At Kakaobuy, we've revolutionized clearance inventory management for footwear resellers through machine learning-driven sales forecasting. Our system provides 72-hour early warnings for stagnating products like New Balance sneakers before they become dead stock.
The 4-Stage Intervention Mechanism
- Phase 1:
- Phase 2:
- Phase 3:
- Phase 4:
- Phase 2:
| Risk Level | Daily Sales Drop | Suggested Discount | Marketing Channel |
|---|---|---|---|
| Yellow | 15-24% | 10-15% off | Email only |
| Orange | 25-34% | Flash sale (20% off) | Mobile push + Email |
| Red | ≥35% | Tiered discounts | All channels + Recommendation engine |
Model Training Methodology
Our algorithm analyzes:
- Historical sell-through rates of comparable SKUs
- Seasonal demand patterns beyond statistical averages
- Price elasticity curves specific to athletic footwear
- Cross-category cannibalization effects
New Balance 550 Implementation Example
When detecting a 38% midweek sales drop for this bestselling model, the system executed three strategic markdown waves:
- Day 1:
- Day 3:
- Day 5:
- Day 3:
Proactive price optimization has helped Kakaobuy partners
"Machine learning turns sales data into predictive power - we now start promotions before customers realize they want discounts."