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.

Learning Curve: A Simulation-Based Approach to Dynamic Pricing

Abstract: By employing dynamic pricing, sellers have the potential to increase their revenue by selling their goods at prices customized to the buyers' demand, the market environment, and the seller's supply at the moment of the transaction. As dynamic pricing becomes a necessary competitive maneuver, and as market mechanisms become more complex, there is a growing need for software agents to be used to automate the task of implementing instantaneous price changes. But prior to using dynamic pricing agents, sellers need to understand the implications of agent pricing strategies on their marketplaces. The following article presents the Learning Curve Simulator, a market simulator designed for analyzing agent pricing strategies in markets under finite time horizons and fluctuation buyer demand. Through an in-depth description of the simulator's capabilities and an example of strategy analysis, we demonstrate the strength of a simulation-based approach to understanding agent pricing strategies.

Speed Matters: The Role of Free Software Offer in Software Diffusion

Abstract: Many software products are available free of charge. While the benefits resulting from network externality have been examined in the related literature, the effect of free offer on the diffusion of new software has not been formally analyzed. We show in this study that even if other benefits do not exist, a software firm can still benefit from giving away fully functioning software. This is due to the accelerated diffusion process and subsequently the increased net present value of future sales. By adapting the Bass diffusion model to capture the impact of free software offer, we provide a methodology to determine the optimal number of free adopters. We show that the optimal free offer solution depends on the discount rate, the length of the demand window, and the ratio of low-valuation to high-valuation free adopters. Our methodology is shown to be applicable for both fixed and dynamic pricing strategies.

A Model of Consumer Inertia with Applications to Dynamic Pricing

Abstract: This paper introduces a decision model of consumer inertia. Consumers exhibit inertia when they have an inherent bias to delay purchases. Inertia may induce consumers to wait even when it is optimal to buy immediately. We embed our decision model within a dynamic pricing context. There is a firm that sells a fixed capacity over two time periods to an uncertain number of both rational and inertial consumers. We find that consumer inertia has both positive and negative effects on profits: it decreases demand (in period one) but intensifies competition among consumers for the product (in period two). We show that our model of inertia is consistent with well-established behavioral regularities, such as loss aversion and probability weighting in the sense of prospect theory, and hyperbolic time preferences. We offer practical recommendations for firms to influence the level of consumer inertia. These include offering returns policies (to mitigate potential consumer losses), providing decision aids (to avoid perception errors), and offering flexible payment options (to lower transaction costs).

Optimal Toll Design Problem in Dynamic Traffic Networks with Joint Route and Departure Time Choice

Abstract: Road pricing is one of the market-based traffic control measures that can influence travel behavior to alleviate congestion on roads. This paper addresses the effects of uniform (constant, fixed) and time-varying (step) tolls on the travel behavior of users on the road network. The problem of determining optimal prices in a dynamic traffic network is considered by applying second-best tolling scenarios imposing tolls only to a subset of links on the network and considering elastic demand. The optimal toll design problem is formulated as a bilevel optimization problem with the road authority (on the upper level) setting the tolls and the travelers (on the lower level) who respond by changing their travel decisions (route and departure time choice). To formulate the optimal toll design problem, the so-called mathematical program with equilibrium constraints (MPEC) formulation was used, considering the dynamic nature of traffic flows on the one hand and dynamic pricing on the other. Until now, the MPEC formu...

A Novel Model for Competition and Cooperation among Cloud Providers

Abstract: Having received significant attention in the industry, the cloud market is nowadays fiercely competitive with many cloud providers. On one hand, cloud providers compete against each other for both existing and new cloud users. To keep existing users and attract newcomers, it is crucial for each provider to offer an optimal price policy which maximizes the final revenue and improves the competitive advantage. The competition among providers leads to the evolution of the market and dynamic resource prices over time. On the other hand, cloud providers may cooperate with each other to improve their final revenue. Based on a service level agreement, a provider can outsource its users’ resource requests to its partner to reduce the operation cost and thereby improve the final revenue. This leads to the problem of determining the cooperating parties in a cooperative environment. This paper tackles these two issues of the current cloud market. First, we solve the problem of competition among providers and propose a dynamic price policy. We employ a discrete choice model to describe the user’s choice behavior based on his obtained benefit value. The choice model is used to derive the probability of a user choosing to be served by a certain provider. The competition among providers is formulated as a non-cooperative stochastic game where the players are providers who act by proposing the price policy simultaneously. The game is modelled as a Markov Decision Process whose solution is a Markov Perfect Equilibrium. Then, we address the cooperation among providers by presenting a novel algorithm for determining a cooperation strategy that tells providers whether to satisfy users’ resource requests locally or outsource them to a certain provider. The algorithm yields the optimal cooperation structure from which no provider unilaterally deviates to gain more revenue. Numerical simulations are carried out to evaluate the performance of the proposed models.