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

• Choose template from references/prd_templates.md

• Fill sections based on discovery work

• Review with engineering for feasibility

• 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 :

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

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:

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

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

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.)