arXiv:2606. 13741v1 Announce Type: new Abstract: This paper presents the design, development, and implementation of a specialized forecast-then-optimize algorithmic pricing tool for sales campaigns in fashion e-commerce.
arXiv:2606. 26787v1 Announce Type: cross Abstract: Traditional dynamic pricing models in large-scale e-commerce suffer from limited interpretability, poor utilization of unstructured information, and misalignment with long-term business objectives such as cumulative Gross Merchandise Value (GMV), Return on Investment (ROI) and milestone achievement.
arXiv:2609.14718v1 Announce Type: new Abstract: Demand forecasting is critical in modern industry, offering opportunities to reduce costs and gain competitive advantage through improved inventory man...
arXiv:2606. 16183v1 Announce Type: cross Abstract: We develop an LLM-powered virtual population model that simulates demand for pricing decisions, in settings where products are described by rich unstructured information, such as text descriptions and images, and where decision makers need not only mean-demand predictions but also uncertainty estimates for counterfactual prices.
arXiv:2607. 13331v1 Announce Type: new Abstract: Retail demand forecasts are reused across replenishment, capacity, labor, and transportation planning cycles.
The paper extends the Expected Cost-per-Mille (eCPM) framework to handle auction and hybrid "Auction with Buy It Now" (ABIN) listings by deriving a marginal eCPM (meCPM) that captures the incremental value of showing an additional impression for items whose prices evolve dynamically. This unified ranking objective allows fixed-price, auction, and ABIN listings to be compared and ranked together. A production implementation was tested via online A/B experiments on a large e-commerce platform, yielding positive revenue gains and statistically significant improvements in user metrics, leading to deployment in production.
arXiv:2607. 16354v1 Announce Type: cross Abstract: Retail demand forecasting remains difficult when demand shifts faster than static forecasting models can be retrained, especially in early demand cycles where newly observed labels are sparse.
arXiv:2608. 12674v1 Announce Type: new Abstract: Maintaining price consistency and executing an Every Day Low Price strategy is critical for global retailers.
arXiv:2609.16952v1 Announce Type: cross Abstract: Feature-based multi-product pricing uses customer characteristics to identify demand heterogeneity and tailor prices across products. Choice model tr...
arXiv:2608. 12680v1 Announce Type: cross Abstract: Item demand forecasting is an integral component of store assortment optimization.
The paper introduces LLP, a Large Language Model–based generative framework for pricing second‑hand products on consumer‑to‑consumer platforms. LLP retrieves similar items to capture market dynamics, then uses LLMs to generate price suggestions, refined through supervised fine‑tuning and group relative policy optimization. A confidence‑based filter rejects unreliable predictions, and experiments show LLP outperforms prior methods, achieving higher static adoption rates when deployed on Xianyu.
arXiv:2606. 11118v1 Announce Type: new Abstract: We study a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers in a discrete-time setting.
arXiv:2607. 09956v1 Announce Type: new Abstract: Pricing food products to balance profitability with consumer welfare is a central challenge for retailers.