Product Bundling Validation

Identify co-purchased Shopify products and validate bundle concepts with Mavera Focus Groups

Scenario

You want product bundles backed by evidence. You analyze order history to find frequently co-purchased products, propose bundle concepts from top pairs, then validate with a Mavera focus group for synthetic audience feedback.

Architecture

Code

Python

import os, requests
from collections import defaultdict
from itertools import combinations

STORE = os.environ["SHOPIFY_STORE"]
TOKEN = os.environ["SHOPIFY_ACCESS_TOKEN"]
MV = os.environ["MAVERA_API_KEY"]
SH = f"https://{STORE}.myshopify.com/admin/api/2024-10/graphql.json"
SH_H = {"X-Shopify-Access-Token": TOKEN, "Content-Type": "application/json"}
MB = "https://app.mavera.io/api/v1"
MH = {"Authorization": f"Bearer {MV}", "Content-Type": "application/json"}

QUERY = """query ($cursor: String) {
  orders(first: 250, after: $cursor) {
    edges { node { lineItems(first: 20) { edges { node { title originalUnitPriceSet { shopMoney { amount } } } } } } }
    pageInfo { hasNextPage endCursor }
  }
}"""

orders, cursor = [], None
while True:
    resp = requests.post(SH, json={"query": QUERY, "variables": {"cursor": cursor} if cursor else {}}, headers=SH_H)
    resp.raise_for_status(); data = resp.json()["data"]["orders"]
    for e in data["edges"]:
        items = [li["node"] for li in e["node"]["lineItems"]["edges"]]
        if len(items) >= 2: orders.append(items)
    if not data["pageInfo"]["hasNextPage"]: break
    cursor = data["pageInfo"]["endCursor"]
print(f"Analyzed {len(orders)} multi-item orders")
counts, prices = defaultdict(int), defaultdict(list)
for items in orders:
    titles = sorted(set(i["title"] for i in items))
    pm = {i["title"]: float(i["originalUnitPriceSet"]["shopMoney"]["amount"]) for i in items}
    for a, b in combinations(titles, 2):
        counts[(a, b)] += 1
        prices[(a, b)].append(pm.get(a, 0) + pm.get(b, 0))

bundles = [{"products": list(p), "count": c, "bundle_price": round(sum(prices[p])/len(prices[p])*0.9, 2)}
           for p, c in sorted(counts.items(), key=lambda x: -x[1])[:10] if c >= 5]
for b in bundles[:5]:
    print(f"  {b['products'][0]} + {b['products'][1]}: {b['count']}x → ${b['bundle_price']}")
descs = [f"Bundle {i+1}: {b['products'][0]} + {b['products'][1]} at ${b['bundle_price']} (10% off)" for i, b in enumerate(bundles[:5])]
fg = requests.post(f"{MB}/focus-groups", json={
    "name": "Shopify Bundle Validation",
    "questions": [f"Would you buy '{bundles[0]['products'][0]}' + '{bundles[0]['products'][1]}' at ${bundles[0]['bundle_price']}?", "Which bundle is most appealing?", "What discount makes a bundle irresistible?", "Mix-and-match or curated sets?"],
    "context": "Testing bundle concepts from purchase data.\n\n" + "\n".join(descs),
}, headers=MH)
fg.raise_for_status(); result = fg.json()
print(f"\nFocus group: {result['id']}")
for r in result.get("responses", []):
    print(f"  Q: {r['question']}\n  A: {r['answer'][:200]}\n")

Example Output

{
  "id": "fg_7d3f1a9e",
  "responses": [
    {
      "question": "Would you buy 'Merino Base Layer' + 'Trail Runner Shorts' at $112.50?",
      "answer": "Absolutely — I already buy these together. Saving $12.50 is a no-brainer."
    },
    {
      "question": "What discount makes a bundle irresistible?",
      "answer": "15% is the sweet spot. At 10% I comparison-shop, at 15% I stop thinking."
    }
  ]
}

Error Handling

Mostly single-item orders

Co-occurrence needs orders with 2+ distinct products. If your store has mostly single-item orders, lower min_count or expand the date range.

Combinatorial explosion on large orders

An order with 20 unique products generates 190 pairs. Cap unique titles per order to the top 10 by quantity to keep memory manageable.