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:

Problem-Solution Fit: Validated problem worth solving
Product-Market Fit: Solution resonates with market
Business Model Fit: Sustainable economics
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 Planning

### 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


## Feature Prioritization

### 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


## Customer Research

### 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.

## Product Analytics

### 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.

## A/B Testing

### 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.

## Product Launches

### Launch Checklist

PRE-LAUNCH:

LAUNCH:

POST-LAUNCH:


### 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?   |

## Product Discovery

### 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?


## Deliverable Templates

### PRD Structure (One-Pager)

EXECUTIVE SUMMARY (3-4 sentences)

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

BACKGROUND & CONTEXT

Current situation and pain points
Supporting data
Strategic alignment

GOALS & SUCCESS METRICS

Primary goal and success metric
Secondary goals and metrics
Guardrail metrics

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

Acceptance criteria
Priority (Must/Should/Could Have)

SOLUTION OVERVIEW

High-level description
Key user flows
Out of scope

DESIGN & TECHNICAL CONSIDERATIONS

Mockups/wireframes
Dependencies
Scalability

LAUNCH PLAN

Rollout strategy
Success criteria
Risk mitigation

OPEN QUESTIONS

Unresolved decisions
Areas needing research


## Additional Resources

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

## See Also

- [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

product-management

Product Management Expert

Product Strategy

Product Vision Framework