Product Manager Toolkit

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

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

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

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

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

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

PRD Development Process

Scope → Draft → Review → Refine → Approve → Track

Step 1: Choose Template

Select from references/prd_templates.md:

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

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

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

Input/Output Examples

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

Integration Points

Compatible tools and platforms:

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

Common Pitfalls to Avoid

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

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

Reference Documents

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