Markdowns
Pricing and discount optimization to recover frozen capital
How it works
Candidate identification
Scans the entire catalog for markdown candidates: products with >60 days supply, declining sales velocity, approaching expiration (for pharma), or flagged as dead stock by the detection module.
Optimal discount calculation
For each candidate, the system tests discount levels (5%–50%) against historical elasticity data. The goal: maximize recovered capital while maintaining a positive margin. No blanket discounts.
Priority ranking
Candidates are ranked by frozen capital × urgency. A $3.3M test strip position with 26K days of supply ranks higher than a $200 cosmetic with 95 days. 812 out of 884 items in our pilot were high priority.
Execution tracking
After markdowns are applied, the system monitors sell-through velocity in real-time. If a discounted item isn't moving fast enough, it escalates with a deeper discount recommendation or a transfer suggestion.
Under the Hood
Urgency = days_on_shelf × frozen_capital. Higher urgency items get deeper discounts and surface first in recommendations.
15–50% based on dead stock criteria and seasonality type. Dead stock → 50%, seasonal overstock → 25%, spot items → 15%.
Estimates recovery at each discount tier. Projects expected cash release and timeline based on historical clearance velocity.
Real-World Example
Real example
812 out of 884 candidates were flagged as high priority. The system recommended tiered discounts that would recover 50% of the frozen capital value within 90 days.