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Agent Skill · 研究检索

customer-segmentation(客户细分)

customer-segmentation 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

customer-segmentation 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 57,063。

总安装 441周安装 18GitHub Stars 19下载 141核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:customer-segmentation(客户细分)
来源仓库:https://github.com/finsilabs/awesome-ecommerce-skills
仓库路径:skills/customer-segmentation
安装命令:
npx skills add https://github.com/finsilabs/awesome-ecommerce-skills --skill customer-segmentation
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/finsilabs/awesome-ecommerce-skills --skill customer-segmentation

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

customer-segmentation 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。适用于客户细分策略相关的资料搜索与内容筛选,可结合来源仓库和原始 README 进一步核验具体用法。

通过 npx skills add 命令从 GitHub 仓库安装,支持主流 AI 宿主环境集成调用。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写操作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/customer-segmentation

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/finsilabs/awesome-ecommerce-skills --skill cus…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

customer-segmentation 怎么安装?

在终端执行 npx skills add https://github.com/finsilabs/awesome-ecommerce-skills --skill customer-segm…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

customer-segmentation 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

customer-segmentation 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

customer-segmentation 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

customer-segmentation 用的人多吗?

公开数据是累计安装 441 次,近一周安装 18 次,下载 141 次,GitHub 19 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

customer-segmentation 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
customer-segmentation

客户细分 · 本页

44119需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Customer Segmentation

Overview

Customer segmentation divides your customer base into groups with similar purchase behavior so marketing campaigns, promotions, and product recommendations can be precisely targeted. Klaviyo, Omnisend, and Metorik all provide RFM-style segmentation out of the box for Shopify and WooCommerce without custom SQL. Only build a custom segmentation system if your platform's tools don't support the segment logic you need.

When to Use This Skill

  • When personalizing email campaigns by lifecycle stage (new, active, at-risk, lapsed)
  • When building suppression lists to avoid wasting ad spend on already-converted customers
  • When identifying "champion" customers for VIP programs and early access campaigns
  • When analyzing which acquisition cohort has the best 90-day retention
  • When syncing behavioral segments to advertising platforms (Meta, Google)

Core Instructions

Step 1: Determine platform and choose the right segmentation tool

PlatformBuilt-in SegmentationRecommended Tool
ShopifyBasic: Admin → Customers → Filters; Advanced: Klaviyo or OmnisendKlaviyo for email + SMS; Lifetimely for cohort analysis
WooCommerceWooCommerce Analytics → Customers (basic filters)Klaviyo + WooCommerce plugin; or Metorik for analytics
BigCommerceCustomer Groups (tier-based); Analytics → CustomersKlaviyo for behavioral segmentation
Custom / HeadlessBuild RFM scoring in SQL; sync to Klaviyo for activationRequired when platform has no segmentation tools

Step 2: Platform-specific setup


Shopify

Option A: Shopify Admin segments (basic, free)

  1. Go to Admin → Customers
  2. Use the filter bar to create segments based on:

- Order count, total spent, last order date - Email subscription status, tags, location - Product purchased

  1. Save the filter as a customer segment
  2. Export the segment to CSV for use in ads or email campaigns

Option B: Klaviyo (recommended for lifecycle segmentation)

Klaviyo syncs automatically with Shopify and provides RFM-style segmentation built on real purchase data.

Key segments to create in Klaviyo:

  1. Champions (high-value, frequent, recent):

- Rule: Ordered at least 3 times AND Last order within 60 days AND Total spent > $200 - Action: Early access to new products, VIP perks, referral program invites

  1. At Risk — High Value:

- Rule: Total spent > $200 AND Last order 90–180 days ago - Action: Win-back flow with personalized offer

  1. Recent First-Time Buyers:

- Rule: Order count equals 1 AND First order within 30 days - Action: Onboarding sequence, encourage second purchase

  1. Lapsed Customers:

- Rule: Last order more than 180 days ago AND Order count >= 2 - Action: Re-engagement campaign with "We've missed you" messaging

  1. Subscribers who never purchased:

- Rule: Email subscriber AND Order count equals 0 - Action: Welcome series with social proof and first-order incentive

To create segments in Klaviyo:

  1. Go to Lists & Segments → Create Segment
  2. Add conditions using the filter builder — Klaviyo has 150+ pre-built filter types including Shopify-specific events
  3. Name the segment clearly (e.g., "At Risk High Value — 90-180 days")
  4. Use the segment in a Flow trigger or as an audience for a Campaign

Klaviyo Predictive Analytics (paid plans):

  • Klaviyo automatically calculates Predicted CLV, Expected Date of Next Order, and Churn Risk per customer
  • Find these under Analytics → Predictive Analytics
  • Use these predictions as segment filter criteria without any custom code

WooCommerce

Option A: Metorik (recommended analytics platform)

  1. Install Metorik (connects via WooCommerce REST API)
  2. Go to Metorik → Segments → Create Segment
  3. Build rule-based segments using purchase history, product affinity, geography, and more
  4. Metorik calculates RFM scores automatically
  5. Export segment to CSV or sync directly to Klaviyo/Mailchimp

Option B: Klaviyo for WooCommerce

  1. Install Klaviyo: Email Marketing for WooCommerce from WordPress.org
  2. Klaviyo syncs your WooCommerce order history and creates profiles for all customers
  3. Build the same lifecycle segments described in the Shopify section above

BigCommerce

Customer Groups for tier-based segmentation:

  1. Go to Customers → Customer Groups → Add Group
  2. Create groups based on purchase behavior rules:

- "VIP" — customers with lifetime spend > $500 - "Repeat Buyers" — customers with 3+ orders

  1. Assign group-specific pricing, category access, or shipping rules

Klaviyo for behavioral segmentation:

  • Install the Klaviyo for BigCommerce app
  • Connect your BigCommerce store
  • Build behavioral segments in Klaviyo using purchase history and event data

Custom / Headless

Build RFM scoring in SQL and sync to an ESP for activation:

-- PostgreSQL: Calculate RFM scores for all customers
WITH customer_rfm AS (
  SELECT
    customer_id,
    EXTRACT(EPOCH FROM (NOW() - MAX(created_at))) / 86400 AS recency_days,
    COUNT(id) AS frequency,
    SUM(subtotal_cents) / 100.0 AS monetary
  FROM orders
  WHERE status NOT IN ('cancelled', 'refunded')
  GROUP BY customer_id
),
rfm_scored AS (
  SELECT
    customer_id,
    recency_days, frequency, monetary,
    NTILE(5) OVER (ORDER BY recency_days DESC) AS r_score,  -- Lower recency = higher score
    NTILE(5) OVER (ORDER BY frequency ASC) AS f_score,
    NTILE(5) OVER (ORDER BY monetary ASC) AS m_score
  FROM customer_rfm
)
SELECT
  customer_id, r_score, f_score, m_score,
  r_score + f_score + m_score AS rfm_total,
  CASE
    WHEN r_score >= 4 AND f_score >= 4 AND m_score >= 4 THEN 'champions'
    WHEN r_score >= 3 AND f_score >= 3 AND m_score >= 3 THEN 'loyal_customers'
    WHEN r_score >= 4 AND f_score <= 2 THEN 'recent_customers'
    WHEN r_score <= 2 AND f_score >= 4 AND m_score >= 4 THEN 'cannot_lose_them'
    WHEN r_score <= 2 AND f_score >= 3 THEN 'at_risk'
    WHEN r_score = 1 AND f_score <= 2 THEN 'lost'
    ELSE 'needs_attention'
  END AS segment
FROM rfm_scored;
// Sync a segment to Klaviyo — batched at 100 profiles per request
export async function syncSegmentToKlaviyo(segmentCustomers: Customer[], klaviyoListId: string) {
  const BATCH_SIZE = 100;
  for (let i = 0; i < segmentCustomers.length; i += BATCH_SIZE) {
    const batch = segmentCustomers.slice(i, i + BATCH_SIZE);
    await fetch(`https://a.klaviyo.com/api/lists/${klaviyoListId}/relationships/profiles/`, {
      method: 'POST',
      headers: {
        Authorization: `Klaviyo-API-Key ${process.env.KLAVIYO_PRIVATE_KEY}`,
        'Content-Type': 'application/json',
        revision: '2024-10-15',
      },
      body: JSON.stringify({
        data: batch.map(c => ({
          type: 'profile',
          attributes: { email: c.email, first_name: c.firstName, last_name: c.lastName },
        })),
      }),
    });
  }
}

// Export suppression list for Meta Ads — hash emails before sending
export async function exportSuppressionListForMeta(lookbackDays = 30): Promise<string[]> {
  const recentBuyers = await db.orders.findMany({
    where: { createdAt: { gte: new Date(Date.now() - lookbackDays * 86400000) }, status: 'completed' },
    select: { customerEmail: true },
    distinct: ['customerEmail'],
  });

  return recentBuyers.map(({ customerEmail }) => {
    const { createHash } = require('crypto');
    return createHash('sha256').update(customerEmail.toLowerCase().trim()).digest('hex');
  });
}

Step 3: Map segments to actions

Every segment should have a clear marketing action:

SegmentRecommended Action
ChampionsVIP early access, referral program invite, no win-back discounts needed
Loyal CustomersLoyalty program enrollment, review request, upsell to next tier
Recent First-Time BuyersSecond-purchase email series (send 7 days after first order)
At Risk — High ValuePersonalized win-back with acknowledgment: "It's been a while"
Cannot Lose ThemDirect outreach, significant offer (20% off), personal email from founder
Lapsed / LostLow-cost re-engagement (newsletter, product announcement); remove from active campaigns

Step 4: Build suppression lists for paid ads

Upload your most recent buyers as suppression lists on Meta and Google to avoid wasting acquisition budget:

  • Meta Business Manager: Audiences → Create Audience → Customer List → Upload hashed email CSV
  • Google Ads: Audience Manager → Customer Match → Upload email list
  • Refresh these suppression lists monthly

Best Practices

  • Refresh segments nightly — Klaviyo and Metorik do this automatically; for custom builds, run the RFM SQL nightly
  • Start with RFM, then layer behavioral signals — purchase recency/frequency/spend is the most reliable foundation; add category affinity and channel preference as you collect more data
  • Always build suppression lists alongside targeting lists — sending re-engagement campaigns to active customers wastes budget and annoys them
  • Validate segment sizes before campaign sends — a segment returning 0 customers usually indicates a logic error; set a minimum threshold check
  • Track which segments respond best to each type of offer — champions rarely need discounts; at-risk customers may respond to free shipping more than percentage off

Common Pitfalls

ProblemSolution
Champions segment shrinks every monthChampions require recent + high frequency — customers naturally graduate out; supplement with "Loyal Customers" for long-term relationship management
RFM scores biased by one very large orderKlaviyo's predicted CLV smooths this out; for custom builds, use median order value in the monetary score rather than total spend
Segment sync to Klaviyo creates duplicate profilesAlways match by email as the primary key when syncing; if duplicates exist, use Klaviyo's profile merge
Cohort analysis shows declining retention but reason unclearSegment cohort by acquisition channel to identify whether specific channels bring lower-quality customers

Related Skills

  • @customer-lifetime-value
  • @personalization-engine
  • @referral-program

平台分布

Codex

35.04%
按下载量换算49

Claude

27.88%
按下载量换算39

Cursor

20.72%
按下载量换算29

Gemini CLI

9.63%
按下载量换算14

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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