# High-Frequency Pricing at Scale for E-Commerce

arXiv: https://arxiv.org/abs/2606.13741 · HTML: https://mlsanity.com/paper/2606.13741/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 11 Jun 2026.

## Builds on (most cited first, 7 of 7)

- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [From Predictive to Prescriptive Analytics](https://mlsanity.com/paper/1402.5481/) (2014)
- [Deep Learning based Forecasting: a case study from the online fashion industry](https://mlsanity.com/paper/2305.14406/) (2023)
- [Promotheus: An End-to-End Machine Learning Framework for Optimizing Markdown in Online Fashion E-commerce](https://mlsanity.com/paper/2207.01137/) (2022)
- [Hierarchical Forecasting at Scale](https://mlsanity.com/paper/2310.12809/) (2023)
- [Price Optimization in Fashion E-commerce](https://mlsanity.com/paper/2007.05216/) (2020)
- [Tricks from the Trade for Large-Scale Markdown Pricing: Heuristic Cut Generation for Lagrangian Decomposition](https://mlsanity.com/paper/2404.02996/) (2024)

## Abstract (arXiv)

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.