A model learns from what your customers actually bought together and in sequence, and produces a ranked set of next offers per person. Stock, margin, eligibility and exclusion rules are applied on top, because the statistically best offer for an out-of-stock product is a bad offer. The result is delivered where your team already works — the ESP, the site, the CRM — and measured against whatever you rank with today.
The homepage block, the email module and the CRM campaign all show the top sellers. Customers who bought them last month get shown them again, and the block earns almost nothing.
A merchandiser can maintain cross-sell rules for a hundred products, not ten thousand. So the long tail is never offered to anyone, and it quietly becomes dead stock.
Recommendation blocks get credit for revenue that would have happened anyway. Without a comparison against a simple popularity baseline, an expensive engine can perform worse than a sorted list.
Purchases, repeats, returns, browsing where you have it, and the product attributes that make items comparable. Returns matter: an offer that gets sent back is a cost, not a conversion.
Collaborative filtering and gradient-boosted ranking are trained and then held against the honest baseline — bestsellers, and last-bought-again. We report the difference before anything ships.
Stock, margin floors, eligibility, subscription state, category exclusions and anything a regulator requires. The model proposes; your rules dispose, and the rules are visible and editable.
Ranked offers land in your ESP for lifecycle emails, in on-site blocks through an API, and as fields in the CRM for the sales team. A recommendation nobody can send is a research project.
A share of customers keeps seeing the old logic. Revenue per customer between the two groups is the number we report monthly, and it is the number that decides whether the engine stays.
We build in your stack rather than moving you onto ours. The list below is what this solution most often connects to — other systems are a scoping question, not a blocker.
Segmentation groups people; this ranks products for one person. They stack well — segments decide the campaign, the engine decides what goes in it for each recipient.
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