Showing papers on "Dynamic pricing published in 2003"

Dynamic Pricing in the Presence of Inventory Considerations: Research Overview, Current Practices, and Future Directions

Abstract: The benefits of dynamic pricing methods have long been known in industries, such as airlines, hotels, and electric utilities, where the capacity is fixed in the short-term and perishable. In recent years, there has been an increasing adoption of dynamic pricing policies in retail and other industries, where the sellers have the ability to store inventory. Three factors contributed to this phenomenon: (1) the increased availability of demand data, (2) the ease of changing prices due to new technologies, and (3) the availability of decision-support tools for analyzing demand data and for dynamic pricing. This paper constitutes a review of the literature and current practices in dynamic pricing. Given its applicability in most markets and its increasing adoption in practice, our focus is on dynamic (intertemporal) pricing in the presence of inventory considerations.

Commissioned Paper: An Overview of Pricing Models for Revenue Management

Abstract: In this paper, we examine the research and results of dynamic pricing policies and their relation to revenue management. The survey is based on a generic revenue management problem in which a perishable and nonrenewable set of resources satisfy stochastic price sensitive demand processes over a finite period of time. In this class of problems, the owner (or the seller) of these resources uses them to produce and offer a menu of final products to the end customers. Within this context, we formulate the stochastic control problem of capacity that the seller faces: How to dynamically set the menu and the quantity of products and their corresponding prices to maximize the total revenue over the selling horizon.

Revenue Management and E-Commerce

Abstract: We trace the history of revenue management in an effort to illustrate a successful e-commerce model of dynamic, automated sales. Our discourse begins with a brief overview of electronic distribution as practiced in the airline industry, emphasizing the fundamental role of central reservation and revenue management systems. Methods for controlling the sale of inventory are then introduced along with related techniques for optimization and forecasting. Research contributions and areas of significant research potential are given special attention. We conclude by looking at how revenue management is practiced outside of the airline industry, its relationship to dynamic pricing, and future directions for the discipline.

Dynamic pricing in internet retail: Effects on consumer trust

Abstract: Individual-level price discrimination, while not a new idea, is more than a theoretical possibility in the Internet age Economic theory argues that dynamic pricing (ie, individual-level price discrimination) is inherently good for the profitability of the firm, because it allows the firm to capture a larger share of the consumer surplus, but anecdotal evidence from recent retail experiments with Internet-based dynamic pricing suggests that consumers react strongly against this practice Using a two-dimensional conceptualization of trust, based on benevolence and competence trust, the current experiment examines how the experience of a dynamic pricing event and the direction of the pricing discrimination (ie, whether one is offered the higher or the lower price) affects both the mean levels of trust and the weight given to the separate trust dimensions in the formation of overall trust Because demand-based pricing, such as dynamic pricing, is generally considered unfair, it is expected that trust levels will be lower and that more weight will be given to benevolence trust Results show that mean benevolence trust is significantly lower (which leads to a marginal decrease in overall trust) and the weight given to benevolence trust in the formation of overall trust substantially increases The direction-of-price-discrimination effects are generally unsupported © 2003 Wiley Periodicals, Inc

Dynamic pricing in the presence of inventory considerations: research overview, current practices, and future directions

Abstract: The beneÞts of dynamic pricing methods have long been known in industries, such as airlines, hotels and electric utilities, where the capacity is Þxed in the short-term and perishable. In recent years, there has been an increasing adoption of dynamic pricing policies in retail and other industries as well, where the sellers have the ability to store inventory. Three factors contributed to this phenomenon: the increased availability of demand data, the ease of changing prices due to new techologies, and the availability of decision-support tools for analyzing demand data and for dynamic pricing. This paper constitutes a review of the literature and current practices in dynamic pricing. Given its applicability in most markets and its increasing adoption in practice, our focus is on dynamic (intertemporal) pricing in the presence of inventory considerations. (Dynamic Pricing; E-commerce; Revenue Management; Inventory)

An Overview of Pricing Models for Revenue Management

Abstract: In this paper, we examine the research and results of dynamic pricing policies and their relation to Revenue Management. The survey is based on a generic Revenue Management problem in which a perishable and non-renewable set of resources satisfy stochastic price-sensitive demand processes over a finite period of time. In this class of problems, the owner (or the seller) of these resources uses them to produce and offer a menu of final products to the end customers. Within this context, we formulate the stochastic control problem of capacity that the seller faces: how to dynamically set the menu and the quantity of products and their corresponding prices in order to maximize the total revenue over the selling horizon.

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.

Integrated dynamic pricing and procurement support for e-commerce advertising channels

Abstract: The present invention provides a virtual system that assists in the procurement of advertising on an Internet vendor site for the sale of products or services. The system links to a user's financial package to get data on the products or services and allows the user to set financial parameters based on the desired financial goals related to the product and advertising. Performance data regarding advertising is accessed and financial rules generated which are applied to generate a target price for advertising or one or more products. The system can acquire advertising automatically or assist in the submission of bids in an auction of advertising. In a preferred embodiment, keywords are purchased on a search engine in an auction.

The Internet as an enabler for dynamic pricing of goods

Abstract: The Internet offers the potential for dynamic pricing for a wide range of products across the supply chain. Dynamic pricing can be formally defined as the buying and selling of goods in markets where prices move quickly in response to supply and demand fluctuations. Unlike physical markets where change occurs slowly because of information delays, change occurs very rapidly on the Internet. In the marketplace, the Internet is a powerful tool for almost instantaneous consumer feedback. For example, prices can be changed dynamically to meet demand because the cost of changing a price may be lower on the Internet than in physical markets. The success of dynamic pricing is helping in the growth of new businesses, including broad-based e-commerce portals new interactive networks. This paper has several objectives. The first objective is to look at factors that affected the use of dynamic pricing in the past. The second objective is to summarize the notion of dynamic pricing over the Internet. The third objective is to examine the different methods for collecting dynamic demand data over the Internet. The final objective is to present two models to optimize the revenue obtained for build-to-forecast and build-to-order environments.

Incentive mechanisms for peer-to-peer systems

Abstract: Most of the existing research in peer-to-peer systems focuses on protocol design and doesn’t consider the rationality of each peer. One phenomenon that should not be ignored is free riding. Some peers simply consume system resources but contribute nothing to the system. In this paper we present an agent-based peer-to-peer system, in which each peer is a software agent and the agents cooperate to search the whole system through referrals. We present a static and a dynamic pricing mechanism to motivate each agent to behave rationally while still achieving good overall system performance. We study the behavior of the agents under two pricing mechanisms and evaluate the impact of free riding using simulations.

Supply Chain Design and Planning – Applications of Optimization Techniques for Strategic and Tactical Models

Abstract: Publisher Summary This chapter describes the optimization models that effectively address the coordination of various decisions concerning the planning and design of the supply chain, and are the promising foundations for the development of Decision Support Systems in this field The chapter focuses on three different problem areas: (1) production/distribution systems, (2) pricing to improve supply chain performance, and (3) logistics network design In the production/distribution systems, the models that are designed to determine the appropriate production, inventory, and transportation policies for a set of manufacturing plants, warehouses, and retailers, are reviewed The chapter extends dynamic pricing techniques to a more general supply chain setting with nonperishable inventory Specifically, the pricing, production, and inventory decisions are considered simultaneously in a finite and an infinite horizon single product environment The objective is to maximize the profit under conditions of periodically varying inventory holding and production costs, and price sensitive, stochastic demand

Revenue Management and Pricing: Case Studies and Applications

Abstract: Introduction, using the book. Cases. 1. Revenue management basics in the charter boat industry. 2. EasyJet: an airline that changed our flying habits. T 3. The Wedding Bell Blues. 4. The Right Price Consultants. 5. Revenue Management in Restaurants. 6. Dynamic Pricing of Distillate Products at Petroleum Terminals. 7. Free Nelson Mandela? 8. The politics & pricing of culture within society. 9. Sex & Saunas. 10. Hotel Demand/Cancellation Analysis and Estimation of Unconstrained Demand using statistical methods. 11. Bolton Wanderers: a case of good practice in the football industry? 12. Unconstraining Demand Data at US Airways. 13. Revenue Management in the Health Care Industry. 14. Revenue Management in Visitor Attractions. 15. To Trust or Not to Trust: variable pricing and the customer. 16. Cases in Legal Aspects. 17. Understanding the Bid Price Approach to Revenue Management.

A Machine-Learning Approach to Optimal Bid Pricing

Abstract: We consider the problem faced by a seller in determining an optimal price to quote in response to a Request for Quote (RFQ) from a prospective buyer. The optimal price is determined by maximizing expected profit, given the underlying seller costs of the bid items and a computed probability of winning the bid as a function of price and other bid features such as buyer characteristics and the degree of competition. An entropy-based information-gain metric is used to quantify the contribution of the extracted features to predicting the win/loss label. A naive Bayes classification model is developed to predict the bid outcome (win or loss) as a function of these features. This model naturally generates the win probability as a function of bid price required to compute the optimal price. Results obtained by applying this model to a database of bid transactions involving computer sales demonstrate statistically significant lift curves for predicting bid outcome. A method for creating additional synthetic bids to improve computation of the win probability function is demonstrated. Finally, the computed optimal prices generated via this approach are compared to the actual bid prices approved by human pricing experts.

Dynamic Pricing and Investment from Static Proxy Models

Abstract: This paper evaluates the use of static cost proxy models in setting forward-looking prices such as the prices set according to the FCC’s TELRIC methodology. First, it compares the time paths of prices and depreciation under traditional regulatory accounting with the prices and depreciation implied by various versions of TELRIC. When TELRIC prices are recomputed at intervals shorter than asset lives, the firm will generally not earn the target rate of return. In these cases, a correction factor must be applied to the TELRIC price path in order for revenues to exactly recover investment cost, including the target rate of return. Next, the paper considers a firm’s cost minimizing investment decisions under two different assumptions about asset obsolescence. In both scenarios, cost minimizing investment paths and implied utilization rates for the firm’s assets are derived under a variety of assumptions about the relevant input parameters. Some implications for TELRIC pricing are then derived.

Smart Pricing: Linking Pricing Decisions with Operational Insights

Abstract: textThe past decade has seen a virtual explosion of information about customers and their preferences This information potentially allows companies to increase their revenues, in particular since modern technology enables price changes to be effected at minimal cost At the same time, companies have taken major strides in understanding and managing the dynamics of the supply chain, both their internal operations and their relationships with supply chain partners These two developments are narrowly intertwined Pricing decisions have a direct effect on operations and visa versa Yet, the systematic integration of operational and marketing insights is in an emerging stage, both in academia and in business practice This article reviews a number of key linkages between pricing and operations In particular, it highlights different drivers for dynamic pricing strategies Through the discussion of key references and related software developments we aim to provide a snapshot into a rich and evolving field

An optimization problem with dynamic route-departure time choice and pricing

Abstract: Dynamic pricing is a strategy that has not yet been widely applied in the area of dynamic traffic management; this might be due to the fact that a time-varying toll has not yet been widely accepted by the authorities. The increasing congestion is therefore pushing the trend to use whatever strategy in order to alleviate delays. The use of Dynamic Network Loading (DNL) and Dynamic Traffic Assignment (DTA) permits one to estimate travel times during different periods of the day and above all during congestion. This allows one to calculate the dynamic path disutilities faced by travelers when these consist of travel time and dynamic tolls. This study combines two avenues of modeling innovation. Firstly it relaxes the assumption of fixed departure times during the evaluation periods. It does so by including a departure time choice model that uses the time dependent path disutility calculated by a Dynamic Traffic Assignment model which incorporates the DNL. Secondly it integrates this with the problem of finding the dynamic tolls that minimize the total system cost. It does so by solving a bilevel optimization in which tolls are set in an upper level problem (aimed at minimizing the system cost), and the dynamic user-optimal response of the transport system (in terms of route choice and departure-time choice) is calculated in a lower level problem. Software was prepared to solve the upper level of the bilevel problem iteratively, at each step determining the user-optimal response of the lower level. The simplification leads the problem to a single level problem in which the strategy is iteratively adapted to the demand. Consequently, this paper aims at finding consistency of user response with respect to pricing and to show how, in some cases pricing is beneficial for the societal purposes

Dynamic pricing of inventory with cancellation demand

Abstract: When an inventory item has such a limited selling period that only a single supply order can be placed to satisfy future demand, a decision-maker must determine the quantity of the order to meet future demand and how to price this stock. Although this problem has received considerable attention, related investigations typically view the demand and selling price as exogenous parameters and assume that customers cannot cancel an order or return the product after purchasing the item. Pricing is, however, an important pervasive marketing vehicle that affects demand, and customers indeed cancel or return their orders after placing them. The newsboy problem is extended here so that demand is price-dependent and customers may cancel their orders. This paper seeks to develop decision rules to maximize the total expected profit over a given planning period. Analysis results demonstrate the feasibility of applying the order-up-to structure to yield the order quantity.

Shattering the Myth of Costless Price Changes: Emerging Perspectives on Dynamic Pricing

Abstract: In this paper, we argue that pricing is all about price changes, and that the costs of price changes are often simultaneously subtle and substantial. We discuss a framework to deal with the dynamics of changing prices. This framework incorporates customer interpretations of price changes, an awareness of the organizational costs of price changes, investments in future pricing processes, and an understanding of the role that supply chains play in price change strategy. The framework can be used at the tactical level to improve the specific price changes chosen and made, at the managerial level to decide whether or not to make a particular price change at all, and at the strategic level to determine what price adjustment processes should be invested in to improve pricing effectiveness in the future.

Reinforcement learning applications in dynamic pricing of retail markets

Abstract: In this paper, we investigate the use of reinforcement learning (RL) techniques to the problem of determining dynamic prices in an electronic retail market. As representative models, we consider a single seller market and a two seller market, and formulate the dynamic pricing problem in a setting that easily generalizes to markets with more than two sellers. We first formulate the single seller dynamic pricing problem in the RL framework and solve the problem using the Q-learning algorithm through simulation. Next we model the two seller dynamic pricing problem as a Markovian game and formulate the problem in the RL framework. We solve this problem using actor-critic algorithms through simulation. We believe our approach to solving these problems is a promising way of setting dynamic prices in multi-agent environments. We illustrate the methodology with two illustrative examples of typical retail markets.

Teaching Pricing and Revenue Optimization

Abstract: Pricing and revenue optimization, defined as the formulation and solution of tactical pricing decisions using constrained optimization, is becoming an increasingly popular subject to be taught at the MBA level. Perhaps the best-known example of pricing and revenue optimization is revenue management whereby airlines, hotels, and other companies seek to maximize operating contribution by opening and closing fare classes. However, a number of other important business problems including markdown management, dynamic pricing for e-commerce, and customized pricing are also parts of pricing and revenue optimization. This article gives an overview of the field for those who are considering teaching the topic to MBA students. I outline the scope of the area, describe specific topics that can be included, present a sample syllabus and provide some guidance based on my own experience and links to additional resources.

A Network Effects Perspective on Software Piracy

Abstract: The software industry frequently maintains that software piracy is no more than theft and a cause of huge losses. Courts and other policy makers easily adopt that straightforward argument. Surprisingly, however, many software publishers do not employ any technological measures to protect their software from piracy and many popular software products are distributed without any means of protection and are easily pirated. This lack of protection is puzzling; a solution for that puzzle is the focus of the article. The first part of the article challenges the conventional explanations that are usually given for the failure to protect software and offers an alternative explanation. The paper argues that not protecting software is a profitable strategy based on several elements: the first element is cross-sectional price discrimination in which the lower tiers of customers do not pay for their software. In the face of network effects that exist in many software markets, such a strategy achieves the most expeditious and widest dissemination of software, maximizes the value of the network, may accelerate the tipping of the market in favor of the more dominant publisher and later create higher barriers to entry. The article analyzes the advantages of such implicit price discrimination over explicit price discrimination and the advantages from complaining about piracy over preventing it. The second element is dynamic pricing in a multiple-period setting. At a second stage, due to a lock-in phenomenon, software publishers are able to hold-up potential ex-pirates who face a threat of litigation and charge them higher prices. According to the basic copyright paradigm each pirated copy is a net loss for the copyright holder. According to the proposed network theory some level of piracy generates higher revenues from other customers and over time. The second part of the article explores whether and to what extent legal doctrines, particularly in antitrust and copyright law should respond to these counter-paradigmatic circumstances.

Dynamic Consumer Profiling and Tiered Pricing Using Software Agents

Abstract: Shopbots or software agents that enable comparison shopping of items from different online sellers have become popular for quick and easy shopping among online buyers. Rapid searches and price comparison by shopbots have motivated sellers to use software agents called pricebots to adjust their prices dynamically so that they can maintain a competitive edge in the market. Existing pricebots charge the same price for an item from all of their customers. Online consumers differ in their purchasing preferences and, therefore, a seller's profit can be increased by charging two different prices for the same good from price-insensitive and price-sensitive consumers. In this paper, we present an algorithm that partitions the buyer population into different segments depending on the buyers' purchase criteria and then charges a different price for each segment. Simulation results of our tiered pricing algorithm indicate that sellers' profits are improved by charging different prices to buyers with different purchase criteria. Price wars between sellers that cause regular price fluctuations in the market, are also prevented when all the sellers in the market use a tiered pricing strategy.

An auction pricing strategy for differentiated service networks

Abstract: We use pricing as an effective strategy to allocate network resources in an efficient way so as to maximize a service provider's revenue. Among all static and dynamic pricing strategies, an auction approach is a widely proposed decentralized mechanism. We propose a scenario where all clients can bid for their required bandwidth as well as the price they are willing to pay. The service provider decides on the admission price and differentiated service provided for each class. These thresholds also provide a future reference for admitting new flows later.

Dynamic pricing and reinforcement learning

Abstract: We consider the problem of optimizing sales revenues based on a parametric model in which the parameters are unknown. The manager has to set the price at a level in order to maximize current revenues and at the same time learn about the parameter values to increase the future revenues. Both demand and price are assumed to be continuous variables. We study several different strategies for learning and show that a one-step look-ahead rule produces good short term performance.

Bundling and pricing for information brokerage: customer satisfaction as a means to profit optimization

Abstract: Traditionally, the study of online dynamic pricing and bundling strategies for information goods is motivated by the value-extracting or profit-generating potential of these strategies. Here we discuss the relatively overlooked potential of these strategies to online learn more about customer's preferences. Based on this enhanced customer knowledge an information broker can - by tailoring the brokerage services more to the demand of the various customer groups - persuade customers to engage in repeated transactions (i.e., generate customer lock-in). To illustrate the discussion, we show by means of a basic consumer model how, with the use of online dynamic bundling and pricing algorithms, customer lock-in can occur. The lock-in occurs because the algorithms can both find appropriate prices and (from the customer's perspective) the most interesting bundles. In the conducted computer experiments we use an advanced genetic algorithm with a niching method to learn the most interesting bundles efficiently and effectively, brokerage; recommender systems.

Method and system of range-based floating pricing for electronic transaction

Abstract: A method and system that allows users to trade items over the Internet with dynamic pricing model is disclosed. The method and system enables a seller to post an item for sale with dynamic pricing settings. The dynamic pricing settings comprise price range, pricing algorithm and price-updating interval Over the item's listing period, the method and system automatically schedule to run the pricing algorithm to update the listing price every price-updating interval and the listing price is floating within the price range. A buyer can place an order on an item and finish the transaction at anytime before the end of the listing period. A buyer can trade directly online or set up an agent to conduct trading on behalf of the buyer.

Meeting QoS Requirements in a Dynamically Priced Commercial Cellular Network

Abstract: With the current trend towards user mobility and ubiquitous computing, cellular networks are becoming an evermore important feature of day-to-day life, albeit an unseen one. Mobile networks are characterised by a scarcity of resources, particularly bandwidth and frequency spectrum. However, for new multimedia applications, such as video telephony, a large amount of these resources is required and, moreover, these applications demand that specific quality of service guarantees are met by the network at all times. In a cellular network, the traffic is highly variable both temporally and spatially. Therefore, dimensioning a network so that it can meet peak-time demand is both uneconomic and inefficient, as most of the time the network will be under-utilised. This leads to frequent and significant congestion in mobile networks, so that, at a certain time and place, users may find it impossible to start a phone call, or an ongoing phone call may be interrupted. Some solutions have been proposed to alleviate the problem of congestion without installing new infrastructure. However, these schemes only improve the network performance for some incoming traffic rates, but cannot meet QoS guarantees at peak-times. Another possible solution to this problem is to attempt to modify the user demand to fit the available resource. This leads to dynamic pricing: charging users according to the current traffic conditions, hence providing negative or positive incentives to regulate the traffic entering the network. As they know the price they will be charged, users can decide whether to make the phone call or not, and the importance of the call will influence their choice. Hence dynamic pricing leads to a natural prioritisation of calls, ensuring that only low priority calls are blocked. Dynamic pricing has been applied successfully in several domains, but its application to cellular networks is an emerging research area and is particularly challenging due to the mobility of users. This project investigates dynamic pricing in cellular networks from a technical perspective. For this purpose, detailed network and traffic models are defined based on an investigation of current research in related areas. These models are implemented in a simulator which is then used to test, refine and improve existing dynamic pricing schemes for both GSM and GSM/GPRS networks.