Product Discovery

Build products customers actually want. Apply Marty Cagan’s Silicon Valley-tested framework to discover solutions that are valuable, usable, feasible, and viable.

When to Use This Skill

• New product development when validating what to build
• Feature prioritization to ensure you’re solving real problems
• Pivot decisions when current direction isn’t working
• Team alignment on what problems to solve
• Risk reduction before committing development resources
• Continuous discovery to maintain product-market fit

Methodology Foundation

What Claude Does vs What You Decide

What This Skill Does

1. Frames the four risks – Value, usability, feasibility, viability
2. Distinguishes discovery from delivery – Different mindsets, different processes
3. Teaches opportunity assessment – Which problems to solve
4. Develops prototyping skills – Test ideas before building
5. Guides customer research – Learn what customers need (not want)
6. Structures continuous discovery – Ongoing learning, not one-time research

How to Use

Assess a Product Opportunity

I'm considering building [feature/product].
Apply product discovery principles to assess this opportunity.
Context: [target customer, current state, hypothesis]

Reduce Risk Before Building

We're about to build [feature].
Help me identify the key risks and design tests to address them.

Set Up Continuous Discovery

I want to implement continuous discovery for my product team.
Help me design a weekly discovery rhythm.

Instructions

Step 1: Understand the Four Risks

## The Four Product Risks

Every product idea has four risks to address BEFORE building:

### 1. Value Risk
"Will customers buy/use this?"

**Questions:**
- Does this solve a real problem?
- Is the problem painful enough to pay/switch for?
- Will users actually adopt this?

**Tests:**
- Customer interviews
- Demand testing
- Fake door tests
- Concierge MVP

### 2. Usability Risk
"Can customers figure out how to use it?"

**Questions:**
- Is it intuitive?
- Can users accomplish their goals?
- What's the learning curve?

**Tests:**
- Prototype testing
- Usability studies
- Wizard of Oz tests
- A/B tests on UX

### 3. Feasibility Risk
"Can we build this?"

**Questions:**
- Do we have the technology?
- Can we do it in reasonable time?
- What are the technical dependencies?

**Tests:**
- Technical spike
- Proof of concept
- Architecture review
- Build vs. buy analysis

### 4. Viability Risk
"Should we build this?"

**Questions:**
- Does it fit our strategy?
- Can we support/maintain it?
- Is it legal/compliant?
- Does the business model work?

**Tests:**
- Business case
- Stakeholder review
- Compliance review
- Financial modeling

Step 2: Discovery vs. Delivery

## Two Tracks: Discovery and Delivery

### Discovery (Figure out WHAT to build)

**Mindset:**
- Embrace uncertainty
- Test assumptions
- Fail fast and cheap
- Learn over deliver

**Activities:**
- Customer interviews
- Prototyping
- Experiments
- Opportunity assessment

**Outcome:**
- Validated problems
- Tested solutions
- Confidence to build
- Clear success metrics

### Delivery (BUILD it right)

**Mindset:**
- Reduce uncertainty
- Execute efficiently
- Ship quality
- Hit timelines

**Activities:**
- Engineering
- QA
- Launch prep
- Documentation

**Outcome:**
- Working software
- Happy customers
- Business impact
- Technical quality

### The Critical Point

Most teams skip discovery and jump to delivery.

**Result:**
- Build features no one wants
- Waste engineering resources
- Miss market opportunities
- Frustrated team, frustrated customers

**The ratio:**
Spend 10-20% of time on discovery to avoid wasting
80-90% of delivery time on wrong things.

Step 3: Opportunity Assessment

## Assessing Product Opportunities

### The Opportunity Assessment Framework

Before committing to solve a problem, answer:

**1. Is this problem worth solving?**
| Factor | Questions |
|--------|-----------|
| **Frequency** | How often does this problem occur? |
| **Intensity** | How painful is it when it happens? |
| **Willingness** | Will people pay/switch to solve it? |
| **Reach** | How many customers have this problem? |

**Scoring:**
- High frequency + High intensity = Strong opportunity
- Low frequency OR Low intensity = Weak opportunity

**2. Can we solve it effectively?**
| Factor | Questions |
|--------|-----------|
| **Capability** | Do we have the skills/tech? |
| **Fit** | Does it align with our strategy? |
| **Uniqueness** | Can we solve it better than alternatives? |
| **Sustainability** | Can we maintain competitive advantage? |

**3. Should we solve it now?**
| Factor | Questions |
|--------|-----------|
| **Urgency** | Is timing critical? |
| **Resources** | Do we have capacity? |
| **Dependencies** | What else needs to happen first? |
| **Opportunity cost** | What are we NOT doing instead? |

### Opportunity Score Card

Opportunity: [Name]

Problem Assessment

• Frequency: [1-5]
• Intensity: [1-5]
• Willingness to pay/switch: [1-5]
• Market size: [1-5] Problem Score: [Average]

Solution Assessment

• Technical feasibility: [1-5]
• Strategic fit: [1-5]
• Competitive advantage: [1-5] Solution Score: [Average]

Timing Assessment

• Urgency: [1-5]
• Resource availability: [1-5] Timing Score: [Average]

Overall: [Problem × Solution × Timing = X]

Recommendation: [Pursue / Park / Pass]

Step 4: Discovery Techniques

## Core Discovery Techniques

### 1. Customer Interviews

**Purpose:** Understand problems, not validate solutions

**Structure:**
1. Context: Understand their current situation
2. Problem: Explore the pain points
3. Impact: How does it affect them?
4. Current solutions: What do they do today?
5. Ideal state: What would "solved" look like?

**Key rules:**
- Ask about past behavior, not future intentions
- Don't pitch, just listen
- Follow the emotion
- Get specific stories

**Questions:**
- "Walk me through the last time this happened..."
- "What did you do? What happened next?"
- "Why was that a problem?"
- "What would have made it better?"

### 2. Prototyping

**Purpose:** Test solutions before building

**Types:**
| Type | Fidelity | Tests | Time |
|------|----------|-------|------|
| **Paper sketch** | Low | Concepts, flow | Hours |
| **Wireframe** | Low-Med | Structure, navigation | Days |
| **Clickable prototype** | Medium | Usability, flow | Days |
| **Wizard of Oz** | High | Full experience | Weeks |

**Principle:**
Use the lowest fidelity that tests your hypothesis.
Higher fidelity = More time = More risk of attachment.

### 3. Experiments

**Purpose:** Test assumptions with real behavior

**Types:**
- **Fake door:** Button for feature that doesn't exist
- **Smoke test:** Landing page before building
- **Concierge:** Manual delivery of automated value
- **A/B test:** Compare variations with real users

**Structure:**
1. Hypothesis: "We believe [X]"
2. Test: "We will test by [Y]"
3. Metric: "We will measure [Z]"
4. Success: "[Number] indicates we should proceed"

### 4. Opportunity Solution Trees

**Purpose:** Map problem space to solution space

**Structure:**

        [Desired Outcome]
               |
┌──────────────┼──────────────┐
|              |              |

[Opportunity 1] [Opportunity 2] [Opportunity 3] | | | ┌─┴─┐ ┌─┴─┐ ┌─┴─┐ | | | | | | [S1] [S2] [S1] [S2] [S1] [S2]

- Start with business outcome
- Break into opportunities (problems to solve)
- Brainstorm solutions for each opportunity
- Test solutions, not opportunities

Step 5: Continuous Discovery

## Weekly Discovery Rhythm

### The Cadence

**Monday: Prep**
- Review last week's learnings
- Prioritize this week's questions
- Schedule interviews/tests

**Tuesday-Thursday: Research**
- Customer interviews (2-3 per week minimum)
- Prototype testing
- Data analysis
- Experiment monitoring

**Friday: Synthesis**
- Consolidate learnings
- Update opportunity assessment
- Share with delivery team
- Plan next week

### The Habits

**1. Weekly customer touchpoint**
- Minimum: 2-3 customer conversations/week
- Mix: Prospects, users, churned customers

**2. Assumption tracking**
- List key assumptions
- Design tests for riskiest ones
- Document learnings

**3. Experiment backlog**
- Always have 2-3 experiments running
- Quick iterations over perfect tests

**4. Cross-functional involvement**
- Engineering in discovery
- Design in customer calls
- PM facilitates, doesn't dictate

### The Anti-Patterns

**Discovery theater:**
- Doing research but not changing decisions
- Fix: Tie discovery directly to roadmap decisions

**Big bang research:**
- Quarterly research sprints instead of continuous
- Fix: Small, frequent over large, infrequent

**Validation bias:**
- Only seeking confirmation
- Fix: Actively try to disprove hypotheses

Examples

Example 1: New Feature Discovery

Input:

“We’re thinking of adding a calendar integration to our project management tool. How do I validate this before we build it?”

Output:

Discovery Plan: Calendar Integration

Risk Assessment

Focus discovery on: Value risk (first), then Usability

Discovery Activities

Week 1: Understand the Problem

Customer Interviews (5-8 interviews)

Target: Current users who manage time-sensitive projects

Questions:

1. “Walk me through how you schedule work for your team right now.”
2. “What happens when deadlines slip? How do you find out?”
3. “What role does your calendar play in project management?”
4. “What’s frustrating about keeping projects and calendars in sync?”
5. “Show me the last time you missed something because of calendar/project disconnect.”

What you’re listening for:

• Is there a real pain? How intense?
• What workarounds do they use today?
• Is this a “nice to have” or “need to have”?

Week 2: Assess Demand

Fake Door Test

1. Add “Calendar Sync (Coming Soon)” button to settings
2. When clicked: “Thanks for your interest! Join the waitlist.”
3. Track: Click rate, waitlist conversions

Success criteria:

• 5% of MAU click the button = Strong signal

• 2-5% = Moderate signal
• <2% = Weak signal

Existing data analysis:

• How many users mention “calendar” in support tickets?
• What integrations do users currently connect?
• Are users with calendar tools more or less engaged?

Week 3: Test Solutions

Prototype Test (if Week 1-2 show signal)

Low-fidelity prototype showing:

1. Calendar sync setup flow
2. How synced events appear
3. Conflict resolution

Test with 5 users:

• Can they set it up without help?
• Does it match their mental model?
• What’s confusing?

Wizard of Oz Test

For 10 users:

1. Manually sync their calendar items to projects
2. Send them a summary email daily
3. Interview after 1 week: Was this valuable?

Decision Framework

Timeline

• Week 1: Interviews → Problem understanding
• Week 2: Fake door + data → Demand validation
• Week 3: Prototype → Solution validation
• Week 4: Synthesize → Build decision

Total discovery investment: 4 weeks, ~40 hours vs. building without discovery: 3-6 months of engineering

Example 2: Continuous Discovery Setup

Input:

“I’m a PM at a B2B SaaS startup. We don’t do any formal discovery—we just build what customers ask for or what the CEO wants. How do I start continuous discovery?”

Output:

Continuous Discovery Kickstart

Start Small, Build Habits

Don’t try to:

• Redesign your entire process
• Stop all current development
• Hire a research team

Do:

• Add one discovery habit per week
• Start with what you control
• Show value quickly

Week 1: Customer Conversation Habit

Action: Schedule 2 customer calls for next week.

Who to talk to:

• 1 customer who recently churned or downgraded
• 1 customer who recently upgraded or is highly engaged

Script: “We’re working to make [product] better for people like you. I’d love 20 minutes to understand how you’re using it and what we could improve. Not a sales call—just learning.”

After each call: Write 3-5 bullet points:

• What problem were they trying to solve?
• What’s working? What’s not?
• What surprised me?

Week 2: Assumption Tracking

Action: For any feature in development, list the top 3 assumptions.

Example format:

Feature: New onboarding flow
Assumptions:
1. Users don't complete onboarding because it's too long
2. Users who complete onboarding retain better
3. Users want to invite teammates during onboarding

Evidence level:
1. Assumption (no evidence)
2. Validated (we have data)
3. Assumption (no evidence)

Share with team: “Here are our assumptions. Which are we most uncertain about? How could we test them?”

Week 3: Add Interview Question

Action: Add one discovery question to every customer call (support, sales, success).

The question: “What’s the biggest challenge you’re facing right now that we don’t currently help with?”

Collect answers: Shared doc/Slack channel where team posts responses.

Weekly review: “We talked to 8 customers. Here’s what we heard about challenges…”

Week 4: Small Experiment

Action: Run one small experiment.

Simple options:

• Survey in-app about a proposed feature
• Fake door for feature idea
• A/B test on onboarding

Share results: “We tested [X]. Here’s what we learned: [Y]. This changes our thinking about [Z].”

Building Executive Buy-In

Don’t ask for permission. Show value.

After 4 weeks, share:

• “We talked to 8 customers. Here’s what we learned.”
• “We ran one experiment. It told us [X].”
• “This prevented us from building [feature] that data shows customers don’t actually need.”

The pitch: “I’m not asking for a big research investment. I’m spending 2-3 hours per week talking to customers and running small tests. It’s already helping us prioritize better.”

Scaling Up

Once habits are established (4-8 weeks):

1. Formalize interview cadence: 2-3/week team standard
2. Create opportunity backlog: Problems we might solve
3. Link to roadmap: Discovery informs what we build
4. Involve engineering: Engineers in customer calls

Example 3: Opportunity Assessment

Input:

“We have three potential features to build: (A) AI-powered suggestions, (B) Team collaboration improvements, (C) Mobile app. How do I assess which to prioritize?”

Output:

Opportunity Assessment: Feature Prioritization

Opportunity A: AI-Powered Suggestions

Problem Assessment

Solution Assessment

Key Risks:

• Value: Will suggestions be good enough to use?
• Feasibility: Do we have ML talent?
• Viability: Training data requirements, cost

Opportunity Score: 8.25

Opportunity B: Team Collaboration

Problem Assessment

Solution Assessment

Key Risks:

• Value: Which specific collaboration features matter?
• Usability: Team features can get complex quickly

Opportunity Score: 16.0

Opportunity C: Mobile App

Problem Assessment

Solution Assessment

Key Risks:

• Viability: Ongoing maintenance cost
• Feasibility: Native vs. cross-platform decisions

Opportunity Score: 6.4

Summary & Recommendation

Recommendation:

1. Prioritize Team Collaboration

   • Highest problem intensity
   • Clear strategic fit
   • Feasible to build

2. Park AI Suggestions for now

   • Validate value risk first
   • Consider: What specific suggestions would be valuable?
   • Test with simple rules before ML

3. Deprioritize Mobile App

   • High effort, limited reach
   • Consider: Progressive web app as interim?
   • Revisit when team collaboration is strong

Next Steps:

• Run 5 interviews focused on team collaboration pain points
• Identify top 3 specific collaboration problems
• Prototype and test before committing to full build

Checklists & Templates

Discovery Kickoff Checklist

## Before Starting Discovery

### Define the Scope
□ What outcome are we trying to achieve?
□ What problem might we solve?
□ Who is the target customer?
□ What's the timeline for decision?

### Identify Assumptions
□ List top 10 assumptions about problem and solution
□ Rank by risk level (if wrong, how bad?)
□ Identify top 3 to test first

### Plan Activities
□ Customer interviews scheduled (minimum 5)
□ Data/analytics to review identified
□ Prototype or experiment designed
□ Success criteria defined

### Align Team
□ Cross-functional team identified
□ Discovery goals shared
□ Calendar blocked for activities

Discovery Summary Template

## Discovery Summary: [Feature/Opportunity]

### Problem Statement
[What problem are we solving? For whom?]

### Research Conducted
- [X] customer interviews
- [X] data analyses
- [X] prototype tests
- [X] experiments

### Key Findings

**What we learned about the problem:**
1.
2.
3.

**What we learned about solutions:**
1.
2.
3.

### Risk Assessment

| Risk | Level | Mitigation |
|------|-------|------------|
| Value | | |
| Usability | | |
| Feasibility | | |
| Viability | | |

### Recommendation
[Build / Don't Build / Need More Discovery]

### If Building, Success Metrics
- Metric 1:
- Metric 2:
- Metric 3:

Skill Boundaries

What This Skill Does Well

• Structuring persuasive content
• Applying copywriting frameworks
• Creating draft variations
• Analyzing competitor approaches

What This Skill Cannot Do

• Guarantee conversion rates
• Replace brand voice development
• Know your specific audience
• Make final approval decisions

References

• Cagan, Marty. “Inspired: How to Create Tech Products Customers Love” (2018)
• Cagan, Marty & Jones, Chris. “Empowered” (2020)
• Torres, Teresa. “Continuous Discovery Habits” (2021)
• Silicon Valley Product Group (SVPG) resources
• Intercom on Product Management

Related Skills

• customer-discovery – Steve Blank’s broader framework
• mom-test – Customer interview techniques
• lean-canvas – Business model validation
• shape-up – Basecamp’s build methodology
• design-sprint – Google Ventures sprint

Skill Metadata

• Mode: cyborg

name: product-discovery
category: product
subcategory: methodology
version: 1.0
author: MKTG Skills
source_expert: Marty Cagan
source_work: Inspired, Empowered
difficulty: intermediate
estimated_value: $10,000+ product consulting engagement
tags: [product, discovery, validation, PM, Cagan, SVPG, risk, prototyping]
created: 2026-01-25
updated: 2026-01-25