Fashion Brand
Promotion Adjustment Rate Prediction for e-Commerce Channel
The fast-fashion e-Commerce industry faces intense competition from rapid trend shifts, seasonal demand and omnichannel sales. Key challenges include predicting promo sales, optimizing SKU inventory and real-time supply chain alignment.
Core pain points are ineffective forecasting, traditional methods using generic models lack SKU-level granularity, causing >30% errors and data scarcity for new products, leading to “cold start” issues and biased manual forecasting.
Core Problems Solved
The solution uses machine learning to predict the “Adj Rate” for promotional periods, enabling data-driven planning for sales, inventory and supply chain. By analyzing historical sales, promotional activities and market trends, the system provides accurate forecasts to optimize promotional strategies and inventory allocation.
Key Features of the Solution
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Multi-Model for
Adj Rate Prediction
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Application of
AutoML and Feature Engineering
Achievements and Benefits
20%
Prediction Accuracy:
Reduced MAPE by 15-20%, with SKU-level forecasts achieving 70% accuracy.
12%
Inventory Efficiency:
Optimized inventory turnover by 12%, reducing overstocking costs by $2.4 million annually.
60%
Operational Cost:
Cut manual forecasting labor by 60%, with AutoML reducing model development time from 3 months to 10hrs.
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