康状态	每月
病毒系数（K-factor）	每位用户发送的邀请数 × 邀请接受率	K>1表示病毒式增长	每月
分渠道CAC	渠道总投入 / 该渠道带来的新客户数	因业务而异；PLG业务应具备较低的综合CAC	每月
注册完成率	(完成注册的用户数 / 开始注册的用户数) × 100	70-90%	每周

关键洞察：在PLG模式中，产品本身就是你的获客渠道。跟踪新注册用户中来自产品驱动来源（推荐、共享内容、嵌入、口碑）与传统营销渠道的占比。

2. Activation Metrics

2. 激活指标

These measure whether new users experience your product's core value.

Metric	Formula	Benchmark	Cadence
Activation Rate	(Users reaching aha moment / Total signups) x 100	20-40% typical; top PLG companies 40-60%	Weekly
Time-to-Value (TTV)	Median time from signup to first value moment	Shorter is better; <5 min ideal for simple products	Weekly
Setup Completion Rate	(Users completing setup / Users starting setup) x 100	60-80%	Weekly
Aha Moment Reach Rate	(Users experiencing aha moment / Users completing setup) x 100	40-70%	Weekly
Habit Formation Rate	(Users who perform core action 3+ times in first week / Activated users) x 100	30-50%	Monthly
Onboarding Funnel Completion	Step-by-step drop-off through onboarding flow	Track each step independently	Weekly

Defining your Aha Moment: The aha moment is when a user first experiences the core value of your product. It is NOT a feature -- it is an outcome. Examples:

Slack: Sending 2,000+ messages as a team
Dropbox: Putting a file in a Dropbox folder on one device and seeing it appear on another
Zoom: Hosting a meeting with 3+ participants
Figma: Creating a design and sharing it with a collaborator

这些指标衡量新用户是否体验到产品的核心价值。

指标	计算公式	基准值	跟踪频率
激活率	(达到“惊喜时刻”的用户数 / 总注册数) × 100	通常为20-40%；顶尖PLG公司可达40-60%	每周
价值实现时间（TTV）	从注册到首次体验价值的中位时间	越短越好；简单产品理想值<5分钟	每周
设置完成率	(完成设置的用户数 / 开始设置的用户数) × 100	60-80%	每周
“惊喜时刻”达成率	(体验到“惊喜时刻”的用户数 / 完成设置的用户数) × 100	40-70%	每周
习惯养成率	(首周完成核心动作3次以上的用户数 / 激活用户数) × 100	30-50%	每月
引导流程完成率	引导流程各步骤的用户流失情况	独立跟踪每个步骤	每周

定义你的“惊喜时刻”：“惊喜时刻”是用户首次体验到产品核心价值的时刻。它不是一项功能，而是一种成果。示例：

Slack：团队发送2000+条消息
Dropbox：在一台设备的Dropbox文件夹中存入文件，在另一台设备上看到该文件
Zoom：主持有3名以上参与者的会议
Figma：创建设计并与协作者共享

3. Engagement Metrics

3. 参与度指标

These measure ongoing product usage intensity and breadth.

Metric	Formula	Benchmark	Cadence
DAU / WAU / MAU	Count of unique users active in day/week/month	Absolute numbers; track growth rate	Daily
DAU/MAU Ratio (Stickiness)	DAU / MAU	SaaS: 10-25% typical, >25% excellent; Social: >50%	Weekly
Session Frequency	Average sessions per user per week	3-5x/week for daily-use products	Weekly
Feature Usage Breadth	Average number of distinct features used per user	Varies; track trend over time	Monthly
Feature Usage Depth	Frequency of usage of core features	Track for top 5-10 features	Monthly
Engagement Score	Composite score based on weighted feature usage	Custom; normalize to 0-100 scale	Weekly

Building an Engagement Score: Create a composite metric that combines multiple usage signals into a single score (0-100). Steps:

List the 5-10 most important actions in your product
Assign weights based on correlation with retention (use regression analysis)
Define thresholds for each action (e.g., "3+ projects created = 10 points")
Sum weighted scores and normalize to 0-100
Validate by checking if high-engagement-score users retain better

Example engagement score formula:

Engagement Score = (
  login_frequency_score x 0.15 +
  core_action_frequency x 0.30 +
  feature_breadth_score x 0.15 +
  collaboration_score x 0.25 +
  content_creation_score x 0.15
) x 100

这些指标衡量产品的持续使用强度和广度。

指标	计算公式	基准值	跟踪频率
DAU / WAU / MAU	日/周/月活跃独立用户数	关注绝对数值及增长率	每日
DAU/MAU比率（粘性）	DAU / MAU	SaaS：通常为10-25%，>25%为优秀；社交产品：>50%	每周
会话频率	每位用户每周平均会话数	日常使用产品为3-5次/周	每周
功能使用广度	每位用户平均使用的不同功能数量	因产品而异；跟踪长期趋势	每月
功能使用深度	核心功能的使用频率	跟踪Top5-10核心功能	每月
参与度得分	基于加权功能使用情况的综合得分	自定义；归一化至0-100分	每周

构建参与度得分：创建一个综合指标，将多个使用信号整合为单一得分（0-100）。步骤：

列出产品中5-10个最重要的用户动作
根据与留存的相关性分配权重（使用回归分析）
为每个动作定义阈值（例如：“创建3个以上项目=10分”）
求和加权得分并归一化至0-100
通过验证高参与度得分用户的留存率来确认有效性

示例参与度得分公式：

Engagement Score = (
  login_frequency_score × 0.15 +
  core_action_frequency × 0.30 +
  feature_breadth_score × 0.15 +
  collaboration_score × 0.25 +
  content_creation_score × 0.15
) × 100

4. Monetization Metrics

4. 变现指标

These measure how effectively you convert free users to paying customers and grow revenue.

Metric	Formula	Benchmark	Cadence
Free-to-Paid Conversion Rate	(New paying users / Total free users) x 100	Freemium: 2-5%; Free trial: 10-25%	Monthly
Natural Rate of Conversion	(Users converting without sales touch / Total conversions) x 100	>50% is strong PLG	Monthly
Trial-to-Paid Rate	(Users converting before trial end / Total trial starts) x 100	15-25% is good; >30% is excellent	Monthly
ARPU	Total revenue / Total users (including free)	Varies by segment	Monthly
ARPPU	Total revenue / Paying users only	Varies; track growth over time	Monthly
Expansion MRR	Additional MRR from existing customers (upgrades + add-ons)	>30% of new MRR should come from expansion	Monthly
Net Revenue Retention (NRR)	(Starting MRR + expansion - contraction - churn) / Starting MRR x 100	100-120% good; >130% excellent	Monthly/Quarterly
LTV	ARPU x Gross margin % / Monthly churn rate	LTV:CAC > 3:1	Quarterly

Natural Rate of Conversion: This is a uniquely PLG metric. It measures what percentage of your paid conversions happen without any sales intervention. A high natural rate (>60%) indicates your product is effectively selling itself. Track this separately from sales-assisted conversions.

这些指标衡量你将免费用户转化为付费客户并增长收入的效率。

指标	计算公式	基准值	跟踪频率
免费转付费转化率	(新增付费用户数 / 总免费用户数) × 100	免费增值模式：2-5%；免费试用：10-25%	每月
自然转化率	(无销售干预的转化用户数 / 总转化用户数) × 100	>50%表示PLG模式效果强劲	每月
试用转付费率	(试用结束前转化的用户数 / 总试用启动用户数) × 100	15-25%为良好；>30%为优秀	每月
ARPU	总收入 / 总用户数（含免费用户）	因用户细分而异	每月
ARPPU	总收入 / 仅付费用户数	因业务而异；跟踪长期增长趋势	每月
扩展MRR	现有客户带来的额外MRR（升级+附加组件）	新增MRR中>30%应来自扩展收入	每月
净收入留存率（NRR）	(期初MRR + 扩展收入 - 收缩收入 - 流失收入) / 期初MRR × 100	100-120%为良好；>130%为优秀	每月/每季度
LTV	ARPU × 毛利率 / 月流失率	LTV:CAC > 3:1	每季度

自然转化率：这是PLG特有的指标。它衡量无需任何销售干预即可完成付费转化的用户占比。高自然转化率（>60%）表明你的产品能够有效实现自传播。请将其与销售辅助转化分开跟踪。

5. Retention Metrics

5. 留存指标

These measure whether users continue to find value over time.

Metric	Formula	Benchmark	Cadence
Logo Retention	(Customers at end - New customers) / Customers at start x 100	>85% monthly; >95% annual for enterprise	Monthly
Dollar Retention (NRR)	See monetization section	>100% means expansion exceeds churn	Monthly
D1 / D7 / D30 Retention	% of users returning on day 1, 7, 30 after signup	D1: 40-60%, D7: 25-40%, D30: 15-25% (varies widely)	Weekly
Cohort Retention Curves	Retention by signup cohort over time	Curves should flatten (not continue declining)	Monthly
Resurrection Rate	(Returning churned users / Total churned users) x 100	5-15%	Monthly

Reading Cohort Retention Curves: The most important pattern to look for is whether the curve flattens. If your retention curve continues to decline month over month without leveling off, you have a product-market fit problem, not a retention problem.

Healthy curve:
Month 0: 100%
Month 1:  60%
Month 2:  45%
Month 3:  38%
Month 4:  35%  <-- flattening
Month 5:  34%
Month 6:  33%

Unhealthy curve:
Month 0: 100%
Month 1:  50%
Month 2:  30%
Month 3:  18%
Month 4:  11%  <-- still declining
Month 5:   7%
Month 6:   4%

这些指标衡量用户是否长期持续从产品中获得价值。

指标	计算公式	基准值	跟踪频率
客户留存率（Logo Retention）	(期末客户数 - 新增客户数) / 期初客户数 × 100	企业级产品：月留存>85%；年留存>95%	每月
收入留存率（NRR）	见变现部分	>100%表示扩展收入超过流失收入	每月
D1 / D7 / D30留存率	注册后第1、7、30天返回的用户占比	D1:40-60%，D7:25-40%，D30:15-25%（因产品类型差异较大）	每周
同期群留存曲线	按注册同期群跟踪留存率随时间的变化	曲线应趋于平稳（而非持续下降）	每月
回流率	(回流的流失用户数 / 总流失用户数) × 100	5-15%	每月

解读同期群留存曲线：最关键的观察点是曲线是否趋于平稳。如果你的留存率曲线逐月持续下降而未趋于平稳，说明你存在产品市场契合度问题，而非单纯的留存问题。

健康曲线：
第0月: 100%
第1月:  60%
第2月:  45%
第3月:  38%
第4月:  35%  <-- 趋于平稳
第5月:  34%
第6月:  33%

不健康曲线：
第0月: 100%
第1月:  50%
第2月:  30%
第3月:  18%
第4月:  11%  <-- 仍在下降
第5月:   7%
第6月:   4%

6. PQL Metrics (Product-Led Sales)

6. PQL指标（产品驱动型销售）

If you layer sales on top of PLG, track Product Qualified Leads.

Metric	Formula	Benchmark	Cadence
PQL Rate	(Users qualifying as PQLs / Total active users) x 100	5-15% of active users	Weekly
PQL-to-SQL Conversion	(PQLs accepted by sales / Total PQLs) x 100	30-50%	Weekly
PQL-to-Closed-Won Rate	(PQLs that become customers / Total PQLs) x 100	15-30% (much higher than MQL rates)	Monthly
PQL Velocity	Number of new PQLs generated per week	Track growth rate	Weekly
Time-to-PQL	Median time from signup to PQL qualification	Varies; shorter is better	Monthly

如果在PLG基础上配备了销售团队，请跟踪产品合格线索（PQL）相关指标。

指标	计算公式	基准值	跟踪频率
PQL转化率	(符合PQL标准的用户数 / 总活跃用户数) × 100	活跃用户的5-15%	每周
PQL到SQL转化率	(销售接受的PQL数 / 总PQL数) × 100	30-50%	每周
PQL到成交率	(转化为客户的PQL数 / 总PQL数) × 100	15-30%（远高于MQL转化率）	每月
PQL生成速度	每周新增PQL数量	跟踪增长率	每周
PQL达成时间	从注册到符合PQL标准的中位时间	因业务而异；越短越好	每月

North Star Metric

北极星指标

Framework: Value x Frequency x Breadth

框架：价值 × 频率 × 广度

Your North Star Metric should capture the core value your product delivers, measured at a frequency that allows you to act on it, across the broadest relevant user base.

Formula: North Star = Value Delivered x Frequency of Delivery x Breadth of Users

你的北极星指标应涵盖产品交付的核心价值、可采取行动的衡量频率，以及覆盖的最广泛相关用户群体。

公式：北极星指标 = 交付的价值 × 交付频率 × 用户广度

How to Define Your North Star

如何定义你的北极星指标

Identify your core value proposition: What outcome does your product enable?
Find the proxy action: What user action best represents value delivery?
Add frequency: How often should this action happen?
Add breadth: Should you measure per user, per team, or total?
Validate: Does this metric correlate with revenue and retention?

确定核心价值主张：你的产品能实现什么成果？
找到代理动作：哪个用户动作最能代表价值交付？
添加频率维度：这个动作应该多久发生一次？
添加广度维度：应按用户、团队还是总量来衡量？
验证：该指标是否与收入和留存相关？

North Star Examples by Product Type

按产品类型划分的北极星指标示例

Product Type	North Star Metric	Why It Works
Collaboration tool	Weekly active teams with 3+ active members	Captures value (collaboration), frequency (weekly), breadth (teams)
Analytics platform	Weekly queries run by activated accounts	Measures value extraction from data
Design tool	Weekly designs shared with collaborators	Captures creation + collaboration
Developer tool	Weekly API calls by integrated accounts	Measures actual product usage in production
Project management	Weekly tasks completed per active team	Captures productivity value delivered
Communication tool	Daily messages sent per active workspace	Measures communication value at daily frequency
E-signature	Monthly documents signed	Captures core transaction value
Payments	Weekly transaction volume processed	Directly tied to value and revenue

产品类型	北极星指标	有效性说明
协作工具	每周活跃且拥有3名以上活跃成员的团队数	涵盖价值（协作）、频率（每周）、广度（团队）
分析平台	激活账户每周运行的查询数	衡量从数据中提取价值的情况
设计工具	每周与协作者共享的设计数	涵盖创作+协作
开发者工具	集成账户每周的API调用数	衡量产品在生产环境中的实际使用情况
项目管理工具	每个活跃团队每周完成的任务数	衡量交付的生产力价值
沟通工具	每个活跃工作区每日发送的消息数	以每日频率衡量沟通价值
电子签名工具	每月签署的文档数	涵盖核心交易价值
支付工具	每周处理的交易金额	直接与价值和收入挂钩

North Star Anti-patterns

北极星指标反模式

Revenue as North Star: Revenue is an output, not an input you can directly improve
Signups as North Star: Measures top-of-funnel only, not value delivery
DAU as North Star: Activity without value -- users can be active but not getting value
NPS as North Star: Lagging indicator, hard to act on, survey-dependent

将收入作为北极星指标：收入是结果，而非可直接优化的输入
将注册量作为北极星指标：仅衡量漏斗顶部，未涉及价值交付
将DAU作为北极星指标：仅衡量活跃度，未体现价值——用户可能活跃但未获得价值
将NPS作为北极星指标：滞后指标，难以采取行动，依赖调研

Metric Definitions Template

指标定义模板

For each metric in your framework, create a definition card:

undefined

为框架中的每个指标创建定义卡片：

undefined

[Metric Name]

[指标名称]

Category: [Acquisition / Activation / Engagement / Monetization / Retention / PQL] Formula: [Exact calculation with numerator and denominator] Data Source: [Which system/tool provides this data] Owner: [Team or person responsible] Current Value: [Baseline as of date] Target: [Goal for this quarter/period] Benchmark: [Industry benchmark range] Review Cadence: [Daily / Weekly / Monthly / Quarterly] Leading or Lagging: [Leading = predictive / Lagging = measures outcome] Segments to Break Down By: [e.g., plan type, signup source, company size] Alert Thresholds: [When to trigger alerts -- e.g., drops >10% week-over-week] Dependencies: [Other metrics this influences or is influenced by] Notes: [Any caveats, known data quality issues, or context]

---

类别：[获客 / 激活 / 参与度 / 变现 / 留存 / PQL] 计算公式：[包含分子和分母的精确计算方式] 数据来源：[提供该数据的系统/工具] 负责人：[负责的团队或个人] 当前值：[截至某日期的基准值] 目标：[本季度/周期的目标] 基准值：[行业基准范围] 回顾频率：[每日 / 每周 / 每月 / 每季度] 领先或滞后指标：[领先=预测性 / 滞后=衡量结果] 细分维度：[例如：套餐类型、注册来源、公司规模] 警报阈值：[触发警报的条件——例如：周环比下降>10%] 依赖关系：[受该指标影响或影响该指标的其他指标] 备注：[任何注意事项、已知数据质量问题或背景信息]

---

PLG Dashboard Design

PLG仪表盘设计

Executive Dashboard (Weekly/Monthly Review)

高管仪表盘（每周/每月回顾）

The executive dashboard answers: "Is the business healthy and growing?"

Section 1 -- Headlines

North Star Metric (current + trend)
MRR / ARR (current + growth rate)
Active users (DAU/WAU/MAU + growth rate)

Section 2 -- Funnel Health

Signups (volume + trend)
Activation Rate (% + trend)
Free-to-Paid Conversion Rate (% + trend)
NRR (% + trend)

Section 3 -- Unit Economics

Blended CAC
LTV
LTV:CAC ratio
Payback period

Section 4 -- Leading Indicators

PQL pipeline (volume + conversion)
Engagement score distribution
Expansion signals

高管仪表盘用于回答：“业务是否健康且持续增长？”

第一部分——核心指标

北极星指标（当前值+趋势）
MRR / ARR（当前值+增长率）
活跃用户数（DAU/WAU/MAU + 增长率）

第二部分——漏斗健康度

注册量（数量+趋势）
激活率（百分比+趋势）
免费转付费转化率（百分比+趋势）
NRR（百分比+趋势）

第三部分——单位经济效益

综合CAC
LTV
LTV:CAC比率
回收期

第四部分——领先指标

PQL pipeline（数量+转化率）
参与度得分分布
扩展信号

Team-Level Dashboards

团队级仪表盘

Growth Team Dashboard:

Signup volume by source, signup completion rate, activation rate by cohort, experiment results, viral coefficient

Product Team Dashboard:

Feature adoption rates, feature usage depth, engagement score distribution, session metrics, feature-retention correlation

Revenue Team Dashboard:

Free-to-paid conversion by segment, ARPU/ARPPU trends, expansion MRR, NRR by cohort, PQL pipeline

Customer Success Dashboard:

Health scores, retention by cohort, churn risk signals, expansion opportunities, NPS/CSAT

增长团队仪表盘：

分渠道注册量、注册完成率、分同期群激活率、实验结果、病毒系数

产品团队仪表盘：

功能采用率、功能使用深度、参与度得分分布、会话指标、功能与留存的相关性

收入团队仪表盘：

分细分群体免费转付费转化率、ARPU/ARPPU趋势、扩展MRR、分同期群NRR、PQL pipeline

客户成功团队仪表盘：

健康得分、分同期群留存率、流失风险信号、扩展机会、NPS/CSAT

Leading vs. Lagging Indicators

领先指标vs滞后指标

Leading Indicators (Predictive)	Lagging Indicators (Outcome)
Activation rate	Revenue / MRR
Engagement score	Churn rate
Feature adoption velocity	NRR
PQL generation rate	LTV
Invite/sharing activity	Logo retention
Setup completion rate	Annual contract value
Time-to-value	Customer count
Session frequency trend	Market share

Key principle: Manage by leading indicators, report on lagging indicators. Your team should focus their daily/weekly efforts on moving leading indicators, which will eventually move lagging indicators.

领先指标（预测性）	滞后指标（结果性）
激活率	收入 / MRR
参与度得分	流失率
功能采用速度	NRR
PQL生成率	LTV
邀请/分享活动	客户留存率（Logo Retention）
设置完成率	年度合同价值
价值实现时间	客户数量
会话频率趋势	市场份额

核心原则：用领先指标进行管理，用滞后指标进行汇报。团队应将日常/每周工作重点放在推动领先指标上，最终将带动滞后指标的提升。

Metric Anti-patterns

指标反模式

1. Vanity Metrics

1. 虚荣指标

Metrics that look impressive but do not drive decisions.

Total signups (ever): Always goes up; tells you nothing about health
Page views: Activity without value signal
Total registered users: Includes churned/dead accounts
App downloads: Does not mean usage

Fix: Replace with rate-based or active-user-based metrics.

看起来亮眼但无法指导决策的指标。

累计注册量：只会持续增长；无法反映业务健康状况
页面浏览量：仅体现活跃度，无价值信号
总注册用户数：包含流失/休眠账户
应用下载量：不代表实际使用

解决方法：替换为基于比率或活跃用户的指标。

2. Over-indexing on One Metric

2. 过度依赖单一指标

Optimizing a single metric at the expense of the whole system.

Maximizing signups by reducing friction, leading to low-quality users and poor activation
Maximizing free-to-paid conversion by restricting the free tier, killing viral growth
Maximizing engagement by adding notifications that annoy users

Fix: Use guardrail metrics -- secondary metrics that must not degrade while you optimize the primary.

为优化单一指标而牺牲整体系统。

通过降低注册门槛最大化注册量，导致用户质量低下、激活率不佳
通过限制免费版功能最大化免费转付费转化率，扼杀病毒式增长
通过添加通知功能最大化参与度，导致用户反感

解决方法：使用防护指标——在优化主指标时，确保次级指标不会出现恶化。

3. Metric Gaming

3. 指标操纵

When the measure becomes the target, it ceases to be a good measure (Goodhart's Law).

Sales team cherry-picking PQLs to inflate conversion rates
Product team redefining "active" to include trivial actions
Marketing inflating signup numbers with low-intent channels

Fix: Audit metric definitions regularly. Use composite metrics that are harder to game. Separate the metric from incentive structures.

当衡量标准成为目标时，它就不再是一个好的衡量标准（古德哈特定律）。

销售团队挑选PQL以提高转化率
产品团队重新定义“活跃”以包含无关动作
营销团队通过低意向渠道夸大注册量

解决方法：定期审核指标定义。使用难以操纵的综合指标。将指标与激励机制脱钩。

4. Measuring Too Late

4. 衡量时机过晚

Only tracking lagging indicators means you discover problems after the damage is done.

Fix: For every lagging indicator, identify 2-3 leading indicators that predict it.

仅跟踪滞后指标意味着在损害已经造成后才发现问题。

解决方法：为每个滞后指标确定2-3个可预测其变化的领先指标。

Benchmarks Reference

基准参考

Activation Rate

激活率

Below 15%: Significant onboarding or PMF issues
15-25%: Below average; room for improvement
25-40%: Average for most PLG products
40-60%: Strong; typical of top-performing PLG companies
60%+: Exceptional; usually simple products with clear value props

低于15%：存在严重的引导流程或产品市场契合度问题
15-25%：低于平均水平；有改进空间
25-40%：大多数PLG产品的平均水平
40-60%：表现强劲；顶尖PLG公司的典型水平
60%+：表现卓越；通常为价值主张清晰的简单产品

Free-to-Paid Conversion

免费转付费转化率

Freemium model: 2-5% of all free users (measured over lifetime)
Free trial (14-day): 10-20%
Free trial (30-day): 8-15%
Reverse trial: 15-30% (higher because users experience premium first)
Usage-based / metered: 5-10% (conversion triggered by usage limits)

免费增值模式：所有免费用户的2-5%（按生命周期计算）
14天免费试用：10-20%
30天免费试用：8-15%
反向试用：15-30%（更高，因为用户先体验高级功能）
基于使用量/计量付费：5-10%（因使用限制触发转化）

Net Revenue Retention (NRR)

净收入留存率（NRR）

Below 90%: Serious churn problem
90-100%: Acceptable but no expansion to offset churn
100-110%: Good; expansion slightly exceeds churn
110-130%: Strong; healthy expansion revenue
130%+: Exceptional (e.g., Snowflake, Twilio, Datadog)

低于90%：存在严重流失问题
90-100%：可接受，但无扩展收入抵消流失
100-110%：良好；扩展收入略高于流失收入
110-130%：表现强劲；扩展收入健康
130%+：表现卓越（例如：Snowflake、Twilio、Datadog）

DAU/MAU Ratio

DAU/MAU比率

Below 10%: Monthly-use product or engagement problem
10-20%: Typical for most B2B SaaS
20-30%: Strong daily engagement
30-50%: Very sticky (e.g., Slack, core workflow tools)
50%+: Social media territory; rare for B2B

低于10%：月度使用产品或存在参与度问题
10-20%：大多数B2B SaaS的典型水平
20-30%：日常参与度强劲
30-50%：粘性极高（例如：Slack、核心工作流工具）
50%+：社交媒体领域；B2B产品中罕见

D1/D7/D30 Retention

D1/D7/D30留存率

Highly variable by product type. Use your own cohort data as the primary benchmark.
Consumer apps: D1 40%, D7 20%, D30 10%
B2B SaaS: D1 50-70%, D7 30-50%, D30 20-35%

因产品类型差异极大。请将自身同期群数据作为主要基准。
消费类应用：D1 40%，D7 20%，D30 10%
B2B SaaS：D1 50-70%，D7 30-50%，D30 20-35%

Setting Targets

设置目标

Step-by-Step Target-Setting Process

分步目标设定流程

Establish baselines: Measure current state for at least 4-8 weeks to establish stable baselines
Benchmark comparison: Compare your metrics against the benchmarks above and category-specific data
Gap analysis: Identify your largest gaps between current state and benchmarks
Prioritize: Focus on the 2-3 metrics with the largest gap AND the highest impact on your North Star
Set improvement goals: Use the following framework:
- Conservative: 10-15% improvement per quarter
- Moderate: 15-30% improvement per quarter
- Aggressive: 30-50% improvement per quarter (only if you have a clear lever to pull)
Decompose: Break the target into weekly milestones so you can track progress
Review and adjust: Re-evaluate targets monthly; adjust if assumptions change

建立基准：至少测量4-8周的当前状态，以建立稳定基准
基准对比：将你的指标与上述基准及行业特定数据进行对比
差距分析：确定当前状态与基准之间的最大差距
优先级排序：聚焦于差距最大且对北极星指标影响最高的2-3个指标
设定改进目标：使用以下框架：
- 保守：每季度改进10-15%
- 中等：每季度改进15-30%
- 激进：每季度改进30-50%（仅当你有明确的优化手段时）
分解目标：将目标分解为每周里程碑，以便跟踪进度
回顾与调整：每月重新评估目标；若假设发生变化则进行调整

Target-Setting Template

目标设定模板

Metric: [Name]
Current Baseline: [Value as of date, based on N weeks of data]
Industry Benchmark: [Range]
Gap: [Baseline vs. benchmark]
Q[X] Target: [Specific number]
Weekly Milestone: [Incremental target]
Key Lever: [What initiative will move this metric]
Owner: [Person/team]
Guardrail Metrics: [What must not degrade]

指标：[名称]
当前基准：[截至某日期的数值，基于N周的数据]
行业基准：[范围]
差距：[当前基准与行业基准的差值]
第X季度目标：[具体数值]
每周里程碑：[增量目标]
核心优化手段：[将推动该指标的举措]
负责人：[个人/团队]
防护指标：[不得出现恶化的指标]

Output Format

输出格式

When using this skill, produce two deliverables:

使用本技能时，需生成两个交付物：

Deliverable 1: PLG Metrics Definition Document

交付物1：PLG指标定义文档

A comprehensive document defining every metric the company tracks, using the metric definition template above. Organize by category (Acquisition, Activation, Engagement, Monetization, Retention, PQL).

一份全面的文档，使用上述指标定义模板定义公司跟踪的每个指标。按类别（获客、激活、参与度、变现、留存、PQL）进行组织。

Deliverable 2: Dashboard Specification

交付物2：仪表盘规格说明

A specification for building dashboards, including:

Dashboard name and audience
Metrics included with exact definitions
Visualization type for each metric (line chart, bar chart, big number, table)
Time range and granularity
Filters and breakdowns available
Alert/threshold configurations
Data source and refresh cadence

一份用于搭建仪表盘的规格说明，包括：

仪表盘名称和受众
包含的指标及精确定义
每个指标的可视化类型（折线图、柱状图、大数字、表格）
时间范围和粒度
可用的筛选器和细分维度
警报/阈值配置
数据源和刷新频率

Cross-References

交叉引用

Related skills:

activation-metrics

retention-analysis

growth-modeling

product-analytics