user-segmentation
Scannednpx machina-cli add skill phuryn/pm-skills/user-segmentation --openclawUser 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)
- Data Preparation: Read and organize all provided user feedback and data
- Behavior Extraction: Identify key behavioral patterns, usage modes, and user journeys
- Needs Analysis: Map jobs-to-be-done, desired outcomes, and pain points for each user
- Clustering: Group users into distinct segments based on behavior and needs similarity
- Validation: Ensure segments are coherent, non-overlapping, and actionable
- 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
Source
git clone https://github.com/phuryn/pm-skills/blob/main/pm-market-research/skills/user-segmentation/SKILL.mdView on GitHub Overview
Analyze feedback to identify at least three distinct behavioral and needs-based user segments. This skill surfaces hidden groups by jobs-to-be-done, behaviors, and motivations, enabling targeted product strategy beyond demographics.
How This Skill Works
Input data such as feedback, interviews, support tickets, product usage logs, and surveys is read and organized. The process follows data preparation, behavior extraction, needs analysis, clustering, validation, and rich segment characterization to produce actionable profiles with representative quotes.
When to Use It
- Segment a user base to tailor product strategy and messaging
- Analyze diverse feedback to surface non-obvious user groups
- Build a segmentation model from usage and support data
- Prioritize features by segment to reduce churn risk
- Validate and refine segments against product usage data
Quick Start
- Step 1: Collect feedback data, usage logs, interviews, and tickets for $ARGUMENTS.
- Step 2: Prepare data, extract behavior patterns, map jobs-to-be-done, and group similar needs.
- Step 3: Run clustering, validate segments, and publish profiles with representative quotes.
Best Practices
- Ground segmentation in behavioral and motivational data, not demographics
- Use representative quotes from actual user feedback
- Ensure segments are distinct and address different core needs
- Assess interdependencies and prioritization tradeoffs between segments
- Flag underrepresented segments and validate with usage data
Example Use Cases
- Power User vs. Casual User: segment by usage frequency and task depth to tailor automation features and onboarding.
- Curious Evaluator: users exploring features, requiring clearer onboarding and trial guidance.
- Price-Sensitive Seeker: segments focused on value, pricing plans, and discount messaging.
- Support-First User: heavy reliance on docs and self-service, needing robust help content and tutorials.
- Occasional Integrator: relies on integrations and automation, prioritizing API stability and connector quality.
