As a leading cross-border e-commerce platform in Asia, Kakaobuy continuously refines its data-driven inventory management methods. This case study showcases our AI-powered system that predicts sales decline patterns and executes smart promotions for footwear categories - notably the recently struggling New Balance line.
Micro-Triggered Early Warning System
Our proprietary machine learning model tracks 17 Key Performance Indicators (KPIs) including:
- Daily sales velocity decline rate (35% threshold trigger)
- Average time-to-cart compared to category benchmarks
- Mobile vs desktop conversion discrepancies
- Warehouse grouping patterns for slow-moving items
When our system detects abnormal indicators like New Balance’s 35%+ daily sales drop (codename: NB23-UG01), the Kakaobuy Inventory Radar
| Risk Level | Days Stock Remaining | Auto-Promotion Type |
|---|---|---|
| Code Yellow (Warning) | 45-60 days | Personalized coupon bundling |
| Code Orange (Critical) | 30-45 days | Multi-tier flash sales with price elasticity testing |
| Code Red (Emergency) | Under 30 days | Last-chance Combo deals + free shipping override |
Precision Promotion Engine
The system's decision matrix evaluates 6 promotional strategies against historical performance data under similar circumstances. For our New Balance case:
- Phase 1:
- Phase 2:
- Final Phase:
- Phase 2:
KPI Improvement After 28 Days:
- ⇧ 68% increase in daily unit sales
- ⇩ 42% reduction in projected warehousing costs
- ⇧ 23% elevation in customer repurchase intent scores
This tech-powered clearance methodology continues evolving - next quarter's upgrade will incorporate weather pattern data and regional sporting events into sales forecasts. Brands interested in partnering with our proactive inventory solution may visit Kakaobuy's Brand Collaboration Portal