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Dynamic pricing

About: Dynamic pricing is a research topic. Over the lifetime, 4144 publications have been published within this topic receiving 91390 citations. The topic is also known as: surge pricing & demand pricing.


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Journal ArticleDOI
TL;DR: In this paper, the authors consider a firm (e.g., retailer) selling a single nonperishable product over a finite-period planning horizon and propose a nonparametric data-driven policy that learns about the demand on the fly and applies learned information to determine replenishment and pricing decisions.
Abstract: We consider a firm (e.g., retailer) selling a single nonperishable product over a finite-period planning horizon. Demand in each period is stochastic and price-dependent, and unsatisfied demands are backlogged. At the beginning of each period, the firm determines its selling price and inventory replenishment quantity, but it knows neither the form of demand dependency on selling price nor the distribution of demand uncertainty a priori, hence it has to make pricing and ordering decisions based on historical demand data. We propose a nonparametric data-driven policy that learns about the demand on the fly and, concurrently, applies learned information to determine replenishment and pricing decisions. The policy integrates learning and action in a sense that the firm actively experiments on pricing and inventory levels to collect demand information with the least possible profit loss. Besides convergence of optimal policies, we show that the regret, defined as the average profit loss compared with that of the optimal solution when the firm has complete information about the underlying demand, vanishes at the fastest possible rate as the planning horizon increases.

37 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigate the optimal pricing strategies for a platform selling electronic products when consumers sequentially learn about product quality from consumer reviews, and propose a tractable and flexible framework to support both operational and strategic decision-making processes.
Abstract: Consumer reviews have become pervasive for e-commerce in recent years, especially for electronic products. In this paper, we investigate the optimal pricing strategies for a platform selling electronic products when consumers sequentially learn about product quality from consumer reviews. We focus on the transient analysis to calibrate how information externalities across the time dimension would distort the seller’s optimal pricing strategies. Facing the “cold start” problem, the seller of high-quality products would choose lower prices to speed up the consumer learning process. Consequently, the optimal prices suffer from downward distortions that increase in product quality in this reputation-riding regime. In the extensions, we propose a tractable and flexible framework to support both operational and strategic decision-making processes. We explore the value of persuasive advertisement, and the results suggest that consumer reviews and marketing efforts are strategic substitutes. In terms of quality control, we find that it would be optimal to invest in quality in the early stages, but stop at a certain time threshold, resulting in a reputation-building and reputation-spending pattern. Finally, we extend the framework to study a duopoly pricing problem. We show that the high-quality seller could strategically accommodate the low-quality seller in the early stages, and wages a price war at later stages.

37 citations

Journal ArticleDOI
TL;DR: In this article, the authors consider a periodic review inventory system with regular and expedited supply modes, and they show that if it is optimal to order from both supplies, the optimal inventory policy is determined by two state-independent thresholds, one for each supply mode, and a list price is set for the product; if only the regular supply is used, they conduct a numerical study to assess and compare the respective benefit of dynamic pricing and supply diversification.
Abstract: We consider a periodic-review inventory system with regular and expedited supply modes. The expedited supply is faster than the regular supply but incurs a higher cost. Demand for the product in each period is random and sensitive to its selling price. The firm determines its order quantity from each supply in each period as well as its selling price to maximize the expected total discounted profit over a finite or an infinite planning horizon. We show that, in each period if it is optimal to order from both supplies, the optimal inventory policy is determined by two state-independent thresholds, one for each supply mode, and a list price is set for the product; if only the regular supply is used, the optimal policy is a state-dependent base-stock policy, that is, the optimal base-stock level depends on the starting inventory level, and the optimal selling price is a markdown price that decreases with the starting inventory level. We further study the operational impact of such supply diversification and show that it increases the firm's expected profit, reduces the optimal safety-stock levels, and lowers the optimal selling price. Thus that diversification is beneficial to both the firm and its customers. Building upon these results, we conduct a numerical study to assess and compare the respective benefit of dynamic pricing and supply diversification.

37 citations

Journal ArticleDOI
TL;DR: In this article, the authors argue that the benefits of dynamic pricing are greatest when they most need them, and that price-responsive demand programs require policymakers to understand and accept the insurance aspects of the dynamic pricing.

37 citations

Journal ArticleDOI
TL;DR: Evaluation results confirm that, in the presence of network delay and transmission error, deviation from the desired power load can be limited for a range of number of users and price sensitivity of users.
Abstract: In our fight against global warming and excessive carbon emission, smart power grid has emerged as a useful tool for its ability to integrate renewable energy sources with traditional energy sources in a single distribution system. Smart power grid depends on consumption scheduling and dynamic pricing to match power supply and demand, and to avoid significant fluctuation in power load. A supply demand mismatch or a large deviation in the power load may cause power outage and damage to equipments. In the literature, various dynamic pricing schemes have been proposed to manage and control power load. However, these existing works often assume perfect communication network performance, where pricing information and control messages can be transmitted to remote users without delay and without transmission error. In practice, communication channel is error prone and network delay is not negligible. In view of the situation, this paper begins by studying the effects of network delay and transmission error on achieving a desired power load through dynamic pricing. We find that these communication network impairments may impose a lower bound on price update interval, and an upper bound on price update step size. Based on the findings, we further propose a heuristics algorithm to determine the price update interval and price update step size, for a given requirement of maximum deviation in power load from a desired level. We have evaluated the proposed algorithm through random event simulations. Evaluation results confirm that, in the presence of network delay and transmission error, deviation from the desired power load can be limited for a range of number of users and price sensitivity of users.

37 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023140
2022262
2021307
2020324
2019346
2018314