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ClaudeWave
Skill17k estrellas del repoactualizado 6d ago

user-segmentation

This Claude Code skill analyzes user feedback data to identify at least three distinct user segments based on behavioral patterns, jobs-to-be-done, and underlying needs rather than demographics. Use it when you have qualitative data like user interviews, support tickets, or feedback and need to uncover hidden customer groups for targeted product strategy, prioritization decisions, or building segmentation models that reveal motivations and pain points across your user base.

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git clone --depth 1 https://github.com/phuryn/pm-skills /tmp/user-segmentation && cp -r /tmp/user-segmentation/pm-market-research/skills/user-segmentation ~/.claude/skills/user-segmentation
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SKILL.md

# 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)
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