Stop manually creating product bundles. Pick a playbook — Starter Kits, Gift Sets, or Overstock Liquidator — and AI generates optimized bundles from your actual co-purchase data.
Each playbook configures the AI bundle generator with different goals — from converting first-time buyers to clearing overstocked inventory.
Clusters 3-4 entry-level products from the same category. Auto-named as "The [Category] Starter Kit" with 15% discount to drive first purchases.
Finds cross-category co-purchases and bundles them as outfit or set pairings. LLM names them with editorial flair — not generic "bundle" language.
Groups consumable products customers re-order together. Higher quantities at 10% discount. Designed to increase lifetime value through effortless re-ordering.
Bundles products in the $25-$75 range from complementary categories. AI generates gift-occasion names like "Self-Care Sunday" or "The BBQ Master Pack".
Targets overstocked products. Pairs slow movers with a bestseller at 20% off. Clears inventory while maintaining perceived value through smart anchoring.
Uses intent signals and trending categories to auto-generate seasonal bundles. Activated by calendar triggers for Back to School, Holiday, Summer, and more.
Each playbook defines the bundle generation rules: how many products, discount percentage, price range, category coherence, and the AI naming style.
The bundle generator builds a graph of your actual order data, detects product clusters using Louvain community detection, and validates each cluster for coherence and pricing.
Each validated cluster gets an AI-generated name that matches the playbook's style — editorial for "Complete The Look", practical for "Replenishment Pack". Pricing is auto-calculated with the playbook's discount.
Traditional bundling approaches are manual — a merchandiser picks products they think go together and creates a discount. This doesn't scale and misses patterns hidden in order data. SellerZoom's bundle generator uses graph clustering on actual co-purchase data to find product groupings that real customers already buy together.
Every pair of products that has been purchased together at least 3 times creates an edge in a co-purchase graph. The edge weight reflects how often those products are bought together and the confidence of the association. Louvain community detection then identifies natural clusters within this graph — groups of products that are frequently purchased as a set.
A bundle of "Blue Widget, Red Widget, and Widget Holder" needs a name that a customer would actually want to click on. Each playbook includes a custom naming prompt that instructs the LLM to generate names matching a specific style. The Gift Set playbook generates names like "Self-Care Sunday" while the Starter Kit playbook generates names like "The Skincare Starter Kit". The LLM sees the actual product titles, categories, and prices to create contextually relevant names.
The Overstock Liquidator playbook specifically targets products flagged as overstocked in your inventory system. By pairing slow-moving items with a bestseller at a deeper discount, it creates perceived value through anchoring — customers feel they're getting a deal on the popular product while the slow mover comes along for the ride. This is more effective than marking down individual products because the bundle context reframes the discount as a deal rather than a desperation sale.
Pick a playbook, generate bundles from your real order data, and start increasing average order value today.
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