DeftMerch use cases by commerce journey
Practical ways commerce teams can test ai merchandising that sorts itself across high-value shopper journeys.
How does DeftMerch sort collections per shopper?
DeftMerch scores every product in a collection against the current shopper: their browsing behavior, price band, size or variant affinity, and purchase history. Business data like inventory depth, margin, and sell through targets shapes the ranking alongside shopper fit. The result is a collection page that leads with what this visitor is most likely to buy while still moving the inventory the business needs to move.
What control do merchandisers keep?
Merchandisers define the strategy, and the engine executes it. Controls include pinned products at fixed positions, boosted categories or attributes, demotions for low stock or low margin items, and global rules like always rank new arrivals in the top rows for loyalists. Every ranking decision is explainable, and a visual preview shows how any collection looks for different shopper types before changes go live.
Use case for new visitors
Use landing context and in-session behavior to reduce choice overload while a shopper is still anonymous. Keep the default experience as a control and avoid assumptions that the available signals cannot support.
Use case for returning customers
Use prior consented interactions and current session intent to reduce repeated discovery work. Do not let old behavior override a shopper who is clearly exploring something new.
Use case for high-intent traffic
Help shoppers compare and decide without adding unnecessary discounts or distractions. Measure completed purchases and margin, not only engagement with the personalized block.
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