High-Frequency Pricing at Scale for E-Commerce
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Computer Science > Machine Learning
Title:High-Frequency Pricing at Scale for E-Commerce
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. 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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.13741 [cs.LG] |
| (or arXiv:2606.13741v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.13741
arXiv-issued DOI via DataCite
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