/user-segmentation
Segment users from feedback data based on behavior, JTBD, and needs. Identifies at least 3 distinct user segments. Use when segmenting a user base, analyzing diverse user feedback, or building a segmentation model.
Claude Code
$ npx -y skills add phuryn/pm-skills --skill user-segmentation --agent claude-code
Codex
$ npx skills add phuryn/pm-skills --skill user-segmentation -a codex -y
Cursor
$ npx skills add phuryn/pm-skills --skill user-segmentation -a cursor -y
Gemini CLI
$ npx skills add phuryn/pm-skills --skill user-segmentation -a gemini-cli -y
GitHub Copilot
$ npx skills add phuryn/pm-skills --skill user-segmentation -a github-copilot -y
OpenCode
$ npx skills add phuryn/pm-skills --skill user-segmentation -a opencode -y
Windsurf
$ npx skills add phuryn/pm-skills --skill user-segmentation -a windsurf -y
SKILL.md
user-segmentation.SKILL.mdname: user-segmentation
description: "Segment users from feedback data based on behavior, JTBD, and needs. Identifies at least 3 distinct user segments. Use when segmenting a user base, analyzing diverse user feedback, or building a segmentation model."
User Segmentation
Purpose
Analyze diverse user feedback to identify at least 3 distinct behavioral and needs-based user segments. This skill surfaces hidden customer groups based on jobs-to-be-done, behaviors, and motivations rather than demographics alone, enabling targeted product strategy.
Instructions
You are an expert behavioral researcher and data analyst specializing in user segmentation and behavioral clustering.
Input
Your task is to segment users for **$ARGUMENTS** based on behavior, jobs-to-be-done, and unmet needs.
If the user provides feedback data, interviews, support tickets, product usage logs, surveys, or other user data, read and analyze them directly. Extract behavioral patterns, motivations, and needs across the user base.
Analysis Steps (Think Step by Step)
1. **Data Preparation**: Read and organize all provided user feedback and data 2. **Behavior Extraction**: Identify key behavioral patterns, usage modes, and user journeys 3. **Needs Analysis**: Map jobs-to-be-done, desired outcomes, and pain points for each user 4. **Clustering**: Group users into distinct segments based on behavior and needs similarity 5. **Validation**: Ensure segments are coherent, non-overlapping, and actionable 6. **Characterization**: Develop rich profiles for each segment with representative quotes
Output Structure
For each identified segment (minimum 3):
**Segment Name & Overview**
- Clear, descriptive segment identifier
- Size: estimated number or percentage of user base
- Brief one-sentence characterization
**Behavioral Characteristics**
- How this segment uses $ARGUMENTS (primary use cases, frequency, depth)
- Typical user journey and key touchpoints
- Technical proficiency or sophistication level
- Integration with other tools or workflows
**Jobs-to-be-Done & Motivations**
- Core job(s) this segment is trying to accomplish
- Underlying motivations and desired outcomes
- Context and frequency of the job
- What success looks like for this segment
**Key Needs & Pain Points**
- Unmet needs specific to this segment's behavior
- Obstacles preventing effective job completion
- Current workarounds or alternative solutions they employ
- Severity and frequency of pain points
**Current Product Fit**
- How well $ARGUMENTS currently serves this segment
- Features or capabilities this segment values most
- Gaps or limitations most frustrating to this segment
- Likelihood to continue using vs. churn risk
**Differentiated Value Proposition**
- What unique value could be unlocked for this segment
- Feature or experience improvements that would maximize fit
- Messaging and positioning most resonant with this segment
**Segment Prioritization**
- Strategic importance: growth potential, revenue impact, alignment with vision
- Implementation difficulty: ease of serving this segment's needs
- Recommendation: invest, maintain, or de-prioritize
Best Practices
- Ground segmentation in behavioral and motivational data, not just demographics
- Use representative quotes and examples from actual user feedback
- Ensure segments are distinct and serve different core needs
- Consider interdependencies between segments and prioritization tradeoffs
- Flag any segments that may be underrepresented in feedback data
- Validate emerging segments against product usage or customer data when available
- Consider adjacent behaviors and cross-segment patterns
---
Further Reading
- [Market Research: Advanced Techniques](https://www.productcompass.pm/p/market-research-advanced-techniques)
- [User Interviews: The Ultimate Guide to Research Interviews](https://www.productcompass.pm/p/interviewing-customers-the-ultimate)
- [Jobs-to-be-Done Masterclass with Tony Ulwick and Sabeen Sattar](https://www.productcompass.pm/p/jobs-to-be-done-masterclass-with) (video course)
Read more
name: user-segmentation description: "Segment users from feedback data based on behavior, JTBD, and needs. Identifies at least 3 distinct user segments. Use when segmenting a user base, analyzing diverse user feedback, or building a segmentation model."
User Segmentation
Purpose
Analyze diverse user feedback to identify at least 3 distinct behavioral and needs-based user segments. This skill surfaces hidden customer groups based on jobs-to-be-done, behaviors, and motivations rather than demographics alone, enabling targeted product strategy.
Instructions
You are an expert behavioral researcher and data analyst specializing in user segmentation and behavioral clustering.
Input
Your task is to segment users for **$ARGUMENTS** based on behavior, jobs-to-be-done, and unmet needs.
If the user provides feedback data, interviews, support tickets, product usage logs, surveys, or other user data, read and analyze them directly. Extract behavioral patterns, motivations, and needs across the user base.
Analysis Steps (Think Step by Step)
1. **Data Preparation**: Read and organize all provided user feedback and data 2. **Behavior Extraction**: Identify key behavioral patterns, usage modes, and user journeys 3. **Needs Analysis**: Map jobs-to-be-done, desired outcomes, and pain points for each user 4. **Clustering**: Group users into distinct segments based on behavior and needs similarity 5. **Validation**: Ensure segments are coherent, non-overlapping, and actionable 6. **Characterization**: Develop rich profiles for each segment with representative quotes
Output Structure
For each identified segment (minimum 3):
**Segment Name & Overview**
- Clear, descriptive segment identifier
- Size: estimated number or percentage of user base
- Brief one-sentence characterization
**Behavioral Characteristics**
- How this segment uses $ARGUMENTS (primary use cases, frequency, depth)
- Typical user journey and key touchpoints
- Technical proficiency or sophistication level
- Integration with other tools or workflows
**Jobs-to-be-Done & Motivations**
- Core job(s) this segment is trying to accomplish
- Underlying motivations and desired outcomes
- Context and frequency of the job
- What success looks like for this segment
**Key Needs & Pain Points**
- Unmet needs specific to this segment's behavior
- Obstacles preventing effective job completion
- Current workarounds or alternative solutions they employ
- Severity and frequency of pain points
**Current Product Fit**
- How well $ARGUMENTS currently serves this segment
- Features or capabilities this segment values most
- Gaps or limitations most frustrating to this segment
- Likelihood to continue using vs. churn risk
**Differentiated Value Proposition**
- What unique value could be unlocked for this segment
- Feature or experience improvements that would maximize fit
- Messaging and positioning most resonant with this segment
**Segment Prioritization**
- Strategic importance: growth potential, revenue impact, alignment with vision
- Implementation difficulty: ease of serving this segment's needs
- Recommendation: invest, maintain, or de-prioritize
Best Practices
- Ground segmentation in behavioral and motivational data, not just demographics
- Use representative quotes and examples from actual user feedback
- Ensure segments are distinct and serve different core needs
- Consider interdependencies between segments and prioritization tradeoffs
- Flag any segments that may be underrepresented in feedback data
- Validate emerging segments against product usage or customer data when available
- Consider adjacent behaviors and cross-segment patterns
---
Further Reading
- [Market Research: Advanced Techniques](https://www.productcompass.pm/p/market-research-advanced-techniques)
- [User Interviews: The Ultimate Guide to Research Interviews](https://www.productcompass.pm/p/interviewing-customers-the-ultimate)
- [Jobs-to-be-Done Masterclass with Tony Ulwick and Sabeen Sattar](https://www.productcompass.pm/p/jobs-to-be-done-masterclass-with) (video course)
72 PM skills and 42 chained workflows across 9 plugins. Claude Code, Cowork, and more. From discovery to strategy, execution, launch, growth, and shipping AI-built code. Designed for Claude Code and Cowork. Skills compatible with other AI assistants.
Other skills on pm-skills.
More skills from other plugins.
i-have-adhd
Shape output for a reader with ADHD: lead with the next action, number multi-step work, restate state across turns, suppress…
distilly
Set up the owner profile so a personal agent knows who it works for (first-run setup, \"get to know me\"), or distill colleague,…
openwork-connect
Search and use the skills, MCP connections, and connected services available through the user's OpenWork organization.
Questions about user-segmentation.
- Which coding agents can use user-segmentation?
- user-segmentation installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.

