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.

Lessons learned from smart grid enabled pricing programs

Abstract: Dynamic pricing is often considered an essential part of demand response programs, particularly when considering the advent of new consumer-facing technologies, which will eventually reshape the relationship between utility and consumer. This paper presents and analyzes case studies of several dynamic pricing programs, including different proposed rates, enabling technologies and incentives. Program successes are evaluated based on a combination of peak load reduction, customer bill impacts and customer satisfaction. An analysis of lessons learned is provided on how various factors can affect the success, scalability and applicability of smart grid demand response programs.

Dynamic Pricing of Perishable Assets under Competition

Abstract: We study dynamic price competition in an oligopolistic market with a mix of substitutable and complementary perishable assets. Each firm has a fixed initial stock of items and competes in setting prices to sell them over a finite sales horizon. Customers sequentially arrive at the market, make a purchase choice and then leave immediately with some likelihood of no-purchase. The purchase likelihood depends on the time of purchase, the product attributes and the current prices. The demand structure includes time-variant linear and MultiNomial Logit demand models as special cases. Assuming deterministic customer arrival rates, we show that any equilibrium strategy has a simple structure, involving a finite set of shadow prices measuring capacity externalities that firms exert on each other: equilibrium prices can be resolved from a one-shot price competition game under the current-time demand structure, taking into account capacity externalities through the time-invariant shadow prices. The former reflects the transient demand side at every moment and the latter captures the aggregate supply constraint over the sales horizon. This simple structure sheds light on dynamic revenue management problems under competition, which helps capture the essence of the problems under demand uncertainty. We show that the equilibrium solutions from the deterministic game provide pre-committed and contingent heuristic policies that are asymptotic equilibria for its stochastic counterpart, when demand and supply are sufficiently large.

Developing User Perceived Value Based Pricing Models for Cloud Markets

Abstract: With the rapid deployment of cloud computing infrastructures, understanding the economics of cloud computing has become a pressing issue for cloud service providers. However, existing pricing models rarely consider the dynamic interactions between user requests and the cloud service provider. Thus, the law of supply and demand in marketing is not fully explored in these pricing models. In this paper, we propose a dynamic pricing model based on the concept of user perceived value that accurately captures the real supply and demand relationship in the cloud service market. Subsequently, a profit maximization scheme is designed based on the dynamic pricing model that optimizes profit of the cloud service provider without violating service-level agreement. Finally, a dynamic closed loop control scheme is developed to adjust the cloud service price and multiserver configurations according to the dynamics of the cloud computing environment such as fluctuating electricity and rental fees. Extensive simulations using the data extracted from real-world applications validate the effectiveness of the proposed user perceived value-based pricing model and the dynamic profit maximization scheme. Our algorithm can achieve up to 31.32 percent profit improvement compared to a state-of-the-art approach.

Pricing strategies on Airbnb: Are multi-unit hosts revenue pros? [Summary]

Abstract: Abstract This study examined the effects of pricing strategies, including price positioning and dynamic pricing, on an Airbnb listing’s revenue with a particular interest on the performance difference between multi-unit and single-unit hosts. A series of econometric analyses were performed using a dataset of 320,243 listings managed by 216,058 hosts in 10 major U.S. markets across a longitudinal period from October 2014 to July 2017. The results suggest while price positioning and dynamic pricing have positive impacts on an Airbnb listing’s revenue performance, a multi-listing host performs better than a single-listing host in driving a listing’s revenue, through (a) positioning a listing at a higher price than the average listing price in a neighborhood and (b) adopting less dynamic pricing strategies. Our study fills the void of pricing research in room-sharing economy literature and generates important insights about the pricing strategies and the consequent performance outcome between two different host types.