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LLM simulates fashion demand for H&M pricing decisions

Researchers have developed a novel LLM-powered virtual population model designed to simulate customer demand for pricing decisions, particularly for products with rich, unstructured information like text descriptions and images. This model represents customers as personas, using an LLM to predict purchase probabilities based on persona and product details. Tested on an H&M fashion dataset, the framework demonstrated superior predictive performance and supported efficient, risk-aware pricing strategies by providing a full demand distribution rather than just a point forecast. AI

IMPACT This LLM application offers a new method for demand simulation and pricing in e-commerce, especially for products with rich descriptions.

RANK_REASON The cluster describes an academic paper published on arXiv detailing a new LLM application.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Chengpiao Huang, Kaizheng Wang ·

    LLM-Powered Virtual Population for Demand Simulation and Pricing

    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 ma…

  2. arXiv cs.CL TIER_1 English(EN) · Kaizheng Wang ·

    LLM-Powered Virtual Population for Demand Simulation and Pricing

    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 als…