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user-segmentation

@phuryn · 收录于 1 周前 · 上游提交 1 周前★ 社区精选

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.

适合你,如果手头有大量用户反馈需要按行为或需求分组

/ 通过 npx 安装 校验哈希
npx oh-my-skill add phuryn/pm-skills/user-segmentation
/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- phuryn/pm-skills/user-segmentation
/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify phuryn/pm-skills/user-segmentation
安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
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怎么用

商店整理自技能原文 · 版本 18468a9 · 表述以原文为准
它做什么

装上后,Claude 会分析你提供的用户反馈数据,识别出至少3个不同的用户群体,并描述每个群体的行为、动机和需求。

什么时候触发

当你需要细分用户群体、分析多样化的用户反馈,或构建用户分群模型时触发。

装好后可以这样说
Claude 会分析数据并输出至少3个用户群体。
Claude 会从工单中提取行为模式并分组。
技能原文 SKILL.md作者撰写 · MIT · 18468a9

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
按 MIT 许可原样转载,未经改动 · 在 GitHub 查看 →

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