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High-Frequency Pricing at Scale for E-Commerce

2026-06-11 · Stefan Birr, Tobias Huelden, Mones Raslan, Adele Gouttes, Andreas Schmitt, Mateusz Koren, Johannes Stephan, Robert Streek, Manuel Kunz, Tim Januschowski 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.

📄 PDF Abstract BibTeX arXiv:2606.13741

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AIGP: An LLM-Based Framework for Long-Term Value Alignment in E-Commerce Pricing

2026-06-25 · Chennan Ma, Yanning Zhang, Siqi Hong, Xiuchong Wang 외 arxiv

Traditional dynamic pricing models in large-scale e-commerce suffer from limited interpretability, poor utilization of unstructured information, and misalignment with long-term business objectives such as cumulative Gros…

Knowledge Distillation Reinforcement Learning

Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning

2018-09-27 · Jiaxi Liu, Yidong Zhang, Xiaoqing Wang, Yuming Deng 외

In this paper we develop an approach based on deep reinforcement learning (DRL) to address dynamic pricing problem on E-commerce platform. We models real-world E-commerce dynamic pricing problem as Markov Decision Proce…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Thompson Sampling for Dynamic Pricing

2018-02-08 · Ravi Ganti, Matyas Sustik, Quoc Tran, Brian Seaman

In this paper we apply active learning algorithms for dynamic pricing in a prominent e-commerce website. Dynamic pricing involves changing the price of items on a regular basis, and uses the feedback from the pricing dec…

Active Learning Thompson Sampling

Anomaly Detection for an E-commerce Pricing System

2019-02-25 · Jagdish Ramakrishnan, Elham Shaabani, Chao Li, Mátyás A. Sustik

Online retailers execute a very large number of price updates when compared to brick-and-mortar stores. Even a few mis-priced items can have a significant business impact and result in a loss of customer trust. Early det…

Anomaly Detection Model Selection Outlier Detection Supervised Anomaly Detection

Dynamic Pricing in Securities Lending Market: Application in Revenue Optimization for an Agent Lender Portfolio

2024-07-18 · Jing Xu, Yung-Cheng Hsu, William Biscarri

Securities lending is an important part of the financial market structure, where agent lenders help long term institutional investors to lend out their securities to short sellers in exchange for a lending fee. Agent len…