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.

Dynamic Pricing of Limited Inventories When Customers Negotiate

Abstract: Although take-it-or-leave-it pricing is the main mode of operation for many retailers, a number of retailers discreetly allow price negotiation when some haggle-prone customers ask for a bargain. At these retailers, the posted price, which itself is subject to dynamic adjustments in response to the pace of sales during the selling season, serves two important roles: (i) it is the take-it-or-leave-it price to many customers who do not bargain, and (ii) it is the price from which haggle-prone customers negotiate down. To effectively measure the benefit of dynamic pricing and negotiation in such a retail environment, one must take into account the interactions among inventory, dynamic pricing, and negotiation. The outcome of the negotiation (and the final price a customer pays) depends on the inventory level, the remaining selling season, the retailer's bargaining power, and the posted price. We model the retailer's dynamic pricing problem as a dynamic program, where the revenues from both negotiation and posted pricing are embedded in each period. We characterize the optimal posted price and the resulting negotiation outcome as a function of inventory and time. We also show that negotiation is an effective tool to achieve price discrimination, particularly when the inventory level is high and/or the remaining selling season is short, even when implementing negotiation is costly.

E-Commerce and Operations Research in Airline Planning, Marketing, and Distribution

Abstract: Many e-commerce principles were pioneered in the airline industry. These include the first business-to-business electronic information exchange and industrywide electronic marketplace. This environment provided unprecedented opportunity for operations research (OR) modeling. By the mid-1980s airlines used customer shopping data to calibrate traveler-demand-and-choice models, analyzed multichannel product-distribution strategies with simulation, and practiced dynamic pricing through yield management. Airlines continue to derive billions of dollars annually from these and derivative models. The availability of reliable, low-cost communications via the Internet is providing new modeling challenges within the airline industry and is also providing similar opportunities in other industries.

Pricing network resources for adaptive applications

Abstract: The Differentiated Services framework (DiffServ) has been proposed to provide multiple Quality of Service (QoS) classes over IP networks. A network supporting multiple classes of service also requires a differentiated pricing structure. In this work, we propose a pricing algorithm in a DiffServ environment based on the cost of providing different levels of services, and on long-term average user resource demand of a service class. We integrate the proposed service-dependent pricing scheme with a dynamic pricing and service negotiation environment by considering a dynamic and congestion-sensitive pricing component. Pricing network services dynamically based on the level of service, usage, and congestion allows a more competitive price to be offered, allows the network to be used more efficiently, and provides a natural and equitable incentive for applications to adapt their service requests according to network conditions. We also develop the demand behavior of adaptive users based on a physically reasonable user utility function. Simulation results show that a congestion-sensitive pricing policy coupled with user rate adaptation is able to control congestion and allows a service class to meet its performance assurances under large or bursty offered loads, even without explicit admission control. Users are able to maintain a stable expenditure, and allowing users to migrate between service classes in response to price increases further stabilizes the individual service prices. When admission control is enforced, congestion-sensitive pricing still provides an advantage in terms of a much lower connection blocking rate at high loads.

Insulated Platform Competition y

Abstract: The externalities that operating system users receive from software developers are among the leading features of those ‘platform’ industries but are rarely incorporated into applied models of imperfect competition. We argue this omission arises from the di culty of collapsing the dynamic pricing characterizing such industries into a static policy analysis model. Given the role these pricing strategies play in coordinating consumer behavior, a theory ignoring them quickly becomes intractable and indeterminate. Postulating that platforms identify and then robustly implement best response allocations, we show platforms play an Insulated Equilibrium that eliminates the need for consumers to coordinate their behavior. This facilitates the analysis of an oligopoly model without unrealistic restrictions imposed for tractability. We use this to illustrate the additional distortion, analogous to that identified by Spence’s (1975) study of a quality-choosing monopolist, arising when platforms determine both their prices and their (externality-driven) level of quality.

The Discovery of Price Responsiveness – A Survey of Experiments Involving Dynamic Pricing of Electricity

Abstract: This paper surveys the results from 126 pricing experiments with dynamic pricing and time-of-use pricing of electricity. These experiments have been carried out across three continents at various times during the past decade. Data from 74 of these experiments are sufficiently complete to allow us to identify the relationship between the strength of the peak to off-peak price ratio and the associated reduction in peak demand or demand response. An “arc of price responsiveness” emerges from our analysis, showing that the amount of demand response rises with the price ratio but at a decreasing rate. We also find that about half of the variation in demand response can be explained by variations in the price ratio. This is a remarkable result, since the experiments vary in many other respects – climate, time period, the length of the peak period, the history of pricing innovation in each area, and the manner in which the dynamic pricing designs were marketed to customers. We also find that enabling technologies such as in-home displays, energy orbs and programmable and communicating thermostats boost the amount of demand response. The results of the paper support the case for widespread rollout of dynamic pricing and time-of-use pricing.