Shopify Admin Order Cancellation Analysis

Purpose

Computes cancellation rate (cancelled orders / total orders) over a configurable window, broken down by cancelReason (CUSTOMER, FRAUD, INVENTORY, DECLINED, OTHER, STAFF). Surfaces shifts in cancellation patterns — for example, a spike in INVENTORY cancellations suggests a stock data integrity problem, while a spike in FRAUD suggests a coordinated attack. Read-only — no mutations.

Prerequisites

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

Parameters

Safety

ℹ️ Read-only skill — no mutations are executed. Safe to run at any time. The analysis uses cancelReason as recorded by Shopify or staff at cancellation time — accuracy depends on staff selecting the correct reason.

Workflow Steps

1. OPERATION: orders — query Inputs: query: "created_at:>='<NOW - days_back days>'", first: 250, select cancelledAt, cancelReason, displayFinancialStatus, totalPriceSet, pagination cursor Expected output: All orders created in the window (cancelled and non-cancelled) for rate calculation; paginate until hasNextPage: false

2. Partition orders into cancelled (cancelledAt != null) and not cancelled. Compute overall rate = cancelled / total.

3. For cancelled orders, group by cancelReason and by time bucket. Compute rate per bucket and per reason.

4. Identify time buckets where any single reason exceeds 2x its trailing 7-bucket average — flag as anomalies.

GraphQL Operations

# orders:query — validated against api_version 2025-01
query OrdersForCancellationAnalysis($query: String!, $after: String) {
  orders(first: 250, after: $after, query: $query) {
    edges {
      node {
        id
        name
        createdAt
        cancelledAt
        cancelReason
        displayFinancialStatus
        displayFulfillmentStatus
        totalPriceSet {
          shopMoney {
            amount
            currencyCode
          }
        }
        customer {
          id
          numberOfOrders
        }
        staffMember {
          id
          name
        }
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

Session Tracking

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

On start, emit:

╔══════════════════════════════════════════════╗
║  SKILL: Order Cancellation Analysis          ║
║  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):

══════════════════════════════════════════════
CANCELLATION ANALYSIS  (<days_back> days, by <bucket>)
  Total orders:        <n>
  Cancelled orders:    <n>  (<pct>%)
  Lost revenue:        $<amount>

  By reason:
    CUSTOMER     <n>  (<pct>%)
    FRAUD        <n>  (<pct>%)
    INVENTORY    <n>  (<pct>%)
    DECLINED     <n>  (<pct>%)
    OTHER        <n>  (<pct>%)

  Anomaly buckets (>2x trailing avg):
    <bucket-key>  reason=<reason>  rate=<pct>%
  Output: cancellation_analysis_<date>.csv
══════════════════════════════════════════════

For format: json, emit:

{
  "skill": "order-cancellation-analysis",
  "store": "<domain>",
  "period_days": 30,
  "bucket": "day",
  "total_orders": 0,
  "cancelled_orders": 0,
  "cancellation_rate": 0,
  "lost_revenue": 0,
  "currency": "USD",
  "by_reason": {
    "CUSTOMER": 0, "FRAUD": 0, "INVENTORY": 0, "DECLINED": 0, "OTHER": 0
  },
  "anomalies": [],
  "output_file": "cancellation_analysis_<date>.csv"
}

Output Format

CSV file cancellation_analysis_<YYYY-MM-DD>.csv with columns: bucket_start, bucket_end, total_orders, cancelled_orders, rate_pct, reason_customer, reason_fraud, reason_inventory, reason_declined, reason_other, lost_revenue, currency

Error Handling

Best Practices

• A baseline cancellation rate of 1–3% is typical; spikes above 5% warrant investigation.
• Sustained INVENTORY cancellations indicate a sync issue between storefront stock and warehouse — pair this skill with multi-location-inventory-audit.
• Sustained FRAUD cancellations indicate either improving fraud filters (good) or a coordinated attack (bad) — cross-reference with order-risk-report.
• High DECLINED rates often correlate with checkout friction or expired payment methods — investigate alongside checkout abandonment data.
• Run weekly to catch reason-mix shifts early; run after every major promotion to confirm cancellations did not spike.