Shopify Admin Customer Acquisition Cost By Source

Purpose

Estimates customer acquisition cost (CAC) for each traffic source by combining the number of new-customer orders attributed to a landing page / referrer with a configurable ad spend input per source. Output answers: "for every dollar spent on source X, how many new customers did we acquire and at what unit cost?" Read-only — no mutations. Provides the data foundation for paid-media budget reallocation.

Prerequisites

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

Parameters

Safety

ℹ️ Read-only skill — no mutations are executed. Safe to run at any time. Ad spend values are caller-provided; this skill does not pull from any ad platform.

Workflow Steps

1. OPERATION: orders — query Inputs: query: "created_at:>='<NOW - days_back days>'", first: 250, select customer { id, numberOfOrders }, customerJourneySummary { firstVisit { landingPage referrerUrl source } }, landingPageUrl, referrerUrl, totalPriceSet, pagination cursor Expected output: All orders in the window with referral and customer attribution; paginate until hasNextPage: false

2. Group orders by normalized source. Resolution order:

   • customerJourneySummary.firstVisit.source if present
   • Else parse domain from referrerUrl
   • Else parse landingPageUrl UTM params (utm_source)
   • Else bucket as direct

3. If new_customers_only: true, drop orders where customer.numberOfOrders > 1 so each customer is counted once

4. Aggregate per source: orders_count, new_customers_count, revenue_attributed

5. Join with ad_spend map: cac = ad_spend[source] / new_customers_count. Sources without spend data report cac: null (organic / unattributed)

6. Filter to sources with orders_count >= min_orders_per_source

GraphQL Operations

graphqlCopy code
# 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
        customerJourneySummary {
          firstVisit {
            landingPage
            referrerUrl
            source
            sourceType
            utmParameters {
              source
              medium
              campaign
            }
          }
        }
        totalPriceSet {
          shopMoney {
            amount
            currencyCode
          }
        }
        customer {
          id
          numberOfOrders
        }
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

Session Tracking

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

On start, emit:

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╔══════════════════════════════════════════════╗
║  SKILL: Customer Acquisition Cost by Source  ║
║  Store: <store domain>                       ║
║  Started: <YYYY-MM-DD HH:MM UTC>             ║
╚══════════════════════════════════════════════╝

After each step, emit:

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[N/TOTAL] <QUERY|MUTATION>  <OperationName>
          → Params: <brief summary of key inputs>
          → Result: <count or outcome>

On completion, emit:

For format: human (default):

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══════════════════════════════════════════════
CAC BY SOURCE  (<days_back> days)
  Orders analyzed:        <n>
  New customers acquired: <n>
  Total ad spend (input): $<amount>
  Blended CAC:            $<amount>

  By Source (sorted by CAC ascending):
    google      Customers: <n>  Spend: $<n>   CAC: $<n>
    meta        Customers: <n>  Spend: $<n>   CAC: $<n>
    direct      Customers: <n>  Spend: —      CAC: organic
    referral    Customers: <n>  Spend: —      CAC: organic

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

For format: json, emit:

jsonCopy code
{
  "skill": "customer-acquisition-cost-by-source",
  "store": "<domain>",
  "period_days": 30,
  "orders_analyzed": 0,
  "new_customers": 0,
  "blended_cac": 0,
  "currency": "USD",
  "by_source": [],
  "output_file": "cac_by_source_<date>.csv"
}

Output Format

CSV file cac_by_source_<YYYY-MM-DD>.csv with columns: source, orders_count, new_customers_count, revenue_attributed, ad_spend, cac, currency

Error Handling

Best Practices

• Provide ad spend for the same window as days_back — mismatched windows produce misleading CAC numbers.
• Pair with customer-cohort-analysis to validate that low-CAC sources also produce high-LTV customers.
• Sources reported as direct often hide attribution leakage — investigate UTM tagging and referrer policies before drawing conclusions.
• Treat output as estimated CAC — Shopify's first-touch attribution does not capture cross-device journeys, so sources that rely on view-through (display, video) will be undercounted.
• Re-run weekly to catch CAC drift before campaigns become unprofitable.