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user-segmentation

Analyze user feedback and usage data to identify at least three customer groups based on behavior, goals, and unmet needs. Build profiles covering pain points, product fit, and priorities for serving each group.

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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.

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Good for

  • Finding customer groups within diverse user feedback
  • Comparing needs across user groups
  • Prioritizing product improvements by customer group
  • Reviewing churn risks and product fit

Files included (1)

  • SKILL.md4.0 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

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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