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.

翻译：本文介绍了为时尚电商销售活动设计、开发并部署的一种专用预测-优化算法定价工具。销售活动带来的独特挑战包括需求模式波动剧烈、定价决策需快速响应，以及需平衡短期收入与长期盈利能力。我们描述了结合梯度提升树的日粒度需求预测与多目标优化框架的方法，该框架能对超过500万件商品同时最大化长期利润与净商品价值。我们的解决方案通过实现预测-优化架构，克服了现有周粒度系统的关键局限，将定价决策时间从数小时缩短至数分钟。我们通过2023-2024年期间在欧洲领先的在线时尚零售商Zalando的12个市场进行的23项A/B测试验证了该方法。实验结果表明，与先前的手动-算法混合方法相比，新定价系统在保持销售额和收入相当的同时，利润提升了约6%。基于这些结果，该算法已成功部署到生产环境，目前负责该公司销售活动中大部分的算法定价决策。