Shopify Admin Order Attribution Report

Purpose

Pulls recent orders, extracts the UTM parameters embedded in each order's landingPageUrl query string, and rolls up revenue, order count, and average order value (AOV) by utm_source, utm_medium, and utm_campaign. Builds a marketing attribution report directly from first-party Shopify order data — no external analytics tool required. 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. Attribution accuracy depends on whether the storefront propagates UTM parameters into the checkout — orders that bypass the storefront (POS, draft orders, subscriptions) will not have landing site URLs.

Workflow Steps

1. OPERATION: orders — query Inputs: query: "created_at:>='<NOW - days_back days>' financial_status:paid", first: 250, select landingPageUrl, referrerUrl, customerJourneySummary, totalPriceSet, pagination cursor Expected output: All paid orders in the window with landing page URLs; paginate until hasNextPage: false

2. For each order, parse landingPageUrl query string and extract utm_source, utm_medium, utm_campaign, utm_term, utm_content. Orders without UTM params are bucketed as (direct/organic) if include_organic: true.

3. Aggregate by the group_by dimension: sum order count, sum revenue (in shop currency), compute AOV = revenue / orders.

4. Sort groups by revenue descending; filter out groups below min_orders.

GraphQL Operations

# orders:query — validated against api_version 2025-01
query OrdersWithAttribution($query: String!, $after: String) {
  orders(first: 250, after: $after, query: $query) {
    edges {
      node {
        id
        name
        createdAt
        landingPageUrl
        referrerUrl
        displayFinancialStatus
        totalPriceSet {
          shopMoney {
            amount
            currencyCode
          }
        }
        customerJourneySummary {
          firstVisit {
            landingPage
            source
            sourceType
            referrerUrl
            utmParameters {
              source
              medium
              campaign
              term
              content
            }
          }
          lastVisit {
            landingPage
            source
            sourceType
            utmParameters {
              source
              medium
              campaign
            }
          }
          momentsCount
        }
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

Session Tracking

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

On start, emit:

╔══════════════════════════════════════════════╗
║  SKILL: Order Attribution Report             ║
║  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):

══════════════════════════════════════════════
ORDER ATTRIBUTION REPORT  (<days_back> days)
  Orders attributed:   <n>
  Total revenue:       $<amount>
  Untagged (direct):   <n>  (<pct>%)

  Top sources by revenue:
    <source>     Orders: <n>   Revenue: $<n>   AOV: $<n>
    <source>     Orders: <n>   Revenue: $<n>   AOV: $<n>
  Output: attribution_report_<date>.csv
══════════════════════════════════════════════

For format: json, emit:

{
  "skill": "order-attribution-report",
  "store": "<domain>",
  "period_days": 30,
  "group_by": "source",
  "orders_attributed": 0,
  "total_revenue": 0,
  "currency": "USD",
  "groups": [
    { "key": "google", "orders": 0, "revenue": 0, "aov": 0 }
  ],
  "output_file": "attribution_report_<date>.csv"
}

Output Format

CSV file attribution_report_<YYYY-MM-DD>.csv with columns: group_key, utm_source, utm_medium, utm_campaign, orders, revenue, aov, currency, pct_of_revenue

Error Handling

Best Practices

• Use group_by: source_medium to distinguish paid traffic (google/cpc) from organic (google/organic).
• A high (direct/organic) percentage usually means UTM tagging is missing on paid campaigns — fix the campaign URLs, not the report.
• Run weekly during active campaigns to track attribution drift; run monthly for steady-state reporting.
• Cross-reference revenue here with ad spend from your ad platforms to compute true ROAS — this skill provides the order-side numerator only.
• For multi-touch attribution, also surface customerJourneySummary.firstVisit vs lastVisit to compare first-click vs last-click models.