PM Discovery

Product discovery frameworks for turning research into product decisions. Use after market research, before implementation planning.

When to Use

• After customer interviews, before synthesizing insights
• When prioritizing features or opportunities
• When validating product hypotheses
• When mapping assumptions to test
• When structuring discovery findings for stakeholders

Discovery Frameworks

1. Customer Interview Synthesis

Interview Question Bank:

## Problem Discovery
- "Walk me through the last time you [did X]..."
- "What's the hardest part about [doing X]?"
- "Why is that hard?" (ask 3x)
- "What have you tried to solve this?"
- "What happened when you tried that?"

## Current Solution Analysis
- "How do you handle [X] today?"
- "How often do you do this?"
- "What would happen if you couldn't do this?"
- "How much time/money does this cost you?"

## Switching Signals
- "Have you looked for other solutions?"
- "What would make you switch?"
- "What's stopping you from switching now?"

## Value Discovery
- "If you could wave a magic wand, what would change?"
- "What would that be worth to you?"
- "Who else cares about this problem?"

Interview Synthesis Template:

## Interview: [Customer Name/Segment]
**Date:** YYYY-MM-DD | **Duration:** X min | **Role:** [Title]

### Problem Quotes (verbatim)
> "[Exact quote about the problem]"
> "[Another revealing quote]"

### Current Behavior
- Does [X] using [current solution]
- Frequency: [daily/weekly/monthly]
- Time spent: [X hours/month]

### Pain Intensity: [1-5]
- 1: Mild annoyance
- 3: Significant friction
- 5: "Hair on fire" problem

### Willingness to Pay Signal
- [ ] Actively searching for solutions
- [ ] Has budget allocated
- [ ] Named a specific price point: $___
- [ ] Would switch immediately if solved

### Key Insight
[One sentence capturing the non-obvious learning]

2. Assumption Mapping

Riskiest Assumption Test (RAT):

## Assumption Map

### Desirability (Will they want it?)
| Assumption | Evidence For | Evidence Against | Risk Level |
|------------|--------------|------------------|------------|
| [Users want X] | [data] | [data] | High/Med/Low |

### Viability (Will it work for the business?)
| Assumption | Evidence For | Evidence Against | Risk Level |
|------------|--------------|------------------|------------|
| [Users will pay $X] | [data] | [data] | High/Med/Low |

### Feasibility (Can we build it?)
| Assumption | Evidence For | Evidence Against | Risk Level |
|------------|--------------|------------------|------------|
| [We can integrate with X] | [data] | [data] | High/Med/Low |

### Riskiest Assumption to Test Next
**Assumption:** [The one with highest risk + least evidence]
**Test:** [Cheapest way to validate/invalidate]
**Success Criteria:** [Specific threshold]
**Timeline:** [Days/weeks]

3. Jobs-to-be-Done (JTBD)

Job Statement Format:

When [situation/trigger],
I want to [motivation/goal],
so I can [expected outcome].

JTBD Canvas:

## Job: [Core functional job]

### Trigger/Situation
- When does this job arise?
- What context are they in?

### Functional Job (what they're trying to do)
[Action verb] + [object] + [clarifying context]
Example: "Organize customer feedback by theme before the weekly product meeting"

### Emotional Job (how they want to feel)
- Feel [emotion] about [situation]
Example: "Feel confident presenting insights to leadership"

### Social Job (how they want to be perceived)
- Be seen as [perception] by [audience]
Example: "Be seen as data-driven by the exec team"

### Current Solutions
| Solution | Hiring Criteria | Firing Criteria |
|----------|-----------------|-----------------|
| [Tool/workaround] | [Why they use it] | [Why they'd stop] |

### Outcome Metrics
What does "job done well" look like?
- Speed: [Complete X in Y minutes]
- Quality: [Z accuracy/completeness]
- Confidence: [Feel certain about decision]

4. Feature Prioritization

RICE Scoring:

RICE Score = (Reach × Impact × Confidence) / Effort

Factor	Definition	Scale
Reach	Users affected per quarter	Actual number
Impact	Effect on users	3=Massive, 2=High, 1=Medium, 0.5=Low, 0.25=Minimal
Confidence	How sure are you?	100%=High, 80%=Medium, 50%=Low
Effort	Person-months to ship	Actual estimate

RICE Table:

| Feature | Reach | Impact | Confidence | Effort | RICE Score |
|---------|-------|--------|------------|--------|------------|
| [Feature A] | 5000 | 2 | 80% | 2 | 4000 |
| [Feature B] | 1000 | 3 | 50% | 1 | 1500 |

ICE Scoring (simpler alternative):

ICE Score = Impact × Confidence × Ease

Factor	Scale
Impact	1-10 (potential value)
Confidence	1-10 (certainty of impact)
Ease	1-10 (implementation simplicity)

5. Opportunity Solution Tree

Structure:

Outcome (measurable business goal)
├── Opportunity 1 (unmet customer need)
│   ├── Solution 1a
│   │   └── Experiment: [test]
│   └── Solution 1b
│       └── Experiment: [test]
├── Opportunity 2 (another need)
│   └── Solution 2a
│       └── Experiment: [test]
└── Opportunity 3
    └── ...

OST Template:

## Outcome
**Goal:** [Measurable objective]
**Current:** [Baseline metric]
**Target:** [Target metric]
**Timeline:** [By when]

## Opportunity Map

### Opportunity 1: [Customer need/pain point]
**Evidence:** [Interview quotes, data]
**Size:** [How many users affected]

**Solutions considered:**
1. **[Solution A]**
   - Effort: [S/M/L]
   - Experiment: [How to test cheaply]
   - Success metric: [What to measure]

2. **[Solution B]**
   - Effort: [S/M/L]
   - Experiment: [How to test cheaply]
   - Success metric: [What to measure]

**Selected:** [Which and why]

6. Product Hypothesis

Hypothesis Format:

## Hypothesis: [Short name]

**We believe that** [building this feature/making this change]
**For** [target user segment]
**Will result in** [expected outcome/behavior change]
**We will know we're right when** [measurable success criteria]

### Riskiest Assumption
[The assumption that if wrong, invalidates the hypothesis]

### Minimum Test
[Cheapest/fastest way to validate]
- Type: [Prototype/Fake door/Concierge/etc]
- Duration: [X days/weeks]
- Sample size: [N users]

### Decision Criteria
- **Ship if:** [specific threshold met]
- **Iterate if:** [mixed signals, specify]
- **Kill if:** [specific threshold not met]

Discovery Synthesis

After gathering insights, synthesize into:

## Discovery Summary: [Feature/Initiative]

### What We Learned
1. [Key insight with evidence]
2. [Key insight with evidence]
3. [Key insight with evidence]

### User Segments & Their Jobs
| Segment | Primary Job | Pain Intensity | Size |
|---------|-------------|----------------|------|
| [Segment A] | [JTBD] | [1-5] | [N users] |

### Prioritized Opportunities
| Rank | Opportunity | Evidence | RICE |
|------|-------------|----------|------|
| 1 | [Opp] | [Quote/data] | [Score] |

### Recommended Next Step
**Do:** [Specific action]
**Test:** [What to validate]
**Success looks like:** [Measurable outcome]

### What We Still Don't Know
- [ ] [Open question to investigate]
- [ ] [Assumption still untested]

Anti-Patterns to Avoid

Anti-Pattern	Why It Fails	Instead Do
Leading questions	Confirms bias, not truth	Ask open-ended, follow with "why"
Hypothetical pricing	People lie about future spending	Ask about current spending
Feature requests as truth	Users describe solutions, not problems	Dig for underlying need
Small sample size decisions	Anecdotes ≠ patterns	Require 5+ signals minimum
Skipping competitor analysis	Reinventing existing solutions	Research before ideating

Integration with Other Skills

• Before PM Discovery: Use problem-research for market pain points
• Before PM Discovery: Use customer-discovery to find user communities
• After PM Discovery: Use /majestic:prd to document requirements
• After PM Discovery: Use /majestic:plan for implementation planning