SellerZoom analyzes co-purchase patterns using graph clustering, then generates ready-to-sell product bundles — complete with AI-written names, descriptions, and discount strategies.
No guesswork. The AI analyzes real co-purchase data from your orders, finds product clusters that naturally sell together, and generates everything you need to launch a bundle.
Products are connected by co-purchase frequency. Louvain community detection finds tightly-knit product clusters that naturally sell together — not random groupings.
The LLM examines each bundle's products and generates a compelling marketing name and description — "The Complete Barista Kit" instead of "Bundle #47."
AI recommends optimal bundle discounts based on individual product margins — just enough to incentivize the bundle without killing your profit.
Bundles appear automatically in the storefront widget as cart upsells. When a shopper adds a bundle product, the complete bundle offer surfaces immediately.
Each bundle includes an AI-generated explanation of why these products work together — displayed in the widget to convince shoppers and increase add-to-cart rates.
Track views, add-to-carts, and revenue per bundle. See which bundles convert and which need refreshing. A/B test bundle discounts with the experiments feature.
SellerZoom builds a product co-purchase graph from your order history. Each edge represents how often two products appear in the same order, weighted by frequency and recency.
Louvain community detection identifies tightly-knit groups of products that naturally sell together. The algorithm finds optimal cluster sizes — typically 2-5 products per bundle.
The LLM examines each cluster, generates a marketing-ready bundle name and description, and recommends a discount percentage based on individual product margins.
Manual product bundling relies on merchandiser intuition — which products "should" go together. But intuition misses patterns hidden in order data. A kitchen store might never think to bundle a cast iron skillet with a specific brand of spatula, but order data shows they're purchased together 40% of the time.
SellerZoom's bundle generator uses graph-based clustering algorithms to find these hidden co-purchase patterns. By building a network graph where products are nodes and co-purchases are edges, the system identifies communities of products that naturally cluster together — the same algorithms used by social networks to detect friend groups.
A bundle called "Products 12, 47, and 89" doesn't sell. A bundle called "The Weekend Baker's Starter Kit" does. SellerZoom uses large language models to examine the products in each bundle and generate compelling, marketable names and descriptions that communicate value to shoppers.
Product bundles work because they reduce decision fatigue. A shopper who needs to furnish a home office faces dozens of individual purchase decisions — desk, chair, lamp, organizer, cable management. A "Home Office Starter Kit" collapses those decisions into one. The shopper gets confidence that the products work together, a slight discount for buying as a set, and the satisfaction of checking off multiple needs at once.
Research in behavioral economics shows that bundles also increase perceived value. A $200 bundle of five items feels like a better deal than buying each item individually for $210 total, even though the actual savings are minimal. The framing as a curated set creates value beyond the sum of the parts.
Co-purchase patterns shift over time due to seasonality, new product launches, and changing customer preferences. A bundle based on winter co-purchase data will underperform in summer. SellerZoom recommends regenerating bundles quarterly, or whenever you add more than 50 new products to your catalog. The generation process takes seconds and always uses the freshest behavioral data.
Where bundles appear on your store matters as much as what's in them. SellerZoom recommends placing bundle recommendations on individual product pages — when a shopper is viewing a product that's part of a bundle, show the complete bundle as a "Complete the Set" recommendation. This contextual placement converts 2–3x better than generic bundle pages because the shopper already has purchase intent for one of the products and the bundle makes the decision to buy more feel natural rather than promotional.
Let AI analyze your order data and generate high-converting bundles with names, descriptions, and discount strategies — automatically.
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