5 research outputs found
Context-Based Dynamic Pricing with Online Clustering
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 products 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 and allow for better pricing decisions. We
evaluate the algorithms using the 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. Our algorithms were
further implemented in a field study at Alibaba with 40 products for 30
consecutive days, and compared to the products which use business-as-usual
pricing policy of Alibaba. The results from the field experiment show that the
overall revenue increased by 10.14%
Revenue Maximization and Learning in Products Ranking
We consider the revenue maximization problem for an online retailer who plans
to display a set of products differing in their prices and qualities and rank
them in order. The consumers have random attention spans and view the products
sequentially before purchasing a ``satisficing'' product or leaving the
platform empty-handed when the attention span gets exhausted. Our framework
extends the cascade model in two directions: the consumers have random
attention spans instead of fixed ones and the firm maximizes revenues instead
of clicking probabilities. We show a nested structure of the optimal product
ranking as a function of the attention span when the attention span is fixed
and design a -approximation algorithm accordingly for the random attention
spans. When the conditional purchase probabilities are not known and may depend
on consumer and product features, we devise an online learning algorithm that
achieves regret relative to the approximation
algorithm, despite of the censoring of information: the attention span of a
customer who purchases an item is not observable. Numerical experiments
demonstrate the outstanding performance of the approximation and online
learning algorithms
Data-Driven Optimization in Revenue Management: Pricing, Assortment Planning, and Demand Learning
This dissertation studies several problems in revenue management involving dynamic pricing, assortment selection, and their joint optimization, through demand learning. The setting in these problems is that customers’ responses to selling prices and product displays are unknown a priori, and the only information the decision maker can observe is sales data. Data-driven optimizing-while-learning algorithms are developed in this thesis for these problems, and the theoretical performances of the algorithms are established. For each algorithm, it is shown that as sales data accumulate, the average revenue achieved by the algorithm converges to the optimal.
Chapter 2 studies the problem of context-based dynamic pricing of online products, which have low sales. 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 products 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. Simulations with both synthetic and real data from Alibaba show that our algorithm performs very well, and a field experiment at Alibaba shows that our algorithm increased the overall revenue by 10.14%.
Chapter 3 investigates an online personalized assortment optimization problem where customers arrive sequentially and make their choices (e.g., click an ad, purchase a product) following the multinomial logit (MNL) model with unknown parameters. We develop several algorithms to tackle this problem where the number of data samples is huge and customers’ data are possibly high dimensional. Theoretical performance for our algorithms in terms of regret are derived, and numerical experiments on a real dataset from Yahoo! on news article recommendation show that our algorithms perform very well compared with benchmarks.
Chapter 4 considers a joint assortment optimization and pricing problem where customers arrive sequentially and make purchasing decisions following the multinomial logit (MNL) choice model. Not knowing the customer choice parameters a priori and subjecting to a display capacity constraint, we dynamically determine the subset of products for display and the selling prices to maximize the expected total revenue over a selling horizon. We design a learning algorithm that balances the trade-off between demand learning and revenue extraction, and evaluate the performance of the algorithm using Bayesian regret. This algorithm uses the method of random sampling to simultaneously learn the demand and maximize the revenue on the fly. An instance-independent upper bound for the Bayesian regret of the algorithm is obtained and numerical results show that it performs very well.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155268/1/semiao_1.pd