Retail markdown optimization is the practice of strategically reducing prices on slow-moving or end-of-life inventory to maximize gross margin while clearing stock by a target date. Rather than blanket discounts, optimization uses demand forecasts, sell-through rates, weeks of supply (WOS), and price elasticity models to recommend the right markdown depth on the right SKUs at the right time. Done well, it can lift margin rates versus reactive end-of-season markdowns.

Merchandising decisions sit at the intersection of trend data, inventory position, sell-through velocity, supplier lead times, and competitive pricing signals. Synthesizing all of that simultaneously — for a category with hundreds of SKUs, across dozens of locations — is exactly the kind of challenge where better data access creates outsized impact.

Databricks Genie enables merchandising leaders to interrogate their entire data environment in natural language. A CMO can ask: 'Which categories are showing week-over-week sell-through deceleration greater than 10%, and what's our current inventory cover at current sell-through rates?' That question surfaces in seconds.

Coop, a cooperative retailer owned by over 4 million members, used Databricks Genie to build "AskCap" — an AI-powered assistant embedded in Microsoft Teams that lets employees query enterprise data using plain-language questions. The result: a 30% retention rate among internal users, with managers and executives now getting instant answers on deep store and market share intelligence without touching a single dashboard.