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User Segmentation

Pawel HurynCommunityCreated by Pawel Huryn

  • Skill
  • Research and analysis
  • Business and productivity
  • MIT
  • No issues found

From the author

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.
— the description by Pawel Huryn

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill acts as a behavioral research assistant that processes raw user data—such as feedback, support tickets, and usage logs—to cluster users into distinct segments. By focusing on Jobs-to-be-Done (JTBD), motivations, and pain points rather than demographics, it helps product teams uncover hidden user groups and prioritize development efforts based on strategic value.

What it does

  • Analyzes diverse user feedback and usage data to extract behavioral patterns.
  • Maps user jobs-to-be-done, motivations, and specific pain points.
  • Clusters users into at least three distinct, actionable segments.
  • Develops comprehensive profiles including product fit and value propositions for each segment.
  • Provides strategic recommendations on whether to invest in, maintain, or de-prioritize specific segments.

When to use it

  • When you need to move beyond demographic data to understand how users actually interact with your product.
  • During the early stages of product strategy or roadmap planning.
  • When analyzing large volumes of qualitative user feedback or interview transcripts.
  • When identifying why different user groups experience varying levels of satisfaction or churn.

Advantages

  • Moves beyond surface-level demographics to uncover deep behavioral insights.
  • Provides a structured framework for prioritizing product features based on segment needs.
  • Helps align product messaging and positioning with specific user motivations.
  • Identifies clear opportunities for product improvement and growth.

Usage examples

“Analyze these 50 customer support tickets to identify three distinct user segments for our project management tool.”
The agent will process the tickets, identify behavioral clusters (e.g., power users vs. casual collaborators), and generate detailed profiles for each, including their specific pain points and strategic priority.
“Based on these interview summaries, segment our user base and tell me which group we should prioritize for the next quarter.”
The agent will synthesize the interview data into distinct segments and provide a recommendation on which group offers the highest growth potential or strategic alignment.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/phuryn/user-segmentation/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip user-segmentation.zip -d .agents/skills

Documentation by Pawel Huryn

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

Files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

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Attribution and licence

Created by
Pawel Huryn
Publisher
Pawel Huryn (Community)
Source
phuryn/pm-skills · pm-market-research/skills/user-segmentation · commit 2a12295
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download user-segmentation.zip