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.

Context

Weekly batch reports can leave merchants working with outdated sales information. By the time a trend shifts, inventory may be heavier than planned, leaving less selling time and forcing deeper markdowns. Decisions also require simultaneous analysis of sell-through, inventory, supplier lead times, competitive pricing, and regional performance.

Approach / What changed

The post describes using Databricks Genie to query commerce data in natural language. Genie brings together e-commerce, store, and wholesale data with supplier lead times, fill rates, margin context, and historical patterns, helping leaders assess which SKUs, timing, discount depth, and locations warrant action. Coop embedded an AI assistant called AskCap in Microsoft Teams.

Takeaways

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

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