mom-test

Files (7)

• SKILL.md
• avoiding-bad-data.md · references/
• case-studies.md · references/
• commitment-advancement.md · references/
• finding-conversations.md · references/
• processing-learning.md · references/
• question-patterns.md · references/

SKILL.md (first lines)

# The Mom Test Framework

Framework for customer conversations that won't lead you astray, based on a fundamental truth: everyone is lying to you -- not maliciously, but because you're asking the wrong questions. 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. Talk about their problems, their lives, and their existing behavior instead of pitching, and ask about specifics in the past, not hypotheticals about the future. Above all, talk less and listen more.

## Scoring

**Goal: 7/7.** Score a conversation (or interview plan) by the seven-row Quick Diagnostic below: **1 point per row that passes.**
- **6-7** = focused on their life and past behavior, concrete facts captured, a real commitment (time/reputation/money) secured, they talked 80%+, and beliefs got updated.
- **4-5** = some past-behavior facts but leaking into hypotheticals, compliments accepted as signal, or ending without an ask.
- **<=3** = a pitch in disguise: leading questions, opinions and fluff, a polite zombie lead, no learning.

Always state the current score out of 7, name the failing diagnostic rows, and give the specific fix for each.

## Framework Sections

### 1. The Mom Test Rules

**Core concept:** Three rules that make it impossible for even your most supportive loved ones to give you false validation, shifting conversations from opinion-gathering to fact-finding.

**Why it works:** People are unreliable predictors of their own future behavior, so opinions are worthless. Past behavior is the only reliable data and 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 could potentially destroy your currently imagined business

**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]" not "Would you use an app that does X?" |
| Feature prioritization | Discover what people do vs. what they say | "Walk me through how you handled this last week" |
| Pricing research | Anchor to existing spending behavior | "What are you currently paying to solve this?" not "Would you pay $X?" |

**Copy patterns:**
- "Tell me about the last time you..."
- "What else have you tried?"
- "Why does that bother you?"

See: [references/question-patterns.md](references/question-patterns.md) when drafting an interview script -- a 5-tier question hierarchy, domain-specific question banks (SaaS/consumer/marketplace), and four formulation exercises.

### 2. Good vs Bad Questions

**Core concept:** Most interview questions are 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:** Asking "would you buy this?" is like asking "will you go to the gym next week?" -- the answer is always yes, the follow-through rarely there. Behavior that already happened 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?" / "How much would you pay?" -- hypothetical, anchored to please you
- 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 with the power to change what you're building -- produce the most useful data

**Product applications:**

| Context | Application | Example |
|---------|-------------|---------|
| Problem validation | Confirm the problem exists and matters | "When did this last come up? What did you do? What didn't work?" |
| Market sizing | Check if enough people share the problem | "Who else in your industry deals with this? How do they handle it?" |
| Competitive analysis | Find real alternatives already in use | "What tools/processes do you currently use for this?" |

**Copy patterns:**
- "What's the hardest part about [doing this thing]?"
- "How often does this come up?"
- "Walk me through what happened the last time this came up"

### 3. Avoiding Compliments and Opinions

**Core concept:** Three types of bad data feel like progress but actively mislead: compliments ("That's a great idea!"), fluff (hypotheticals, maybes, future promises), and ideas (feature requests disconnected from real problems). Deflecting these and digging for truth is the core skill.

**Why it works:** Compliments are the fool's gold of customer development -- they feel amazing but contain zero information about whether anyone will pay or use the product. Only specifics about real past behavior and genuine commitments provide signal.

**Key insights:**
- Compliments: deflect immediately and return to concrete facts about how they handle the problem today
- Fluff: generic claims ("I usually," "I always," "I would never") are worthless without a specific instance
- Ideas: dig into the motivation behind every feature request -- what's driving it, when they last needed it
- Fishing for compliments ("Don't you think this would be useful?") is unconscious validation-seeking
- Symptom of a bad conversation: you walk away feeling great but with no concrete facts or commitments

**Product applications:**

| Context | Application | Example |
|---------|-------------|---------|
| Post-demo feedback | Deflect "this looks awesome" | "Thanks! What part of your current workflow would this replace?" |
| Feature requests | Dig for the underlying job | "Why do you want that? Can you show me the last time you needed it?" |
| Investor conversations | Separate encouragement from interest | Ask for customer intros, not "great idea" feedback |

**Copy patterns:**
- "Thanks, but to make sure I'm not wasting your time -- what does your current process look like?"
- "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?"

See: [references/avoiding-bad-data.md](references/avoiding-bad-data.md) when a conversation feels good but yields no facts -- how to spot and deflect the three bad-data types (compliments, fluff, ideas) in real time.

### 4. Commitment and Advancement

**Core concept:** The currency of a customer conversation is commitment, not compliments. End every conversation with a clear advance toward adoption or a clear rejection -- the worst outcome is a "zombie lead" who is polite but never commits.

**Why it works:** Saying "I'd definitely buy that" costs nothing; offering an intro, a deposit, or a pilot invests something real. Commitment closes the dangerous gap between what people say and what they do.

**Key insights:**
- Commitment currencies: time (meeting, trial), reputation (intro, testimonial), money (deposit, pre-order, letter of intent)
- Advancing moves the relationship toward a sale; spinning wheels produces pleasant, useless meetings
- Know your "ask" before the meeting -- the minimum commitment that proves this is real
- A "no" is more valuable than a "maybe" -- you can learn from it and move on
- If they won't give you their time, they definitely won't give you their money

**Product applications:**

| Context | Application | Example |
|---------|-------------|---------|

From wondelai/skills, MIT.

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