为时尚电商设计高频率定价系统，实现分钟级决策与6%利润提升。
High-Frequency Pricing at Scale for E-Commerce
- 用梯度提升树做日粒度需求预测，结合多目标优化框架。
- 23次实验显示利润提升约6%，销量和收入持平。
- 适合需要快速响应的电商促销场景，尤其大型零售平台。
本文介绍了为时尚电商销售活动设计、开发并实施的一种专用“预测-优化”算法定价工具。销售活动面临需求波动大、决策速度快，且需平衡短期收入与长期利润等挑战。我们的方法结合日粒度需求预测（采用梯度提升树）与多目标优化框架，同时最大化超过500万商品的长期利润与净商品价值。相比现有周粒度系统，该方案通过“预测-优化”架构将定价决策时间从数小时缩短至分钟级。在欧洲领先时尚电商平台Zalando 2023–2024年跨12个市场的23次A/B测试中验证，新系统实现约6%的利润增长，同时保持销量与收入水平不变。基于结果，系统已成功上线生产环境，现主导公司销售活动的大部分算法定价决策。
原文摘要 · Abstract (English)
This paper presents the design, development, and implementation of a specialized forecast-then-optimize algorithmic pricing tool for sales campaigns in fashion e-commerce. Sales events present unique challenges for pricing including volatile demand patterns, rapid pricing decisions, and the need to balance short-term revenue with long-term profitability. We describe our approach combining daily-resolution demand forecasting using gradient-boosted trees with a multi-objective optimization framework that maximizes both long-term profit and net merchandise value for more than 5 million articles. Our solution addresses key limitations of existing weekly-granularity systems by implementing a forecast-then-optimize architecture that reduces pricing decision time from hours to minutes. We validate our approach through 23 A/B tests across 12 markets during 2023-2024 sales campaigns at Zalando, one of Europe's leading online fashion retailers. Experimental results demonstrate that the new pricing system achieves approximately 6% higher profit while maintaining equivalent performance on sales and revenue compared to the previous manual-algorithmic hybrid approach. Based on these results, the algorithm was successfully deployed to production and now handles the majority of algorithmic pricing decisions for sales campaigns at the company.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。