lean-analytics
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ChineseLean Analytics
Lean Analytics
A data discipline for startups distilled from Alistair Croll and Benjamin Yoskovitz's Lean Analytics: separate metrics that change decisions from numbers that merely flatter, then point the whole company at the One Metric That Matters for your business model and stage. Use it to choose metrics, audit dashboards, set targets, and plan instrumentation.
这是从Alistair Croll和Benjamin Yoskovitz所著的《Lean Analytics》中提炼出的一套创业公司数据方法论：将能驱动决策的指标与仅能自我满足的数值区分开来，然后让整个公司聚焦于符合自身商业模式与发展阶段的One Metric That Matters（OMTM）。可借助它来选择指标、审核仪表盘、设置目标并规划指标采集方案。
Core Principle
核心原则
Focus on the one metric that matters right now — everything else is noise that feels like progress. Startups die from lack of focus more often than lack of data. The discipline is knowing your business model, knowing your stage, and tracking the single number that tells you whether the riskiest part of the business is working. A metric earns attention only if it changes what you do next.
聚焦当下最关键的核心指标——其他所有数据都是看似有进展的噪音。 创业公司死于缺乏聚焦的情况远多于死于缺乏数据。这套方法论的关键在于明确自身商业模式、所处发展阶段，并跟踪那个能告诉你业务最具风险的部分是否运转正常的单一指标。只有当一个指标能改变你下一步行动时，它才值得关注。
Scoring
评分标准
Goal: 10/10. Rate metric choices, dashboards, and instrumentation plans 0-10 against these principles. Report the current score and the specific changes needed to reach 10/10.
- 9-10: One OMTM matched to model and stage, paired counter-metric, a line in the sand with a pre-committed miss response, cohorted and segmented data
- 7-8: Mostly actionable ratios and a plausible OMTM, but no explicit target, weak cohorting, or too many "key" metrics
- 5-6: Actionable and vanity metrics mixed; dashboard exists but rarely changes a decision; model and stage never named
- 3-4: Vanity metrics dominate — totals, cumulative charts, blended averages; metrics copied from other companies
- 0-2: No instrumentation, or numbers chosen to impress investors rather than drive decisions
目标：10/10分。依据以下原则，对指标选择、仪表盘和指标采集方案进行0-10分评级。报告当前得分以及达到10/10分所需的具体改进措施。
- 9-10分：OMTM与商业模式和阶段匹配，搭配对应反指标，设定明确目标线及未达标时的预设应对方案，数据按群组和细分维度划分
- 7-8分：大部分为可落地的比率指标，OMTM合理，但未设定明确目标，群组分析薄弱，或“关键”指标过多
- 5-6分：可落地指标与虚荣指标混杂；仪表盘存在但极少驱动决策；未明确商业模式和发展阶段
- 3-4分：虚荣指标占主导——总数、累计图表、混合平均值；指标照搬其他公司
- 0-2分：未部署指标采集，或选择的数值仅用于打动投资者而非驱动决策
Framework
框架体系
1. Good Metrics vs Vanity Metrics
1. 优质指标vs虚荣指标
Core concept: A good metric is comparative (versus last week, versus another cohort), understandable (the team can recall and debate it), a ratio or rate (not an ever-growing total), and behavior-changing — if a number won't change what you do, stop measuring it. Vanity metrics — total signups, page views, cumulative anything — only go up and only make you feel good.
Why it works: The output of analytics is decisions, not data. Ratios are inherently comparative and operable, while totals hide decay: total registered users rises even while the product bleeds actives. Forcing every metric through the "what will we do differently?" test converts reporting into learning.
Key insights:
- Work the lens pairs: qualitative vs quantitative (interviews reveal why, numbers reveal how much), exploratory vs reporting (exploration finds your unfair advantage; reporting keeps the lights on), leading vs lagging (complaints predict churn before churn happens), correlated vs causal
- Correlation finds the lever; only an experiment proves it — find metrics that move together, then change one for a randomized group to test causality
- Cohorts make time honest: compare users by signup month, or real improvement vanishes inside blended averages
- Segments make comparisons honest: split by channel, plan, and geography — a flat aggregate often hides one segment soaring and another collapsing
- Averages lie under skew: whales and lurkers are different businesses, so read medians and percentiles
- A cumulative up-and-to-the-right chart is the single most reliable vanity tell
Applications:
| Context | Application | Example |
|---|---|---|
| Dashboard audit | Rewrite each total as a ratio | Total signups → % of visitors activating within 7 days |
| Board reporting | Show cohorts, not cumulative curves | Retention by signup month replaces "users over time" |
| Feature decision | Demand a behavior-changing metric | "If D7 retention doesn't rise 10%, the feature comes out" |
See references/good-metrics.md when auditing a dashboard or running a metric through the four tests — full test definitions, the 10-row vanity rewrite table, a worked cohort-retention example, segmentation rules, the correlation-to-causation experiment loop, and a metric-definition template.
核心概念：优质指标具备可比性（与上周对比、与其他群组对比）、易懂性（团队能记住并展开讨论）、是比率或速率（而非持续增长的总数），且能改变行为——如果一个数值无法改变你的行动，就停止衡量它。虚荣指标——总注册量、页面浏览量、任何累计数值——只会持续上升，仅能让你自我感觉良好。
为何有效：分析的产出是决策，而非数据。比率天生具备可比性和可操作性，而总数会掩盖衰退：总注册用户数持续增长的同时，产品的活跃用户可能在不断流失。让每个指标都通过“这会让我们采取什么不同行动？”的测试，就能将报告转化为学习。
关键见解：
- 运用多组视角：定性vs定量（用户访谈揭示原因，数据揭示程度）、探索性分析vs报告性分析（探索性分析找到你的竞争优势；报告性分析维持日常运营）、领先指标vs滞后指标（用户投诉会在流失发生前预测流失）、相关性vs因果性
- 相关性能找到杠杆，但只有实验能证明因果性——找到联动的指标，然后针对随机分组调整其中一个指标来测试因果关系
- 群组分析让时间维度更真实：按注册月份对比用户，否则真实的改进会被混合平均值掩盖
- 细分维度让对比更真实：按渠道、套餐、地域拆分数据——平淡的总数据背后往往隐藏着一个细分群体增长迅猛而另一个群体崩溃的情况
- 平均值在数据倾斜时会说谎：大额用户与潜水用户是完全不同的业务，因此要关注中位数和百分位数
- 持续上升的累计图表是最可靠的虚荣指标特征
应用场景：
| 场景 | 应用方式 | 示例 |
|---|---|---|
| 仪表盘审核 | 将每个总数改写为比率 | 总注册量 → 7天内激活的访客占比 |
| 董事会汇报 | 展示群组数据，而非累计曲线 | 按注册月份划分的留存率替代“用户随时间变化” |
| 功能决策 | 要求提供能改变行为的指标 | “如果7日留存率未提升10%，该功能将被移除” |
如需审核仪表盘或对指标进行四项测试，请参阅references/good-metrics.md——其中包含完整的测试定义、10行虚荣指标改写表格、群组留存分析示例、细分规则、从相关到因果的实验循环，以及指标定义模板。
2. The One Metric That Matters (OMTM)
2. One Metric That Matters (OMTM)
Core concept: At any moment there is one number that matters above all others — the one that tells you whether the current riskiest assumption is working. Pick it, display it everywhere, and let it drive every experiment until you graduate to the next stage.
Why it works: The OMTM answers the most important question you have right now, forces you to draw a line in the sand so "good" is defined before results arrive, and focuses the entire company. A dashboard of forty numbers diffuses accountability; one number creates a shared scoreboard and a culture of experimentation.
Key insights:
- The OMTM rotates — it is the metric that matters now, not forever; passing a stage gate or pivoting changes it
- Pair it with a counter-metric so it can't be gamed: activation speed paired with 30-day retention, sales velocity paired with refund rate
- A line in the sand has three parts: a target number, a date, and a pre-committed answer to "what do we do if we miss?"
- "Good enough" is a decision made in advance, not a discovery made after — otherwise the goalposts move
- If the team can't agree on the OMTM, you haven't agreed what the riskiest part of the business is — that argument is the valuable part
- Collect many metrics, but watch one — the rest live in drill-down reports, not on the wall
Applications:
| Context | Application | Example |
|---|---|---|
| Quarterly planning | One OMTM per stage; experiments ladder up to it | Stickiness stage → all bets target week-4 retention |
| Dashboard design | OMTM big, 4-6 supporting metrics small | Wall display: paid conversion 3.2% huge; CAC, churn, NPS below |
| Team alignment | Pre-commit the miss response | "Under 10% by March 1 → we pivot to the agency segment" |
Ethical boundary: The line in the sand disciplines the company's bets, not individuals — turning the OMTM into personal quotas invites gaming and hides truth.
See references/omtm.md when choosing or rotating the OMTM, pairing a counter-metric, or drawing the line in the sand — the six-step selection procedure, the 6x3 stage x model matrix, a 7-row counter-metric gaming table, line-in-the-sand and rotation-trigger rules, and three worked examples.
核心概念：在任何时刻，都有一个凌驾于所有指标之上的核心数值——它能告诉你当前最具风险的假设是否成立。选定它，在全公司展示，让它驱动所有实验，直到进入下一个发展阶段。
为何有效：OMTM能回答你当下最重要的问题，迫使你设定明确的目标线，在结果出来前就定义“达标”的标准，并让整个公司聚焦。一个包含40个指标的仪表盘会分散责任；而单一指标能打造共享的计分板，培养实验文化。
关键见解：
- OMTM是动态变化的——它是当下最关键的指标，而非永久指标；进入新阶段或业务转型后，它会随之改变
- 为它搭配反指标，避免被钻空子：激活速度搭配30天留存率，销售速度搭配退款率
- 目标线包含三个部分：目标数值、截止日期、未达标时的预设应对方案
- “足够好”是提前做出的决策，而非事后的发现——否则目标会不断变动
- 如果团队无法就OMTM达成一致，说明你们尚未明确业务最具风险的部分——这场争论本身就有价值
- 可以收集多个指标，但重点关注一个——其余指标放在深度报告中，而非展示在显眼位置
应用场景：
| 场景 | 应用方式 | 示例 |
|---|---|---|
| 季度规划 | 每个阶段对应一个OMTM；所有实验都围绕它展开 | 粘性阶段 → 所有举措以提升第4周留存率为目标 |
| 仪表盘设计 | OMTM放大展示，4-6个辅助指标缩小展示 | 墙面展示：付费转化率3.2%（大字体）；CAC、流失率、NPS（小字体） |
| 团队对齐 | 预设未达标应对方案 | “3月1日前未达到10% → 我们转向企业客户细分市场” |
伦理边界：目标线约束的是公司的举措，而非个人——将OMTM转化为个人配额会引发钻空子行为，掩盖真实情况。
如需选择或更换OMTM、搭配反指标、设定目标线，请参阅references/omtm.md——其中包含六步选择流程、6×3阶段×模式矩阵、7行反指标防钻空子表格、目标线与更换触发规则，以及三个实操示例。
3. Metrics by Business Model
3. 按商业模式划分的指标
Core concept: Your business model dictates which metrics exist and which matter. Lean Analytics defines six archetypes — e-commerce, SaaS, free mobile app, media site, user-generated content, and two-sided marketplace — each with its own metric tree and its own definition of "working."
Why it works: Copying another company's north star fails because metrics encode the mechanics of a model: a marketplace lives or dies on liquidity, a SaaS business on churn, a media site on engaged attention. Naming your model first turns "what should we measure?" from a brainstorm into a lookup.
Key insights:
- E-commerce runs on conversion rate, average order value, and repurchase rate — annual repurchase under ~40% means acquisition mode, over ~60% loyalty mode, and each mode has a different playbook
- SaaS runs on MRR, churn, LTV:CAC, expansion, and time-to-value; free mobile apps run on downloads → DAU/MAU, percent paying, and ARPDAU vs ARPPU (whales skew every average)
- Media runs on audience, engaged time (not raw pageviews), CTR, and RPM; UGC runs on the engagement funnel — visitor → voyeur → commenter → creator — plus content per user and spam rate
- Marketplaces run on liquidity: listings, fill/sell-through rate, time-to-transaction, take rate, buyer/seller ratio — GMV is vanity until multiplied by take rate
- Hybrid businesses must pick ONE primary model to own the OMTM; the secondary model contributes counter-metrics, not equal billing
- The model also dictates instrumentation: define each metric's formula and source up front, or every team computes "churn" differently
Applications:
| Context | Application | Example |
|---|---|---|
| New product instrumentation | Name the model, install its metric tree | Subscription box → primary model SaaS; churn tracked before AOV |
| North-star debate | Derive from model mechanics, don't copy | Marketplace adopts fill rate, not a SaaS-style MRR target |
| Investor dashboard | Report the model's canonical ratios | SaaS deck: MRR growth, net churn, LTV:CAC, CAC payback |
See references/business-model-metrics.md when instrumenting a product or picking a model's canonical ratios — metric trees for all six models with formulas, instrumentation notes, measurement failure modes, and hybrid-model guidance.
核心概念：你的商业模式决定了哪些指标存在、哪些指标重要。Lean Analytics定义了六种典型商业模式——电商、SaaS、免费移动应用、媒体网站、用户生成内容（UGC）、双边市场——每种模式都有自己的指标体系和“运转正常”的定义。
为何有效：照搬其他公司的北极星指标会失败，因为指标承载着商业模式的运行逻辑：双边市场的生死取决于流动性，SaaS业务的关键是流失率，媒体网站的核心是用户参与时长。先明确自身模式，就能将“我们应该衡量什么？”从头脑风暴转化为精准匹配。
关键见解：
- 电商的核心是转化率、平均订单价值和复购率——年复购率低于约40%意味着处于获客模式，高于约60%则处于忠诚度模式，两种模式的策略完全不同
- SaaS的核心是MRR、流失率、LTV:CAC、扩展收入和价值实现时间；免费移动应用的核心是下载量→DAU/MAU、付费用户占比，以及ARPDAU vs ARPPU（大额用户会扭曲所有平均值）
- 媒体业务的核心是受众规模、参与时长（而非单纯页面浏览量）、CTR和RPM；UGC业务的核心是参与漏斗——访客→浏览者→评论者→创作者——加上人均内容量和垃圾内容率
- 双边市场的核心是流动性： listings数量、成交率、交易时长、抽成率、买卖双方比例——GMV在乘以抽成率之前只是虚荣指标
- 混合业务必须选择一种主模式来确定OMTM；次要模式仅提供反指标，而非同等地位
- 模式还决定了指标采集方案：提前定义每个指标的公式和数据源，否则不同团队计算“流失率”的方式会不一致
应用场景：
| 场景 | 应用方式 | 示例 |
|---|---|---|
| 新产品指标采集 | 明确模式，部署对应指标体系 | 订阅盒 → 主模式为SaaS；先跟踪流失率，再关注平均订单价值 |
| 北极星指标争论 | 从模式逻辑推导，而非照搬 | 双边市场采用成交率，而非SaaS风格的MRR目标 |
| 投资者仪表盘 | 报告模式的标准比率 | SaaS融资演示：MRR增长率、净流失率、LTV:CAC、CAC回收期 |
如需为产品部署指标采集或选择模式的标准比率，请参阅references/business-model-metrics.md——其中包含六种模式的指标体系（带公式）、采集注意事项、测量失败场景，以及混合模式指导。
4. Metrics by Stage: The Lean Analytics Stages
4. 按阶段划分的指标：Lean Analytics创业阶段
Core concept: Startups move through five stages — Empathy, Stickiness, Virality, Revenue, Scale — and each has a gate. The OMTM is the intersection of business model and current stage; working on a later stage's metric before passing the current gate is the canonical startup mistake.
Why it works: Sequencing prevents waste. Virality poured into a product that doesn't retain is a leaky bucket; paid acquisition before unit economics burns runway with precision. Each gate de-risks the next, larger investment of money and time.
Key insights:
- Empathy: have 15+ problem interviews shown a painful, frequent problem people will pay to fix? The metric is mostly conversation notes — and that's correct at this stage
- Stickiness: do people use it repeatedly on their own? Track retention cohorts and core-action engagement; don't pour users into a leaky bucket
- Virality: do users bring users? Track viral coefficient AND cycle time — shortening the cycle often grows you faster than raising the coefficient, and inherent virality beats incentivized invites
- Revenue: does a dollar in return more than a dollar out, soon enough? Revenue per customer, CAC payback, gross margin
- Scale: channels, partners, and new markets — metrics shift from product risk to ecosystem and operations
- Gates are evidence, not time: a flattening retention curve exits Stickiness; positive unit economics within payback tolerance exits Revenue
Applications:
| Context | Application | Example |
|---|---|---|
| Growth-spend decision | Check the stickiness gate first | D30 retention at 4% → fix onboarding before buying ads |
| Roadmap prioritization | Stage picks the OMTM; OMTM picks the work | Stickiness stage ships onboarding fixes, not a referral program |
| Fundraising narrative | Pitch the passed gate and its evidence | "Week-4 retention flat at 35% — raising to scale acquisition" |
See references/five-stages.md when locating your stage or deciding whether you've passed a gate — the per-stage playbook with gating metrics, exit-criteria checklists, premature-scaling symptoms, and funding/runway interactions.
核心概念：创业公司会经历五个阶段——共情、粘性、病毒性、营收、规模化——每个阶段都有一个门槛。OMTM是商业模式与当前阶段的交集；在未跨越当前阶段门槛前就关注下一阶段的指标，是创业公司的典型错误。
为何有效：按顺序推进能避免浪费。将病毒性策略投入到留存率低的产品中，就像往漏水的桶里倒水；在单位经济模型跑通前就投入付费获客，会精准消耗现金流。每个门槛都能降低下一轮更大规模资金和时间投入的风险。
关键见解：
- 共情阶段：是否有15+次问题访谈显示用户存在痛苦且频繁的问题，并且愿意付费解决？此阶段的指标主要是访谈记录——这在当前阶段是合理的
- 粘性阶段：用户是否会主动重复使用产品？跟踪群组留存率和核心行为参与度；不要往漏水的桶里导入用户
- 病毒性阶段：用户是否会带来新用户？跟踪病毒系数和循环时间——缩短循环时间往往比提高病毒系数更能推动增长，且内在病毒性优于激励性邀请
- 营收阶段：投入1美元能否在足够短的时间内获得超过1美元的回报？跟踪单客营收、CAC回收期、毛利率
- 规模化阶段：渠道、合作伙伴、新市场——指标从产品风险转向生态系统和运营
- 门槛是基于证据，而非时间：留存曲线趋于平稳意味着跨越粘性阶段；在可接受的回收期内实现正向单位经济意味着跨越营收阶段
应用场景：
| 场景 | 应用方式 | 示例 |
|---|---|---|
| 增长投入决策 | 先检查粘性门槛 | 30日留存率为4% → 在购买广告前先优化新用户引导 |
| roadmap优先级 | 阶段决定OMTM；OMTM决定工作内容 | 粘性阶段优先发布新用户引导优化，而非推荐计划 |
| 融资叙事 | 展示已跨越的门槛及证据 | “第4周留存率稳定在35%——融资用于规模化获客” |
如需定位自身阶段或判断是否已跨越门槛，请参阅references/five-stages.md——其中包含各阶段的策略手册、门槛指标、提前规模化的症状，以及融资与现金流的关联。
5. Baselines and Lines in the Sand
5. 基准值与目标线
Core concept: A metric without a target is trivia. Use published baselines as starting heuristics — not laws — to define "good enough," then draw your line in the sand: a number, a date, and a pre-committed action if you miss.
Why it works: Baselines convert open-ended measurement into falsifiable bets. Knowing that ~5% monthly churn is the early-SaaS ceiling tells you whether to optimize or rebuild; without a line, every result can be rationalized and no experiment can fail.
Key insights:
- Early SaaS: ~5% monthly customer churn is the upper bound of viable; healthy companies push toward ~2% or lower
- Habitual and social apps: DAU/MAU around 20%+ signals real engagement; casual mobile apps average roughly 14% day-30 retention, so plan for steep decay
- Conversion: e-commerce typically converts ~1-3% of visitors; landing pages on good paid traffic usually convert low single digits — 25-30% is exceptional, not a planning number
- A viral coefficient above 1 is rare and fleeting; treat virality as CAC reduction and optimize cycle time before coefficient
- No benchmark for your case? Measure your current value, improve relative to it, and watch the derivative — 5% weekly improvement compounds into category-leading numbers
- Benchmarks shift by market, channel, price point, and era — always re-derive against your own cohorts before adopting someone else's number
Applications:
| Context | Application | Example |
|---|---|---|
| Target setting | Baseline → line in the sand → pre-commitment | "Churn under 4% by Q3 or we rebuild onboarding" |
| Anomaly triage | Compare to your own baseline before benchmarks | Conversion fell 2.4% → 1.9% in a week — investigate the release |
| Channel evaluation | Re-derive benchmarks per channel | Paid social converts 0.8%, search 4% — budget follows the line |
See references/case-studies.md when you want a full worked walkthrough — three scenarios: SaaS dashboard to OMTM, marketplace liquidity discovery, and a mobile app fixing stickiness before growth.
核心概念：没有目标的指标只是无用信息。将公开基准值作为初始参考（而非定律）来定义“足够好”，然后设定你的目标线：一个数值、一个截止日期、未达标时的预设行动。
为何有效：基准值将开放式测量转化为可验证的赌注。知道早期SaaS的月度流失率上限约为5%，就能判断是优化还是重建；没有目标线，任何结果都能被合理化，没有实验会失败。
关键见解：
- 早期SaaS：月度客户流失率约5%是可行的上限；健康的公司会努力将其降至约2%或更低
- 习惯类和社交类应用：DAU/MAU达到20%+意味着真实的用户参与；休闲移动应用的30日留存率平均约为14%，因此要做好用户快速流失的准备
- 转化率：电商通常能将1-3%的访客转化为客户；优质付费流量的着陆页转化率通常为个位数——25-30%是特例，而非规划标准
- 病毒系数超过1的情况罕见且短暂；将病毒性视为CAC降低的手段，先优化循环时间再提高病毒系数
- 没有适合你的基准值？测量当前数值，相对自身进行改进，并关注变化率——每周5%的提升会累积成行业领先的数值
- 基准值会随市场、渠道、价格点和时代变化——在采用他人的数值前，务必基于自身群组重新推导
应用场景：
| 场景 | 应用方式 | 示例 |
|---|---|---|
| 目标设定 | 基准值 → 目标线 → 预设承诺 | “第三季度前将流失率降至4%以下，否则重建新用户引导” |
| 异常排查 | 先与自身基准值对比，再参考行业基准 | 转化率在一周内从2.4%降至1.9% → 调查最近的版本发布 |
| 渠道评估 | 按渠道重新推导基准值 | 付费社交转化率为0.8%，搜索渠道为4% → 预算向搜索渠道倾斜 |
如需完整的实操案例，请参阅references/case-studies.md——包含三个场景：SaaS仪表盘到OMTM的优化、双边市场流动性探索、移动应用在增长前修复粘性问题。
Common Mistakes
常见错误
| Mistake | Why It Fails | Fix |
|---|---|---|
| A dashboard with 40 metrics | Diffuses focus; nobody owns anything | One OMTM big, 4-6 supporting metrics, archive the rest |
| Celebrating cumulative charts | Totals can't go down, so they hide decay | Plot rates, conversions, and cohort retention instead |
| Copying another company's north star | Metrics encode model mechanics you don't share | Derive the OMTM from your model × stage |
| Skipping cohorts | Blended averages mask whether the product improves | Track each signup cohort separately over time |
| Optimizing virality before stickiness | Growth multiplies churn — the leaky bucket | Pass the retention gate, then build invite loops |
| Measuring what's easy, not what's risky | Decisions still get made on gut | Instrument the riskiest assumption first |
| No line in the sand | Every result gets rationalized; experiments can't fail | Pre-commit target, date, and miss response |
| Confusing correlation with causation | You pump a metric that doesn't drive the outcome | Run a controlled experiment before investing |
| 错误 | 失败原因 | 解决方案 |
|---|---|---|
| 包含40个指标的仪表盘 | 分散注意力；无人对指标负责 | 放大展示一个OMTM，保留4-6个辅助指标，其余存档 |
| 庆祝累计图表 | 总数只会上升，掩盖衰退 | 改为绘制比率、转化率和群组留存率 |
| 照搬其他公司的北极星指标 | 指标承载着你不具备的模式逻辑 | 从自身模式×阶段推导OMTM |
| 跳过群组分析 | 混合平均值掩盖产品真实改进 | 按注册时间单独跟踪每个群组的表现 |
| 在粘性达标前优化病毒性 | 增长会放大流失——如同漏水的桶 | 先跨越留存门槛，再构建邀请循环 |
| 衡量容易获取的指标，而非风险最高的指标 | 决策仍依赖直觉 | 先为最具风险的假设部署指标采集 |
| 未设定目标线 | 任何结果都能被合理化；实验不会失败 | 以书面形式设定目标、截止日期和未达标应对方案 |
| 将相关性混淆为因果性 | 投入资源优化的指标无法驱动结果 | 在投入前先进行受控实验 |
Quick Diagnostic
快速诊断
| Question | If No | Action |
|---|---|---|
| Can you name your OMTM right now? | Focus is diffused across a dashboard | Pick one metric from current model × stage |
| Would this metric change what you do next? | You're reporting, not deciding | Drop it, or define the decision it gates |
| Is it a ratio or rate, not a total? | Vanity risk — totals only go up | Rewrite as a conversion, retention, or per-user rate |
| Do you know your business model archetype? | Wrong metric tree installed | Name one of the six models; adopt its metrics |
| Do you know your stage (Empathy → Scale)? | Probably optimizing a later stage too early | Find the first unpassed gate; that's your stage |
| Is there a target with a date and a miss plan? | Goalposts will move after results | Draw the line in the sand in writing |
| Is the data cohorted and segmented? | Averages are hiding the truth | Build cohort tables; split by channel and segment |
| Is a counter-metric guarding the OMTM? | The OMTM will be gamed | Pair it, e.g. signup growth × 30-day retention |
| 问题 | 如果答案为否 | 行动方案 |
|---|---|---|
| 你能立刻说出当前的OMTM吗？ | 注意力分散在整个仪表盘上 | 从当前模式×阶段中选择一个指标 |
| 这个指标会改变你下一步的行动吗？ | 你只是在做报告，而非决策 | 移除该指标，或定义它所驱动的决策 |
| 它是比率或速率，而非总数吗？ | 存在虚荣风险——总数只会上升 | 将其改写为转化率、留存率或单客速率 |
| 你知道自身的商业模式类型吗？ | 部署了错误的指标体系 | 明确六种模式中的一种；采用其对应指标 |
| 你知道自身所处的阶段（共情→规模化）吗？ | 可能过早优化下一阶段的指标 | 找到第一个未跨越的门槛；那就是你的当前阶段 |
| 是否有包含日期和未达标方案的目标？ | 结果出来后目标会变动 | 以书面形式设定目标线 |
| 数据是否按群组和细分维度划分？ | 平均值掩盖了真实情况 | 构建群组表格；按渠道和细分维度拆分数据 |
| 是否有反指标来监督OMTM？ | OMTM会被钻空子 | 为其搭配反指标，例如注册量增长×30日留存率 |
Further Reading
延伸阅读
- "Lean Analytics: Use Data to Build a Better Startup Faster" by Alistair Croll & Benjamin Yoskovitz
- "The Lean Startup" by Eric Ries
- "Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing" by Ron Kohavi, Diane Tang & Ya Xu
- 《Lean Analytics: Use Data to Build a Better Startup Faster》 作者：Alistair Croll & Benjamin Yoskovitz
- 《The Lean Startup》 作者：Eric Ries
- 《Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing》 作者：Ron Kohavi, Diane Tang & Ya Xu
About the Authors
作者简介
Alistair Croll is an entrepreneur and analyst who co-founded web performance company Coradiant, founded Solve For Interesting, and chairs Startupfest among other technology conferences. Benjamin Yoskovitz is a founding partner at venture studio Highline Beta and a serial founder and startup investor. They wrote Lean Analytics for Eric Ries's Lean Series.
Alistair Croll 是企业家兼分析师，联合创立了Web性能公司Coradiant，创立了Solve For Interesting，并担任Startupfest等科技会议的主席。Benjamin Yoskovitz 是风险工作室Highline Beta的创始合伙人，也是连续创业者和创业投资者。他们为Eric Ries的精益系列撰写了《Lean Analytics》。