/shopify-admin-rfm-customer-segmentation

name: shopify-admin-rfm-customer-segmentation
role: customer-ops
description: "Read-only: scores every customer on Recency, Frequency, and Monetary value to segment them into actionable groups (Champions, Loyal, At-Risk, Lost)."
toolkit: shopify-admin, shopify-admin-execution
api_version: "2025-01"
graphql_operations:
  - customers:query
  - orders:query
status: stable
compatibility: Claude Code, Cursor, Codex, Gemini CLI

Purpose

Performs full RFM (Recency, Frequency, Monetary) analysis across the entire customer base. Each customer is scored 1-5 on three dimensions — how recently they purchased, how often they purchase, and how much they spend — then classified into actionable segments: Champions, Loyal Customers, Potential Loyalists, At-Risk, Hibernating, and Lost. Read-only — no mutations.

Prerequisites

• Authenticated Shopify CLI session: `shopify store auth --store <domain> --scopes read_orders,read_customers`
• API scopes: `read_orders`, `read_customers`

Parameters

| Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | store | string | yes | — | Store domain (e.g., mystore.myshopify.com) | | days_back | integer | no | 365 | Lookback window for order history | | segments | integer | no | 5 | Number of quintile buckets per dimension (3 or 5) | | min_orders | integer | no | 1 | Minimum orders for a customer to be scored | | tag_customers | boolean | no | false | If true, add RFM segment tag to customer (requires write_customers scope) | | format | string | no | human | Output format: `human` or `json` |

Safety

> ℹ️ Read-only by default. If `tag_customers: true`, will add tags via customerUpdate mutation — use `dry_run: true` first.

RFM Segment Definitions

| Segment | R Score | F Score | M Score | Description | |---------|---------|---------|---------|-------------| | Champions | 5 | 5 | 5 | Best customers — recent, frequent, high spend | | Loyal Customers | 3-5 | 4-5 | 4-5 | Consistent buyers with strong spend | | Potential Loyalists | 4-5 | 2-3 | 2-3 | Recent buyers who could become loyal | | New Customers | 5 | 1 | 1-2 | Just made first purchase | | Promising | 4 | 1-2 | 1-2 | Recent but low frequency — nurture them | | Need Attention | 3 | 3 | 3 | Average across all dimensions — slipping | | About to Sleep | 2-3 | 2 | 2 | Below average recency and frequency | | At Risk | 1-2 | 4-5 | 4-5 | Were great customers, haven't bought recently | | Hibernating | 1-2 | 1-2 | 1-3 | Low on all dimensions — nearly lost | | Lost | 1 | 1-2 | 1-5 | Haven't bought in a very long time |

Workflow Steps

1. **OPERATION:** `orders` — query **Inputs:** `query: "created_at:>='<NOW - days_back days>'"`, `first: 250`, select `createdAt`, `totalPriceSet`, `customer { id, email, firstName, lastName, numberOfOrders }`, pagination cursor **Expected output:** All orders in window with customer linkage; paginate until complete

2. Aggregate per customer:

• **Recency** = days since last order
• **Frequency** = total number of orders in window
• **Monetary** = total spend in window

3. Score each dimension 1-5 using quintile bucketing:

• Sort all customers by each metric
• Divide into N equal-sized groups (quintiles)
• Assign scores (5 = best for recency [most recent], frequency [most frequent], monetary [highest spend])

4. Map (R, F, M) score combination to named segment using the definitions above

5. **OPERATION:** `customers` — query (enrichment) **Inputs:** Customer IDs from each segment for contact details **Expected output:** Email, name, tags for top customers in each segment

GraphQL Operations

# orders:query — validated against api_version 2025-01
query OrdersForRFM($query: String!, $after: String) {
  orders(first: 250, after: $after, query: $query) {
    edges {
      node {
        id
        createdAt
        totalPriceSet { shopMoney { amount currencyCode } }
        customer {
          id
          email
          firstName
          lastName
          numberOfOrders
        }
      }
    }
    pageInfo { hasNextPage endCursor }
  }
}

# customers:query — validated against api_version 2025-01
query CustomerDetails($query: String, $after: String) {
  customers(first: 250, after: $after, query: $query) {
    edges {
      node {
        id
        email
        firstName
        lastName
        numberOfOrders
        totalSpentV2 { amount currencyCode }
        tags
        createdAt
      }
    }
    pageInfo { hasNextPage endCursor }
  }
}

Session Tracking

**Claude MUST emit the following output at each stage. This is mandatory.**

**On start**, emit:

╔══════════════════════════════════════════════╗
║  SKILL: RFM Customer Segmentation           ║
║  Store: <store domain>                       ║
║  Started: <YYYY-MM-DD HH:MM UTC>             ║
╚══════════════════════════════════════════════╝

**After each step**, emit:

[N/TOTAL] <QUERY|MUTATION>  <OperationName>
          → Params: <brief summary of key inputs>
          → Result: <count or outcome>

**On completion**, emit:

For `format: human` (default):

══════════════════════════════════════════════
RFM SEGMENTATION REPORT  (<days_back> days)
  Customers scored:     <n>
  ─────────────────────────────
  Champions:            <n> (<pct>%)  Avg spend: $<n>
  Loyal Customers:      <n> (<pct>%)  Avg spend: $<n>
  Potential Loyalists:  <n> (<pct>%)  Avg spend: $<n>
  At Risk:              <n> (<pct>%)  Avg spend: $<n>
  Hibernating:          <n> (<pct>%)  Avg spend: $<n>
  Lost:                 <n> (<pct>%)  Avg spend: $<n>

  Top Champions:
    <name> (<email>)  R:<n> F:<n> M:<n>  Spend: $<n>
  Top At-Risk (win-back candidates):
    <name> (<email>)  Last order: <date>  Lifetime: $<n>
  Output: rfm_segments_<date>.csv
══════════════════════════════════════════════

For `format: json`, emit:

{
  "skill": "rfm-customer-segmentation",
  "store": "<domain>",
  "