Recommendation Engine

Surface the next order before your customer has to go looking for it.

The problem it solves

Generic "customers also bought" widgets rank by overall popularity, which breaks down fast in B2B buying, where two accounts in completely different lines of business should never see the same suggestions. The order history needed to do this properly already sits in your systems, it just isn't connected to a model that can use it.

What it does for your business

Maps full purchase graphs

Learns which accounts buy alike across your entire catalog, not just which two items were bought together once.

Surfaces recommendations where they convert

Quote building, cart, reorder screens, and email are all natural places to place a ranked suggestion for that specific account.

Works across B2B and B2C

Account-level recommendations for a wholesale channel and individual personalization for a direct storefront, from the same underlying model.

Measures its own lift

Attach rate and order value are tracked against a holdout group, so the impact is a measured number, not an assumption.

Where this moves the needle

Higher cross-sell attach rate Higher average order value More line items per order Better email-to-order conversion
How it works
Collaborative filtering or graph-embedding models are trained over your account-by-product purchase matrix and retrained as new orders accumulate, then served in real time wherever a recommendation surface needs it.
What we need from you
Order history with line-item detail, your product catalog with categories and attributes, and storefront event tracking if you want on-site placements as well.

See the Recommendation Engine on data like yours

A short walkthrough on sample data, including how attach-rate lift is measured before and after rollout.

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