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.

Purpose The purpose of this paper is to study the optimal contact policies for customers that belong to the mass affluent market. Design/methodology/approach The authors formulate a stochastic dynamic programming model to determine the optimal frequency of contacts in order to maximize the expected return of the company. Findings The authors show that personalized marketing strategies provide a competitive advantage to companies that contact their customers directly through, for example, phone calls or meetings. The authors show that a threshold policy is only optimal for customers with increasing sensitivity to contact. In all other cases, optimal policies might have a less intuitive structure. The authors also study the importance of the size of the customer database and determine the optimal maximum recency when maintenance costs are present. Practical implications Contact policies should be tailored for each company/industry individually, due to their sensitivity to customers’ purchasing behavior.

This paper examines the ways in which a service provider's policies on pricing and service level affect the size of its customer base and profitability. The analysis begins with the development of a customer behavior model that uses customer satisfaction and depth of relationship as mediators of the impact of price and service level on profitability. Based on this model of customer behavior, the system is analyzed as a queuing network from which the properties of the aggregate population's behavior are derived. The analysis reveals the counterintuitive result that a policy that involves a decrease in prices or an increase in service level may lead to a smaller customer base. However, this policy may also lead to higher profits. The novelty of this result lies in the explanation of the phenomenon: that when the customer base decreases due to a change in prices or service quality, companies may experience gains in profit that result not from a decrease in costs associated with serving fewer customers but from an increase in revenues resulting from the indirect effects of the lower prices or higher level of service on customer behavior. The application of optimization techniques to the model developed in this paper yields optimality conditions through which managers can assess the long-term profitability of their pricing and service-level policies.

A service encounter is an experience that extends over time. Therefore, its effective management must include the control of the timing of the delivery of each of the service's elements and the enhancement of the customer's experience between and during the delivery of the various elements. This paper provides a conceptual framework that links the duration of a service encounter to behaviors that have been shown to affect profitability. Analysis of the framework reveals a wide gap between the behavioral assumptions typically made in operations research (OR) and operations management (OM) models and the state of the art in the marketing and psychology literature. The central motivations behind this paper are (1) to help the OR and OM community bridge this gap by bringing to its attention recent findings from the behavioral literature that have implications for the design of queueing systems for service firms and (2) to identify opportunities for further research.

This paper addresses the problem of how to determine the composition and price of a bundle so as to maximize the total expected profit. To motivate the problem, we use as a setting a high‐tech manufacturing company that operates in a competitive environment, is not a leader in the industry, and is constantly reacting to bundles introduced by the leader. Bundles are sets of components that must meet technical constraints. The company's objective is to build a bundle and offer it in a market where it will compete with other bundles. Consumers purchase the bundle that maximizes their utility after examining all available bundles. The company selection of the bundle's components and its price is made in light of the bundles against which it will be competing and the uncertainty in the consumer choice process. The optimal decision could be found by solving a nonlinear mixed integer program, which is difficult to solve. Instead, we propose an efficient solution procedure to determine the optimal composition of the bundle and the price at which it should be offered. The paper concludes with a brief discussion of extensions of the research to cases that consider multiple segments of customers and/or multiple bundles.

This paper studies optimal pricing policies for a family of substitute perishable products with demand correlation. Potential buyers arrive according to an exogenous stochastic process. At each demand epoch, the arriving customer observes the set of substitute products for which there is still inventory available together with their corresponding prices. Based on this information, the customer either buys one of the available products at the posted price, or leaves the system without purchasing anything. We propose a simple choice model to capture buyers’ purchasing behavior from which a price-sensitive demand function is derived. In this context, we study the seller’s problem of optimally selecting a pricing policy that maximizes expected cumulative revenues over a finite selling horizon.

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This paper proposes a framework for analysing admission controls as pricing schemes for shared services. Token bucket admission-control mechanisms are considered as pricing schemes. To analyse the buyer's problem of choosing optimal parameters for token bucket schemes, an important performance metric of token bucket mechanisms is considered, the long-run probability of being denied service, which is equivalent to the threshold-crossing probability of two-sided or one-sided regulated, random walks. For the buyer's problem, the paper gives approximations for these hard-to-calculate metrics, shows that the problem is convex under mild assumptions, and provides a closed-form solution to an approximation of the problem when demand is normally distributed.

In this paper, we present a performance analysis of a 2-dimensional preemptive priority queueing system with state-dependent arrivals. Using a Markovian formulation we first compute the steady state distribution for the queue length of both classes. Then, waiting times and busy periods are characterized through (i) first and second moments and (ii) the approximation of their cumulative distribution functions (cdf) and Laplace–Stieltjes transforms (LST). We derive these approximations connecting bounds in the Laplace domain with bounds on the original time domain. We also, study the behavior of the inter-departure time for each class. Finally, we conclude the paper with a set of computational experiments testing our results.

In this paper we discuss the concept of tradeoff curves in the context of the design of manufacturing systems that can be represented as open queuing networks. These curves are a characteristic of a system and allow us to understand the tradeoffs among different performance measures. We review the algorithms in the literature to derive these curves and illustrate their application in evaluating the efficiency in the system, in deciding how much capacity to have, how to allocate resources between the reduction of uncertainty and the introduction of new technologies, and how to assess the impact of changes in product throughput and product mix. The methodology is illustrated with an example derived from an actual application in the semiconductor industry.

In this paper we propose a methodology to set prices of perishable items in the context of a retail chain with coordinated prices among its stores and compare its performance with actual practice in a real case study. We formulate a stochastic dynamic programming problem and develop heuristic solutions that approximate optimal solutions satisfactorily. To compare this methodology with current practices in the industry, we conducted two sets of experiments using the expertise of a product manager of a large retail company in Chile. In the first case, we contrast the performance of the proposed methodology with the revenues obtained during the 1995 autumn-winter season. In the second case, we compare it with the performance of the experienced product manager in a “simulation-game” setting. In both cases, our methodology provides significantly better results than those obtained by current practices.

In this paper we review the literature on product development from a services perspective. We identify similarities in the creation and evolution of products and services, and discuss three types of knowledge that are commonly required in a development process: the sequence of steps or procedural plan that must be followed; the understanding of what components integrate the design and how they interact (architectural knowledge); and the principles and models that describe physical or human behavior in the system that is being designed. For each step of a generic development process we review the methods and tools that are widely used in product development and may be successfully applied to service development. To illustrate the notion of architectural knowledge in the service context, we introduce an example of a service operation structure and discuss important aspects of its components. Finally we explain the role of models in the development of products and services and argue how they can help design intangible elements. We conclude the paper by identifying gaps in the literature and suggesting directions for future research.

In a service organization, a number of mechanisms may be used to match a limited supply of services with an unpredictable demand for those services. Tactical and operational mechanisms, which actually enhance the organization's performance, may either increase absolute capacity and efficiency, or shift the demand from peak periods to off-peak periods. These mechanisms differ in the complexity of their design and implementation; some require only qualitative analysis while others call for mathematical models or analytic tools like simulation, queuing theory, and mathematical programming. Finally, perceptual mechanisms, which alter only the customer's perceptions of the organization's performance, may also be used to maintain customer satisfaction when delays in service are unavoidable. Most service firms will want to use a mix of these mechanisms; airline companies have used many different mechanisms with favorable results.

This paper studies intertemporal pricing policies when selling seasonal products in retail stores. We first present a continuous time model where a seller faces a stochastic arrival of customers with different valuations of the product. For this model, we characterize the optimal pricing policies as functions of time and inventory. We use this model as a benchmark against which we compare more realistic models that consider periodic pricing reviews. We show that the structure of the optimal pricing policies in this case is consistent with the procedures observed in practice; retail stores successively discount the product during the season and promote a liquidation sale at the end of the planning horizon. We also show that the loss experienced when implementing periodic pricing reviews instead of continuous policies is small when the appropriate number of reviews is chosen. Several interesting economic insights emerge from our analysis. For example, uncertainty in the demand for new products leads to higher prices, larger discounts, and more unsold inventory. Finally, we study the effect of announced discount policies on prices and profits. We show that stores that have adopted this type of strategy usually set prices such that with high probability the merchandise is sold during the first periods and the largest discounts rarely take place.

In this article Gabriel Bitran and Susana Mondschein discuss the most important features of the mailing process in the catalog sales industry. They contrast a ‘conventional’ approach for mailing and inventory decisions, that is recommended in the specialized literature and has been observed in several catalog companies, with an analytical methodology proposed by the authors. They show, that in general, the latter leads to systematically better solutions in terms of the companies' profitability. They also present an optimal method for computing the customer lifetime value and discuss the importance of this concept when making decisions concerning the acquisition of new customers, retention of old customers, and valuation of a catalog company.

Catalog sales are among the fastest growing businesses in the U.S. The most important asset a company in this industry has is its list of customers, called the house list. Building a house List is expensive, since the response rate of names from rental lists is low. Cash management therefore plays a central role in this capital intensive business,This paper studies optimal mailing policies in the catalog sales industry when there is limited access to capital, We consider a stochastic environment given by the random responses of customers and a dynamic evolution of the house list. Given the size of real problems, it is impossible to compute the optimal solutions. We therefore develop a heuristic based on the optimal solutions of simplified versions of the problem. The performance of this heuristic is evaluated by comparing its outcome with an upper bound derived for the original problem. Computational experiments show that it bt haves satisfactorily.The methodology presented permits the evaluation of potential catalog Ventures thus proving useful to entrepreneurs in this industry.