User Segmentation

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

Download the skill as a ZIP:
https://autogineer.com/agent-skills/phuryn/user-segmentation/download

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

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

Files

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