Showing papers on "Dynamic pricing published in 2002"

Dynamic Pricing, Advanced Metering, and Demand Response in Electricity Markets

Abstract: In this monograph, we present an overview and analysis of the possible approaches to bringing an active demand side into electricity markets. In section I, we describe the ways in which economic incentives can be introduced on the demand side. We discuss the fundamental economics of establishing these incentives and the economic loss from systems that lack demand-side participation, and we analyze the effect of these incentives on the efficiency and competitiveness of the market. In section II, we move from the fundamentals to specific issues of implementing time-varying prices. We begin by describing illustrative Realtime Pricing and Critical Peak Pricing tariffs that are in use today. We then address the actual development of dynamic retail prices. In section III, we examine the ways in which customers respond to time-varying and dynamic prices. We discuss both the potential responses that are envisioned by those who study optimization of power use and the actual responses that have taken place in pilot and long-term programs. We conclude in section IV by advocating much wider use of dynamic retail pricing, under which prices faced by end-use customers can be adjusted frequently and on short notice to reflect changes in wholesale prices and the supply/demand balance.

Analyzing Complex Strategic Interactions in Multi-Agent Systems

Abstract: We develop a model for analyzing complex games with repeated interactions, for which a full game-theoretic analysis is intractable. Our approach treats exogenously specified, heuristic strategies, rather than the atomic actions, as primitive, and computes a heuristic-payoff table specifying the expected payoffs of the joint heuristic strategy space. We analyze two games based on (i) automated dynamic pricing and (ii) continuous double auction. For each game we compute Nash equilibria of previously published heuristic strategies. To determine the most plausible equilibria, we study the replicator dynamics of a large population playing the strategies. In order to account for errors in estimation of payoffs or improvements in strategies, we also analyze the dynamics and equilibria based on perturbed payoffs.

Bid Together, Buy Together: On the Efficacy of Group-Buying Models in Internet-Based Selling.

Abstract: In recent years, the advent of electronic commerce has led to the creation of many new and interesting business models for Internet-based selling. In this paper, we will explore a variant of the typical dynamic pricing mechanism, in which buyers and sellers actively engage in the price discovery process, that emphasizes the power of group buying. Dynamic pricing approaches are used by many well known Internet-based firms, including firms that offer online auctions such as eBay and Amazon.com. A group-buying discount is a dynamic pricing mechanism that mimics the general approach of traditional “discount shopping clubs.” Group buying pricing mechanisms permit buyers to aggregate their purchasing power and obtain lower prices than they otherwise would be able to get individually. However, with the recent closing of Mercata.com, a leading group-buying Web site, and the change in strategic direction of another market leader, Mobshop.com, the future of group-buying discount business models in Internet-based selling is no longer clear. In this essay, we will: (1) introduce the innovations associated with group-buying business models in Internet-based selling; (2) characterize the operational aspects of dynamic pricing mechanisms for group-buying through a discussion of a series of mini-cases with different firms that are widely recognized as the innovators in this area; (3) assess the quality of their business models relative to other new business models for Internet-based selling; and (4) draw conclusions about their sustainability in light of competitive forces in the marketplace.

Dynamic Mechanism Design for Online Commerce

Abstract: Motivated by electronic commerce, this paper is a mechanism design study for sellers of multiple identical items. In the market environment we consider, participants are risk neutral and time-sensitive, with the same discount factor; potential buyers have unit demand and arrive sequentially according to a renewal process; and valuations are drawn independently from the same regular distribution. From the Revelation Principle, we can restrict our attention to direct dynamic mechanisms taking a sequence of valuations and arrival epochs as a strategic input. We define two properties (discreteness and stability), and prove that under a regularity assumption on the inter-arrival time distribution, we may at no cost of generality consider only mechanisms satisfying them. This effectively reduces the mechanism input to a sequence of valuations, allowing us to formulate the problem as a dynamic program (DP). Because this DP is equivalent to a well-known infinite horizon asset-selling problem, we can finally characterize the optimal mechanism as a sequence of posted prices increasing with each sale. Our numerical study indicates that, with uniform valuations, the benefit of dynamic pricing over a fixed posted price may be small. Besides, posted prices are preferable to online auctions for a large number of items or high interest rate, but in other cases auctions are close to optimal and significantly more robust.

Applications of flexible pricing in business-to-business electronic commerce

Abstract: The increasingly dynamic nature of business-to-business electronic commerce has produced a recent shift away from fixed pricing and toward flexible pricing. Flexible pricing, as defined here, includes both differential pricing, in which different buyers may receive different prices based on expected valuations, and dynamic-pricing mechanisms, such as auctions, where prices and conditions are based on bids by market participants. In this paper we survey ongoing work in flexible pricing in the context of the supply chain, including revenue management, procurement, and supply-chain coordination. We review negotiation mechanisms for procurement, including optimization approaches to the evaluation of complex, multidimensional bids. We also discuss several applications of flexible pricing on the sell side, including pricing strategies for response to requests for quotes, dynamic pricing in a reverse logistics application, and pricing in the emerging area of hosted applications services. We conclude with a discussion of future research directions in this rapidly growing area.

Downlink power allocation for multi-class CDMA wireless networks

Abstract: We use a utility based power allocation framework in the downlink to treat multi-class CDMA wireless services in a unified way. Our goal is to obtain a power allocation which maximizes the total system utility. Natural utility functions for each mobile are non-concave. Hence we cannot use existing techniques on convex optimization problems to derive a social optimal solution. We propose a simple distributed algorithm to obtain an approximation to the social optimal power allocation. The algorithm is based on dynamic pricing and allows partial cooperation between mobiles and the base station. The algorithm consists of two stages. At the first stage, the base station selects mobiles to which power is allocated, considering their partial-cooperative nature. This is called partial-cooperative optimal selection, since in a partial-cooperative setting and pricing scheme, this selection is optimal and satisfies system feasibility. At the next stage, the base station allocates power to the selected mobiles. This power allocation is a social optimal power allocation among mobiles in the partial-cooperative optimal selection, thus, we call it a partial-cooperative optimal power allocation. We compare the partial-cooperative optimal power allocation with the social optimal power allocation for the single class case. From these results, we infer that the system utility obtained by partial-cooperative optimal power allocation is quite close to the system utility obtained by social optimal allocation.

Pricing on the Internet

Abstract: Conventional theories suggest that the Internet will drive down prices and lead to perfectly competitive prices. However, there is contradictory evidence indicating that online prices are not absolutely lower than offline stores. Regardless, the Internet gives rise to many opportunities for leveraging pricing strategies, in research and testing capabilities, customer segmentation, dynamic pricing, product differentiation, developing brand loyalty, including shipping and handling in the profitability analysis, offering multiple versions, and creating or participating in electronic marketplaces. The trading platform of eBay, Priceline’s reverse auction, and price comparison Web sites are examples of novel Internet pricing models that are helping create a new pricing paradigm.

Dynamic pricing and inventory control with learning

Abstract: Optimal operating policies and corresponding managerial insight are developed for the decision problem of coordinating supply and demand when (i) both supply and demand can be influenced by the decision maker and (ii) learning is pursued. In particular, we determine optimal stocking and pricing policies over time when a given market parameter of the demand process, though fixed, initially is unknown. Because of the initially unknown market parameter, the decision maker begins the problem horizon with a subjective probability distribution associated with demand. Learning occurs as the firm monitors the market's response to its decisions and then updates its characterization of the demand function. Of primary interest is the effect of censored data since a firm's observations often are restricted to sales. We find that the first-period optimal selling price increases with the length of the problem horizon. However, for a given problem horizon, prices can rise or fall over time, depending on how the scale parameter influences demand. Further results include the characterization of the optimal stocking quantity decision and a computationally viable algorithm. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 303–325, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10013

Dynamic Pricing and Lead‐Time Policies for Make‐to‐Order Systems*

Abstract: Make-to-order firms use different approaches for managing their lead-times and pricing in the face of changing market conditions. A particular firm's approach may be largely dictated by environmental constraints. For example, it makes little sense to carefully manage lead-time if its effect on demand is muted, as it can be in situations where leadtime is difficult for the market to gauge or requires investment to estimate. Similarly, it can be impractical to change capacity and price. However, environmental constraints are likely to become less of an issue in the future with the expanding e-business infrastructure, and this trend raises questions into how to manage effectively the marketing mix of price and lead-time in a more “friction-free” setting. We study a simple model of a make-to-order firm, and we examine policies for adjusting price and capacity in response to periodic and unpredictable shifts in how the market values price and lead-time. Our analysis suggests that maintaining a fixed capacity while using lead-time and/or price to absorb changes in the market will be most attractive when stability in throughput and profit are highly valued, but in volatile markets, this stability comes at a cost of low profits. From a pure profit maximization perspective, it is best to strive for a short and consistent lead-times by adjusting both capacity and price in response to market changes.

Mark-down pricing: An empirical analysis of policies and revenue potential at one apparel retailer

Abstract: The results of an analysis of sales and price data from a speciality retailer of women's apparel are reported. The data set contains 184 styles sold during the Spring 1993 season. A demand model similar to those in the existing literature is hypothesised, fit to the data, and then analysed to obtain estimates of revenues under various pricing policies. Both full information and adaptive policies are considered. The optimal prices suggested by the models are compared with those of the study company and the revenues generated by various policies are estimated. The analysis suggests that if the firm had made smaller mark-downs earlier in the sales season, it would have increased its revenues significantly. The results also indicate that model-based pricing schemes can potentially increase revenue by approximately 4 per cent.

Method and apparatus for allocating interviews based on dynamic pricing or auctions using electronic networks

Abstract: An electronic interview auction method for auctioning an interview for a job, wherein the auction is conducted using at least an auctioneer's computer and one or more bidders' computers communicating electronically over a network, wherein a client may be the auctioneer, or the client and auctioneer may be separate. The method contemplates establishing criteria for the auction and storing the criteria on the auctioneer's computer, allowing potential bidders to access the stored criteria from a bidder's computer by electronic communication with the auctioneer's computer, storing in the auctioneer's computer submitted bid information, including relevant job-related information and a monetary bid, automatically selecting one or more winning bids based on the stored monetary bids, and allowing the user to select other bids with monetary values less than the monetary value of the highest bid.

A SLA framework for QoS provisioning and dynamic capacity allocation

Abstract: Any QoS scheme must be designed from the perspective of pricing policies and service level agreements (SLAs). Although there has been enormous amount of research in designing mechanisms for delivering QoS, its applications has been limited due to the missing link between QoS, SLA and pricing. Therefore the pricing policies in practice are very simplistic (fixed price per unit capacity with fixed capacity allocation or pricing based on peak or 95-percentile load etc.). The corresponding SLAs also provide very limited QoS options. This leads to provisioning based on peak load, under-utilization of resources and high costs. We present a SLA based framework for QoS provisioning and dynamic capacity allocation. The proposed SLA allows users to buy a long term capacity at a pre-specified price. However, the user may dynamically change the capacity allocation based on the instantaneous demand. We propose a three tier pricing model with penalties (TTPP) SLA that gives incentives to the users to relinquish unused capacities and acquire more capacity as needed. This work may be viewed as a pragmatic first step towards a more dynamic pricing scenario. We solve the admission control problem arising in this scheme using the concept of trunk reservation. We also show how the SLA can be used in virtual leased-line service for VPNs, and Web hosting service by application service providers (ASPs). Using Web traces we demonstrate the proposed SLA can lead to more efficient usage of network capacity by a factor of 1.5 to 2. We show how this translates to payoffs to the user and the service provider.

Customizing Prices in Online Markets

Abstract: Dynamic pricing is the dynamic adjustment of prices to consumers depending on the value these customers attribute to a good. Underlying the concept of dynamic pricing is what marketers call price customization. Price customization is the charging of different prices to end consumers based on a discriminatory variable. Internet technology will serve as a great enabling tool for making dynamic pricing accessible to many industries.

Dynamic pricing system and method

Abstract: A dynamic pricing system and method that enables prices for sellable objects to be dynamically adjusted using pricing factors corresponding to attributes of the sellable objects. Product administrators are enabled to define sellable products and extended attributes for the products, while pricing administrators are enabled to define price lists for the sellable products and extended attributes. Sales representatives build sellable objects, such as quotes, orders, shopping carts, etc, by adding products that customers would like to purchase to the sellable object. Additionally, extended attributes may be selected for all or a portion of the products. Using Static Pricing, an initial or static price is determined for the sellable objects. The static price may then be dynamically adjusted prior to or at the point of a sales transaction or offer using pricing factors corresponding to combinations of various attributes of the sellable object and/or products from which it is built.

Multilocation Combined Pricing and Inventory Control

Abstract: We consider the problem of managing inventories and dynamically adjusting retailer prices in distribution systems with geographically dispersed retailers. More specifically, we analyze the following single item, periodic review model. The distribution of demand in each period, at a given retailer, depends on the item's price according to a stochastic demand function. These stochastic demand functions may vary by retailer and by period. The replenishment process consists of two phases: In some or all periods, a distribution center may place an order with an outside supplier. This order arrives at the distribution center after an "order leadtime" and is then, in the second phase, allocated to the retailers. Allocations arrive after a second "allocation leadtime."We develop an approximate model that is tractable and in which an optimal policy of simple structure exists. The approximate model thus provides analytically computable approximations for systemwide profits and other performance measures. Moreover, the approximate model allows us to prove how various components of the optimal strategy (i.e., prices and order-up-to levels) respond to shifts in the model parameters, e.g., to shifts in the retailers' demand functions. In addition, we develop combined pricing, ordering, and allocation strategies and show that the system's performance under these strategies is well gauged by the above approximations. We use this model to assess the impact of different types of geographic dispersion on systems with dynamically varying prices and how different system parameters (e.g., leadtimes, coefficients of variation of individual retailers' demand, price elasticities) contribute to this impact. Similarly, we use the model to gauge the benefits of coordinated replenishments under dynamic pricing, and how these benefits increase as the allocation decisions of the systemwide orders to individual retailers are postponed to a later point in the overall replenishment leadtime.We report on a comprehensive numerical study based on data obtained from a nationwide department store chain.

Private Demands and Demands For Privacy: Dynamic Pricing and the Market for Customer Information

Abstract: Consumer privacy and the market for customer information in electronic retailing are investigated. The value of customer information derives from the ability of firms to identify individual consumers and charge them personalized prices. Two settings are studied, a closed privacy regime in which sale of customer information is forbidden and an open privacy regime in which it is permitted. Consumers fare poorly and firms fare well under an open privacy regime when consumers are myopic. In such settings the opportunity to sell information often gives firms incentives to charge 'experimental' prices. When consumers are farsighted relative to firms, however, they may undermine the market for customer information by strategically rejecting offers. In this case, firms are always better off committing to keep customer information private.

Static and Dynamic Pricing of Excess Capacity in a Make-to-Order Environment

Abstract: Recent years have seen advances in research and management practice in the area of pricing, and particularly in dynamic pricing and revenue management. At the same time, researchers and managers have made dramatic improvements in production and supply chain management. The interactions between pricing and production/supply chain performance, however, are not as well understood. Can a firm benefit from knowing the status of the supply chain or production facility when making pricing decisions? How much can be gained if pricing decisions explicitly and optimally account for this status? This paper addresses these questions by examining a make-to-order manufacturer that serves two customer classes - core customers who pay a fixed negotiated price and are guaranteed job acceptance, and "fill-in" customers who make job submittal decisions based on the instantaneous price set by the firm for such orders. We examine four pricing policies that span a range of complexity and required knowledge about the status of the production system at the manufacturer, including the optimal policy of setting a different price for each possible state of the queue. We demonstrate properties of the optimal policy, and we illustrate numerically the financial gains a firm can achieve by following this policy vs. simpler pricing policies. The four policies we consider are (1) state-independent (static) pricing, (2) allowing fill-in orders only when the system is idle, (3) setting a uniform price up to a cut-off state, and (4) general state-dependent pricing. Although general state-dependent pricing is optimal in this setting, we find that charging a uniform price up to a cut-off state performs quite well in many settings and presents an attractive trade-off between ease of implementation and profitability. Thus, a fairly simple heuristic policy may actually out-perform the optimal policy when costs of design and implementation are taken into account.

Differential join prices for parallel queues: social optimality, dynamic pricing algorithms and application to Internet pricing

Abstract: We consider a system of identical parallel queues served by a single server and distinguished only by the price charged at entry. A Poisson stream of customers joins the queue by a greedy policy that minimizes a 'disutility' that combines price and congestion. A special case of linear disutility is analyzed for which it is shown that the individually optimal greedy queue join policy is nearly socially optimal. For this queueing system, a Markov decision theoretic framework is formulated for dynamic pricing in the general case. This queueing system has application in the pricing of Internet services.

Threshold pricing in dynamically priced

Abstract: In a telecommunications system where the price of communications is established by the network operator (“the seller”) in accordance with actual or predicted demand in such fine increments that it becomes “dynamic” from the user's perspective because the prices vary to such a high degree that individual users (“buyers”) are unable to execute a rational economic decision without incurring prohibitively high transaction costs. The present invention provides a method to reduce the transaction costs of such a system by determining and storing individual users' “threshold prices” for a variety of communications and circumstances. By comparing the network operator's offered price for the communication with the user's pre-determined threshold price, and executing a decision automatically based on the results of the comparison, the invention significantly reduces the transaction costs to the user and network operator to the point where dynamic pricing becomes viable and beneficial for both the seller and buyer.

Effect of Pricing Intervals on the Congestion-Sensitivity of Network Service Prices

Abstract: Several proposals have been made for congestionsensitive pricing of the Internet. One key implementation obstacle for these dynamic pricing schemes is the necessity of frequent price updates whereas the structure of wide area networks does not allow frequent price updates for many reasons, such as roundtrip-times are very large for some cases. As the networks allow infrequent price updates, more control is achieved by the pricing schemes with more frequent price updates. So an important issue to investigate is to find a maximum value for the interval (i.e. pricing interval) over which price updates occur, such that the level of congestion control can remain in a desired range. This paper presents our modeling and analysis work for the length of pricing intervals. To represent the level of control over congestion, we use correlation between prices and congestion measures. After developing approximate models for the correlation, we find and prove that the correlation degrades at most inversely proportional to an increase in the pricing interval. We also find that the correlation degrades with an increase in mean or variance of the incoming traffic.

Dynamic Pricing of Information Products Based on Reinforcement Learning: A Yield-Management Approach

Abstract: Pricing of information services gains an increasing importance in an IT environment, which is characterized by more and more decentralized computing resources (e.g. P-2-P computing). Even if pricing theory represents a kernel domain of economic research the pricing problem related to automated information production processes could not be handled satisfactory. This stems from the combination of high fixed costs with negligible variable costs. Especially in airline industries this problem is addressed by heuristics in the so called "Yield Management" (YM) domain. The paper presented here, shows the transferability of these methods to the information production and services domain. Pricing a bundle of complementary resources can not be solved by the simple addition of value functions. Therefore we introduce Machine Learning (ML) techniques to master complexity. Artificial Neural Networks (ANN) are used for the joint representation of the multidimensional value functions and Genetic Algorithms (GA) should help train them in a first effort. While this does not lead to outstanding results, we try Reinforcement Learning (RL) in a second approach. This ML method provides encouraging results for efficient adaptive pricing of resource attribution related to the multidimensional YM problem.

Simplification of network dynamics in large systems

Abstract: We show that significant simplicity can be exploited for pricing-based control of large networks. We first consider a general loss network with Poisson arrivals and arbitrary holding time distributions. In dynamic pricing schemes, the network provider can charge different prices to the user according to the current utilization level of the network and other factors. We show that, when the system becomes large, the performance (in terms of expected revenue) of an appropriately chosen static pricing scheme, whose price is independent of the current network utilization, approaches that of the optimal dynamic pricing scheme. Further, we show that, under certain conditions, this static price is independent of the route the flows take. This indicates that we can use the static scheme, with its much simpler structure, to control large communication networks. We then extend the result to the case of dynamic routing, and show that the performance of an appropriately chosen static pricing scheme, with bifurcation probability determined by average parameters, can also approach that of the optimal dynamic routing scheme when the system is large. Finally, we study the control of elastic flows and show that there exist schemes with static parameters whose performance can approach that of the optimal dynamic resource allocation scheme (in the large system limit). We also identify the applications of our results to QoS routing and rate control for real-time streaming.

Optimal dynamic pricing of railway passenger ticket

Abstract: A passenger ticket pricing system represents the marketing degree of a passenger transportation system. By many years reformation, Chinese railway passenger transportation has changed its fixed pricing system and floating the passenger ticket prices some time. Comparing with west countries, China is behind in this field. More researches and operations have been done in west countries. In this paper, the optimal dynamic pricing policy and the practical policy of Chinese railway passenger ticket are researched. Based on the developing trend of Chinese railway passenger ticket pricing and the western researches, a formulation is given which can be used to find the optimal dynamic pricing policy and the practical policy. These results supply theoretic bases for designing pricing subsystem of the Passenger Ticket Selling and Reserving System of Chinese Railway.

A novel, practical pricing strategy for congestion control and differentiated services

Abstract: Pricing is an effective tool to control congestion and achieve QoS provisioning for multiple differentiated levels of service. In this paper, we propose a practical, flexible and computationally simple pricing strategy that can achieve QoS provisioning in differentiated services networks with multiple priority classes at close to peak efficiency, while also maintaining stable transmission rates from end-users. In contrast to previous work in which dynamic pricing strategies are based on the state of congestion alone, our strategy adds a separate price component for the preferential service received by a packet. In addition, it utilizes a user-centric approach where a user is not charged a higher price unless preferential service is actually delivered.

The Stochastic Knapsack Revisited: Structure, Switch-Over Policies, and Dynamic Pricing

Abstract: The stochastic knapsack originated from a model that studies admission control in telecommunication. In recent years, a variation of the model has become a basic tool in studying problems that arise in revenue management and dynamic/∞exible pricing; and it is in this context that our study is undertaken. Based on a dynamic programming formulation, we identify certain properties of the value function, which lead to a lower- and upper-orthant structure of the optimal policy. This motivates a class of control that we call switch-over policies, in which the switch-over times are optimally derived via convex programming. Based on these policies, we develop pricing models to optimize the price reductions over the decision horizon.

Dynamic pricing: a strategic advantage for electronic retailers

Abstract: We develop an analytical framework to investigate the competitive implications of dynamic pricing technologies (DPT), which enable precise inferring of consumers’ valuations for firms’ products and personalized pricing. These technologies enable first-degree price discrimination: firms charge different prices to different consumers, based on their willingness to pay. We first show that, even though the monopolist makes a higher profit with DPT, its optimal quality is the same with or without DPT. Next we show that in a duopoly setting, dynamic pricing adds value only if it is associated with product differentiation. We then consider a model of vertical product differentiation, and show how dynamic pricing on the Internet affects firms’ choices of quality differentiation in a competitive scenario. We find that when the high quality firm adopts DPT both firms raise their quality. Conversely, when the low quality firm adopts DPT, both firms lower their quality. While it is optimal for the firm adopting DPT to increase product differentiation, the non-DPT firm seeks to reduce differentiation by moving closer in the quality space. Our model also points out firms’ optimal pricing strategies with DPT, which may be non-monotonic in consumer valuations. Finally, we show that consumer surplus is highest when both firms adopt DPT. Thus, despite the threat of first-degree price discrimination, dynamic pricing with competing firms can lead to an overall increase in consumer welfare.