  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.
