product-management

Product Vision Framework

```

VISION COMPONENTS:

TARGET CUSTOMER:

• Who are we building for?
• What segments? What personas?

CUSTOMER NEED:

• What problem are we solving?
• What job to be done?

KEY BENEFIT:

• Primary value proposition
• Why customers will choose us

DIFFERENTIATOR:

• What makes us unique?
• Competitive advantage

AMAZON PRESS RELEASE FORMAT:

• Headline
• Summary (who, what, when, where, why)
• Problem statement
• Solution description
• Customer quote
• How to get started

```

Product-Market Fit

```

PMF INDICATORS:

QUANTITATIVE:

• 40%+ would be "very disappointed" without product (Sean Ellis)
• Strong organic growth/referrals
• Low churn, high retention
• Improving unit economics

QUALITATIVE:

• Customers actively advocating
• Word of mouth driving acquisition
• Pull from market (not push)
• Customers expanding usage

PMF SURVEY:

"How would you feel if you could no longer use [product]?"

• Very disappointed → Target 40%+
• Somewhat disappointed
• Not disappointed

PMF STAGES:

1. Problem-Solution Fit: Validated problem worth solving
2. Product-Market Fit: Solution resonates with market
3. Business Model Fit: Sustainable economics
4. Scale: Growth mechanics work

```

Jobs to Be Done (JTBD)

```

JOB STATEMENT:

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

FORCES OF PROGRESS:

Push: Current pain/frustration

Pull: Attraction to new solution

Anxiety: Concerns about switching

Habit: Comfort with status quo

```

See [Customer Research Methods](./references/customer-research-methods.md) for detailed JTBD methodology and interview techniques.

Roadmap Types

| Type | Timeframe | Audience | Detail Level |

| ------------- | ---------- | ------------------- | ------------ |

| Vision | 2-5 years | Board, executives | Themes |

| Strategic | 1-2 years | Leadership | Initiatives |

| Release | 3-6 months | Teams, stakeholders | Features |

| Sprint | 2-4 weeks | Dev team | User stories |

OKR Framework for Product

```

PRODUCT OKR STRUCTURE:

OBJECTIVE: [Qualitative goal]

KEY RESULT 1: [Metric] from [X] to [Y]

KEY RESULT 2: [Metric] from [X] to [Y]

KEY RESULT 3: [Metric] from [X] to [Y]

EXAMPLE:

O: Become the preferred solution for enterprise customers

KR1: Increase enterprise NPS from 40 to 60

KR2: Reduce enterprise churn from 8% to 4%

KR3: Increase enterprise ACV from $50K to $75K

```

RICE Framework

```

RICE SCORE = (Reach x Impact x Confidence) / Effort

REACH: How many customers affected per quarter

• Count: Number of users, customers, transactions

IMPACT: Effect on individual customer

• 3 = Massive
• 2 = High
• 1 = Medium
• 0.5 = Low
• 0.25 = Minimal

CONFIDENCE: How sure are we

• 100% = High confidence
• 80% = Medium
• 50% = Low

EFFORT: Person-months of work

• Engineering time
• Design time
• PM time

EXAMPLE:

| Feature | Reach | Impact | Conf | Effort | RICE |

|---------|-------|--------|------|--------|------|

| A | 5000 | 2 | 80% | 3 | 2667 |

| B | 1000 | 3 | 100% | 1 | 3000 |

| C | 10000 | 1 | 50% | 5 | 1000 |

```

ICE Framework

```

ICE SCORE = Impact x Confidence x Ease

IMPACT (1-10):

How much will this move our key metric?

CONFIDENCE (1-10):

How sure are we about impact estimate?

EASE (1-10):

How easy to implement?

Note: Simpler than RICE, good for quick decisions

```

MoSCoW Method

| Category | Definition | Guidance |

| --------------- | --------------------------- | --------------------- |

| Must Have | Non-negotiable for release | Core functionality |

| Should Have | Important but not critical | High value, can defer |

| Could Have | Nice to have | If time permits |

| Won't Have | Out of scope (this release) | Future consideration |

Kano Model

```

CATEGORIES:

BASIC (Must-be):

• Expected features
• Absence causes dissatisfaction
• Example: Login functionality

PERFORMANCE (Linear):

• More is better
• Satisfaction proportional to fulfillment
• Example: Speed, capacity

DELIGHTERS (Excitement):

• Unexpected features
• Absence doesn't cause dissatisfaction
• Presence greatly increases satisfaction
• Example: Innovative features

```

Research Methods

| Method | When to Use | Sample Size | Time |

| ------------------- | ------------------------ | ----------- | --------- |

| User Interviews | Deep understanding | 5-15 | 2-4 weeks |

| Surveys | Quantify findings | 100-1000+ | 1-2 weeks |

| Usability Tests | Validate designs | 5-8 | 1-2 weeks |

| A/B Tests | Compare options | 1000+ | 2-4 weeks |

| Analytics | Understand behavior | N/A | Ongoing |

| Card Sorting | Information architecture | 15-30 | 1 week |

| Diary Studies | Long-term behavior | 10-20 | 2-4 weeks |

See [Customer Research Methods](./references/customer-research-methods.md) for detailed interview frameworks, persona templates, and usability testing protocols.

Key Metrics Framework

```

PIRATE METRICS (AARRR):

ACQUISITION:

• How do users find us?
• Metrics: Traffic, signups, installs

ACTIVATION:

• First positive experience
• Metrics: Onboarding completion, first value

RETENTION:

• Do they come back?
• Metrics: DAU/MAU, cohort retention

REVENUE:

• Do they pay?
• Metrics: Conversion, ARPU, LTV

REFERRAL:

• Do they tell others?
• Metrics: NPS, referral rate, viral coefficient

```

Product Health Metrics

| Metric | Formula | Target |

| -------------------- | --------------------------------- | -------- |

| DAU/MAU | Daily users / Monthly users | 20-50%+ |

| Activation Rate | Completed setup / Signups | 40-60%+ |

| Feature Adoption | Users using feature / Total users | Varies |

| Time to Value | Days to first value | Minimize |

| Power Users | Heavy users / Total users | 15-25% |

See [Analytics and Experimentation](./references/analytics-and-experimentation.md) for detailed cohort analysis, retention benchmarks, and event tracking strategies.

Experiment Framework

```

EXPERIMENT DESIGN:

HYPOTHESIS:

If we [change], then [metric] will [improve/decrease] because [rationale].

METRICS:

• Primary: The metric you're trying to move
• Secondary: Other metrics to monitor
• Guardrails: Metrics that shouldn't degrade

SAMPLE SIZE:

Use calculator based on:

• Baseline conversion rate
• Minimum detectable effect (MDE)
• Statistical significance (usually 95%)
• Power (usually 80%)

DURATION:

• At least 1 business cycle
• Adequate sample size
• Account for novelty effects

```

Decision Framework

• Ship: Stat sig + practical sig + no negative guardrails
• Iterate: Directionally positive but not stat sig, or mixed results
• Kill: No effect or negative impact
• Investigate: Unexpected results, large variance, segment differences

See [Analytics and Experimentation](./references/analytics-and-experimentation.md) for detailed statistical concepts, common pitfalls, and segmentation analysis.

Launch Checklist

```

PRE-LAUNCH:

• [ ] Feature complete and tested
• [ ] Documentation ready
• [ ] Support team trained
• [ ] Marketing materials prepared
• [ ] Sales team enabled
• [ ] Beta feedback incorporated
• [ ] Success metrics defined

LAUNCH:

• [ ] Staged rollout plan
• [ ] Monitoring dashboards live
• [ ] War room established
• [ ] Communication sent
• [ ] Feature flags enabled

POST-LAUNCH:

• [ ] Monitor metrics and feedback
• [ ] Address critical issues
• [ ] Gather early learnings
• [ ] Celebrate wins
• [ ] Retrospective scheduled

```

Go-to-Market Plan

| Element | Description |

| --------------------- | ---------------------------- |

| Target Segment | Who is this for? |

| Value Proposition | Why will they care? |

| Pricing | How will we charge? |

| Distribution | How will they get it? |

| Messaging | What will we say? |

| Enablement | How will teams sell/support? |

| Measurement | How will we track success? |

Discovery Techniques

| Technique | Purpose | When to Use |

| ----------------------- | -------------------- | ----------------- |

| Opportunity Mapping | Identify problems | Early discovery |

| Story Mapping | Visualize journeys | Planning releases |

| Design Sprints | Rapid prototyping | Big bets |

| Fake Door Tests | Validate demand | Before building |

| Wizard of Oz | Test concepts | Complex features |

| Concierge MVP | Manual service first | New markets |

Opportunity Assessment

```

OPPORTUNITY CANVAS:

PROBLEM:

What problem are we solving?

Who has this problem?

How do they solve it today?

EVIDENCE:

What data supports this?

Customer quotes/feedback?

Market research?

SOLUTION:

What are we proposing?

Why will it work?

What's the MVP?

ASSUMPTIONS:

What must be true?

What risks exist?

How will we validate?

OUTCOME:

Success metrics?

Business impact?

Customer impact?

```

PRD Structure (One-Pager)

```

1. EXECUTIVE SUMMARY (3-4 sentences)

• What: One-line description
• Why: Core problem being solved
• Who: Target users
• Success: How we'll measure it

1. BACKGROUND & CONTEXT

• Current situation and pain points
• Supporting data
• Strategic alignment

1. GOALS & SUCCESS METRICS

• Primary goal and success metric
• Secondary goals and metrics
• Guardrail metrics

1. USER STORIES

Format: "As a [persona], I want to [action], so that [benefit]"

• Acceptance criteria
• Priority (Must/Should/Could Have)

1. SOLUTION OVERVIEW

• High-level description
• Key user flows
• Out of scope

1. DESIGN & TECHNICAL CONSIDERATIONS

• Mockups/wireframes
• Dependencies
• Scalability

1. LAUNCH PLAN

• Rollout strategy
• Success criteria
• Risk mitigation

1. OPEN QUESTIONS

• Unresolved decisions
• Areas needing research

```

For comprehensive product management frameworks and methodologies:

• [Product Strategy Expert](./references/product-strategy-expert.md) - Complete PM reference guide
• [Customer Research Methods](./references/customer-research-methods.md) - Interview frameworks, personas, usability testing
• [Analytics and Experimentation](./references/analytics-and-experimentation.md) - Retention analysis, A/B testing, event tracking

• [Data Science](../data-science/SKILL.md) - Analytics and ML
• [Marketing](../marketing/SKILL.md) - Go-to-market strategy
• [Business Strategy](../business-strategy/SKILL.md) - Strategic planning