product-manager-toolkit

For Feature Prioritization

```bash

python scripts/rice_prioritizer.py sample # Create sample CSV

python scripts/rice_prioritizer.py sample_features.csv --capacity 15

```

For Interview Analysis

```bash

python scripts/customer_interview_analyzer.py interview_transcript.txt

```

For PRD Creation

1. Choose template from references/prd_templates.md
2. Fill in sections based on discovery work
3. Review with stakeholders
4. Version control in your PM tool

Feature Prioritization Process

1. Gather Feature Requests

- Customer feedback

- Sales requests

- Technical debt

- Strategic initiatives

1. Score with RICE

```bash

# Create CSV with: name,reach,impact,confidence,effort

python scripts/rice_prioritizer.py features.csv

```

- Reach: Users affected per quarter

- Impact: massive/high/medium/low/minimal

- Confidence: high/medium/low

- Effort: xl/l/m/s/xs (person-months)

1. Analyze Portfolio

- Review quick wins vs big bets

- Check effort distribution

- Validate against strategy

1. Generate Roadmap

- Quarterly capacity planning

- Dependency mapping

- Stakeholder alignment

Customer Discovery Process

1. Conduct Interviews

- Use semi-structured format

- Focus on problems, not solutions

- Record with permission

1. Analyze Insights

```bash

python scripts/customer_interview_analyzer.py transcript.txt

```

Extracts:

- Pain points with severity

- Feature requests with priority

- Jobs to be done

- Sentiment analysis

- Key themes and quotes

1. Synthesize Findings

- Group similar pain points

- Identify patterns across interviews

- Map to opportunity areas

1. Validate Solutions

- Create solution hypotheses

- Test with prototypes

- Measure actual vs expected behavior

PRD Development Process

1. Choose Template

- Standard PRD: Complex features (6-8 weeks)

- One-Page PRD: Simple features (2-4 weeks)

- Feature Brief: Exploration phase (1 week)

- Agile Epic: Sprint-based delivery

1. Structure Content

- Problem → Solution → Success Metrics

- Always include out-of-scope

- Clear acceptance criteria

1. Collaborate

- Engineering for feasibility

- Design for experience

- Sales for market validation

- Support for operational impact

rice_prioritizer.py

Advanced RICE framework implementation with portfolio analysis.

Features:

• RICE score calculation
• Portfolio balance analysis (quick wins vs big bets)
• Quarterly roadmap generation
• Team capacity planning
• Multiple output formats (text/json/csv)

Usage Examples:

```bash

# Basic prioritization

python scripts/rice_prioritizer.py features.csv

# With custom team capacity (person-months per quarter)

python scripts/rice_prioritizer.py features.csv --capacity 20

# Output as JSON for integration

python scripts/rice_prioritizer.py features.csv --output json

```

customer_interview_analyzer.py

NLP-based interview analysis for extracting actionable insights.

Capabilities:

• Pain point extraction with severity assessment
• Feature request identification and classification
• Jobs-to-be-done pattern recognition
• Sentiment analysis
• Theme extraction
• Competitor mentions
• Key quotes identification

Usage Examples:

```bash

# Analyze single interview

python scripts/customer_interview_analyzer.py interview.txt

# Output as JSON for aggregation

python scripts/customer_interview_analyzer.py interview.txt json

```

prd_templates.md

Multiple PRD formats for different contexts:

1. Standard PRD Template

- Comprehensive 11-section format

- Best for major features

- Includes technical specs

1. One-Page PRD

- Concise format for quick alignment

- Focus on problem/solution/metrics

- Good for smaller features

1. Agile Epic Template

- Sprint-based delivery

- User story mapping

- Acceptance criteria focus

1. Feature Brief

- Lightweight exploration

- Hypothesis-driven

- Pre-PRD phase

RICE Framework

```

Score = (Reach × Impact × Confidence) / Effort

Reach: # of users/quarter

Impact:

- Massive = 3x

- High = 2x

- Medium = 1x

- Low = 0.5x

- Minimal = 0.25x

Confidence:

- High = 100%

- Medium = 80%

- Low = 50%

Effort: Person-months

```

Value vs Effort Matrix

```

Low Effort High Effort

High QUICK WINS BIG BETS

Value [Prioritize] [Strategic]

Low FILL-INS TIME SINKS

Value [Maybe] [Avoid]

```

MoSCoW Method

• Must Have: Critical for launch
• Should Have: Important but not critical
• Could Have: Nice to have
• Won't Have: Out of scope

Customer Interview Guide

```

1. Context Questions (5 min)

- Role and responsibilities

- Current workflow

- Tools used

1. Problem Exploration (15 min)

- Pain points

- Frequency and impact

- Current workarounds

1. Solution Validation (10 min)

- Reaction to concepts

- Value perception

- Willingness to pay

1. Wrap-up (5 min)

- Other thoughts

- Referrals

- Follow-up permission

```

Hypothesis Template

```

We believe that [building this feature]

For [these users]

Will [achieve this outcome]

We'll know we're right when [metric]

```

Opportunity Solution Tree

```

Outcome

├── Opportunity 1

│ ├── Solution A

│ └── Solution B

└── Opportunity 2

├── Solution C

└── Solution D

```

North Star Metric Framework

1. Identify Core Value: What's the #1 value to users?
2. Make it Measurable: Quantifiable and trackable
3. Ensure It's Actionable: Teams can influence it
4. Check Leading Indicator: Predicts business success

Funnel Analysis Template

```

Acquisition → Activation → Retention → Revenue → Referral

Key Metrics:

• Conversion rate at each step
• Drop-off points
• Time between steps
• Cohort variations

```

Feature Success Metrics

• Adoption: % of users using feature
• Frequency: Usage per user per time period
• Depth: % of feature capability used
• Retention: Continued usage over time
• Satisfaction: NPS/CSAT for feature

Writing Great PRDs

1. Start with the problem, not solution
2. Include clear success metrics upfront
3. Explicitly state what's out of scope
4. Use visuals (wireframes, flows)
5. Keep technical details in appendix
6. Version control changes

Effective Prioritization

1. Mix quick wins with strategic bets
2. Consider opportunity cost
3. Account for dependencies
4. Buffer for unexpected work (20%)
5. Revisit quarterly
6. Communicate decisions clearly

Customer Discovery Tips

1. Ask "why" 5 times
2. Focus on past behavior, not future intentions
3. Avoid leading questions
4. Interview in their environment
5. Look for emotional reactions
6. Validate with data

Stakeholder Management

1. Identify RACI for decisions
2. Regular async updates
3. Demo over documentation
4. Address concerns early
5. Celebrate wins publicly
6. Learn from failures openly

1. Solution-First Thinking: Jumping to features before understanding problems
2. Analysis Paralysis: Over-researching without shipping
3. Feature Factory: Shipping features without measuring impact
4. Ignoring Technical Debt: Not allocating time for platform health
5. Stakeholder Surprise: Not communicating early and often
6. Metric Theater: Optimizing vanity metrics over real value

This toolkit integrates with:

• Analytics: Amplitude, Mixpanel, Google Analytics
• Roadmapping: ProductBoard, Aha!, Roadmunk
• Design: Figma, Sketch, Miro
• Development: Jira, Linear, GitHub
• Research: Dovetail, UserVoice, Pendo
• Communication: Slack, Notion, Confluence

```bash

# Prioritization

python scripts/rice_prioritizer.py features.csv --capacity 15

# Interview Analysis

python scripts/customer_interview_analyzer.py interview.txt

# Create sample data

python scripts/rice_prioritizer.py sample

# JSON outputs for integration

python scripts/rice_prioritizer.py features.csv --output json

python scripts/customer_interview_analyzer.py interview.txt json

```