/shopify-admin-repeat-purchase-rate

name: shopify-admin-repeat-purchase-rate
role: order-intelligence
description: "Read-only: calculates what percentage of customers place 2+ orders within N days, segmented by product or collection."
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

Calculates the repeat purchase rate — the percentage of customers who return to place at least one more order within a defined window — and segments it by first-purchase product or collection. Identifies which products drive the highest repeat purchase behavior. Read-only — no mutations.

Prerequisites

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

Parameters

| Parameter | Type | Required | Default | Description | |-----------|------|----------|---------|-------------| | store | string | yes | — | Store domain (e.g., mystore.myshopify.com) | | days_back | integer | no | 90 | Acquisition window — customers first purchased in this period | | repeat_window | integer | no | 90 | Days after first purchase to look for a repeat order | | segment_by | string | no | none | Segment repeat rate by: `product`, `none` | | format | string | no | human | Output format: `human` or `json` |

Safety

> ℹ️ Read-only skill — no mutations are executed. Safe to run at any time.

Workflow Steps

1. **OPERATION:** `customers` — query **Inputs:** `query: "created_at:>='<NOW - days_back days>'"`, `first: 250`, select `id`, `numberOfOrders`, `createdAt` **Expected output:** Customers acquired in window

2. **OPERATION:** `orders` — query **Inputs:** `query: "created_at:>='<NOW - days_back + repeat_window days>'"`, `first: 250`, select `customer { id }`, `createdAt`, `lineItems { product { id, title } }`, pagination cursor **Expected output:** Orders to build per-customer purchase history and first-product mapping

3. For each acquired customer: if they have ≥ 2 orders within `repeat_window` days → repeat purchaser

4. Calculate overall rate; if `segment_by: product`, group by first-purchased product

GraphQL Operations

# customers:query — validated against api_version 2025-01
query AcquiredCustomers($query: String!, $after: String) {
  customers(first: 250, after: $after, query: $query) {
    edges {
      node {
        id
        createdAt
        numberOfOrders
        defaultEmailAddress {
          emailAddress
        }
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

# orders:query — validated against api_version 2025-01
query CustomerOrderHistory($query: String!, $after: String) {
  orders(first: 250, after: $after, query: $query) {
    edges {
      node {
        id
        createdAt
        customer {
          id
        }
        lineItems(first: 5) {
          edges {
            node {
              product {
                id
                title
              }
            }
          }
        }
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

Session Tracking

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

**On start**, emit:

╔══════════════════════════════════════════════╗
║  SKILL: Repeat Purchase Rate                 ║
║  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):

══════════════════════════════════════════════
REPEAT PURCHASE RATE
  Acquisition window:  <days_back> days
  Repeat window:       <repeat_window> days
  Customers acquired:  <n>
  Repeat purchasers:   <n>
  Repeat rate:         <pct>%

  By First Product:
    "<product>"  Acquired: <n>  Repeat: <pct>%
  Output: repeat_purchase_<date>.csv
══════════════════════════════════════════════

For `format: json`, emit:

{
  "skill": "repeat-purchase-rate",
  "store": "<domain>",
  "acquisition_days": 90,
  "repeat_window_days": 90,
  "customers_acquired": 0,
  "repeat_purchasers": 0,
  "repeat_rate_pct": 0,
  "by_product": [],
  "output_file": "repeat_purchase_<date>.csv"
}

Output Format

CSV file `repeat_purchase_<YYYY-MM-DD>.csv` with columns: `customer_id`, `first_order_date`, `first_product`, `total_orders`, `is_repeat`, `days_to_repeat`, `total_spent`

Error Handling

| Error | Cause | Recovery | |-------|-------|----------| | `THROTTLED` | API rate limit exceeded | Wait 2 seconds, retry up to 3 times | | Guest checkout customers | No customer record to link orders | Exclude from analysis | | Insufficient history | Store newer than window | Analyze available period |

Best Practices

• A repeat rate of 25–35% within 90 days is a healthy baseline for most non-subscription ecommerce stores.
• Products with high repeat rates are your "gateway" products — prioritize them in acquisition campaigns.
• Use `segment_by: product` to identify which products create loyal customers vs. one-time buyers.
• Pair with `customer-cohort-analysis` for a deeper view of long-term retention trends.