mom-test
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ChineseThe Mom Test Framework
The Mom Test 沟通框架
Framework for having useful customer conversations that won't lead you astray. Based on a fundamental truth: everyone is lying to you -- not because they're malicious, but because you're asking the wrong questions. Your mom will tell you your idea is great because she loves you. Investors, friends, and even potential customers will do the same. The Mom Test provides rules for asking questions so good that even your mom can't lie to you.
这是一套能帮你开展有效客户对话、避免被误导的框架。基于一个基本事实：每个人都在“骗”你——并非恶意，而是因为你问错了问题。你的妈妈会说你的想法很棒，因为她爱你；投资者、朋友甚至潜在客户也会这么做。The Mom Test提供的提问规则，能让就算是你的妈妈也无法对你说假话。
Core Principle
核心原则
Good customer conversations are about their life, not your idea. The moment you mention what you're building, people switch from sharing truth to performing politeness. They tell you what you want to hear. The antidote is simple: talk about their problems, their lives, and their existing behavior instead of pitching your solution. Ask about specifics in the past, not hypotheticals about the future. And above all, talk less and listen more.
优质的客户对话要聚焦他们的生活，而非你的想法。 一旦你提及正在打造的产品，人们就会从分享真实想法切换到礼貌应对模式，说你想听的话。解决方法很简单：聊他们的问题、生活和现有行为，而非推销你的解决方案。询问过去的具体情况，而非对未来的假设。最重要的是：少说话，多倾听。
Scoring
对话评分
Goal: 10/10. When reviewing or planning customer conversations, rate them 0-10 based on adherence to the principles below. A 10/10 means questions focus entirely on the customer's life and past behavior, with no leading, no pitching, and clear commitment signals; lower scores indicate gaps to address. Always provide the current score and specific improvements needed to reach 10/10.
目标：10/10分。 在复盘或规划客户对话时，根据以下原则给对话打分（0-10分）。10分意味着所有问题完全聚焦客户的生活与过往行为，没有引导性提问、没有推销，且获得了明确的承诺信号；分数越低说明存在需要改进的漏洞。评分时需给出当前分数，以及达到10分的具体改进方向。
Framework Sections
框架模块
1. The Mom Test Rules
1. The Mom Test 核心规则
Core concept: Three simple rules that, when followed, make it impossible for even your most supportive loved ones to give you false validation. The rules shift conversations from opinion-gathering to fact-finding.
Why it works: Opinions are worthless because people are unreliable predictors of their own future behavior. Past behavior is the only reliable data. By focusing on what people have actually done rather than what they say they would do, you extract facts that can genuinely inform product decisions.
Key insights:
- Rule 1: Talk about their life, not your idea -- never mention your solution until the end (if at all)
- Rule 2: Ask about specifics in the past, not generics or hypotheticals about the future
- Rule 3: Talk less, listen more -- aim for them to speak 80% of the time
- A question fails the Mom Test if the answer is always "yes" regardless of whether the business will succeed
- Good questions are ones that could potentially destroy your currently imagined business
- You want facts and commitments, not compliments and opinions
- The best learning happens when you shut up and let awkward silences do the work
Product applications:
| Context | Application | Example |
|---|---|---|
| Idea validation | Ask about the problem, never the solution | "Tell me about the last time you tried to [problem area]" instead of "Would you use an app that does X?" |
| Feature prioritization | Discover what people actually do vs. what they say | "Walk me through how you handled this last week" reveals real workflow |
| Pricing research | Anchor to existing spending behavior | "What are you currently paying to solve this?" instead of "Would you pay $X?" |
Copy patterns:
- "Tell me about the last time you..."
- "What happened next?"
- "How are you dealing with that currently?"
- "Can you walk me through your process?"
- "What else have you tried?"
Ethical boundary: Never weaponize someone's honest answers against them. The Mom Test earns trust by respecting people's time and honesty -- using vulnerability data to manipulate sales crosses the line.
See: references/question-patterns.md
核心概念： 三条简单规则，只要遵循，就算是最支持你的人也无法给你虚假的肯定。这些规则将对话从收集意见转向挖掘事实。
为何有效： 意见毫无价值，因为人们无法可靠预测自己未来的行为。过往行为是唯一可信的数据。通过聚焦人们实际做过的事，而非他们说自己会做的事，你能提取真正可指导产品决策的事实。
关键要点：
- 规则1：聊他们的生活，而非你的想法——除非必要，否则绝不提及你的解决方案
- 规则2：询问过去的具体细节，而非泛泛之谈或对未来的假设
- 规则3：少说话，多倾听——目标是让对方说80%的话
- 如果一个问题不管你的业务能否成功，答案都是“是”，那它就不符合The Mom Test
- 好的问题应该有能力推翻你当前构想的业务
- 你要的是事实和承诺，不是赞美和意见
- 最有价值的学习发生在你闭嘴、让尴尬的沉默发挥作用的时候
产品场景应用：
| 场景 | 应用方式 | 示例 |
|---|---|---|
| 想法验证 | 聊问题，而非解决方案 | 问“跟我说说你上次尝试解决[问题领域]时的情况”，而非“你会用一款能做X的应用吗？” |
| 功能优先级排序 | 挖掘人们实际做的事，而非他们说的事 | “跟我走一遍你上周是怎么处理这个问题的”能揭示真实的工作流程 |
| 定价调研 | 以现有消费行为为锚点 | 问“你现在为解决这个问题花多少钱？”，而非“你愿意为X支付多少钱？” |
常用提问句式：
- “跟我说说你上次……的情况”
- “接下来发生了什么？”
- “你现在是怎么处理这个问题的？”
- “能跟我走一遍你的处理流程吗？”
- “你还尝试过哪些方法？”
道德边界： 绝不能利用他人的诚实回答来对付他们。The Mom Test通过尊重他人的时间和诚实来建立信任——利用这些脆弱的数据来操纵销售是越界行为。
参考：references/question-patterns.md
2. Good vs Bad Questions
2. 优质问题vs劣质问题
Core concept: Most customer interview questions are fundamentally broken because they ask people to predict the future, evaluate hypothetical products, or confirm your assumptions. Good questions anchor in observable past behavior and extract concrete facts.
Why it works: Humans are terrible at predicting their own behavior. Asking "would you buy this?" is like asking "will you go to the gym next week?" -- the answer is always yes, the follow-through is rarely there. Questions about what people have already done are reliable because behavior has already happened and can't be rationalized away.
Key insights:
- Bad: "Do you think it's a good idea?" -- always gets a yes
- Bad: "Would you buy a product that does X?" -- hypothetical, meaningless
- Bad: "How much would you pay for X?" -- people anchor to what you want to hear
- Good: "How are you dealing with this problem today?" -- reveals actual behavior
- Good: "What have you tried before and why did you stop?" -- reveals past decisions
- Good: "Where does the money come from for solutions like this?" -- reveals real budgets
- The scariest questions (ones you're afraid to ask) usually produce the most useful data
- Ask questions that have the power to change what you're building
Product applications:
| Context | Application | Example |
|---|---|---|
| Problem validation | Confirm the problem exists and matters enough | "When did this last come up? What did you do? What didn't work?" |
| Market sizing | Understand if enough people have this problem | "Who else in your company/industry deals with this? How do they handle it?" |
| Competitive analysis | Discover real alternatives people already use | "What tools/processes do you currently use for this?" |
Copy patterns:
- "What's the hardest part about [doing this thing]?"
- "Why was that hard?"
- "How often does this come up?"
- "What does a perfect week look like for this workflow?"
- "Talk me through the last time this happened"
Ethical boundary: Never use leading or loaded questions that anchor the respondent toward your desired answer. Your job is to learn, not to sell.
See: references/question-patterns.md
核心概念： 大多数客户访谈问题本质上是无效的，因为它们要求人们预测未来、评估假设产品或确认你的预设。优质问题锚定可观察的过往行为，提取具体事实。
为何有效： 人类不擅长预测自己的行为。问“你会买这个吗？”就像问“你下周会去健身房吗？”——答案永远是“是”，但实际行动很少。询问人们已经做过的事是可靠的，因为行为已经发生，无法被合理化。
关键要点：
- 劣质问题：“你觉得这是个好主意吗？”——答案永远是“是”
- 劣质问题：“你会买一款能做X的产品吗？”——假设性问题，毫无意义
- 劣质问题：“你愿意为X支付多少钱？”——人们会根据你想听的话来回答
- 优质问题：“你现在是怎么处理这个问题的？”——揭示实际行为
- 优质问题：“你之前尝试过什么，为什么放弃了？”——揭示过往决策
- 优质问题：“解决这类问题的预算来自哪里？”——揭示真实的预算情况
- 最可怕的问题（你不敢问的问题）通常能产生最有用的数据
- 要问那些能改变你产品方向的问题
产品场景应用：
| 场景 | 应用方式 | 示例 |
|---|---|---|
| 问题验证 | 确认问题真实存在且足够重要 | “这个问题上次出现是什么时候？你做了什么？哪些方法没用？” |
| 市场规模评估 | 了解是否有足够多的人存在这个问题 | “你公司/行业里还有谁会遇到这个问题？他们是怎么处理的？” |
| 竞品分析 | 挖掘人们实际在使用的替代方案 | “你现在用什么工具/流程来处理这个问题？” |
常用提问句式：
- “做这件事最难的部分是什么？”
- “为什么这么难？”
- “这个问题多久出现一次？”
- “这个工作流程的完美状态是什么样的？”
- “跟我说说上次这个问题发生时的情况”
道德边界： 绝不能使用引导性或带偏见的问题，把受访者引向你想要的答案。你的工作是学习，不是推销。
参考：references/question-patterns.md
3. Avoiding Compliments and Opinions
3. 避开赞美与意见干扰
Core concept: There are three types of bad data that feel like progress but actively mislead you: compliments ("That's a great idea!"), fluff (hypothetical statements, maybes, future promises), and ideas (feature requests disconnected from real problems). Learning to deflect these and dig for truth is the core skill of customer conversations.
Why it works: Compliments are the fool's gold of customer development. They feel amazing -- "Everyone loves our idea!" -- but they contain zero information about whether anyone will actually pay for or use your product. Fluff and opinions give the illusion of validation without any concrete evidence. Only specifics about real past behavior and genuine commitments provide signal.
Key insights:
- Compliments: deflect immediately and get back to concrete facts ("Thanks -- but let me understand how you're actually handling this today")
- Fluff: generic claims ("I usually," "I always," "I would never") are worthless without a specific instance
- Ideas: when someone suggests a feature, dig into the motivation ("That's interesting -- what's driving that? Tell me about the last time you needed something like that")
- The "would you buy this?" trap: the answer is always yes because saying no feels rude
- Fishing for compliments: unconsciously seeking validation ("Don't you think this would be really useful?")
- Symptoms of a bad conversation: you walk away feeling great but have no concrete facts or commitments
Product applications:
| Context | Application | Example |
|---|---|---|
| Post-demo feedback | Deflect "this looks awesome" to get actionable data | "Thanks! What part of your current workflow would this actually replace?" |
| Feature requests | Dig for the underlying job behind the request | "Why do you want that? Can you show me the last time you needed it?" |
| Investor conversations | Separate encouragement from real interest | Ask for intros to customers, not just "great idea" feedback |
Copy patterns:
- "Thanks, but to make sure I'm not wasting your time -- what does your current process look like?"
- "Interesting. Can you tell me about a specific time that happened?"
- "When you say you'd 'definitely' use this, what would you stop using?"
- "That's a great feature idea -- what problem would it solve for you specifically?"
Ethical boundary: Do not manipulate people into false commitments. Deflecting compliments is about getting to truth, not about pressuring someone into a sale.
See: references/avoiding-bad-data.md
核心概念： 有三种看似有价值实则会误导你的无效信息：赞美（“这个想法太棒了！”）、空话（假设性陈述、模糊的“可能”、未来的承诺）、脱离实际问题的功能建议。学会避开这些信息并挖掘真相，是客户对话的核心技能。
为何有效： 赞美是客户开发中的“愚人金”。它们让人感觉良好——“每个人都喜欢我们的想法！”——但完全无法说明是否有人真的会付费使用你的产品。空话和意见给人一种得到肯定的错觉，但没有任何具体证据。只有关于过往真实行为和真实承诺的细节才是有效信号。
关键要点：
- 赞美：立即转移话题，回到具体事实（“谢谢——但我想了解你现在实际是怎么处理这个问题的”）
- 空话：没有具体实例的泛泛之谈（“我通常……”“我总是……”“我绝不会……”）毫无价值
- 功能建议：当有人提出功能需求时，深挖背后的动机（“这很有意思——是什么让你有这个需求？跟我说说你上次需要类似功能时的情况”）
- “你会买这个吗？”的陷阱：答案永远是“是”，因为说“不”显得不礼貌
- 寻求赞美：无意识地想要得到肯定（“你不觉得这个很有用吗？”）
- 糟糕对话的征兆：结束后你感觉很好，但没有任何具体事实或承诺
产品场景应用：
| 场景 | 应用方式 | 示例 |
|---|---|---|
| 演示后反馈 | 避开“这个看起来很棒”的赞美，获取可行动的数据 | “谢谢！你当前的工作流程中，哪部分可以被这个产品替代？” |
| 功能需求 | 深挖需求背后的真实痛点 | “你为什么想要这个功能？能跟我说说你上次需要它时的情况吗？” |
| 投资者沟通 | 区分鼓励与真实兴趣 | 要求介绍客户，而非只听“好主意”这类反馈 |
常用提问句式：
- “谢谢，但为了不浪费你的时间——你现在的处理流程是什么样的？”
- “这很有意思。能跟我说说具体发生过的一次情况吗？”
- “当你说‘肯定会用’的时候，你会放弃使用什么现有工具？”
- “这是个很棒的功能想法——它能帮你解决什么具体问题？”
道德边界： 绝不能操纵人们做出会后悔的承诺。避开赞美是为了获取真相，而非强迫对方下单。
参考：references/avoiding-bad-data.md
4. Commitment and Advancement
4. 承诺与推进
Core concept: The currency of a customer conversation is not compliments -- it's commitment. Real interest shows up as willingness to invest something of value: time, reputation, or money. Every conversation should end with a clear "advance" (moving toward a sale/adoption) or a clear "rejection" (which is also valuable data). The worst outcome is a "zombie lead" -- someone who is polite but never commits.
Why it works: Talk is cheap. When someone says "I'd definitely buy that," it costs them nothing. When someone offers to introduce you to their boss, puts a deposit down, or agrees to a pilot program, they're investing something real. The gap between what people say and what they do is the most dangerous trap in customer development. Commitment closes that gap.
Key insights:
- Commitment currencies: time (meeting, trial), reputation (intro, testimonial), money (deposit, pre-order, letter of intent)
- Advancing: the conversation moves the relationship closer to a sale or adoption
- Spinning wheels: pleasant conversations that never progress and produce zombie leads
- Always know your "ask" before the meeting -- what's the minimum commitment that proves this is real?
- A "no" is more valuable than a "maybe" -- at least you can learn from it and move on
- First meeting ask: "Would you be open to a 15-minute trial next week?"
- If they won't give you their time, they definitely won't give you their money
Product applications:
| Context | Application | Example |
|---|---|---|
| Early validation | Request a commitment that tests real interest | "Can I follow up with a prototype next week for 15 minutes of your time?" |
| B2B sales | Advance toward a decision-maker meeting | "Could you introduce me to the person who handles the budget for this?" |
| Pre-launch | Collect pre-orders or letters of intent | "We're launching in 8 weeks -- would you like to be in the first cohort at 40% off?" |
Copy patterns:
- "What's the next step here?"
- "Who else should I talk to about this?"
- "Would you be willing to try a prototype next week?"
- "Can I put you on the early access list?"
- "If I built this, would you be willing to pilot it for 30 days?"
Ethical boundary: Never pressure people into commitments they'll regret. The goal is to separate real interest from politeness, not to close a sale prematurely.
See: references/commitment-advancement.md
核心概念： 客户对话的“硬通货”不是赞美，而是承诺。真正的兴趣表现为愿意投入有价值的东西：时间、声誉或金钱。每一次对话都应该以明确的“推进”（向销售/采用迈进）或明确的“拒绝”（这也是有价值的数据）结束。最糟糕的结果是“僵尸线索”——对方很礼貌，但从不做出承诺。
为何有效： 说空话很容易。当有人说“我肯定会买”，这对他们来说毫无成本。当有人主动介绍你认识他们的老板、支付定金或同意参与试点项目时，他们才是投入了真实的东西。客户开发中最危险的陷阱就是人们说的和做的之间的差距。承诺能缩小这个差距。
关键要点：
- 承诺的形式：时间（会面、试用）、声誉（介绍、推荐）、金钱（定金、预购、意向书）
- 推进：对话让关系向销售或产品采用更近一步
- 原地打转：愉快但毫无进展的对话，只会产生僵尸线索
- 会面之前一定要明确你的“诉求”——什么是能证明对方真的感兴趣的最小承诺？
- “不”比“可能”更有价值——至少你能从中学习并继续前进
- 第一次会面的诉求：“下周你愿意花15分钟试用一下吗？”
- 如果对方连时间都不愿意付出，那他们肯定不会花钱
产品场景应用：
| 场景 | 应用方式 | 示例 |
|---|---|---|
| 早期验证 | 要求能测试真实兴趣的承诺 | “下周我可以给你展示原型，你愿意花15分钟看看吗？” |
| B2B销售 | 推进与决策者的会面 | “你能介绍我认识负责这个预算的人吗？” |
| 上线前准备 | 收集预购或意向书 | “我们8周后上线——你愿意以40%的折扣加入首批用户吗？” |
常用提问句式：
- “接下来我们该怎么做？”
- “我还应该和谁聊聊这个问题？”
- “下周你愿意试用一下原型吗？”
- “我能把你加入早期访问名单吗？”
- “如果我做出来了，你愿意试用30天吗？”
道德边界： 绝不能强迫人们做出会后悔的承诺。目标是区分真实兴趣和礼貌应对，而非过早达成销售。
参考：references/commitment-advancement.md
5. Finding Conversations
5. 找到对话对象
Core concept: You don't need a formal meeting to learn from customers. The best customer conversations happen casually -- at industry events, through warm intros, in online communities, or over coffee. Formal "customer interview" framing triggers performance mode where people tell you what they think you want to hear. Casual conversations produce more honest data.
Why it works: When you say "Can I interview you about your problems?", people put on armor. They become polished, guarded, and performative. When you say "I'm trying to learn about the industry -- can I buy you coffee?", people open up. The framing of the conversation determines the quality of the data you receive.
Key insights:
- Cold outreach: keep it short, lead with their expertise, don't pitch
- Warm intros: the best source -- one good advisor can open dozens of doors
- Industry events and meetups: go where your customers already gather
- Online communities: participate genuinely before asking questions
- Landing pages: use "learn more" signups to find engaged prospects
- Keep it casual: "I'm trying to learn" beats "I'm doing customer research"
- Vision/framing/weakness/pedestal/ask: a five-part structure for getting meetings
- Advisors as a distribution channel: formalize relationships with well-connected people
Product applications:
| Context | Application | Example |
|---|---|---|
| Pre-idea exploration | Immerse yourself in the target community | Attend 3 industry events and have 20 casual conversations before writing a line of code |
| B2B prospecting | Use warm intros through advisors and investors | "Our advisor [Name] suggested I talk to you about how you handle [problem area]" |
| Consumer research | Intercept people at the point of behavior | Talk to people in line at the store, at the gym, at the coworking space |
Copy patterns:
- "I'm researching how [industry] handles [problem] -- could I learn from your experience over a 15-minute coffee?"
- "[Mutual contact] suggested I talk to you because you know a lot about [area]"
- "I'm not trying to sell anything -- I'm just trying to understand the space"
- "I'm thinking about starting something in [space] and want to make sure I'm not delusional"
Ethical boundary: Never disguise a sales call as a learning conversation. If you already have a product and are selling, be transparent. The Mom Test is for genuine learning, not for covert pitching.
See: references/finding-conversations.md
核心概念： 你不需要正式的访谈就能从客户那里学习。最好的客户对话是随意发生的——在行业活动上、通过熟人介绍、在在线社区里，或者喝咖啡的时候。正式的“客户访谈”框架会触发人们的表演模式，说你想听的话。随意的对话能产生更诚实的数据。
为何有效： 当你说“我能采访你关于你的问题吗？”，人们会变得警惕、拘谨、刻意表现。当你说“我正在了解这个行业——我能请你喝杯咖啡，向你学习吗？”，人们会敞开心扉。对话的框架决定了你能获得的数据质量。
关键要点：
- 陌生 outreach：保持简短，突出对方的专业能力，不要推销
- 熟人介绍：最好的渠道——一个好的顾问能为你打开很多门
- 行业活动和聚会：去你的客户已经聚集的地方
- 在线社区：先真诚参与，再提问
- 落地页：用“了解更多”的注册来找到感兴趣的潜在客户
- 保持随意：“我正在学习”比“我在做客户调研”效果更好
- 愿景/框架/弱点/推崇/诉求：获取会面机会的五步法
- 顾问作为渠道：与人脉广的人建立正式关系
产品场景应用：
| 场景 | 应用方式 | 示例 |
|---|---|---|
| 想法探索阶段 | 融入目标群体 | 在写一行代码之前，参加3场行业活动，进行20次随意的对话 |
| B2B潜在客户开发 | 通过顾问和投资者进行熟人介绍 | “我们的顾问[姓名]建议我跟你聊聊你是怎么处理[问题领域]的” |
| 消费者调研 | 在人们产生行为的场景下接触他们 | 和商店排队的人、健身房的人、联合办公空间的人聊天 |
常用沟通句式：
- “我正在研究[行业]如何处理[问题]——我能花15分钟向你学习，请你喝咖啡吗？”
- “[共同联系人]建议我跟你聊聊，因为你对[领域]很了解”
- “我不是来推销的——我只是想了解这个领域”
- “我想在[领域]做点事，想确保自己没有想错”
道德边界： 绝不能把销售伪装成学习对话。如果你已经有产品并在销售，要保持透明。The Mom Test是为了真正的学习，而非秘密推销。
参考：references/finding-conversations.md
6. Processing and Learning
6. 处理对话数据并学习
Core concept: Customer conversations are only useful if you process them properly. Raw notes must be distilled into beliefs, updated regularly, and shared with your team. Without a system, you'll cherry-pick quotes that confirm your biases and ignore signals that challenge your assumptions.
Why it works: Memory is unreliable and biased toward recent and emotionally charged information. Without structured note-taking and review, teams selectively remember the data that confirms what they already believe. Processing conversations as a team prevents any single person's bias from dominating the narrative.
Key insights:
- Take notes during or immediately after -- never rely on memory
- Separate facts (what they said and did) from interpretations (what you think it means)
- Share raw notes with your team, not filtered summaries
- Update your three key beliefs: the problem, the customer segment, and the solution
- Know when to stop talking and start building -- when conversations start repeating, you've learned enough
- Conversations are for learning, not for convincing yourself you're right
- Use a simple spreadsheet: who, date, key quotes, facts, commitments, and belief changes
Product applications:
| Context | Application | Example |
|---|---|---|
| Team alignment | Share notes in weekly standups to build shared understanding | Review 5 conversations per week as a team and update the belief board |
| Pivot decisions | Track when evidence contradicts your core beliefs | If 8 of 10 conversations reveal a different problem than expected, pivot |
| Feature validation | Count how many people mention a problem unprompted | A problem mentioned by 7 of 10 people is real; one mentioned by 1 of 10 might not be |
Copy patterns:
- "Here are the exact quotes from this week's conversations"
- "Our current belief is X -- here's what confirms it and what challenges it"
- "We've heard this from N of M people -- is that enough signal?"
- "Time to stop talking and build -- conversations are repeating"
Ethical boundary: Never misrepresent or selectively quote customer conversations to justify a predetermined conclusion. Honest processing means accepting uncomfortable truths.
See: references/processing-learning.md
核心概念： 只有经过妥善处理，客户对话才是有用的。原始笔记必须提炼成观点，定期更新，并与团队分享。如果没有一套体系，你会只挑选能证实你偏见的内容，忽略挑战你假设的信号。
为何有效： 记忆不可靠，且偏向于近期和情绪化的信息。如果没有结构化的记笔记和复盘流程，团队会选择性地记住能证实他们已有观点的数据。团队一起处理对话数据，能防止个人偏见主导决策。
关键要点：
- 对话中或结束后立即记笔记——绝不依赖记忆
- 区分事实（对方说的和做的）和解读（你认为这意味着什么）
- 与团队分享原始笔记，而非经过过滤的总结
- 更新你的三个核心观点：问题、客户群体、解决方案
- 知道何时停止对话开始开发——当对话内容开始重复时，说明你已经学够了
- 对话是为了学习，而非说服自己是对的
- 用简单的表格记录：对话对象、日期、关键引用、事实、承诺、观点变化
产品场景应用：
| 场景 | 应用方式 | 示例 |
|---|---|---|
| 团队对齐 | 在每周站会上分享笔记，建立共同认知 | 每周复盘5次对话，更新团队的观点看板 |
| 转型决策 | 跟踪与核心观点矛盾的证据 | 如果10次对话中有8次揭示了不同的问题，就需要转型 |
| 功能验证 | 统计有多少人主动提到某个问题 | 10次对话中有7次提到的问题是真实的；1次提到的问题可能不重要 |
常用沟通句式：
- “这是本周对话的原始引用”
- “我们当前的观点是X——这是支持和挑战这个观点的证据”
- “我们已经从M个人中的N个那里听到了这个——这是足够的信号吗？”
- “该停止对话开始开发了——内容已经重复了”
道德边界： 绝不能歪曲或选择性引用客户对话，来证明你预先确定的结论。诚实处理意味着接受令人不适的真相。
参考：references/processing-learning.md
Common Mistakes
常见错误
| Mistake | Why It Fails | Fix |
|---|---|---|
| Pitching your idea instead of asking about their life | Triggers politeness, produces compliments instead of facts | Don't mention your idea until the very end, if at all |
| Asking "would you buy this?" | People always say yes to hypotheticals; it costs them nothing | Ask what they've already done: "How much are you spending on this now?" |
| Accepting compliments as validation | "Great idea!" contains zero information about future behavior | Deflect immediately: "Thanks -- but what are you doing about this today?" |
| Talking too much | You learn nothing while talking; you learn everything while listening | Set a timer: they should talk 80% of the time or more |
| Not having a clear ask at the end | Produces zombie leads -- pleasant conversations that go nowhere | Know your advance before the meeting: trial, intro, pre-order |
| Running formal "interview" sessions | Triggers performance mode where people filter their answers | Keep it casual: coffee, hallway conversations, Slack DMs |
| Not processing notes as a team | Individual bias filters raw data into confirmation of existing beliefs | Share raw notes weekly and update shared beliefs together |
| 错误 | 为何失效 | 解决方法 |
|---|---|---|
| 推销你的想法，而非聊对方的生活 | 触发礼貌应对，只会得到赞美而非事实 | 除非必要，否则绝不提及你的想法 |
| 问“你会买这个吗？” | 人们对假设性问题的答案永远是“是”，毫无意义 | 问他们已经做过的事：“你现在为解决这个问题花多少钱？” |
| 把赞美当成肯定 | “好主意！”完全无法说明未来的行为 | 立即转移话题：“谢谢——但你现在是怎么处理这个问题的？” |
| 说得太多 | 说话的时候你学不到任何东西，倾听才能学到 | 设定时间目标：让对方说80%的话 |
| 对话结束时没有明确的诉求 | 产生僵尸线索——礼貌但不感兴趣 | 会面之前明确你的推进诉求：试用、介绍、预购 |
| 开展正式的“访谈” | 触发表演模式，人们会过滤自己的回答 | 保持随意：喝咖啡、走廊聊天、Slack私信 |
| 不与团队一起处理笔记 | 个人偏见会把原始数据过滤成能证实已有观点的内容 | 每周分享原始笔记，一起更新团队的共同观点 |
Quick Diagnostic
快速诊断
| Question | If No | Action |
|---|---|---|
| Did the conversation focus on their life and past behavior, not your idea? | You ran a pitch, not a Mom Test conversation | Redo with zero mention of your solution |
| Did you get concrete facts about what they've already done? | You collected opinions and hypotheticals, which are meaningless | Ask about the last time they experienced the problem and what they did |
| Did they give you a commitment (time, reputation, or money)? | You may have a zombie lead -- polite but not interested | Ask for a specific next step: trial, intro, or pre-order |
| Did they do most of the talking? | You talked too much and learned too little | Practice silence; let awkward pauses work for you |
| Did you learn something that could change what you're building? | You asked safe questions that confirmed what you already believed | Ask the scary questions you've been avoiding |
| Did you update your beliefs based on the conversation? | You're collecting data but not learning from it | Review notes with your team and update your problem/segment/solution beliefs |
| Can you summarize the key facts (not opinions) from the conversation? | You didn't take good notes or you're confusing opinions for facts | Separate facts from interpretations in your notes immediately after |
| 问题 | 如果答案为“否” | 行动 |
|---|---|---|
| 对话聚焦于对方的生活和过往行为，而非你的想法？ | 你做了一次推销，而非符合The Mom Test的对话 | 重新对话，绝不提及你的解决方案 |
| 你得到了关于对方实际做过的事的具体事实？ | 你收集的是意见和假设，毫无意义 | 问对方上次遇到这个问题时的情况，以及他们做了什么 |
| 对方给了你承诺（时间、声誉或金钱）？ | 你可能得到了僵尸线索——礼貌但不感兴趣 | 提出具体的下一步：试用、介绍或预购 |
| 对方说的话更多？ | 你说得太多，学得太少 | 练习沉默；让尴尬的停顿发挥作用 |
| 你学到了能改变产品方向的东西？ | 你问的是安全的问题，只是证实了你已有的观点 | 问你一直回避的“可怕”问题 |
| 你根据对话更新了你的观点？ | 你在收集数据，但没有从中学习 | 和团队一起复盘笔记，更新你的问题/客户群体/解决方案观点 |
| 你能总结对话中的关键事实（而非意见）？ | 你没有记好笔记，或者把意见当成了事实 | 对话结束后立即区分笔记中的事实和解读 |
Reference Files
参考文件
- question-patterns.md: Good vs bad question examples, the three rules in depth, question formulation exercises
- commitment-advancement.md: Commitment currencies, advancing vs spinning wheels, how to push for commitment
- avoiding-bad-data.md: Compliments, fluff, ideas -- the three types of bad data and how to deflect them
- finding-conversations.md: Where to find people, cold vs warm approaches, keeping conversations casual
- processing-learning.md: Note-taking, team sharing, updating beliefs, knowing when to stop talking
- case-studies.md: Realistic scenarios showing Mom Test principles applied to SaaS, consumer, B2B, and marketplace contexts
- references/question-patterns.md: 优质与劣质问题示例、三条规则的深入解读、提问练习
- references/commitment-advancement.md: 承诺的形式、推进vs原地打转、如何争取承诺
- references/avoiding-bad-data.md: 赞美、空话、功能建议——三种无效信息及避开方法
- references/finding-conversations.md: 寻找对话对象的渠道、陌生vs熟人介绍、保持对话随意
- references/processing-learning.md: 记笔记、团队分享、更新观点、知道何时停止对话
- references/case-studies.md: 展示The Mom Test原则在SaaS、消费者产品、B2B和平台场景中应用的真实案例
Further Reading
延伸阅读
This skill is based on The Mom Test methodology developed by Rob Fitzpatrick. For the complete framework, examples, and deeper insights, read the original book:
此方法基于Rob Fitzpatrick开发的The Mom Test方法论。如需完整框架、更多示例和深入见解，请阅读原版书籍：
About the Author
关于作者
Rob Fitzpatrick is an entrepreneur, author, and educator who has founded multiple venture-backed startups and learned the hard way that most customer conversations are useless. After years of collecting misleading feedback and building products nobody wanted, he distilled the principles of effective customer conversations into The Mom Test (2013), which became one of the most recommended books in the startup ecosystem. The book has been translated into over 20 languages and is required reading at accelerators including Y Combinator, Techstars, and 500 Startups. Fitzpatrick has also written The Workshop Survival Guide and Write Useful Books, applying the same evidence-based approach to education and publishing. He teaches and advises startups across Europe and the US, and is known for his direct, practical style that prioritizes actionable frameworks over theory. He is based in the UK.
Rob Fitzpatrick 是一位创业者、作家和教育者，创立过多家获得风险投资的初创公司，从惨痛教训中意识到大多数客户对话都是无用的。在多年收集误导性反馈、打造无人问津的产品后，他将有效客户对话的原则提炼成了The Mom Test（2013年），成为创业圈最受推荐的书籍之一。该书已被翻译成20多种语言，是Y Combinator、Techstars和500 Startups等加速器的必读书目。Fitzpatrick还著有《The Workshop Survival Guide》和《Write Useful Books》，将同样基于证据的方法应用于教育和出版领域。他在欧洲和美国为初创公司提供教学和咨询服务，以直接、实用的风格著称，优先考虑可操作的框架而非理论。他现居英国。