Recep Yusuf Bekci, Stefanus Jasin, Joline Uichanco · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28708 · open-access PDF
As grandes cadeias de varejo devem adaptar as decisões de assortimentos e preços às preferências dos clientes locais, mas mesmo os varejistas com registros de vendas extensos geralmente têm dados limitados para estimar a demanda no nível da loja ou do mercado local, particularmente em categorias de produtos com janelas de vendas curtas.Estimativa local (ajustando um modelo de demanda para cada loja por conta própria) pode, portanto, ser pouco confiável, enquanto a agregação de dados entre lojas melhora a precisão da estimativa ao custo de ignorar a heterogeneidade da demanda local.
Machine translation (Meta M2M100); the English original follows.
Large retail chains must tailor assortment and pricing decisions to local customer preferences, yet even retailers with extensive sales records often have limited data for estimating demand at the store or local market level, particularly in product categories with short selling windows. Local estimation (fitting a demand model for each store on its own) can therefore be unreliable, while pooling data across stores improves estimation precision at the cost of ignoring local demand heterogeneity. We study how retailers can balance these competing effects through transfer learning under a multin
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