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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 15·5 min read
Demand Transfer Estimation at Scale via Restricted Logit Modeling
TL;DR
A new method for estimating Demand Transfer (DT) coefficients allows for efficient item demand forecasting in large item assortments, improving accuracy in predicting customer behavior. This approach can handle item universes exceeding one million products, enhancing store assortment optimization.
✦ Why It Matters
Implement this DT estimation method to enhance your demand forecasting accuracy for large product assortments today.
Key Takeaways
- Utilize Demand Transfer coefficients for better demand predictions.
- Apply the new method to large item assortments efficiently.
- Leverage historical transaction data for improved accuracy.
- Consider customer substitution behavior in forecasting models.
How It Works
The method estimates Demand Transfer coefficients by analyzing how demand for a target item shifts to other items when the target is removed. This allows for independent demand forecasts to be adjusted based on the relationships between items, improving overall accuracy in large assortments.