Recep Yusuf Bekci, Stefanus Jasin, Joline Uichanco · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28708 · open-access PDF
बड़े खुदरा श्रृंखलाओं को स्थानीय ग्राहकों की प्राथमिकताओं के अनुरूप श्रृंखला और मूल्य निर्धारण निर्णयों को अनुकूलित करना होगा, फिर भी व्यापारी के पास व्यापक बिक्री रिकॉर्ड के साथ अक्सर स्टोर या स्थानीय बाजार स्तर पर मांग का अनुमान लगाने के लिए सीमित डेटा होता है, विशेष रूप से छोटे बिक्री विंडो के साथ उत्पाद श्रेणियों में।
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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