Context-Based Dynamic Pricing with Online Clustering
This addresses the challenge of insufficient data for dynamic pricing of low-sale products in e-commerce, offering a practical solution for retailers like Alibaba, though it is incremental by building on existing clustering and pricing methods.
The paper tackles the dynamic pricing problem for low-sale online products by proposing policies that use online clustering to group products with similar demand patterns, improving demand estimation and increasing revenue, as demonstrated with real data from Alibaba.
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 dataset 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.