AI Merchandising
The use of AI to determine which products to show, how to price or bundle them, and to which customers, based on behavioral, transactional, and catalog data.
What It Is
AI merchandising covers product recommendations, dynamic bundling, personalized search ranking, and assortment decisions: deciding what a specific buyer sees and in what order.
It spans a spectrum from reactive personalization (recommending based on past behavior, like ‘customers also bought’) to predictive merchandising, which anticipates a need before any explicit signal from the buyer.
In a large catalog, the practical effect is surfacing relevant products that would otherwise be buried, and doing so differently for buyers with genuinely different purchasing patterns.
In B2B Commerce Context
In B2B commerce, merchandising decisions are constrained in ways B2C tactics aren’t: contract pricing, account-specific catalogs, and tiered access change what ‘personalized’ even means for a given account.
A practical example
AI merchandising can also work against overstock: resurfacing slow-moving inventory specifically to the accounts statistically likely to need it, rather than discounting broadly.
When You Need It
- Your catalog is large or complex enough that relevant items get buried for specific buyers.
- B2B accounts have genuinely distinct buying patterns by vertical, region, or role.
- You want to increase average order value without resorting to broad discounting.
- Your merchandising team can't manually curate assortment at the pace the catalog grows.
Start with your highest-volume account segments before extending personalization catalog-wide.
What It Is Not
- AI merchandising is not the same as a generic 'customers also bought' widget bolted onto a storefront. It ties into pricing, inventory, and account rules, not just co-purchase patterns.
- It is not a replacement for merchandising strategy. The AI executes personalization at scale; humans still decide what the assortment and pricing strategy should be.
- It is not a direct port of B2C tactics. B2B constraints such as contract pricing, account tiers, and approval chains change what recommendations are even permissible to show.
Comparison
| Capability | AI Merchandising | Rule-Based Merchandising |
|---|---|---|
| Personalization basis | Behavioral and transactional patterns | Fixed rules (e.g., ‘always show category X first’) |
| Adaptability | Adjusts as buyer behavior changes | Requires manual rule updates |
| Scale | Handles large, fast-changing catalogs | Effort grows with catalog size |
| Account awareness | Reflects account-specific pricing and history | Typically uniform across accounts |
See also
Ready to see it in action?
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