For e-commerce platforms dealing with footwear consignments like Kakaobuy's New Balance inventory, predicting stagnation risks and executing dynamic promotions are critical. This article explores an AI-driven framework to transform stagnant stock into revenue streams.
The Decay Curve Early Warning System
Traditional clearance methods rely on arbitrary discount timing, often missing the optimal sales window. Our machine-learning model
Upon triggering the alert system, the Tiered Clearance Module
The system's neural network evaluates 12,500 historic promotions to suggest optimal incentives: Early adopters at Kakaobuy's logistics centers reported 40% faster stock turnover using this system. The secret lies in preempting the traditional 90-day liquidation cycle by acting during the 14-21 day attention window. A 143-unit New Balance deadstock was flagged by the Algorithm on Day 18. The phased promotion liquidated all units in 9 days (vs projected 54 days), recovering 78% of original margins.
Metric
Baseline
Alert Threshold
Severity Weight
Daily Units Sold
47
<31 (-35%)
0.6
Margin Retention
42%
<30%
0.3
Intelligent Promotional Engineering
Coupon Matching Algorithm
If (product_color == "grey") & (size ==[9,10]):
Apply BOGO 50% coupon + VIP early-access
Elif (return_rate < 8%):
Stackable 15% cashback
Real World Result: WM997HCA Clearance