Context-based dynamic pricing with online clustering

• 成果类型: Article
• 署名作者: Miao, Sentao; Chen, Xi; Chao, Xiuli; Liu, Jiaxi; Zhang, Yidong
• 署名单位: McGill University; New York University; University of Michigan System; University of Michigan; Amazon.com; Alibaba Group
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13783
• 发表日期: 2022
• 页码: 3559-3575
• 关键词: Dynamic pricing; low-sale product; online clustering; Regret Analysis

摘要

We consider a context-based dynamic pricing problem of online products, which have low sales. Sales data from Alibaba, a major global online retailer, illustrate the prevalence of low-sale products. For these products, existing single-product dynamic pricing algorithms do not work well due to insufficient data samples. To address this challenge, we propose pricing policies that concurrently perform clustering over product demand and set individual pricing decisions on the fly. By clustering data and identifying products that have similar demand patterns, we utilize sales data from products within the same cluster to improve demand estimation for better pricing decisions. We evaluate the algorithms using regret, and the result shows that when product demand functions come from multiple clusters, our algorithms significantly outperform traditional single-product pricing policies. Numerical experiments using a real data set from Alibaba demonstrate that the proposed policies, compared with several benchmark policies, increase the revenue. The results show that online clustering is an effective approach to tackling dynamic pricing problems associated with low-sale products.