Shopify Admin Cross Sell Opportunity Finder

Purpose

Finds products that are almost always purchased alone (single-item orders) and identifies potential cross-sell partners based on category affinity, price complementarity, and customer overlap. While frequently-bought-together finds existing patterns, this skill finds MISSING patterns — products that SHOULD be cross-sold but aren't. Read-only — no mutations.

Prerequisites

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

Parameters

Safety

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

Workflow Steps

1. OPERATION: orders — query Inputs: query: "created_at:>='<NOW - days_back days>'", first: 250, select lineItems { product { id, title, productType, vendor }, quantity, originalTotalSet }, pagination cursor Expected output: All orders with product data

2. For each product, calculate:

   • Total orders containing this product
   • Solo orders (product is the only item) vs. multi-item orders
   • Solo rate = solo_orders / total_orders × 100
   • Average order value when solo vs. when multi-item

3. Flag products with solo rate ≥ solo_threshold as "cross-sell candidates"

4. OPERATION: products — query (enrichment) Inputs: Product IDs for solo items and potential partners Expected output: Product type, vendor, price, collections for affinity matching

5. For each solo product, suggest cross-sell partners:

   • Same vendor, different product type (complementary)
   • Same product type, different price tier (good-better-best)
   • Products bought by the same customer cohort in separate orders
   • Price complementarity: partner price should be 20-50% of main product price (impulse add-on range)

GraphQL Operations

graphqlCopy code
# orders:query — validated against api_version 2025-01
query OrdersForCrossSell($query: String!, $after: String) {
  orders(first: 250, after: $after, query: $query) {
    edges {
      node {
        id
        customer { id }
        lineItems(first: 50) {
          edges {
            node {
              product { id title productType vendor }
              quantity
              originalTotalSet { shopMoney { amount currencyCode } }
            }
          }
        }
      }
    }
    pageInfo { hasNextPage endCursor }
  }
}

graphqlCopy code
# products:query — validated against api_version 2025-01
query ProductEnrichment($ids: [ID!]!) {
  nodes(ids: $ids) {
    ... on Product {
      id
      title
      productType
      vendor
      priceRangeV2 {
        minVariantPrice { amount currencyCode }
      }
      totalInventory
      status
    }
  }
}

Session Tracking

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

On start, emit:

Copy code
╔══════════════════════════════════════════════╗
║  SKILL: Cross-Sell Opportunity Finder        ║
║  Store: <store domain>                       ║
║  Started: <YYYY-MM-DD HH:MM UTC>             ║
╚══════════════════════════════════════════════╝

After each step, emit:

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

On completion, emit:

For format: human (default):

Copy code
══════════════════════════════════════════════
CROSS-SELL OPPORTUNITY REPORT  (<days_back> days)
  Products analyzed:         <n>
  High solo-rate products:   <n>
  ─────────────────────────────
  TOP CROSS-SELL OPPORTUNITIES:

  "<product A>"  (solo rate: <pct>%, <n> orders)
    → Suggested partner: "<product B>" (same vendor, complementary type)
    → Price fit: $<main> + $<partner> = $<combined>
    → Potential AOV lift: +$<amount> per order

  Revenue opportunity: $<total> (if <pct>% of solo orders add partner)

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

Output Format

CSV file cross_sell_opportunities_<YYYY-MM-DD>.csv with columns: product_id, product_title, total_orders, solo_orders, solo_rate, suggested_partner_id, suggested_partner_title, affinity_type, potential_aov_lift

Error Handling

Best Practices

• Products with 80%+ solo rate and high order volume are biggest AOV opportunities.
• Implement suggested pairs as "Customers also bought" or cart drawer recommendations.
• Cross-reference with frequently-bought-together to see what IS working vs. what's missing.
• Use with discount-ab-analysis to test a "buy X, get Y at 15% off" promotion.