Retail markdown optimization: from reactive markdowns to proactive

Summary

Retail markdown optimization addresses the gap between changing demand signals and delayed merchandising action. The post defines it as strategically reducing prices on slow-moving or end-of-life inventory, using demand forecasts, sell-through rates, weeks of supply, and price-elasticity models to choose which SKUs to mark down, when to start, how deep to discount, and where to act. It presents Databricks Genie as a natural-language interface across e-commerce, store, and wholesale data, with supplier, margin, and historical-pattern context; one example is Coop’s Microsoft Teams assistant, AskCap, which reported a 30% retention rate among internal users. Earlier detection of sell-through deceleration can give merchants more time to protect margin, adjust open-to-buy, and reallocate capital, while Genie supports decisions rather than making them.

Takeaways

1. 1

   Markdown optimization uses demand forecasts, sell-through rates, weeks of supply, and price elasticity to determine which SKUs to discount, when to begin, how deeply to cut prices, and where to act.

2. 2

   A single SKU may require different markdown decisions by store or cluster because it can be overstocked in one region while selling well in another.

3. 3

   Coop’s AskCap assistant, built with Databricks Genie and embedded in Microsoft Teams, reported a 30% retention rate among internal users.