mom-test

What this skill does

# The Mom Test Framework

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.

## Core Principle

**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.

## Scoring

**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.

## Framework Sections

### 1. The Mom Test Rules

**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](references/question-patterns.md)

### 2. Good vs Bad Questions

**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](references/question-patterns.md)

### 3. Avoiding Compliments and Opinions

**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 p