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Firms sometimes engage in myopic management (e.g., cutting marketing spending, providing lenient credit to customers to improve short-term results). Although marketing is at the center of such myopic management, there are few insights on whether a marketing department could prevent it. To address this gap, the authors examine the role of powerful marketing departments in preventing myopic marketing spending and revenue management. They hypothesize that there are internal and external enablers of marketing department power (i.e., a chief executive officer with marketing experience, the firm’s power over its customers, analyst coverage, and institutional stock ownership) that help a powerful marketing department prevent myopic management. They test the hypotheses using a panel of 781 publicly listed U.S. firms between 2000 and 2015. As hypothesized, when the firm has (1) a chief executive officer with a marketing background and (2) power over its customers, increasing marketing department power decreases the likelihood of both myopic marketing spending and myopic revenue management; increasing marketing department power and analyst coverage decreases the likelihood of myopic marketing spending. The findings highlight powerful marketing leadership as a hitherto overlooked way to prevent myopic management and improve firm performance.

Traditional revenue management relies on long and stable historical data and predictable demand patterns. However, meeting those requirements is not always possible. Many industries face demand volatility on an ongoing basis, an example would be air cargo which has much shorter booking horizon with highly variable batch arrivals. Even for passenger airlines where revenue management (RM) is well-established, reacting to external shocks is a well-known challenge that requires user monitoring and manual intervention. Moreover, traditional RM comes with strict data requirements including historical bookings (or transactions) and pricing (or availability) even in the absence of any bookings, spanning multiple years. For companies that have not established a practice in RM, that type of extensive data is usually not available. We present a data-driven approach to RM which eliminates the need for demand forecasting and optimization techniques. We develop a methodology to generate bid prices using historical booking data only. Our approach is an ex-post greedy heuristic to estimate proxies for marginal opportunity costs as a function of remaining capacity and time-to-departure solely based on historical booking data. We utilize a neural network algorithm to project bid price estimations into the future. We conduct an extensive simulation study where we measure our methodology’s performance compared to that of an optimally generated bid price using dynamic programming (DP) and compare results in terms of both revenue and load factor. We also extend our simulations to measure performance of both data-driven and DP generated bid prices under the presence of demand misspecification. Our results show that our data-driven methodology stays near a theoretical optimum (< 1% revenue gap) for a wide-range of settings, whereas DP deviates more significantly from the optimal as the magnitude of misspecification is increased. This highlights the robustness of our data-driven approach.

This book focuses on the application of revenue management in the manufacturing industry. Though previous books have extensively studied the application of revenue management in the service industry, little attention has been paid to its application in manufacturing, despite the fact that applying it in this context can be highly profitable and instrumental to corporate success. With this work, the author demonstrates that the manufacturing industry also fulfills the prerequisites for the application of revenue management. The book includes a summary of empirical studies that effectively illustrate how revenue management is currently being applied across Europe and North America, and what the profit potential is -- Backcover.

Many revenue management problems require making capacity control and pricing decisions for multiple products. The decisions for the different products interact because either the products use a common pool of resources or the customers choose and substitute among the products. When pricing airline tickets, for example, different itinerary products use the capacities on common flight legs and the customers choose and substitute among different itinerary products that serve the same origin-destination pair. Finding the optimal capacity control and pricing decisions in such problems can be challenging because one needs to simultaneously consider the capacities available to serve a large pool of products. In “An Approximation Algorithm for Network Revenue Management under Nonstationary Arrivals,” Ma, Rusmevichientong, Sumida, and Topaloglu develop efficient methods to make decisions with performance guarantees in high-dimensional capacity control and pricing problems. We provide an approximation algorithm for network revenue management problems. In our approximation algorithm, we construct an approximate policy using value function approximations that are expressed as linear combinations of basis functions. We use a backward recursion to compute the coefficients of the basis functions in the linear combinations. If each product uses at most L resources, then the total expected revenue obtained by our approximate policy is at least 1 / ( 1 + L ) of the optimal total expected revenue. In many network revenue management settings, although the number of resources and products can become large, the number of resources used by a product remains bounded. In this case, our approximate policy provides a constant-factor performance guarantee. Our approximate policy can handle nonstationarities in the customer arrival process. To our knowledge, our approximate policy is the first approximation algorithm for network revenue management problems under nonstationary arrivals. Our approach can incorporate the customer choice behavior among the products, and allows the products to use multiple units of a resource, while still maintaining the performance guarantee. In our computational experiments, we demonstrate that our approximate policy performs quite well, providing total expected revenues that are substantially better than its theoretical performance guarantee.

This paper presents the results of the second round of the Revenue Administration Fiscal Information Tool (RA-FIT) country survey in an aggregated manner for all respondents and by income group. Notwithstanding regional biases and some data quality issues with the sample, broad insights and trends are discernible from the data, and the results form part of an evolving series that will continue to develop and grow with the International Survey On Revenue Administration (ISORA), the successor survey to RA-FIT conducted by the IMF in collaboration with the Inter-American Center of Tax Administrations (CIAT), the Intra-European Organisation of Tax Administration (IOTA), and the Organisation for Economic Co-operation and Development (OECD). This paper expands on a previous one, which covered the first round of RA-FIT (Lemgruber and others 2015),1 and aims to allow countries to access information about key measures in revenue administration. Unlike the first paper, this one does not cover issues specific to customs administration but focuses rather on tax administration data.

Motivated by the prevalence of ride-sharing platforms, in “Spatial Pricing in Ride-Sharing Networks,” Bimpikis, Candogan, and Saban explore the impact of the demand pattern for rides across a network’s locations on a platform’s optimal pricing and compensation policy, profits, and consumer surplus. They explicitly account for the pricing problem’s spatial dimension and the fact that the drivers endogenously determine whether and where to provide service. Their first contribution is to develop a tractable model to study a platform operating on a network of locations that may differ in both the size of their potential demand and the destination preferences of riders. Second, they provide a characterization of the platform’s optimal policy and identify “balancedness” of the demand pattern as a property that captures the profit potential of a given network. Finally, they discuss the benefits and limitations of a number of alternative pricing and compensation schemes. We explore spatial price discrimination in the context of a ride-sharing platform that serves a network of locations. Riders are heterogeneous in terms of their destination preferences and their willingness to pay for receiving service. Drivers decide whether and where to provide service so as to maximize their expected earnings given the platform’s pricing and compensation policy. Our findings highlight the impact of the demand pattern on the platform’s prices, profits, and the induced consumer surplus. In particular, we establish that profits and consumer surplus at the equilibrium corresponding to the platform’s optimal pricing and compensation policy are maximized when the demand pattern is “balanced” across the network’s locations. In addition, we show that they both increase monotonically with the balancedness of the demand pattern (as formalized by its structural properties). Furthermore, if the demand pattern is not balanced, the platform can benefit substantially from pricing rides differently depending on the location from which they originate. Finally, we consider a number of alternative pricing and compensation schemes that are commonly used in practice and explore their performance for the platform. The e-companion is available at https://doi.org/10.1287/opre.2018.1800 .

Thompson sampling is a randomized Bayesian machine learning method, whose original motivation was to sequentially evaluate treatments in clinical trials. In recent years, this method has drawn wide attention, as Internet companies have successfully implemented it for online ad display. In “Online network revenue management using Thompson sampling,” K. Ferreira, D. Simchi-Levi, and H. Wang propose using Thompson sampling for a revenue management problem where the demand function is unknown. A main challenge to adopt Thompson sampling for revenue management is that the original method does not incorporate inventory constraints. However, the authors show that Thompson sampling can be naturally combined with a linear program formulation to include inventory constraints. The result is a dynamic pricing algorithm that incorporates domain knowledge and has strong theoretical performance guarantees as well as promising numerical performance results. Interestingly, the authors demonstrate that Thompson sampling achieves poor performance when it does not take into account domain knowledge. Finally, the proposed dynamic pricing algorithm is highly flexible and is applicable in a range of industries, from airlines and internet advertising all the way to online retailing. We consider a price-based network revenue management problem in which a retailer aims to maximize revenue from multiple products with limited inventory over a finite selling season. As is common in practice, we assume the demand function contains unknown parameters that must be learned from sales data. In the presence of these unknown demand parameters, the retailer faces a trade-off commonly referred to as the “exploration-exploitation trade-off.” Toward the beginning of the selling season, the retailer may offer several different prices to try to learn demand at each price (“exploration” objective). Over time, the retailer can use this knowledge to set a price that maximizes revenue throughout the remainder of the selling season (“exploitation” objective). We propose a class of dynamic pricing algorithms that builds on the simple, yet powerful, machine learning technique known as “Thompson sampling” to address the challenge of balancing the exploration-exploitation trade-off under the presence of inventory constraints. Our algorithms have both strong theoretical performance guarantees and promising numerical performance results when compared with other algorithms developed for similar settings. Moreover, we show how our algorithms can be extended for use in general multiarmed bandit problems with resource constraints as well as in applications in other revenue management settings and beyond. The online appendix is available at https://doi.org/10.1287/opre.2018.1755 .