Low-cost carriers and escalading fuel costs are placing increased pressure on US legacy carriers to reposition traditional revenue management techniques towards more customer-centric approaches. In this context, recent critiques of revenue management models question the validity of assumptions used to describe passenger cancellation and no-show behavior. Since forecasts of cancellation and no-shows are used to determine overbooking levels, i.e, authorization levels in excess of capacity, concerns related to possible missed revenue opportunities are justifiable. The goal of this research is to explore the impact of time-to-event forecasts of cancellations on airlines' revenue streams. To determine the intensity of the cancellation process, a discrete time proportional odds (DTPO) model with a prospective time scale was estimated for a sample of tickets provided by the Airline Reporting Corporation. Empirical results based on 2004 data from eight domestic US markets indicate that the intensity of the cancellation process is strongly influenced both by the time from ticket purchase and the time before flight departure, as well as several other covariates, including departure day of week, market, and group size. In order to assess potential revenue benefits associated with the DTPO formulation, a modified simulation experiment of a "single-resource capacity control" was designed. Simulation results indicate that time-to-event cancellation forecasts can generate revenue gains up to 2%. Overall, this research provides new insights into the transitional properties associated with the cancellation process, which will help airlines to improve their overbooking strategies.

The application of revenue management (RM) is changing more rapidly than ever before, driven as an important factor of the daily operation to keep prices competitive and to create real-time optimal pricing. In the age of the Internet and social media, negotiated fixed rates have become outmoded. Consumers now have access to online rate comparisons and real time reviews. They think more strategically when making purchasing decisions. Thus, they become more demanding. This research provides an empirical study of revenue management and pricing with an emphasis given to the hospitality industry. The aim of this research is to examine the gap between the theoretical approach and the empirical analysis, the rationality between the implementation of dynamic pricing approaches and the impact on the customer. Furthermore, the research examines the perception of consumers’ willingness to pay when using the Name-Your-Own-Price (NYOP) mechanism, which allows customers to have a greater influence on the amount they are prepared to pay. Instead of posting a price, the seller waits for a potential buyer’s offer, which he or she can either accept or reject. Finally, this study examines, whether the use of social media plays a decisive role in the online purchase environment used by the hospitality sector and the effect it has on a consumer’s willingness to pay. Accordingly, hotel revenue managers will be able to use the findings of this study to effectively plan their short-term, and long-term pricing strategies to generate a stronger revenue management performance for their property, namely to increase the RevPAR (revenue per available room). The research can be useful to businesses, as empirical data and tests were employed to determine what kind of impact the different pricing policies have on the long-term profit optimization. These practical and theoretical elements of the field reinforce each other‚ as well as to a large extent, the constructive interplay of theory and practice. The research is twofold, the holistic approach, which discusses the development of the theoretical dimension, is complemented by the practical analysis of the collected data of the surveys. This approach ensures the relevant observation of ‘real-time’ data and the evaluation of the set of hypotheses. The study conducted two large scale interrelated structured surveys. The first structural survey (NYOP) provides a better understanding of the final consumer, by using the name-your-own-price mechanism and by observing the extended role of social media in the booking procedure. Hypotheses were tested and in the second survey in-depth data from revenue managers and executives working across the tourism industry was collected, in an attempt to measure the use of pricing strategies within the industry. The research contributes to the theory by empirical testing how the extended RM objectives influence RM and pricing. It provides a clear picture of the necessary elements for a successful implementation of pricing strategies. Finally, the study has implications for the consumer. Thus, the researcher investigates consumer’s perception to the NYOP model and the expanding role of social media to the consumer-booking pattern.

abstract: Revenue management is at the core of airline operations today; proprietary algorithms and heuristics are used to determine prices and availability of tickets on an almost-continuous basis. While initial developments in revenue management were motivated by industry practice, later developments overcoming fundamental omissions from earlier models show significant improvement, despite their focus on relatively esoteric aspects of the problem, and have limited potential for practical use due to computational requirements. This dissertation attempts to address various modeling and computational issues, introducing realistic choice-based demand revenue management models. In particular, this work introduces two optimization formulations alongside a choice-based demand modeling framework, improving on the methods that choice-based revenue management literature has created to date, by providing sensible models for airline implementation. The first model offers an alternative formulation to the traditional choice-based revenue management problem presented in the literature, and provides substantial gains in expected revenue while limiting the problem’s computational complexity. Making assumptions on passenger demand, the Choice-based Mixed Integer Program (CMIP) provides a significantly more compact formulation when compared to other choice-based revenue management models, and consistently outperforms previous models. Despite the prevalence of choice-based revenue management models in literature, the assumptions made on purchasing behavior inhibit researchers to create models that properly reflect passenger sensitivities to various ticket attributes, such as price, number of stops, and flexibility options. This dissertation introduces a general framework for airline choice-based demand modeling that takes into account various ticket attributes in addition to price, providing a framework for revenue management models to relate airline companies’ product design strategies to the practice of revenue management through decisions on ticket availability and price. Finally, this dissertation introduces a mixed integer non-linear programming formulation for airline revenue management that accommodates the possibility of simultaneously setting prices and availabilities on a network. Traditional revenue management models primarily focus on availability, only, forcing secondary models to optimize prices. The Price-dynamic Choice-based Mixed Integer Program (PCMIP) eliminates this two-step process, aligning passenger purchase behavior with revenue management policies, and is shown to outperform previously developed models, providing a new frontier of research in airline revenue management. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2016

abstract: Hotel amenities and their influence on consumer choice have been extensively studied by academics. These have largely focused on consumer preferences vacation modes and the psychographic characteristics of travelers. Revenue managers make practical use of this information by attempting to match available hotel rooms with traveler demands for accommodations, setting prices that maximize profits for the hospitality company. The experienced revenue manger is able to determine the most profitable price schedule for a room types across many distribution channels. This study was conducted to test the use of choice modeling for objectively assessing dollar values of three basic amenities for consumers (room type, kitchen availability and price). Researcher used paired comparisons modeled as a conditional logit. This study used market segmentation and choice modeling to determine the value of amenities for an aggregate group and 16 more homogenous groups. Market segmentation and choice modeling allowed this study to segment markets into more homogenous groups, and by doing that allowed for calculation of customer willingness to pay for additional amenities. Results from this study confirm that customers are willing to pay for kitchen $65.43 on top of their room value. All responders generally agree to liking an extra bedroom in their hotel room and they are willing to pay $37.39 more than for a studio room. A surprising result is that it seems based on the results that responders generally do not like to have a second bedroom and they are not willing to pay for it. By knowing customer willingness to pay, it can be assured that customers always feel they are getting a high value out of the transaction and increase the likelihood of future transactions. The significance of this research is the concrete numbers that can be, and already have been, applied immediately in the hospitality industry, and is positively impacting business revenue and customer experience. / Dissertation/Thesis / M.S. Community Resources and Development 2014

The purpose of the study was to determine customer perceptions of fairness concerning pricing policies charged by the hotel industry, and to examine how different outcomes in pricing policies affect customer perceptions of fairness. Convenience-Interception survey sampling was used to collect 460 sample data at the Dallas Love Field Airport. After analyzing data, one can infer that when revenue management information was provided, customers are satisfied. Further, age, education, Airline FFP enrolled and redeem miles, and pricing based on marketing channels plays an imperative role in this study.

Queue buildup is a problem that every fast food restaurant would like to reduce, as its effect on customer satisfaction and restaurant revenue is obviously negative. This thesis looks at how a dynamic menu management system would affect fast food restaurants, specifically how the promotion of fast products, in certain situations, would affect total revenue and queue lengths. A neural network was used to categorize products depending on their preparation time and to predict queue waiting times. A simulation approach was used to find out how the menu management system would affect the restaurant. The results are that in situations where long queues are formed, the promotion of fast products increases revenue by 7.8% and decreases queue lengths by 13.3%. In situations where little to no queues are formed, the same system still decreases queue lengths by 4.7%, but also decreases revenue by 0.2%

Thesis (MTech (Business Administration))--Cape Peninsula University of Technology, 2019 / Financial sustainability of water and sewerage provision is undermined by consumers’ inability and reluctance to pay service fees. Consequently, the escalation of operational and maintenance costs of water and sewerage provision is a major challenge facing South African municipalities. This study examines the provision of water and sewerage services and presents a strategic framework to manage revenue collection to achieve water and sewerage provision financial sustainability. The study applied interpretive epistemology research method. Content analysis was used to collect and analyse data for an improved understanding of service provision and revenue collection mantra. Unlike the other method such as the exploratory research method that provides a description of the observed phenomenon, an interpretive epistemology methodology provides deeper insights into management strategies that are required in order to achieve sustainable water and sewerage provision. The main objective of this study was to investigate factors affecting financial sustainability of water and sewerage provision in Cape Town. The qualitative data were analysed using content method of analysis to gain better meaning of the phenomenon being studied. The results show that although the water and sanitation department of the participating organisation is making efforts to generate revenue through implementing water and sanitation services payments, there are some residents who refuse to pay for services, resulting in revenue loss. The other reason for loss of revenue is unaccounted water which is the focus of the water and sewerage, demand management and strategy section; this is as a result of leaks, illegal connections, unregistered car wash bays, unmetered connections and informal settlement areas which do not get enough subsidies. The reasons advanced for unwillingness to pay for water and sanitation services by consumers were unemployment, poverty, unequal distribution of services and general ignorance. This study has found evidence of a reluctance toward service payment by participating consumers – requiring strategies to encourage rates payments.

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[pt] No contexto da indústria de adquirência, uma adquirente é uma empresa que facilita a comunicação entre um varejista (online ou loja física) e os bancos emissores. Para um adquirente, é crucial determinar o potencial transacional de cada varejista para orientar estratégias adequadas de precificação e gestão de risco. Neste trabalho, propomos uma estrutura para avaliar adequadamente o potencial transacional de qualquer varejista usando as transações de seus pares. A estrutura proposta é baseada na construção de um contrafactual probabilístico que usa a regressão não paramétrica do kernel Nadaraya-Watson para modelar diferentes padrões sazonais, tendências e ciclos de negócios. Propomos uma metodologia integrada de processamento de dados para separar e validar os dados não afetados por intervenções para construir nosso modelo contrafactual probabilístico não paramétrico. O framework proposto é um poderoso sistema de suporte à decisão para gestão de receitas de uma adquirente, com aplicações diretas para precificação, detecção de churn e, de forma mais geral, gerenciamento de receita. Os resultados empíricos corroboram a eficácia do método em relação aos benchmarks relevantes. / [en] In the payment industry context, a merchant acquirer is a firm that facilitates communication between a retailer (online or brick–and–mortar store) and the issuing banks. For an acquirer, it is crucial to determine the transactional potential of each retailer to guide proper pricing and risk management strategies. In this work, we propose a framework to properly assess the transactional potential of any retailer using the transactions of its peers. The proposed framework is based on the construction of a probabilistic counterfactual that uses non-parametric Nadaraya-Watson kernel regression to model differing seasonal patterns, trends and business cycles. We propose an integrated data processing methodology to separate and validate the data not affected by interventions to construct our non-parametric probabilistic counterfactual model. The proposed framework is a powerful decision support system for a merchant acquirer revenue management, with direct applications to pricing, churn detection and, more generally, revenue management. Empirical results corroborate the effectiveness of the method against relevant benchmarks.

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