| name | product-discovery |
| description | Product discovery and market research expert. Use when validating product ideas, conducting market research, user interviews, competitive analysis, or opportunity assessment. Covers JTBD, Kano model, and Value Proposition Canvas. |
Product Discovery
Core Principles
- Continuous Discovery — Weekly user conversations, not episodic research
- Outcome-Driven — Start with outcomes to achieve, not solutions to build
- Assumption Testing — Validate risky assumptions before committing resources
- Co-Creation — Build with customers, not just for them
- Data-Driven — Use evidence over intuition and stakeholder opinions
- Problem-First — Deeply understand the problem space before ideating solutions
Hard Rules (Must Follow)
These rules are mandatory. Violating them means the skill is not working correctly.
No Solution-First Thinking
Never start with a solution. Always define the problem and outcome first.
❌ FORBIDDEN:
"We should build a search bar for the product page"
"Let's add AI recommendations"
"Users need a mobile app"
✅ REQUIRED:
"Problem: Users can't find products (40% exit rate on catalog)
Outcome: Reduce exit rate to 20%
Possible solutions:
1. Search bar with filters
2. AI-powered recommendations
3. Better category navigation
4. Visual product browsing"
Evidence-Based Decisions
Never assume user needs without evidence from real user research.
❌ FORBIDDEN:
- "Users probably want X" (assumption without data)
- "Our competitor has X, so we need it too" (copycat without validation)
- "The CEO thinks we should build X" (HiPPO without evidence)
- "It's obvious users need X" (intuition without validation)
✅ REQUIRED:
- "5 out of 8 interviewed users mentioned X as a pain point"
- "Analytics show 60% of users abandon at step 3"
- "Prototype test: 7/10 users completed task successfully"
- "Survey (n=500): 45% rated feature as 'must have'"
Minimum Interview Threshold
Never validate a problem with fewer than 5 user interviews per segment.
❌ FORBIDDEN:
- "We talked to 2 users and they loved the idea"
- "One customer requested this feature"
- "Based on a quick chat with sales..."
✅ REQUIRED:
| Segment | Interviews | Key Finding |
|---------|------------|-------------|
| Power Users | 6 | 5/6 struggle with X |
| New Users | 5 | 4/5 drop off at onboarding |
| Churned | 5 | 3/5 cited missing feature Y |
Minimum per segment: 5 interviews
Confidence increases with more interviews
Falsifiable Assumptions
Every assumption must be testable and falsifiable with clear success criteria.
❌ FORBIDDEN:
- "Users will like the new design" (not falsifiable)
- "This will improve engagement" (no success criteria)
- "The feature will be useful" (vague)
✅ REQUIRED:
| Assumption | Test | Success Criteria | Result |
|------------|------|------------------|--------|
| Users will complete onboarding in new flow | Prototype test with 10 users | >70% completion | TBD |
| Users prefer visual search | A/B test | >10% lift in conversions | TBD |
| Price point is acceptable | Landing page test | >3% conversion | TBD |
Quick Reference
When to Use What
| Scenario | Framework/Tool | Output |
|---|
| Validate product idea | Product Opportunity Assessment | Go/no-go decision |
| Size market opportunity | TAM/SAM/SOM | Market size estimates |
| Understand user needs | User Research (interviews, surveys) | User insights, pain points |
| Analyze competition | Competitive Analysis | Competitive landscape map |
| Discover user motivations | Jobs-to-be-Done (JTBD) | Job stories, outcomes |
| Prioritize features | Kano Model | Feature categorization |
| Define value proposition | Value Proposition Canvas | Value prop statement |
| Test product concept | Lean Startup / MVP | Validated learnings |
| Map opportunities | Opportunity Solution Tree | Prioritized opportunities |
Continuous Discovery Habits
The Product Trio
Discovery is led by three roles working together weekly:
Product Manager → Defines outcomes, owns roadmap
Designer → Explores solutions, tests usability
Engineer → Assesses feasibility, proposes technical solutions
Weekly Activities
## 1. Customer Interviews (Weekly)
- Schedule 3-5 interviews per week minimum
- Mix of current users, churned users, prospects
- Focus on understanding problems, not pitching solutions
- Record and share insights with team
## 2. Assumption Testing (Weekly)
- Identify riskiest assumptions about solutions
- Design quick tests (prototypes, landing pages, fake doors)
- Run experiments with real users
- Measure results against success criteria
## 3. Opportunity Mapping (Ongoing)
- Build opportunity solution tree
- Map customer needs to potential solutions
- Prioritize based on impact and feasibility
- Update as you learn
Discovery vs Delivery
Discovery (What to Build) Delivery (How to Build It)
├─ Customer interviews ├─ Sprint planning
├─ Prototype testing ├─ Development
├─ Assumption validation ├─ QA testing
├─ Market research ├─ Deployment
└─ Opportunity assessment └─ Post-launch monitoring
Key difference: Discovery reduces risk BEFORE committing to build
Product Opportunity Assessment
Marty Cagan's 10 Questions
Before starting any product initiative, answer these questions:
## 1. Problem Definition
**What problem are we solving?**
- Be specific and measurable
- Validate it's a real problem (not assumed)
## 2. Target Market
**For whom are we solving this problem?**
- Define specific user segments
- Size the addressable market (TAM/SAM/SOM)
## 3. Opportunity Size
**How big is the opportunity?**
- Revenue potential
- User growth potential
- Strategic value
## 4. Success Metrics
**How will we measure success?**
- Leading indicators (usage, engagement)
- Lagging indicators (revenue, retention)
- Define targets upfront
## 5. Alternative Solutions
**What alternatives exist today?**
- Direct competitors
- Indirect solutions
- Current user workarounds
## 6. Our Advantage
**Why are we best suited to solve this?**
- Unique capabilities
- Market position
- Technical advantages
## 7. Strategic Fit
**Why now? Why us?**
- Market timing
- Strategic alignment
- Resource availability
## 8. Dependencies
**What do we need to succeed?**
- Technical dependencies
- Partnership requirements
- Regulatory considerations
## 9. Risks
**What could go wrong?**
- Market risk (will anyone want it?)
- Execution risk (can we build it?)
- Monetization risk (will they pay?)
Competitive disadvantage
Lost revenue
Market opportunity window
Value vs Effort Framework
Quick prioritization of opportunities:
High Value, Low Effort → Do First (Quick Wins)
High Value, High Effort → Plan Strategically (Big Bets)
Low Value, Low Effort → Do Later (Fill Gaps)
Low Value, High Effort → Don't Do (Money Pit)
Discovery Methods
When to Use What Method
## Generative Research (What problems exist?)
Use when: Starting new product area, exploring unknown space
Methods:
- Ethnographic field studies
- Contextual inquiry
- Diary studies
- Open-ended interviews
## Evaluative Research (Does our solution work?)
Use when: Testing specific solutions, validating designs
Methods:
- Usability testing
- Prototype testing
- A/B testing
- Concept testing
## Quantitative Research (How much? How many?)
Use when: Need statistical validation, measuring impact
Methods:
- Surveys
- Analytics analysis
- A/B experiments
- Market sizing
## Qualitative Research (Why? How?)
Use when: Understanding motivations, uncovering insights
Methods:
- User interviews
- Focus groups
- Customer advisory boards
- User observation
Interview Best Practices
## Preparation
- Define research goals and hypotheses
- Create interview guide (but stay flexible)
- Recruit right participants (6-8 per segment)
- Schedule 45-60 min sessions
## During Interview
✓ Ask open-ended questions ("Tell me about...")
✓ Follow up with "Why?" 5 times to get to root cause
✓ Listen more than talk (80/20 rule)
✓ Ask about past behavior, not future hypotheticals
✓ Look for workarounds and pain points
✓ Record and take notes
✗ Don't ask leading questions
✗ Don't pitch your solution
✗ Don't ask "Would you use X?" (people lie)
✗ Don't multi-task while interviewing
## Example Questions
- "Walk me through the last time you [did task]"
- "What's most frustrating about [current solution]?"
- "How are you solving this problem today?"
- "What would make [task] easier for you?"
- "Tell me more about that..."
Survey Best Practices
## When to Survey
✓ Validate findings from qualitative research
✓ Measure satisfaction or sentiment at scale
✓ Prioritize features (Kano surveys)
✓ Segment users by behavior/needs
## Survey Design
- Keep it short (<10 min to complete)
- One question per screen on mobile
- Mix question types (multiple choice, scale, open-ended)
- Avoid leading or biased questions
- Test survey with 5 people before sending
## Question Types
- Multiple choice → Segmentation, categorization
- Likert scale (1-5) → Satisfaction, importance
- Open-ended → Qualitative insights
- Ranking → Prioritization
- NPS (0-10) → Loyalty measurement
## Distribution
- In-app surveys (high response, biased to engaged users)
- Email surveys (broader reach, lower response)
- Incentivize thoughtful responses ($10 gift card, early access)
- Follow up with interviews for interesting responses
2025 Trends in Product Discovery
AI-Powered Research
## AI Tools for Discovery
- **Insight synthesis** — AI analyzes interview transcripts, identifies patterns
- **Synthetic personas** — AI-generated user proxies for rapid testing
- **Market intelligence** — AI tracks competitor moves, pricing changes
- **Survey analysis** — Automated sentiment analysis, theme extraction
- **Trend detection** — AI identifies emerging market trends early
## Examples
- Crayon → Competitive intelligence automation
- Glimpse → Trend detection from web data
- Delve AI → Automated persona creation
- Attest → AI-powered survey insights
- Quantilope → Machine learning research automation
## Best Practices
✓ Use AI to scale research, not replace human insight
✓ Validate AI findings with real user conversations
✓ Combine AI analysis with qualitative depth
✗ Don't rely solely on synthetic users
✗ Don't skip talking to real customers
Continuous Discovery at Scale
## Modern Approach
- Discovery is embedded in every sprint, not a phase
- Weekly user touchpoints (interviews, tests, feedback)
- Rapid experimentation (dozens of tests running)
- Fast pivots based on evidence (days, not months)
## Team Structure
- Product trios own discovery for their area
- Centralized research team supports (tools, methods)
- Customer success shares feedback loop
- Data analysts provide quantitative insights
## Cadence
- Weekly: Customer interviews, prototype tests
- Bi-weekly: Opportunity review, assumption validation
- Monthly: Market analysis, competitive review
- Quarterly: Strategic discovery (new markets, big bets)
Opportunity Solution Tree
What It Is
Visual framework for mapping the path from outcome to solution:
OUTCOME (Business goal)
|
┌────────┴────────┐
│ │
OPPORTUNITY 1 OPPORTUNITY 2
│ │
├─ Solution A ├─ Solution C
├─ Solution B └─ Solution D
└─ Solution C
How to Build One
## Step 1: Define Outcome
Start with measurable business outcome
Example: "Increase Day 30 retention from 20% to 30%"
## Step 2: Map Opportunities
Discover customer needs/pain points through research
Example: "Users don't understand core features"
## Step 3: Generate Solutions
For each opportunity, brainstorm multiple solutions
Example:
- Better onboarding tutorial
- In-app tooltips
- Interactive product tour
## Step 4: Test Assumptions
For each solution, identify riskiest assumption and test
Example: "Users will complete a 5-step tutorial"
Test: Build simple prototype, test with 10 users
## Step 5: Compare Solutions
Use evidence to choose best path forward
Build what tests validate, discard what fails
Benefits
✓ Visualizes multiple paths to outcome
✓ Prevents jumping to first solution
✓ Encourages broad exploration before narrowing
✓ Documents why decisions were made
✓ Keeps team aligned on priorities
Extended Reference
Detailed material starting at ## Integrating Discovery with Delivery has been moved to reference/extended.md to keep this skill concise. Load that reference when the task requires the moved examples, command catalogs, checklists, platform details, or implementation templates.
