Product Manager Toolkit

Essential tools and frameworks for modern product management, from discovery to delivery.

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Table of Contents

• Quick Start
• Core Workflows

- Feature Prioritization

- Customer Discovery

- PRD Development

• Tools Reference

- RICE Prioritizer

- Customer Interview Analyzer

• Input/Output Examples
• Integration Points
• Common Pitfalls

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Quick Start

For Feature Prioritization

# Create sample data file
python scripts/rice_prioritizer.py sample

Run prioritization with team capacity
python scripts/rice_prioritizer.py sample_features.csv --capacity 15

For Interview Analysis

python scripts/customer_interview_analyzer.py interview_transcript.txt

For PRD Creation

• Choose template from references/prd_templates.md
• Fill sections based on discovery work
• Review with engineering for feasibility
• Version control in project management tool

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Core Workflows

Feature Prioritization Process

Gather → Score → Analyze → Plan → Validate → Execute

#### Step 1: Gather Feature Requests

• Customer feedback (support tickets, interviews)
• Sales requests (CRM pipeline blockers)
• Technical debt (engineering input)
• Strategic initiatives (leadership goals)

#### Step 2: Score with RICE

# Input: CSV with features
python scripts/rice_prioritizer.py features.csv --capacity 20

See references/frameworks.md for RICE formula and scoring guidelines.

#### Step 3: Analyze Portfolio

Review the tool output for:

• Quick wins vs big bets distribution
• Effort concentration (avoid all XL projects)
• Strategic alignment gaps

#### Step 4: Generate Roadmap

• Quarterly capacity allocation
• Dependency identification
• Stakeholder communication plan

#### Step 5: Validate Results

Before finalizing the roadmap:

• [ ] Compare top priorities against strategic goals
• [ ] Run sensitivity analysis (what if estimates are wrong by 2x?)
• [ ] Review with key stakeholders for blind spots
• [ ] Check for missing dependencies between features
• [ ] Validate effort estimates with engineering

#### Step 6: Execute and Iterate

• Share roadmap with team
• Track actual vs estimated effort
• Revisit priorities quarterly
• Update RICE inputs based on learnings

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Customer Discovery Process

Plan → Recruit → Interview → Analyze → Synthesize → Validate

#### Step 1: Plan Research

• Define research questions
• Identify target segments
• Create interview script (see references/frameworks.md)

#### Step 2: Recruit Participants

• 5-8 interviews per segment
• Mix of power users and churned users
• Incentivize appropriately

#### Step 3: Conduct Interviews

• Use semi-structured format
• Focus on problems, not solutions
• Record with permission
• Take minimal notes during interview

#### Step 4: Analyze Insights

python scripts/customer_interview_analyzer.py transcript.txt

Extracts:

• Pain points with severity
• Feature requests with priority
• Jobs to be done patterns
• Sentiment and key themes
• Notable quotes

#### Step 5: Synthesize Findings

• Group similar pain points across interviews
• Identify patterns (3+ mentions = pattern)
• Map to opportunity areas using Opportunity Solution Tree
• Prioritize opportunities by frequency and severity

#### Step 6: Validate Solutions

Before building:

• [ ] Create solution hypotheses (see references/frameworks.md)
• [ ] Test with low-fidelity prototypes
• [ ] Measure actual behavior vs stated preference
• [ ] Iterate based on feedback
• [ ] Document learnings for future research

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PRD Development Process

Scope → Draft → Review → Refine → Approve → Track

#### Step 1: Choose Template

Select from references/prd_templates.md:

| Template | Use Case | Timeline |

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

| Standard PRD | Complex features, cross-team | 6-8 weeks |

| One-Page PRD | Simple features, single team | 2-4 weeks |

| Feature Brief | Exploration phase | 1 week |

| Agile Epic | Sprint-based delivery | Ongoing |

#### Step 2: Draft Content

• Lead with problem statement
• Define success metrics upfront
• Explicitly state out-of-scope items
• Include wireframes or mockups

#### Step 3: Review Cycle

• Engineering: feasibility and effort
• Design: user experience gaps
• Sales: market validation
• Support: operational impact

#### Step 4: Refine Based on Feedback

• Address technical constraints
• Adjust scope to fit timeline
• Document trade-off decisions

#### Step 5: Approval and Kickoff

• Stakeholder sign-off
• Sprint planning integration
• Communication to broader team

#### Step 6: Track Execution

After launch:

• [ ] Compare actual metrics vs targets
• [ ] Conduct user feedback sessions
• [ ] Document what worked and what didn't
• [ ] Update estimation accuracy data
• [ ] Share learnings with team

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Tools Reference

RICE Prioritizer

Advanced RICE framework implementation with portfolio analysis.

Features:

• RICE score calculation with configurable weights
• Portfolio balance analysis (quick wins vs big bets)
• Quarterly roadmap generation based on capacity
• Multiple output formats (text, JSON, CSV)

CSV Input Format:

name,reach,impact,confidence,effort,description
User Dashboard Redesign,5000,high,high,l,Complete redesign
Mobile Push Notifications,10000,massive,medium,m,Add push support
Dark Mode,8000,medium,high,s,Dark theme option

Commands:

# Create sample data
python scripts/rice_prioritizer.py sample

Run with default capacity (10 person-months)
python scripts/rice_prioritizer.py features.csv

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

JSON output for integration
python scripts/rice_prioritizer.py features.csv --output json

CSV output for spreadsheets
python scripts/rice_prioritizer.py features.csv --output csv

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Customer Interview Analyzer

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 per section
• Theme and quote extraction
• Competitor mention detection

Commands:

# Analyze interview transcript
python scripts/customer_interview_analyzer.py interview.txt

JSON output for aggregation
python scripts/customer_interview_analyzer.py interview.txt json

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Input/Output Examples

→ See references/input-output-examples.md for details

Integration Points

Compatible tools and platforms:

| Category | Platforms |

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

| Analytics | Amplitude, Mixpanel, Google Analytics |

| Roadmapping | ProductBoard, Aha!, Roadmunk, Productplan |

| Design | Figma, Sketch, Miro |

| Development | Jira, Linear, GitHub, Asana |

| Research | Dovetail, UserVoice, Pendo, Maze |

| Communication | Slack, Notion, Confluence |

JSON export enables integration with most tools:

# Export for Jira import
python scripts/rice_prioritizer.py features.csv --output json > priorities.json

Export for dashboard
python scripts/customer_interview_analyzer.py interview.txt json > insights.json

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Common Pitfalls to Avoid

| Pitfall | Description | Prevention |

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

| Solution-First | Jumping to features before understanding problems | Start every PRD with problem statement |

| Analysis Paralysis | Over-researching without shipping | Set time-boxes for research phases |

| Feature Factory | Shipping features without measuring impact | Define success metrics before building |

| Ignoring Tech Debt | Not allocating time for platform health | Reserve 20% capacity for maintenance |

| Stakeholder Surprise | Not communicating early and often | Weekly async updates, monthly demos |

| Metric Theater | Optimizing vanity metrics over real value | Tie metrics to user value delivered |

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Best Practices

Writing Great PRDs:

• Start with the problem, not the solution
• Include clear success metrics upfront
• Explicitly state what's out of scope
• Use visuals (wireframes, flows, diagrams)
• Keep technical details in appendix
• Version control all changes

Effective Prioritization:

• Mix quick wins with strategic bets
• Consider opportunity cost of delays
• Account for dependencies between features
• Buffer 20% for unexpected work
• Revisit priorities quarterly
• Communicate decisions with context

Customer Discovery:

• Ask "why" five times to find root cause
• Focus on past behavior, not future intentions
• Avoid leading questions ("Wouldn't you love...")
• Interview in the user's natural environment
• Watch for emotional reactions (pain = opportunity)
• Validate qualitative with quantitative data

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Quick Reference

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

Interview Analysis
python scripts/customer_interview_analyzer.py interview.txt

Generate sample data
python scripts/rice_prioritizer.py sample

JSON outputs
python scripts/rice_prioritizer.py features.csv --output json
python scripts/customer_interview_analyzer.py interview.txt json

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Reference Documents

• references/prd_templates.md - PRD templates for different contexts
• references/frameworks.md - Detailed framework documentation (RICE, MoSCoW, Kano, JTBD, etc.)