Product Manager Toolkit

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

RICE Prioritizer Example

Input (features.csv):

name,reach,impact,confidence,effort
Onboarding Flow,20000,massive,high,s
Search Improvements,15000,high,high,m
Social Login,12000,high,medium,m
Push Notifications,10000,massive,medium,m
Dark Mode,8000,medium,high,s

Command:

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

Output:

============================================================
RICE PRIORITIZATION RESULTS
============================================================

📊 TOP PRIORITIZED FEATURES

1. Onboarding Flow
   RICE Score: 16000.0
   Reach: 20000 | Impact: massive | Confidence: high | Effort: s

2. Search Improvements
   RICE Score: 4800.0
   Reach: 15000 | Impact: high | Confidence: high | Effort: m

3. Social Login
   RICE Score: 3072.0
   Reach: 12000 | Impact: high | Confidence: medium | Effort: m

4. Push Notifications
   RICE Score: 3840.0
   Reach: 10000 | Impact: massive | Confidence: medium | Effort: m

5. Dark Mode
   RICE Score: 2133.33
   Reach: 8000 | Impact: medium | Confidence: high | Effort: s

📈 PORTFOLIO ANALYSIS

Total Features: 5
Total Effort: 19 person-months
Total Reach: 65,000 users
Average RICE Score: 5969.07

🎯 Quick Wins: 2 features
   • Onboarding Flow (RICE: 16000.0)
   • Dark Mode (RICE: 2133.33)

🚀 Big Bets: 0 features

📅 SUGGESTED ROADMAP

Q1 - Capacity: 11/15 person-months
   • Onboarding Flow (RICE: 16000.0)
   • Search Improvements (RICE: 4800.0)
   • Dark Mode (RICE: 2133.33)

Q2 - Capacity: 10/15 person-months
   • Push Notifications (RICE: 3840.0)
   • Social Login (RICE: 3072.0)

Customer Interview Analyzer Example

Read the detailed procedure and examples when working on this part of the task.

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.)