arXiv:2605.22820v1 Announce Type: new Abstract: We propose the Integrable Context-Dependent Demand Network (ICDN), a demand-first neural model for multiproduct retail demand. The model learns log-demand as a smooth, context-conditioned function of log-prices, allowing elasticities to be derived exactly from the learned demand surface. On the Dominick's beer dataset, ICDN improves out-of-sample generalization over a directed log-log benchmark and yields more stable, economically plausible elasticity estimates, especially for weakly identified cross-price effects.
The continuous development in AI and machine learning techniques, particularly in neural networks, enables more sophisticated and granular analysis of complex economic phenomena like demand elasticity.
This development allows retailers and economists to derive more accurate and stable elasticity estimates for multi-product demand, leading to better pricing strategies and resource allocation.
Retailers can now leverage a demand-first neural model to understand customer behavior with greater precision, moving beyond traditional econometric models with less stable cross-price effect estimates.
- · Large multi-product retailers
- · AI/ML model developers
- · Pricing strategists
- · Economic researchers
- · Companies relying on outdated pricing models
- · Retailers with limited data infrastructure
Improved pricing strategies lead to optimized revenue for retailers.
More accurate demand forecasting could reduce waste and improve supply chain efficiency for consumer goods.
Enhanced competition in retail sectors as firms leverage superior demand understanding, potentially driving down consumer prices or increasing product variety.
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Read at arXiv cs.LG