Shopify Admin Frequently Bought Together

Purpose

Analyzes order history to discover which products are frequently purchased together. Calculates co-occurrence frequency, lift scores, and confidence metrics to generate data-driven cross-sell recommendations and bundle candidates. 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 } }, pagination cursor Expected output: All orders with product-level line items

2. For each order with 2+ distinct products, generate all product pair combinations

3. Build co-occurrence matrix:

   • Support = number of orders containing both products
   • Confidence(A→B) = P(B|A) = support(A,B) / support(A)
   • Lift = confidence(A→B) / P(B) — lift > 1.0 means positive association

4. OPERATION: products — query (enrichment) Inputs: Product IDs from top pairs for titles, images, prices Expected output: Product details for display

5. Rank pairs by lift score (descending), filter by min_support

GraphQL Operations

graphqlCopy code
# orders:query — validated against api_version 2025-01
query OrderLineItems($query: String!, $after: String) {
  orders(first: 250, after: $after, query: $query) {
    edges {
      node {
        id
        lineItems(first: 50) {
          edges {
            node {
              product { id title }
              quantity
            }
          }
        }
      }
    }
    pageInfo { hasNextPage endCursor }
  }
}

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

Session Tracking

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

On start, emit:

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╔══════════════════════════════════════════════╗
║  SKILL: Frequently Bought Together           ║
║  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):

Copy code
══════════════════════════════════════════════
FREQUENTLY BOUGHT TOGETHER  (<days_back> days, <n> orders analyzed)
  Unique product pairs found: <n>
  Pairs meeting min_support:  <n>

  TOP PAIRS BY LIFT:
  #1  "<product A>" + "<product B>"
      Support: <n> orders  Lift: <n>x  Confidence: <pct>%

  #2  "<product A>" + "<product B>"
      Support: <n> orders  Lift: <n>x  Confidence: <pct>%

  BUNDLE CANDIDATES (high support + high lift):
    "<product A>" + "<product B>"  →  Suggested bundle price: $<n>

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

Output Format

CSV file fbt_pairs_<YYYY-MM-DD>.csv with columns: product_a_id, product_a_title, product_b_id, product_b_title, support, confidence_a_to_b, confidence_b_to_a, lift, combined_avg_price

Error Handling

Best Practices

• Use days_back: 180 or 365 for sufficient sample size.
• Pairs with lift > 2.0 are strong bundle candidates.
• Use results to create manual product bundles or configure upsell apps.
• Cross-reference with top-product-performance to ensure paired items are high-performing.
• Products with high confidence A→B but low confidence B→A suggest directional upsells (show B when A is in cart).