High-Frequency Pricing at Scale for E-Commerce

电商领域的大规模高频定价

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. 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.

摘要： 本文介绍了一款专为时尚电商促销活动设计的“先预测、后优化”算法定价工具的设计、开发与实现过程。促销活动为定价带来了独特的挑战，包括波动的需求模式、快速的定价决策需求，以及平衡短期收入与长期盈利能力的必要性。

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.

我们描述了一种结合了基于梯度提升树（Gradient-Boosted Trees）的日级需求预测与多目标优化框架的方法，该框架旨在为超过 500 万种商品实现长期利润和商品净值（NMV）的最大化。我们的解决方案通过实施“先预测、后优化”的架构，解决了现有周级粒度系统中的关键局限性，将定价决策时间从数小时缩短至数分钟。

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.

我们通过欧洲领先的在线时尚零售商 Zalando 在 2023-2024 年促销活动期间进行的 23 次 A/B 测试，验证了该方法。实验结果表明，与此前的人工与算法混合模式相比，新的定价系统在保持销售额和营收表现相当的前提下，利润提升了约 6%。基于这些结果，该算法已成功部署至生产环境，目前承担了该公司促销活动中大部分的算法定价决策工作。