User Segmentation
- 1.9k installs
- 24.9k repo stars
- Updated July 3, 2026
- phuryn/pm-skills
user-segmentation clusters users into JTBD-based behavioral segments from feedback data.
About
The user-segmentation skill analyzes diverse user feedback to identify at least three behavioral and needs-based segments using jobs-to-be-done rather than demographics alone. Inputs include feedback, interviews, support tickets, usage logs, and surveys for a named product. Steps cover data preparation, behavior extraction, needs analysis, clustering, validation for non-overlapping actionable groups, and characterization with representative quotes. Each segment profile documents name, estimated size, behavioral characteristics, journeys, JTBD motivations, pain points, and product implications. Agents think step by step, ensure minimum three segments, and surface hidden customer groups for targeted strategy. Use when segmenting a user base, analyzing mixed feedback, or building a segmentation model from qualitative and quantitative inputs. Identifies minimum three distinct behavioral segments. Uses JTBD, behaviors, and needs instead of demographics alone. Accepts feedback, tickets, logs, interviews, and surveys.
- Identifies minimum three distinct behavioral segments.
- Uses JTBD, behaviors, and needs instead of demographics alone.
- Accepts feedback, tickets, logs, interviews, and surveys.
- Outputs segment profiles with quotes and product implications.
- Validates segments are coherent, non-overlapping, and actionable.
User Segmentation by the numbers
- 1,911 all-time installs (skills.sh)
- +77 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Ranked #80 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Security screen: LOW risk (skills.sh audit)
- Data as of Aug 5, 2026 (Skillselion catalog sync)
user-segmentation capabilities & compatibility
- Capabilities
- behavior extraction · needs clustering · segment profiling · quote characterization
- Use cases
- data analysis · research
What user-segmentation says it does
Analyze diverse user feedback to identify at least 3 distinct behavioral and needs-based user segments.
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| Installs | 1.9k |
|---|---|
| repo stars | ★ 24.9k |
| Security audit | 3 / 3 scanners passed |
| Last updated | July 3, 2026 |
| Repository | phuryn/pm-skills ↗ |
Who are the distinct user groups hidden in our feedback and usage data?
Segment users from feedback and behavior data into at least three distinct JTBD-based segments with rich profiles.
Who is it for?
Product teams analyzing mixed qualitative and quantitative user data.
Skip if: Real-time ML clustering pipelines without human-readable profiles.
When should I use this skill?
User asks to segment users, analyze feedback diversity, or build segmentation model.
What you get
At least three segment profiles with behaviors, JTBD, pains, and quotes.
- Behavioral segmentation model
- JTBD-based user segment profiles
By the numbers
- Identifies at least 3 distinct user segments
Files
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
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Further Reading
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FAQ
How many segments are required?
At least three distinct behavioral and needs-based segments.
What data sources can be used?
Feedback, interviews, support tickets, usage logs, and surveys.
Are demographics the primary axis?
No. Jobs-to-be-done, behaviors, and motivations drive clustering.
Is User Segmentation safe to install?
skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.