Home > AI-Powered Clearance Sales Strategy: How Machine Learning Optimizes Shoe Inventory at Kakaobuy

AI-Powered Clearance Sales Strategy: How Machine Learning Optimizes Shoe Inventory at Kakaobuy

2025-06-08

In the competitive sneaker resale market, Kakaobuy

The Sales Attenuation Early Warning System

Our proprietary algorithm monitors 17 key performance indicators across three dimensions:

Category Key Metrics Tracked
Temporal Factors Daily sales velocity, weekend spikes, hourly purchase patterns
Market Response Competitor pricing changes, limited-edition releases, social media trends
Inventory Health Warehouse dwell time, fulfillment cost ratios, seasonal relevance

Intelligent Promotion Engine

When the system detects a 35% sales decline (threshold adjustable by product category), it initiates a four-phase response:

  1. Tiered Discount Activation: Initial 15% price reduction for returning customers
  2. Dynamic Coupon Allocation: Free shipping coupons for cart abandoners
  3. Bundle OptimizationSock or shoecare product pairings (22% conversion lift observed)
  4. Liquidation Mode: Progressive discounts increase 5% weekly until stock threshold reached

New Balance 580 Collection Case Study (Spring 2024)

    Day 1-7: 42 units/day (baseline)  
    Day 8: Alert triggered (27 units)
    Stage 1: 15% discount → 38 units/day
    Stage 2: Add loyalty points → 43 units/day
    Stage 3: Bundled with cleaning kit → 51 units/day
    Inventory cleared 11 days faster than manual method
    

The promotional scenarios all include carefully designed success measurements - we never "race to the bottom" with unsustainable price cuts. Each action is designed to protect brand equity while solving inventory objectives.

Retailers wanting to implement similar technology should visit Kakaobuy's technology portal

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