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
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| store | string | yes | — | Store domain (e.g., mystore.myshopify.com) |
| days_back | integer | no | 30 | Lookback window for return resolutions |
| compare_days_back | integer | no | 0 | Optional prior period for comparison (0 = no comparison) |
| 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
-
OPERATION:
returns— query Inputs:query: "created_at:>='<NOW - days_back days>'",first: 250, pagination cursor Expected output: Returns withrefunds { totalRefundedSet },exchangeLineItems, and resolution status -
Categorize each return resolution:
- Exchange: return has
exchangeLineItemswith quantity > 0 - Store credit: return has
refundswith gift card or store credit payment - Refund: return has cash/card refund with no exchange
- Exchange: return has
-
OPERATION:
orders— query (ifcompare_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
| Error | Cause | Recovery |
|---|---|---|
THROTTLED |
API rate limit exceeded | Wait 2 seconds, retry up to 3 times |
| No resolved returns in window | No completed returns in period | Exit with summary: 0 returns |
| Exchange line items empty but status indicates exchange | In-progress exchange | Count as pending, exclude from ratio |
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_backto 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.
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