| name | shopify-admin-average-order-value-trends |
| role | finance |
| description | Read-only: tracks AOV over time buckets and segments by new vs. returning customers. |
| toolkit | shopify-admin, shopify-admin-execution |
| api_version | 2025-01 |
| graphql_operations | ["orders:query","customers:query"] |
| status | stable |
| compatibility | Claude Code, Cursor, Codex, Gemini CLI |
Purpose
Calculates Average Order Value (AOV) over configurable time buckets (daily, weekly, monthly) and segments results by new vs. returning customers. Tracks AOV trends to measure the impact of upsell programs, bundle offers, or free shipping thresholds. Read-only — no mutations.
Prerequisites
- Authenticated Shopify CLI session:
shopify store auth --store <domain> --scopes read_orders,read_customers
- API scopes:
read_orders, read_customers
Parameters
| Parameter | Type | Required | Default | Description |
|---|
| store | string | yes | — | Store domain (e.g., mystore.myshopify.com) |
| days_back | integer | no | 90 | Total lookback window |
| bucket | string | no | week | Time bucket: day, week, or month |
| 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: orders — query
Inputs: query: "created_at:>='<NOW - days_back days>'", first: 250, select totalPriceSet, customer { id, numberOfOrders }, createdAt, pagination cursor
Expected output: All orders in window; paginate until hasNextPage: false
-
Classify each order: if customer.numberOfOrders == 1 → new customer order; else → returning
-
OPERATION: customers — query (optional enrichment for cohort context)
Inputs: Recent customers for new vs. repeat segmentation validation
-
Group orders by time bucket; calculate AOV per bucket and per customer segment
GraphQL Operations
query AOVData($query: String!, $after: String) {
orders(first: 250, after: $after, query: $query) {
edges {
node {
id
name
createdAt
totalPriceSet {
shopMoney {
amount
currencyCode
}
}
customer {
id
numberOfOrders
}
}
}
pageInfo {
hasNextPage
endCursor
}
}
}
query NewVsReturningCustomers($query: String!, $after: String) {
customers(first: 250, after: $after, query: $query) {
edges {
node {
id
numberOfOrders
createdAt
}
}
pageInfo {
hasNextPage
endCursor
}
}
}
Session Tracking
Claude MUST emit the following output at each stage. This is mandatory.
On start, emit:
╔══════════════════════════════════════════════╗
║ SKILL: Average Order Value Trends ║
║ 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):
══════════════════════════════════════════════
AOV TRENDS (<days_back> days, bucket: <bucket>)
Orders analyzed: <n>
Overall AOV: $<amount>
New customer AOV: $<amount>
Returning AOV: $<amount>
Period Orders AOV New AOV Returning AOV
────────────────────────────────────────────────────
2026-W14 <n> $<n> $<n> $<n>
Output: aov_trends_<date>.csv
══════════════════════════════════════════════
For format: json, emit:
{
"skill": "average-order-value-trends",
"store": "<domain>",
"period_days": 90,
"overall_aov": 0,
"new_customer_aov": 0,
"returning_customer_aov": 0,
"by_period": [],
"output_file": "aov_trends_<date>.csv"
}
Output Format
CSV file aov_trends_<YYYY-MM-DD>.csv with columns:
period, order_count, aov, new_customer_orders, new_customer_aov, returning_orders, returning_aov, currency
Error Handling
| Error | Cause | Recovery |
|---|
THROTTLED | API rate limit exceeded | Wait 2 seconds, retry up to 3 times |
| Guest checkout orders | No customer record | Count in totals but exclude from new/returning segmentation |
| No orders in window | New store or quiet period | Exit with 0 AOV |
Best Practices
- A free shipping threshold increase or bundle introduction should show up as an AOV lift in the week/month it launched — use this report to measure the impact.
- Returning customer AOV is typically higher than new — a shrinking gap may indicate loyalty erosion.
bucket: week is best for campaign measurement; bucket: month for long-term trend tracking.
- Guest checkout orders cannot be segmented as new vs. returning — for stores with high guest checkout rates, the segmentation will under-count new customers.