Shopify Admin Exchange Vs Refund Ratio

Purpose

Analyzes return resolutions to calculate the split between exchanges (revenue retained), store credit (revenue deferred), and refunds (revenue lost). Tracks this as a revenue recovery metric over time. Read-only — no mutations.

Prerequisites

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

Parameters

Safety

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

Workflow Steps

1. OPERATION: returns — query Inputs: query: "created_at:>='<NOW - days_back days>'", first: 250, pagination cursor Expected output: Returns with refunds { totalRefundedSet }, exchangeLineItems, and resolution status

2. Categorize each return resolution:

• Exchange: return has exchangeLineItems with quantity > 0
• Store credit: return has refunds with gift card or store credit payment
• Refund: return has cash/card refund with no exchange

3. OPERATION: orders — query (if compare_days_back > 0) Inputs: Same logic for prior period to compute trend

GraphQL Operations

# returns:query — validated against api_version 2025-01
query ReturnResolutions($query: String!, $after: String) {
  returns(first: 250, after: $after, query: $query) {
    edges {
      node {
        id
        status
        createdAt
        totalQuantity
        order {
          id
          name
        }
        exchangeLineItems(first: 10) {
          edges {
            node {
              id
              quantity
              lineItem {
                variant {
                  id
                  title
                }
              }
            }
          }
        }
        refunds(first: 5) {
          id
          totalRefundedSet {
            shopMoney {
              amount
              currencyCode
            }
          }
        }
        returnLineItems(first: 20) {
          edges {
            node {
              refundableQuantity
              returnReason
            }
          }
        }
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

# orders:query — validated against api_version 2025-01
query OrdersInPeriod($query: String!, $after: String) {
  orders(first: 250, after: $after, query: $query) {
    edges {
      node {
        id
        name
        totalPriceSet {
          shopMoney {
            amount
            currencyCode
          }
        }
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

Session Tracking

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

On start, emit:

╔══════════════════════════════════════════════╗
║  SKILL: Exchange vs Refund Ratio             ║
║  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):

══════════════════════════════════════════════
EXCHANGE vs REFUND RATIO  (<days_back> days)
  Total returns resolved:  <n>

  Resolution breakdown:
    Exchange (revenue kept):    <n>  (<pct>%)
    Store credit (deferred):    <n>  (<pct>%)
    Refund (revenue lost):      <n>  (<pct>%)

  Revenue recovery rate: <pct>%
  (exchanges + store credit as % of all returns)

  Output: exchange_refund_ratio_<date>.csv
══════════════════════════════════════════════

For format: json, emit:

{
  "skill": "exchange-vs-refund-ratio",
  "store": "<domain>",
  "period_days": 30,
  "total_returns": 0,
  "exchanges": { "count": 0, "pct": 0 },
  "store_credit": { "count": 0, "pct": 0 },
  "refunds": { "count": 0, "pct": 0 },
  "revenue_recovery_rate_pct": 0,
  "output_file": "exchange_refund_ratio_<date>.csv"
}

Output Format

CSV file exchange_refund_ratio_<YYYY-MM-DD>.csv with columns: return_id, order_name, resolution_type, exchange_sku, refund_amount, currency, created_at

Error Handling

Best Practices

• A revenue recovery rate above 30% (exchanges + store credit) is generally a strong signal for fashion/apparel; set your own benchmark based on category.
• Use compare_days_back to track whether returns policy changes (e.g., "exchange only" periods) improved the recovery rate.
• Pair with return-reason-analysis — high "wrong size" return reasons paired with low exchange rates may indicate size guidance issues in product listings.
• Run before and after launching an exchange incentive (e.g., bonus store credit for exchanging instead of refunding) to measure impact.