---
name: shopify-admin-vip-customer-identifier
role: marketing
description: "Identifies top-spending customers (top N% by lifetime value or order frequency) and exports a VIP candidate list; optionally tags qualified customers as VIPs."
toolkit: shopify-admin, shopify-admin-execution
api_version: "2025-01"
graphql_operations:
  - customers:query
  - orders:query
  - customerUpdate:mutation
status: stable
compatibility: Claude Code, Cursor, Codex, Gemini CLI
---

## Purpose
Ranks customers by lifetime spend and order frequency, identifies the top N% (by value, frequency, or both), and outputs a CSV of VIP candidates. Optionally applies a VIP tag to qualified customers via `customerUpdate`. Used to build loyalty segments, prioritize white-glove support, or seed exclusive-access campaigns. The lifetime spend and order count are pulled directly from Shopify customer aggregates — no external CRM required.

## Prerequisites
- Authenticated Shopify CLI session: `shopify store auth --store <domain> --scopes read_customers,read_orders,write_customers`
- API scopes: `read_customers`, `read_orders`, `write_customers` (only if `tag_customers: true`)

## Parameters

| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| store | string | yes | — | Store domain (e.g., mystore.myshopify.com) |
| format | string | no | human | Output format: `human` or `json` |
| dry_run | bool | no | true | Preview VIP list without applying tags |
| rank_by | string | no | spend | Ranking strategy: `spend` (lifetime value), `frequency` (order count), or `both` (composite score) |
| top_pct | float | no | 5 | Top percentile to qualify as VIP (e.g., 5 = top 5%) |
| min_orders | integer | no | 2 | Minimum lifetime orders to be eligible |
| min_spend | float | no | 0 | Minimum lifetime spend (shop currency) to be eligible |
| tag_customers | bool | no | false | If true, apply VIP tag to qualified customers via `customerUpdate` |
| tag | string | no | vip | Tag string applied when `tag_customers: true` |

## Safety

> ⚠️ When `tag_customers: true`, Step 3 executes `customerUpdate` mutations that mutate customer tag lists. Tags persist until manually removed. Run with `dry_run: true` first to confirm the VIP list and qualifying thresholds. The default is `dry_run: true` — you must explicitly set `dry_run: false` and `tag_customers: true` to apply tags.

## Workflow Steps

1. **OPERATION:** `customers` — query
   **Inputs:** `first: 250`, `query: "orders_count:>=<min_orders>"`, select `id`, `displayName`, `defaultEmailAddress { emailAddress }`, `numberOfOrders`, `amountSpent { amount currencyCode }`, `tags`, pagination cursor
   **Expected output:** All customers meeting `min_orders` threshold; paginate until `hasNextPage: false`

2. **OPERATION:** `orders` — query (only when ranking by frequency, for recency annotation)
   **Inputs:** For each top candidate: `query: "customer_id:<id>"`, `first: 1`, `sortKey: CREATED_AT`, `reverse: true`
   **Expected output:** Most recent order per candidate to annotate the export

3. Filter to `amountSpent.amount >= min_spend`. Score each customer: `spend` → spend; `frequency` → orders; `both` → 0.6 × normalized spend + 0.4 × normalized frequency. Take the top `top_pct%`.

4. **OPERATION:** `customerUpdate` — mutation (only if `tag_customers: true` and `dry_run: false`)
   **Inputs:** `input: { id: <customer_id>, tags: [...existing_tags, <tag>] }`
   **Expected output:** `customer.id`, `customer.tags`, `userErrors`

## GraphQL Operations

```graphql
# customers:query — validated against api_version 2025-01
query VIPCandidateCustomers($first: Int!, $after: String, $query: String) {
  customers(first: $first, after: $after, query: $query) {
    edges {
      node {
        id
        displayName
        firstName
        lastName
        defaultEmailAddress {
          emailAddress
        }
        numberOfOrders
        amountSpent {
          amount
          currencyCode
        }
        tags
        createdAt
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}
```

```graphql
# orders:query — validated against api_version 2025-01
query VIPLastOrder($query: String!) {
  orders(first: 1, query: $query, sortKey: CREATED_AT, reverse: true) {
    edges {
      node {
        id
        name
        createdAt
        totalPriceSet {
          shopMoney { amount currencyCode }
        }
      }
    }
  }
}
```

```graphql
# customerUpdate:mutation — validated against api_version 2025-01
mutation CustomerUpdateVipTag($input: CustomerInput!) {
  customerUpdate(input: $input) {
    customer {
      id
      displayName
      tags
    }
    userErrors {
      field
      message
    }
  }
}
```

## Session Tracking

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

**On start**, emit:
```
╔══════════════════════════════════════════════╗
║  SKILL: VIP Customer Identifier              ║
║  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>
```

If `dry_run: true`, prefix every mutation step with `[DRY RUN]` and do not execute it.

**On completion**, emit:

For `format: human` (default):
```
══════════════════════════════════════════════
VIP CUSTOMER REPORT
  Customers scanned:    <n>
  Eligible (≥ min):     <n>
  VIPs (top <pct>%):    <n>
  Threshold spend:      $<amount>
  Threshold orders:     <n>
  Customers tagged:     <n>  (or "skipped — dry_run")

  Top 10 VIPs by <rank_by>:
    <name>  Spend: $<amount>  Orders: <n>  Last: <date>
  Output: vip_customers_<date>.csv
══════════════════════════════════════════════
```

For `format: json`, emit:
```json
{
  "skill": "vip-customer-identifier",
  "store": "<domain>",
  "dry_run": true,
  "rank_by": "spend",
  "outcome": {
    "customers_scanned": 0,
    "eligible": 0,
    "vips_identified": 0,
    "threshold_spend": 0,
    "customers_tagged": 0,
    "errors": 0,
    "output_file": "vip_customers_<date>.csv"
  }
}
```

## Output Format
CSV file `vip_customers_<YYYY-MM-DD>.csv` with columns:
`customer_id`, `name`, `email`, `lifetime_spend`, `currency`, `orders_count`, `last_order_date`, `composite_score`, `rank`, `tag_applied`

## Error Handling
| Error | Cause | Recovery |
|-------|-------|----------|
| `THROTTLED` | API rate limit exceeded | Wait 2 seconds, retry up to 3 times |
| `userErrors` on customerUpdate | Customer not found or tag conflict | Log, skip, continue |
| Fewer eligible than `top_pct%` | Small customer base | Lower `min_orders`/`min_spend` |
| Multi-currency stores | `currencyCode` varies | Convert via shop default before ranking |

## Best Practices
- Use `rank_by: both` to balance whales with loyalists — pure spend ranking can over-index on one-time large purchases.
- Re-run quarterly with a date-stamped tag (e.g., `vip-2026-Q2`) so lapsed VIPs roll off rather than accumulating permanently.
- Pair with `customer-win-back` — VIPs who become inactive should be flagged for high-priority re-engagement.
- Run with `dry_run: true` first; review the threshold spend value to confirm the cutoff matches your VIP definition.