Product Affinity Cross Sell

Purpose

Applies market basket analysis to your order history to surface product pairs that customers naturally buy together. For every co-purchased pair it calculates support (how often the pair appears), confidence (given product A, how likely is B?), and lift (how much more likely than chance). The output is actionable input for product bundles, "frequently bought together" widgets, cross-sell email flows, and homepage recommendations. Read-only — no mutations are executed.

Prerequisites

Parameters

Workflow Steps

Inputs: first: 250, query: "created_at:>='' created_at:<=''", pagination cursor; select lineItems with product { id, title } and quantity; skip orders with a single line item

Expected output: All multi-item orders in range; paginate until hasNextPage: false; build a product frequency map (product_id → order_count) and a pair frequency map ((product_a_id, product_b_id) → co_occurrence_count)

GraphQL Operations

# orders:query (multi-item basket analysis) — validated against api_version 2025-01
query OrdersForAffinityAnalysis($first: Int!, $after: String, $query: String) {
  orders(first: $first, after: $after, query: $query) {
    edges {
      node {
        id
        createdAt
        lineItems(first: 50) {
          edges {
            node {
              quantity
              product {
                id
                title
                tags
              }
            }
          }
        }
      }
    }
    pageInfo {
      hasNextPage
      endCursor
    }
  }
}

Session Tracking

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

On start, emit:

╔══════════════════════════════════════════════╗
║  SKILL: product-affinity-cross-sell          ║
║  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):

══════════════════════════════════════════════
OUTCOME SUMMARY
  Orders analysed:      <n>
  Unique products:      <n>
  Pairs evaluated:      <n>
  Pairs above threshold:<n>
  Date range:           <start> to <end>
  Sort by:              <lift|confidence|support>
  Errors:               0
  Output:               product_affinity_<date>.csv
══════════════════════════════════════════════

Followed by an inline ranked table of the top top_n pairs:

For format: json, emit:

{
  "skill": "product-affinity-cross-sell",
  "store": "<domain>",
  "started_at": "<ISO8601>",
  "completed_at": "<ISO8601>",
  "dry_run": false,
  "steps": [
    { "step": 1, "operation": "OrdersForAffinityAnalysis", "type": "query", "params_summary": "<date_range_start> to <date_range_end>", "result_summary": "<n> orders, <n> multi-item baskets", "skipped": false }
  ],
  "outcome": {
    "orders_analysed": 0,
    "unique_products": 0,
    "pairs_evaluated": 0,
    "pairs_above_threshold": 0,
    "date_range_start": "<date_range_start>",
    "date_range_end": "<date_range_end>",
    "sort_by": "lift",
    "results": [],
    "errors": 0,
    "output_file": "product_affinity_<date>.csv"
  }
}

Output Format

CSV file product_affinity_.csv with one row per qualifying pair:

Error Handling

Best Practices