Showing papers in "Journal of Revenue and Pricing Management in 2019"
TL;DR: In this article, the authors propose a definitional framework for dynamic pricing for the airline industry and provide a general definition of dynamic pricing, and describe three mechanisms for price selection: assortment optimization, dynamic price adjustment, and continuous pricing.
Abstract: The phrase “dynamic pricing” has entered the airline revenue management lexicon in recent years with the emergence of new distribution capabilities that could allow airlines to adjust ticket prices more frequently. Yet each airline, vendor, and academic has a unique (and often incongruous) view of what dynamic pricing encompasses. To date, there has not been a definition of dynamic pricing proposed in the literature that clearly delineates how these mechanisms are different from traditional airline pricing and revenue management. In this paper, we propose a definitional framework for dynamic pricing for the airline industry. After providing a general definition of dynamic pricing, we describe three mechanisms for price selection—assortment optimization, dynamic price adjustment, and continuous pricing. Depending on the implementation and the information available to the airline at the time of pricing, each of these mechanisms could be used to adjust prices relatively infrequently or, at the limit, on a transaction-by-transaction basis. We close by linking recent technological developments in airline pricing, revenue management, and distribution to the three mechanisms introduced in the framework.
22 citations
17 citations
TL;DR: In this paper, a conditional demand forecast error metric is proposed to compare demand forecasts to historical bookings conditional on the set of fare classes that were open at the time of booking.
Abstract: Since accurate demand forecasts are a key input to any airline revenue management system, it is reasonable to assume that an improvement in demand forecast accuracy would lead to increased revenues. However, this relationship has often been called into question. Past work has not conclusively proven that more accurate demand forecasts lead to higher revenue, causing researchers and practitioners to debate whether the concept of demand forecast accuracy itself is “myth or reality.” In this paper, we demonstrate that it is possible to consistently link demand forecast accuracy to airline revenue. After discussing why traditional demand forecast error metrics have struggled to demonstrate this relationship, we evaluate a novel conditional demand forecast error metric which compares demand forecasts to historical bookings conditional on the set of fare classes that were open at the time of booking. We prove under some mild assumptions that minimizing conditional demand forecast error will maximize revenue under any fare structure and customer choice behavior. These theoretical findings are supported by simulations in both a simple, single-leg model and in a complex multiple-airline network in the Passenger Origin–Destination Simulator. We find that price elasticity parameter bias of ± 10% can reduce revenues by up to about 1%, while price elasticity parameter bias of ± 20% can reduce revenues by up to 4%. We close by discussing the implications of the findings for revenue management practitioners.
15 citations
TL;DR: A typology of pricing schemes for cloud services is presented and a decision model based on the perspectives of both cloud providers and corporate customers is developed to maximize the total profit for cloud providers.
Abstract: As cloud computing rapidly penetrates enterprise computing markets, the paradigm of enterprise computing shifts from client–server data centers to cloud-based data centers. While cloud computing is leading technological innovations to a new level, it is challenging for cloud providers to make informed decisions in regard to pricing of their services. This study presents a typology of pricing schemes for cloud services and develops a decision model based on the perspectives of both cloud providers and corporate customers to maximize the total profit for cloud providers. The model is operationalized with an illustration using real pricing data. A sensitivity analysis provides valuable insights into the pricing dynamics between cloud providers and cloud customers.
12 citations
TL;DR: In this paper, the authors tested discounts presented to customers in euros or percentages at three regular price levels (low, medium, or high) and found that the normal (or regular) price influences the discount attractiveness when the discount is presented in absolute (monetary) terms.
Abstract: Generally, discounting prices increases unit sales but decreases profit margins. If presenting a discount more attractively, will profits increase with sales? We tested discounts presented to customers in euros or percentages at three regular price levels (low, medium, or high). Store visitors were surveyed to determine the discount that would make a promoted product attractive to them. Results align with previous literature: the normal (or regular) price influences the discount attractiveness when the discount is presented in absolute (monetary) terms. However, percentage discounts were perceived similarly between price levels. The amount saved was not only considered: the discount attractiveness was also compared to some reference discount. Second: relative and absolute promotions’ references differ: the evaluations in absolute terms led to higher attractive discount levels.
12 citations
TL;DR: In this article, the authors analyze automated repricing strategies with data-driven price anticipations under duopoly competition and derive optimized self-adaptive pricing strategies that anticipate price reactions of the competitor and take the evolution of the reference price into account.
Abstract: Online markets have become highly dynamic and competitive. Many sellers use automated data-driven strategies to estimate demand and to update prices frequently. Further, notification services offered by marketplaces allow to continuously track markets and to react to competitors’ price adjustments instantaneously. To derive successful automated repricing strategies is challenging as competitors’ strategies are typically not known. In this paper, we analyze automated repricing strategies with data-driven price anticipations under duopoly competition. In addition, we account for reference price effects in demand, which are affected by the price adjustments of both competitors. We show how to derive optimized self-adaptive pricing strategies that anticipate price reactions of the competitor and take the evolution of the reference price into account. We verify that the results of our adaptive learning strategy tend to optimal solutions, which can be derived for scenarios with full information. Finally, we analyze the case in which our learning strategy is played against itself. We find that our self-adaptive strategies can be used to approximate equilibria in mixed strategies.
11 citations
TL;DR: In this article, the scientific nature of value based on expectation and information has been clarified, and its link to valuation approaches in real estate, as well as their informational sources of price, cost, and income (PCI).
Abstract: Real estate analysis, in general, and valuation, in particular, are being challenged more and more by the expectational and informational properties of value. Both properties evolve inversely in the state of consciousness; the expectation in events of desire is continuously collapsing into experiences. Expressions for events are the basis of information, used in models to explain and approximate the future expectation in value. The exclusive use of information limits the explicative/predictive capacity of models and thus risky, because they omit the inversely evolving effects of expectation, simultaneously attached to the same event. To reliably fathom the risk, the global reference position of value needs to be kept in perspective as regards the work of the opposing mechanisms of expectation and information. This research clarifies the scientific nature of value based on these mechanisms and in practical terms ascertains its link to valuation approaches in real estate, as well as their informational sources of price, cost, and income (PCI).
10 citations
TL;DR: The extent of applicability and synergies among both menu analysis and revenue management approaches was identified and it was highlighted that as both approaches become more sophisticated, the practicality of implementation will deprive.
Abstract: Menu analysis and revenue management approaches contribute to improving a restaurant’s profitability. Yet, both approaches are often implemented independently with constraints. This paper explores the potential of integrating both approaches to improve strategy formulation. Hence, this paper identified the extent of applicability and synergies among both approaches. Findings highlighted that as both approaches become more sophisticated, the practicality of implementation will deprive. The synergies identified the potential to integrate both approaches’ performance indicators, cost efficiency data and strategies. Understanding the applicability and synergies of both approaches will lay the foundation for an effective integrated menu analysis and revenue management framework.
10 citations
TL;DR: This paper addresses the frequently encountered situation of observing only a few sales events at the individual product level and proposes variants of small demand forecasting methods to be used for unconstraining, which are able to approximate convex, concave or homogeneous booking curves.
Abstract: Sales data often only represent a part of the demand for a service product owing to constraints such as capacity or booking limits. Unconstraining methods are concerned with estimating the true demand from such constrained sales data. This paper addresses the frequently encountered situation of observing only a few sales events at the individual product level and proposes variants of small demand forecasting methods to be used for unconstraining. The usual procedure is to aggregate data; however, in that case we lose information on when restrictions were imposed or lifted within a given booking profile. Our proposed methods exploit this information and are able to approximate convex, concave or homogeneous booking curves. Furthermore, they are numerically robust due to our proposed group-based parameter optimization. Empirical results on accuracy and revenue performance based on data from a major car rental company indicate revenue improvements over a best practice benchmark by statistically significant 0.5–1.4% in typical scenarios.
9 citations
TL;DR: In this article, the authors study overall price (un)fairness as an aggregate of distributive, procedural, informational, and interpersonal justice and study what differences exist between price fairness and unfairness.
Abstract: Price is the cost consumers pay to stay with the manufacturer. In turn, the firms should make the consumers feel that their sacrifice is fair. Marketers should understand the differences between price fairness and unfairness. Most research manipulates causes price unfairness and assumes reverse is fairness. We study overall price (un)fairness as an aggregate of distributive, procedural, informational, and interpersonal justice and study what differences exist between price fairness and unfairness. Consumer’s sense of equity from the transaction and their understanding of the pricing policy contribute equally toward creating price fairness perceptions. Violation of equity has the greatest impact on price unfairness perceptions, followed by consumer's inability to understand procedures. For services, both distributive and procedural dimensions are equally important. Consumers display stronger preference to stay with the service provider in case of fair price perceptions than their preference to leave because of unfairness. Customers show a status quo bias. Positive communication influences them to repurchase.
8 citations
TL;DR: In this paper, the authors investigated the relationship between booking lead time and hotel room rates while controlling for various booking, room and hotel characteristics, and found that early bookings are associated with the highest room rates, while late bookings have the lowest ones.
Abstract: This paper investigates the relationship between booking lead time and hotel room rates while controlling for various booking, room and hotel characteristics. Data are based on big data drawn from a hotel reservation database covering about 123,000 bookings over a 5-year period. Quantile estimations for online bookings show that early bookings are associated with the highest room rates, while late bookings have the lowest ones. For lower priced rooms of leisure guests booked offline, there is a U-shaped relationship with the lowest prices booked between 10 and 24 days before the check-in day. Similarly for business guests, a U-shaped pattern can be found for high-priced bookings. Overall, price variations between bookings at different points in time range between 10% for external online bookings and up to 28% for offline bookings of leisure guests. The results for hotel bookings stand in contrast to empirical evidence for airfares and train tickets.
TL;DR: In this paper, the authors present Van Westendorp price sensitivity model to assess the range of acceptable price as well as the optimal prices for 2GB and 5GB data packs to understand the consumer's willingness to pay (WTP) for 4G data services.
Abstract: The recent launch of 4G in India initiated disruption in telecom sector with price wars. Knowing the consumers’ willingness to pay (WTP) is vital step toward the survival in such a turbulent situation. We present Van Westendorp price sensitivity model to assess the range of acceptable price as well as the optimal prices for 2 GB and 5 GB data packs to understand the consumer’s WTP for 4G data services. The price sensitivity meter identifies the acceptable range and optimal price points. The estimates obtained are similar to the current prices of the data packs in the same telecom circle which validates suitability of the price sensitivity model.
TL;DR: In this article, a multi-objective optimization model is proposed to derive an integrated net present value-based supply chain configuration for a manufacturing enterprise incorporating the effect of third-party logistics service providers in an uncertain demand scenario.
Abstract: A well-designed supply chain configuration yields positive net value by creating benefits, reducing costs, and improving firm’s profitability. Nowadays, supply chain also includes third-party logistics service providers (3PLs) which are usually contracted by the supplier or manufacturer to supply integrated logistics services to the buyers or consumers. Efficient utilization of 3PLs is expected to bring benefits such as reducing total costs thereby maximizing profits. The purpose of this research paper is to propose a multi-objective optimization model to derive an integrated net present value-based supply chain configuration for a manufacturing enterprise incorporating the effect of third-party logistics service providers in an uncertain demand scenario. Firstly, the paper presents the conceptual framework considering the third-party logistics service providers for a manufacturing enterprise and thereafter a multi-objective optimization model is proposed to find a compromise solution to NPV maximization and total cost minimization. The model also makes use of Chance Constraint methodology to handle demand uncertainties.
TL;DR: In this paper, the authors analyzed the occurrence of the bullwhip effect in pricing under three game scenarios (i.e., a simultaneous, a wholesale-leading, and a retail-leading) for three types of demand functions (e.g., a log-concave, an isoelastic, and negative exponential).
Abstract: Bullwhip effect in pricing (BP) refers to the amplified variability of prices in a supply chain. This paper analyzes the occurrence of BP under three game scenarios (e.g. a simultaneous, a wholesale-leading, and a retail-leading) for three types of demand functions (e.g. a log-concave, an isoelastic, and a negative exponential). Cost pass-throughs and BP ratios are calculated analytically for an N-stage supply chain, and then the price fluctuations in various supply chain game structures are illustrated through simulations. The results indicate that in the case of optimal markup pricing games, the occurrence of BP depends on the demand functions. This study also shows that, BP occurs in varying magnitudes for different types of games. Finally, a relation between price variations and corresponding markup profits are also discussed.
Abstract: Goods and Service Tax (GST), a destination-based unified taxation system, was implemented on 1st July 2017 in India replacing 17 different indirect taxes with the vision to create a seamless common market. A country like India, with the population of over 1.3 billion and heterogeneous distribution of wealth having a federal structure, opted for six different tax slab and dual-GST structure. In some countries, GST has been successful but in others, it has failed. We have attempted to investigate the barriers to the smooth implementation of GST. In this paper, we have identified 12 barriers to GST implementation. Using interpretive structural modelling (ISM), this study finds the driving and dependence nature of different barriers to develop a structural model. The results of analysis found that lack of skilled manpower, lack of clarity of GST provisions, political unwillingness, and lack of policy for proper division of tax are the major barriers in the implementation of GST. In addition to the above analysis, MICMAC analysis is utilized to cluster the barriers in four categories as per their relative driving and dependence powers.
TL;DR: In this paper, the authors show that participative pricing mechanisms, such as pay-what-you-want pricing, can create a price image that is as low as the traditional posted price mechanism with discounts.
Abstract: Retailers often use monetary promotions (e.g., discounts) to sell excess capacity, increase short-term revenue, and create a low price image. However, the frequent use of discounting may lower consumers’ internal reference price, until consumers are not willing to pay anything above the promotional price. This issue can be solved by the use of participative pricing mechanisms, under which the retailer does not set an explicit price to be paid. The results of this experimental study indicate that participative pricing mechanisms, such as pay-what-you-want pricing, create a price image that is as low as the traditional posted price mechanism with discounts.
TL;DR: This research analyzes the scenario where a business first invests in “on-premise” capacity and also procures the excess demand requirements through the public cloud provider utilizing the pay-as-you-go pricing model.
Abstract: In recent business practice, firms, to fulfill their IT requirements, are using both dedicated “on-premise” capacity infrastructure and “on-demand” capacity requirements provided by companies such as AWS, OpenStack, and VMware. In this research, we analyze the scenario where a business first invests in “on-premise” (or in-house) capacity and also procures the excess demand requirements through the public cloud provider utilizing the pay-as-you-go pricing model. We study the impact of factors such as demand correlation in buyers’ market and demand load profile on the capacity decision. We find various cloud computing strategies and link them with real-life business practices.
TL;DR: Airfare forecasts across the prediction outcomes suggested by Hopper, KAYAK, and FareHack show that, despite the importance of accurate forecasting, there is no best website for the optimal recommendation—prediction accuracy varies based on the number of days before the departure date.
Abstract: Customers are uncertain about the best time to get the lowest airfares due to the practice of dynamic pricing in revenue management. Even with the help of price-prediction platforms, the optimal time to buy the lowest-priced airfare remains unclear. This study compares airfare forecasts across the prediction outcomes suggested by Hopper, KAYAK, and FareHack. The data were recorded daily over a 77-day period, focusing on ten one-way route airfares during this time. The results show that, despite the importance of accurate forecasting, there is no best website for the optimal recommendation—prediction accuracy varies based on the number of days before the departure date.
TL;DR: In this article, the authors proposed a new era of retailing in the hospitality industry, where an infinite number of options can be inventory controlled on the price demand curve, improving customer satisfaction.
Abstract: Attribute-based pricing is the new era of retailing in the hospitality industry. This approach to room pricing improves customer satisfaction, since customers can purchase exactly the type of room they want for a memorable stay. For hoteliers, attribute-based pricing provides an opportunity to overhaul existing legacy rate structures with rate simplification and also generate incremental revenues since an infinite number of options can be inventory controlled on the price demand curve.
TL;DR: In this paper, the authors study the impact of green consumers on the profit of a retailer and a manufacturer in a short-life-cycle product remanufacturing scenario and propose two scenarios, i.e., independently optimized profit scenario under Stackelberg game with manufacturer as the leader, and integrated scenario.
Abstract: Remanufacturing has been studied quite extensively during the last decades, as one of product recovery options that could extend product’s useful life and mitigate the environmental impact. Rapid innovation in technology accelerates obsolescence and hence product life cycles are getting shorter and wastes build up faster. Remanufacturing of short life-cycle product is an important alternative to mitigate the waste. However, since there are impacts in implementing remanufacturing, such as cannibalization, segmentation, and lower willingness to pay, pricing has become a significant aspect to ensure successful remanufacturing business. Several studies show that there exists green segment consumer, who has higher preference to purchase environmental-friendly products and higher willingness to pay for such products. In this paper, we study manufacturer’s and retailer’s pricing decision of short life-cycle product considering the green segment consumers, and propose two scenarios i.e. independently optimized profit scenario under Stackelberg game with manufacturer as the leader, and integrated scenario. The results show that the integrated scenario achieves higher profit compared to the independent one, and both players are better off under the integrated decision. We also find that the existence of green segment increases the profit of manufacturer and retailer to a certain level, before it erodes manufacturer’s profit when the green level is too high. In addition, price sensitivity of new and remanufactured products, demand’s speed of change, and remanufacturing cost influence the optimum prices as well as the optimum green level. For firms that are engaging in remanufacturing, managerial insights are also provided to assist managers in making pricing decisions when there exist green consumers.
TL;DR: In this article, a new way of segmentation based on consumers' valuation uncertainty (as measured by the range of willingness-to-pay or WTP) is proposed, and the highest profit is attained when both valuation and valuation uncertainty are considered for optimal pricing.
Abstract: Traditional price discrimination of a monopoly depends on the knowledge of consumers’ valuation (as measured by willingness-to-pay or WTP). Information about consumer uncertainty on WTP, however, has not been considered explicitly as a way of segmentation for pricing purpose. Based on analytical modeling results at the individual level and simulations of survey data at the segment level, this paper proposes a new way of segmentation based on consumers’ valuation uncertainty (as measured by the range of WTP). Highest profit is attained when both valuation and valuation uncertainty are considered for optimal pricing. Moreover, compared to a segment with low valuation uncertainty, a segment with higher valuation uncertainty tends to have a flatter demand curve. Therefore, if the floor WTP has a non-negative correlation with the range of WTP, high valuation uncertainty leads to a higher optimal price as compared to low valuation uncertainty.
TL;DR: In this paper, the authors conduct a thorough secondary research inquiry focused on assessing the level of penetration of the pricing function in global Fortune 500 firms and find that a moderate amount of penetration exists in these firms.
Abstract: Over the last 40 years, the pricing discipline has made tremendous progress in its organization and professionalization. More firms adopt progressive pricing approaches and invest in pricing technical tools. Progress has been made, but there is much more that is needed to bring pricing to the level of adoption seen in the supply chain and analytics functions. Very little is published on the level of penetration and adoption of pricing in general. In this paper, we conduct a thorough secondary research inquiry focused on assessing the level of penetration of the pricing function in global Fortune 500 firms. Our findings reveal a moderate level of penetration (22%) of the pricing function in these firms. The findings establish reference measures for future studies and provide benchmark information for pricing executives wishing to establish a pricing function and to justify greater pricing investments.
TL;DR: In this article, the Stackelberg model was adopted to analyze the optimal revenue sharing rates between two parties when the platform provider acts as a leader, while the service providers are followers.
Abstract: Because of its ability to generate many revenue streams, the platform business has received much attention in recent years, and the revenue sharing problem between platform providers and service providers is a key issue. We adopted the Stackelberg model to analyze the optimal revenue sharing rates between these two parties when the platform provider acts as a leader, while the service providers are followers. We derive the closed form of optimal revenue sharing rates as the equilibrium of the Stackelberg model. Numerical experiments with graphical illustrations are presented to demonstrate the optimality of revenue sharing rates whereby the two parties maximize their own profits. In addition, on the basis of the results of optimal revenue sharing rates, the present study performs sensitivity analyses with regard to various exogenous variables that could affect the optimal revenue sharing rates. Our findings indicate that potential demand was the most significant factor in affecting optimal revenue sharing. Therefore, decision makers should carefully monitor their platform business markets to maximize the profits of all the parties involved.
TL;DR: In this article, a single resource is sold through multiple fare classes with a corresponding stochastic, but not necessarily independent, demand, and the authors explicitly account for any level of positive or negative dependence and focus on the traditional macro-level demand model.
Abstract: This paper extends the fundamental static revenue management capacity control problem by incorporating statistical dependence. A single-resource is sold through multiple fare classes each with a corresponding stochastic, but not necessarily independent, demand. We explicitly account for any level of positive or negative dependence and focus on the traditional macro-level demand model in order to provide distribution-free bounds on the foundational expected marginal seat revenue heuristics, both without and with buy-up. We illustrate for the case with three fare classes and demand drawn from (i) normal distributions, and (ii) normal and exponential distributions.
TL;DR: Three parametric forecasting models that jointly estimate the volume component and the choice component of airline demand and can also be used as an input for optimization models, for joint seat allocation and pricing are developed.
Abstract: We develop three parametric forecasting models that jointly estimate the volume component and the choice component of airline demand. These models—JFM-WPA, JFM-PA, and JFM-WTPU—account for (a) demand volume by considering the average demand of an O–D market, booking curve, seasonality and day-of-the-week indices, and (b) customer behavior by including the maximum willingness-to-pay of the customer and the choice attributes of the available options. We use a mixed logit function to formulate the willingness-to-pay and customer choice behavior. JFM-WPA excludes price from the set of choice attributes. JFM-PA considers price as one of the choice attributes. The utilities of maximum willingness-to-pay and choices are combined in JFM-WTPU. We propose a sequential method to estimate the forecasting models. We utilize the data generated by Airline Planning and Operations Simulator (APOS) from real airline historic data. We compare the models and present the results. These demand models can also be used as an input for optimization models, for joint seat allocation and pricing.
TL;DR: The results of the 2017 Dynamic Pricing Challenge as mentioned in this paper showed that the relative performance of algorithms varies substantially across different market dynamics, which confirms the intrinsic complexity of pricing and learning in the presence of competition.
Abstract: This paper presents the results of the Dynamic Pricing Challenge, held on the occasion of the 17th INFORMS Revenue Management and Pricing Section Conference on June 29–30, 2017 in Amsterdam, The Netherlands. For this challenge, participants submitted algorithms for pricing and demand learning of which the numerical performance was analyzed in simulated market environments. This allows consideration of market dynamics that are not analytically tractable or can not be empirically analyzed due to practical complications. Our findings implicate that the relative performance of algorithms varies substantially across different market dynamics, which confirms the intrinsic complexity of pricing and learning in the presence of competition.
TL;DR: In this article, the authors suggest four changes that may lead more firms to use demand principles when they set prices: shift their focus from profit-optimization to profit-improvement opportunities (e.g., increasing price so it is closer to the demand function), develop pricing heuristics that better meet firm needs, and add new pricing topics to classroom discussions.
Abstract: Applied economists have been trying to sell businesses on the value of demand-based pricing for many years. Their arguments promoting demand-based pricing have not been particularly persuasive and cost-based pricing continues to be a dominant price-setting method. This paper suggests four changes that may lead more firms to use demand principles when they set prices. Applied economists should acknowledge the benefits of other price-setting processes, shift their focus from profit-optimization to profit-improvement opportunities (e.g., increasing price so it is closer to the demand function), develop pricing heuristics that better meet firm needs, and add new pricing topics to classroom discussions.
TL;DR: A new improved integer linear model formulation is developed here which by explicitly assigning booking requests to cabins derives a feasible and revenue-maximizing capacity allocation.
Abstract: The cruise industry is a profitable field for the application of revenue management methods. Existing model formulations for booking limit determination usually assume that the different elements of booking requests are independent. In this work, it is shown that this approach can lead to non-feasible capacity allocations, which consequently are neither optimal nor applicable in practical planning situations. Therefore, a new improved integer linear model formulation is developed here which by explicitly assigning booking requests to cabins derives a feasible and revenue-maximizing capacity allocation. The model and its results are illustrated with a real-world sized case study.
TL;DR: In this article, a new mathematical model was proposed to optimize airline expected revenue from buy-back according to the probability of passenger acceptance, which can be applied for many different compensation schemes that could be put in place by an airline to spur some of their passengers to sell back air tickets to the airline.
Abstract: During the reservation period of some flights, the demand significantly fluctuates due to changes in the business environment. Some demand variations can hardly be anticipated by airline revenue management systems. Therefore, it often happens that the sales policy applied at a given time in a flight turns out to be suboptimal a posteriori. A mechanism of ticket buy-back can then be an interesting tool for the airlines aiming at boosting revenue from these flights. This paper addresses the main stages of a buy-back process triggered by an airline. The process includes the revenue management-based solutions to support the selection of the tickets and the computation of the proposed prices to get them back. The main contribution of this paper is a new mathematical model which optimizes airline-expected revenue from buy-back according to the probability of passenger acceptance. This model can be applied for many different compensation schemes that could be put in place by an airline to spur some of their passengers to sell back air tickets to the airline. Three of them are further analyzed. We simulate buy-back campaigns for four flights with data drawn from real operations and compare additional revenue due to buy-back, according to the selected compensation scheme. Results emphasize our intuition that business benefits can be expected from a well-automated mechanism of ticket buy-back and resell in the airline industry. Depending on the flight and demand characteristics, up to over $$10\%$$
additional revenue can be expected to be added on top of the revenue obtained from a standard revenue management system on a single flight.
TL;DR: In this article, the authors provide empirical evidence on how hotel cancellation policies are changing in recent years and demonstrate that while hotels are experimenting with stricter cancellation windows, their cancellation penalties do not appear to become stricter.
Abstract: This study provides empirical evidence on how hotel cancellation policies are changing in recent years. The findings demonstrate that while hotels are experimenting with stricter cancellation windows, their cancellation penalties do not appear to become stricter. We discuss this counterintuitive discrepancy, explaining the rational with theoretical models of price discrimination and empirical observations of consumer behaviour, both within the framework of hotel revenue management practices.