The Experts below are selected from a list of 12915 Experts worldwide ranked by ideXlab platform
Larry Weatherford - One of the best experts on this subject based on the ideXlab platform.
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intelligent aggressiveness using forecast multipliers hybrid forecasting fare adjustment and unconstraining methods to Increase Revenue
Decision Sciences, 2017Co-Authors: Larry WeatherfordAbstract:Many studies have begun the exploration of airlines using intelligent aggressiveness (IA) in unidimensional directions (e.g., forecast multipliers alone). This article uses the sophisticated passenger origin–destination simulator (PODS) to examine the Revenue impact of four different IA levers—forecast multipliers, unconstraining, hybrid forecasting (HF) and fare adjustment (FA). We also explore the impacts in two different origin–destination networks. Due to the competitive nature of PODS (two or four airlines competing) and its allowance for customer choice, we are able to assess all the implications, including the impact of spill, upgrades and recapture. We find that with a single IA lever, independent of the network and demand level, in a more-restricted fare environment, the optimal lever is almost always HF with moderate-to-aggressive estimates of willingness-to-pay, with Revenue gains of 0.4–4.3% in a large global network, and gains of 1.7–4.2% in a domestic network, depending on demand level and optimization method used. We also test two additional, less-restricted fare environments and find that Revenue improvements have a wider range (0.8–6.3%) with a single lever in the larger network. Finally, we explore the impacts of allowing the competitors to use basic IA and the airline of interest to use multiple IA levers.
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intelligent aggressiveness combining forecast multipliers with various unconstraining methods to Increase Revenue in a global network with four airlines
Journal of Revenue and Pricing Management, 2015Co-Authors: Larry WeatherfordAbstract:Airlines are always searching for ways to maximize Revenues in the hyper-competitive environment that they exist in. This article uses the sophisticated Passenger Origin-Destination Simulator (PODS) simulator to examine the Revenue impact of four levels of forecast multipliers (FM) in combination with three different methods of unconstraining – Expectation Maximization (EM), Projection Detruncation (PD) and Booking Curve (BC). Owing to the competitive nature of PODS (four airlines competing for customers) and its allowance for customer choice, we are able to assess all the implications of these FM levels in combination with unconstraining, including the impact of spill, upgrades and recapture. We find that in this fully/semi-restricted fare environment, under leg optimization and either EM or PD, the optimal level is generally FM of 1.1, independent of demand level, and that under BC unconstraining, the optimal level is FM equal to 1.2. When using realistic booking data from major global airlines to calibrate PODS’ largest global network (U1), we show that becoming more intelligently aggressive with FM can lead to Revenue gains of 0.2–0.6 per cent under EM or PD unconstraining and gains of 0.3–1.2 per cent under BC unconstraining. Under network optimization, it is best not to use FM under any unconstraining method, independent of demand level.
Alex Roman - One of the best experts on this subject based on the ideXlab platform.
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updating fare collection systems help transit operations Increase efficiencies Revenue
Metrologia, 2009Co-Authors: Alex RomanAbstract:This article describes how updating fare collection systems can Increase Revenue and eliminate fare evasion. New technologies also provide tools that improve service and Increase efficiencies. The article describes Anaheim (California) Transit Network, which serves the greater Anaheim resort area, including Disneyland. The agency’s fares Increased twenty times after installing validating fare boxes. Other systems, like Dallas Area Rapid Transit, improved fare collection in 2004 and eliminated paper tickets. In addition, the agency will begin installing vending machines on platforms of every station in fall of 2009. The article also describes the data gathering capabilities of the new fare collection systems, which enable agencies to track ridership by actual count as well as classifications of ridership.
Luis Nunes - One of the best experts on this subject based on the ideXlab platform.
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predicting hotel booking cancellations to decrease uncertainty and Increase Revenue
Tourism & Management Studies, 2017Co-Authors: Nuno Antonio, Ana De Almeida, Luis NunesAbstract:Booking cancellations have a substantial impact in demand-management decisions in the hospitality industry. Cancellations limit the production of accurate forecasts, a critical tool in terms of Revenue management performance. To circumvent the problems caused by booking cancellations, hotels implement rigid cancellation policies and overbooking strategies, which can also have a negative influence on Revenue and reputation. Using data sets from four resort hotels and addressing booking cancellation prediction as a classification problem in the scope of data science, authors demonstrate that it is possible to build models for predicting booking cancellations with accuracy results in excess of 90%. This demonstrates that despite what was assumed by Morales and Wang (2010) it is possible to predict with high accuracy whether a booking will be canceled. Results allow hotel managers to accurately predict net demand and build better forecasts, improve cancellation policies, define better overbooking tactics and thus use more assertive pricing and inventory allocation strategies.
Narayan Rangaraj - One of the best experts on this subject based on the ideXlab platform.
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Revenue management in railway operations: A study of the Rajdhani Express, Indian Railways
Transportation Research Part A: Policy and Practice, 2008Co-Authors: Rohit Bharill, Narayan RangarajAbstract:Revenue management is widely practiced in the transport industry, but the bulk of the published literature deals with the airline industry. We consider the case of passenger services in the premium segment of Indian Railways (IR) and illustrate an application of the principles of Revenue management. The strategy of overbooking is interpreted in terms of waitlist management by IR and cancellation action of customers. An attempt is made to derive elasticity estimates between key mode choices internal to the railways and finally, Revenue management through differential pricing is suggested as a means to Increase Revenue on average. © 2008 Elsevier Ltd. All rights reserved.
Preston R Mcafee - One of the best experts on this subject based on the ideXlab platform.
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when does improved targeting Increase Revenue
Electronic Commerce, 2016Co-Authors: Patrick Hummel, Preston R McafeeAbstract:In second-price auctions, we find that improved targeting via enhanced information disclosure decreases Revenue when there are two bidders and Increases Revenue if there are at least four symmetric bidders with values drawn from a distribution with a monotone hazard rate. With asymmetries, improved targeting Increases Revenue if the most frequent winner wins less than 30.4% of the time under a model in which shares are well defined, but can decrease Revenue otherwise. We derive analogous results for position auctions. Finally, we show that Revenue can vary nonmonotonically with the number of bidders who are able to take advantage of improved targeting.
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when does improved targeting Increase Revenue
The Web Conference, 2015Co-Authors: Patrick Hummel, Preston R McafeeAbstract:In second price auctions with symmetric bidders, we find that improved targeting via enhanced information disclosure decreases Revenue when there are two bidders and Increases Revenue if there are at least four bidders. With asymmetries, improved targeting Increases Revenue if the most frequent winner wins less than 30.4% of the time, but can decrease Revenue otherwise. We derive analogous results for position auctions. Finally, we show that Revenue can vary non-monotonically with the number of bidders who are able to take advantage of improved targeting.
