Assortment & Merchandising Optimizer
Purpose
Continuously improve store-level assortments and digital merchandising rules by learning from sales, margin, shopper behavior, and local context to drive sell-through and category profitability.
Primary users
Merchandising and category managers, retail planning teams, eCommerce merchandising teams, and marketing/campaign planning teams.
Where it fits (process/stage/trigger)
Runs on a recurring cadence (daily/weekly) and before key trading moments (seasonal transitions, promo waves, range resets), triggered by performance drift, inventory constraints, or campaign planning cycles.
Key capabilities / workflow
Ingests store/SKU performance and behavioral signals, validates data completeness, forecasts local demand, quantifies substitution and halo effects, optimizes assortments under space and supply constraints, runs store-cluster test-and-learn, then feeds measured learnings into both merchandising updates and marketing planning.
Inputs
Store/SKU sales and margin, footfall and basket data, local demographics, seasonality signals, inventory and supply constraints, online search/browse logs, and promotion history.
Outputs / Deliverables
Assortment change recommendations by store/cluster, digital ranking and badging rules, localized marketing inputs (featured products and themes), and performance impact summaries with measured uplift.
Value
Improves sell-through and category profit, increases local relevance, reduces stockouts and overstocks through constraint-aware decisions, and tightens alignment between merchandising actions and marketing plans via a closed feedback loop.
