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Shioda, Romy 1977. "Restaurant revenue management." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/28250.

Ciocan, Dragos Florin. "High dimensional revenue management." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/108211.

Uichanco, Joline Ann Villaranda. "Data-driven revenue management." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41728.

Githiri, Duncan. "Airline revenue management performance measurement of South African Airways origin-destination revenue management." Thesis, Rhodes University, 2017. http://hdl.handle.net/10962/59188.

Zickus, Jeffrey S. (Jeffrey Stuart) 1973. "Forecasting for airline network revenue management : revenue and competitive impacts." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/10103.

Martens, Tobias von. "Kundenwertorientiertes Revenue-Management im Dienstleistungsbereich." Wiesbaden : Gabler, 2009. http://dx.doi.org/10.1007/978-3-8349-9503-2.

Defregger, Florian. "Revenue management for manufacturing companies /." kostenfrei, 2009. http://deposit.d-nb.de/cgi-bin/dokserv?idn=997408154.

Chen, Lijian. "Stochastic programming in revenue management." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1150314352.

Thraves, Cortés-Monroy Charles Mark. "New applications in Revenue Management." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112085.

Konig, Matthias. "Risk considerations in revenue management." Thesis, Lancaster University, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.547943.

Martens, Tobias von. "Kundenwertorientiertes Revenue-Management im Dienstleistungsbereich." Wiesbaden Gabler, 2008. http://d-nb.info/992494346/04.

Ďurica, Peter. "Revenue management a jeho využitie v hotelových prevádzkach." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-162376.

Skyba, Stanislav. "Využití renevue managementu k řízení ziskovosti letecké linky." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-318134.

Popescu, Andreea. "Air cargo revenue and capacity management." Diss., Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-11202006-095545/.

Mohaupt, Michael. "Forschungsansatz zur Unsicherheitsproblematik im Revenue Management." Universitätsbibliothek Chemnitz, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-70707.

Pak, Kevin. "Revenue Management: New Features and Models." [Rotterdam]: Erasmus Research Institute of Management (ERIM), Erasmus University Rotterdam ; Rotterdam : Erasmus University Rotterdam [Host], 2005. http://hdl.handle.net/1765/6771.

Cooper, William L. "Revenue management, auctions, and perishable inventories." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/25805.

Wang, Jingbo. "Estimation and optimization in revenue management." Thesis, University of Oxford, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522810.

Armar, Nii A. "Cargo revenue management for space logistics." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/62971.

Mak, Chung Yu. "Revenue impacts of airline yield management." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/26838.

Fry, Daniel G. "Demand driven dispatch and revenue management." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/99548.

Andersson, Karl, and Henrik Wittgren. "Restaurangbesökarens inställning till Restaurant Revenue Management." Thesis, Örebro universitet, Restaurang- och hotellhögskolan, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-51732.

Farias, Vivek Francis. "Revenue management beyond "estimate, the optimize" /." May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

Barocio, Cots Ruben 1970. "Revenue management under demand driven dispatch." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/9481.

Hao, Eric (Eric C. ). "Ancillary revenues in the airline industry : impacts on revenue management and distribution systems." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/89854.

Veselová, Erika. "Modely sieťového revenue manažmentu." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-165297.

Forsman, Tomas, and Isak Lindstrand. "Restaurant Revenue Management : En studie om hur Revenue Management kan implementeras på restauranger för att öka lönsamhet." Thesis, Örebro universitet, Restaurang- och hotellhögskolan, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-61243.

Schmidt, Henning. "Simultaneous control of demand and supply in revenue management with flexible capacity." Clausthal-Zellerfeld Papierflieger, 2009. http://d-nb.info/993813461/04.

Holubec, Jakub. "Využití revenue managementu v ubytovacích zařízeních." Master's thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-136279.

Barz, Christiane. "Risk-averse capacity control in revenue management." Berlin : Springer, 2007. http://dx.doi.org/10.1007/978-3-540-73014-9.

Wong, Sau-lim Tim. "Airline revenue management passenger right and protection /." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B31633183.

Terciyanli, Erman. "Alternative Mathematical Models For Revenue Management Problems." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610711/index.pdf.

Eroglu, Fatma Esra. "Service Models For Airline Revenue Management Problems." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613490/index.pdf.

Chahar, Kiran. "Revenue and order management under demand uncertainty." Connect to this title online, 2008. http://etd.lib.clemson.edu/documents/1219855173/.

Fernandes, A. T. "Spectrum management for revenue maximisation in DSL." Thesis, University of Cambridge, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.598991.

Strauss, Arne Karsten. "Optimisation in choice-based network revenue management." Thesis, Lancaster University, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543995.

王守廉 and Sau-lim Tim Wong. "Airline revenue management: passenger right and protection." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B31633183.

Charania, Aamer 1970. "Incorporating sell-up in airline revenue management." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/10105.

Cusano, Andrew Jacob 1978. "Airline revenue management under alternative fare structures." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/26900.

Boer, Sanne Vincent de 1976. "Advances in airline revenue management and pricing." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/16926.

Bratu, Stephane (Stephane J.-C) 1970. "Network value concept in airline revenue management." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/9939.

D'Huart, Olivier (Olivier Edouard Marie). "A competitive approach to airline revenue management." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/60708.

Liu, Tieming Ph D. Massachusetts Institute of Technology. "Revenue management models in the manufacturing industry." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33736.

Remy, Detlev. "Revenue management in for-profit higher education." Thesis, University of Surrey, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.665501.

Riedel, Silvia. "Forecast combination in revenue management demand forecasting." Thesis, Bournemouth University, 2008. http://eprints.bournemouth.ac.uk/9640/.

Yousef-Sibdari, Soheil. "Essays in Revenue Management and Dynamic Pricing." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/27127.

Bodea, Tudor Dan. "Choice-based revenue management a hotel perspective /." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24739.

Joshi, Kapil. "Modeling alternate strategies for airline revenue management." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000557.

Becher, Michael. "Integrated capacity and price control in Revenue Management a fuzzy system approach." Wiesbaden Gabler, 2007. http://d-nb.info/986403490/04.

Schröder, Anika. "Dynamic Pricing für parallele Flüge /." Clausthal-Zellerfeld : Papierflieger, 2008. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016710472&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

• Open access
• Published: 05 January 2021

Determinants of revenue management practices and their impacts on the financial performance of hotels in Kenya: a proposed theoretical framework

• Michael Murimi   ORCID: orcid.org/0000-0002-9722-3344 1 ,
• Billy Wadongo 1 &
• Tom Olielo 1  

Future Business Journal volume  7 , Article number:  2 ( 2021 ) Cite this article

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This conceptual paper aims at identifying a theoretical framework for the determinants of revenue management (RM) practices and their impacts on the financial performance of hotels. To create this framework, a two-phased process is employed where the first stage involves an explicit examination of the literature related to practices of revenue management and their determinants and to hotel financial performance. The second stage involves an enhancement of the framework. The theoretical structure is developed based on past theoretical explanations, and empirical analysis is conducted in the fields of revenue management. The researchers propose a theoretical framework illustrating how revenue management practices and their determinants affect the financial performance of Kenyan hotels. The use of contingency theory and its justifications and inadequacies among studies on revenue management in hotels is highlighted. The methods highlighted by the reviewed theoretical framework may be utilized to organize revenue management (RM) practices and their determinants for Kenyan hotels. Measurements for the financial performance of hotels are also described. Last, the researchers call for empirical research that authenticates the proposed model using a cross-sectional survey. The present work can inspire scholars and specialists to determine how RM practices and their determinants impact the financial performance of hotels. By assimilating knowledge from numerous disciplines, this paper emphasizes aggregated awareness surrounding the conceptualization of RM, RM practices adopted in hotels, and the financial performance of hotels.

Introduction

The hotel sector is a fashionable sector and a significant player in development in Kenya. Kenya’s hotel sector is composed of classified and non-classified establishments according to the Tourism Regulatory Authority of Kenya. A total of 225 establishments are classified with a one-to-five star rating and host a total of 16,156 rooms and 26,786 beds [ 1 ]. Comparative data from the Kenya National Bureau of Statistics (KNBS) and CIEC data reveal that the occupancy rates of hotels in different regions are below average and vary greatly. Kenya’s Hotel Bed occupancy rate was at 30.800% in 2019, reflecting a decrease from the previous level of 32.500% in 2018, and averaged at 36.250% from 2002 to 2019 [ 2 ]. Occupancy problems changes during peak seasons. Most hotels contract themselves out, due to inadequate space and rooms resulting from previous low season bookings associated with high discounts, such that when the high season arrives, they are incapable of realizing the maximum revenues possible [ 3 , 4 ].

Adverse effects of contingency factors such as seasonality and internal determinants within the sector will continue to influence Kenya’s hotel industry, denying hotels not only stable occupancy rates but also chances to achieve the maximum possible hotel room rates and total revenues. Irrespective of the massive assurances and enhancements of systems of revenue management used by hospitality facilities, there has been inadequate research on the impacts of RM practices within this sector in Kenya. This paper focuses on determining factors of RM practices, practices of RM applied in hotels, and the financial performance of Kenyan hotels. Further, the paper specifies the relationships between these determinants and the financial performance of hotels. The paper in turn provides pertinent information for diagnosing and discovering appropriate explanations for declining occupancy levels among Kenyan hotels.

Contingency theory offers a guide for the development of propositions for study. There has been an absence of replications of contingency investigations of diverse sceneries such as those of the hotel sector in developing countries [ 5 ]. Further, a limited focus on current facets of RM practices restricts the capacity to generalize and revive contingency theory through other major academic domains [ 6 ]. The use of recommended perspectives from contingency research in determining and opposing the connections between aspects of contingency and RM practices supporting this proposed theoretical framework has followed [ 6 ]. Throughout the years, researchers have confirmed an association between factors shaping contingency and performance in different organizations [ 6 , 7 , 8 , 9 ]. Last, this paper responds to earlier demands for more studies of the hotel sector related to practices of revenue management [ 10 , 11 , 12 ].

This section presents the main body of the paper under the following subheadings: practices of revenue management in hotels; financial performance of hotels; relationships between revenue management practices and the financial performance of hotels; and contributions of contingency theory to the present study and research implications and contributions (Fig.  1 ).

Proposed theoretical framework (Authors, 2020)

Practices of revenue management in hotels

Expansion and developments of revenue management may be fortified through performance management outfits, including marketing and appraising, booking automation, and the improvement of Worldwide Distribution Systems [ 13 ]. Experts of revenue management rely on tools such as reliable data, valuing, and user-friendliness and demand anticipation to ensure the expansion of organizational capacity [ 14 ]. Practices of revenue management are substantial when the following supplementary circumstances prevail; fixed capacity, transitory inventory, market fragmentation of demand, reservation structure where products and services are sold before their consumption; and changes in demands and low costs of marginal deals and high pricing policies [ 15 ]. Revenue management deeds are intricate and span several areas [ 16 ].

Revenue management research has drawn attention in recent decades from scholars and professionals focused on areas such as RM uses, processes, and structures. There has been a rise in RM models such as the hedonic price model for measuring the impacts of various factors on hotel prices [ 17 ], the successive consumer decision procedures model [ 18 ], dynamic and deterministic programming models, used to manage matters of RM in hotels [ 19 ], RM implementation and strategy models [ 10 ], the choice model [ 20 ], the room intensification integrated model for hotel proceeds [ 21 ], forecasting models [ 22 ], and the multinomial logit model for RM [ 23 ].

Few studies have explored the expansion and approval of revenue management practices within hospitality facilities. One such study is grounded on theories of transaction cost economics and the resource-based view [ 10 ], while others are based on grounded theory [ 24 , 25 ] and the theory of systems [ 11 ]. Contingency theory-based research in the domain of revenue management practices has been scarce. Consequently, it is essential to address this research gap. To date, the cumulative implications of practicing revenue management mostly in hospitality facilities have not been adequately covered in scholarly work. As far as strategies are concerned, practices of RM in hotels are entering into strategic roles from tactical roles, including those of advertising, sales, accounting, and channel distribution [ 26 ]. For instance, as changes take place, revenue management must use appropriate tools such as mobile applications and social media [ 26 , 27 , 28 ]. The use of social media in combination with RM functions results in novel practices that produce useful content for customers, generating additional revenues [ 29 ]. Revenue streams from workspaces, catering, food and beverage services, and retail, among other services, when effectively coordinated with revenues from room bookings, increase total hotel revenues and empower facilities to realize their goals of amplifying profits in extremely stern markets [ 30 ].

Research on revenue management is in its infancy [ 10 ]. More empirical studies in hospitality management should be done with a focus on key areas such as policy execution in RM [ 11 ]. Future empirical studies on RM should scrutinize whether findings are based on conditions of the hotel sectors or if hotels are failing to implement practical RM systems for various motives [ 12 ]. Research on the growing significance of ancillary hotel revenue has been limited. Previous studies provide little empirical proof of sensible applications of RM and of such systems in the hospitality sector. There is a continuous demand for empirical investigations on revenue management in the hospitality sector. Further, there is limited research on RM practices applied in the hotel sector from [ 10 , 11 , 12 ]. Therefore, a wide gap exists between the hypothetical development of RM and authentic applications of RM in hotels as highlighted by academicians and professionals, laying a foundation for the development of our theoretical framework.

Determinants of revenue management

The following internal features of a hotel have been revealed to have an impact on aspects of revenue management. Star ratings show a significant association with RevPAR and have a considerable impact on revenue management [ 31 ]. Sainaghi [ 32 ] suggested that when a hotel facility is located in a central location, this increases the approximated worth of its RevPAR. Hotel size shows an indirect relation between the number of guest rooms and RevPAR, and the number of employees adds value to occupancy and has an impact on RevPAR [ 32 ]. Hotel size and scale have been found to affect decision making regarding revenue management functions [ 33 ] . Founding and market orientation have an indirect relationship to RevPAR [ 32 ]. It is thus hypothesized that internal hotel factors such as room rates are related to the RM practices and financial performance of hotels.

Seasonality has some effects on hotel performance as a result of the misshaped schemes that result in alternative ways of using products in the tourism and hospitality industry [ 34 ]. Computerized RM necessitates the gathering of information and its interpretation for managerial use [ 35 ]. The hotel sector can be effected by delayed periods of vulnerability and unpredictability, economic volatility, variability in political circumstances, fear-based oppression, and pandemics [ 36 ]. Environmental dynamism resulting from changes in the market, clients, competition, and customer behavior significantly influences organizational performance [ 37 ]. Environmental complexities such as customer centralization, the differentiation of product and services, labor accessibility, and techniques brought about by technology have a positive influence on the performance of an organization [ 38 ]. It is thus hypothesized that there are relationships between seasonality, environmental dynamism, uncertainty, technological changes to RM practices and financial performance.

Financial performance of hotels

Hotel performance is believed to be the most influential facets hotel operation, affecting a hotel’s competitiveness among competitors and long-term effects on the financial sustainability of hotel [ 39 ]. The performance of a hotel is measured based on the total activities of various sub-sectors of the hotel industry [ 40 ]. In the recent past, researchers have carried out various studies on hotel performance and its metrics [ 12 , 33 , 41 , 42 , 43 , 44 ]. A mixed-method strategy that involves implementation strategies is linked to the highest levels of RevPAR [ 33 ]. Wadongo et al. [ 43 ] emphasized the need to codify and describe metrics for the performance of hotel indicators. The financial performance of hotels is usually quantified from the total revenue per available room [ 45 ]. Other metrics include the gross operating profit per available sq ft, revenue per available room [ 42 ], and the average rate per room [ 44 ]. These metrics are used as financial performance indicators for hotels and may be construed in returns [ 46 ]. Returns on investment may be used to measure performance [ 47 , 48 ].

Even though financial performance may be the focal impetus for embracing RM practices in hotels, empirical studies examining how RM practices are related to the financial performance of hotel have been limited. While studies on the financial performance of hotels have applied at most two indicators of performance, the proposed theoretical framework applies more metrics, including the gross operating profit per room, occupancy rates, the average rate per room, revenue per available room, and the total revenue per available room.

Relationships between RM practices and the financial performance of hotels

Several scholars have studied and found an association between hotel performance and RM practices, including payment policies regarding reservations [ 11 ], policies related to RM [ 49 ], pricing policies [ 50 ], revenue forecasting techniques [ 51 ], price optimization [ 52 ], social media strategies [ 29 ], accurate demand forecasting [ 53 ], non-mixed pricing [ 54 ], forecasting [ 55 ], RM system user benefit measurement [ 12 ], procedural room revenue maximization [ 56 ], and enhanced frameworks for the management of demand and optimization of prices [ 57 ].

More empirical hospitality management studies on key areas such as policy execution in RM have been called for Hernandez [ 11 ]. Future empirical studies on RM should scrutinize whether they are based on situational features of a hotel or if hotels are failing to implement practical RM systems for various purposes [ 12 ]. There is a need to understand cutting-edge revenue management strategies adopted in numerous settings and to further contribute to this emerging discipline by determining whether RM concepts can be generalized [ 10 , 11 ]. It is thus hypothesized that RM practices and policies and their implementation, techniques, and systems affect hotel financial performance.

Contributions of contingency theory to the present study

Contingency theory was developed from influential literature of the mid-1960s. The premise of contingency theory is that there is no exceptional arrangement performance management structure used by all or any organization at all times, though different organizations rely on influential and significant contingent situations [ 7 , 9 , 58 ]. Organization performance management is influenced by contingency factors such as innovation, technology, strategies, organizational initiatives, and external factors [ 6 ]. Hotel performance has also been found to be influenced by contingency elements such as a hotel’s dimensions, size, quality, and proximity to destinations such as towns and airports [ 8 ]. This hypothetical paper relies on the views of contingency research presented by Wadongo [ 6 ]. Wadongo proposes how to determine and oppose the connections between aspects of contingency and RM practices that provide an explanation for the projected theoretical framework. Over the years, researchers have confirmed an association between factors of contingency and performance in different organizations [ 6 , 7 , 8 , 9 ]. While past contingency research has conducted studies of one or two variables based on choice fit and linkage impacts, this is uncertain due to common elements among contingency factors. Further, most of these studies present hypothetical and methodological inadequacies resulting from examining few variables measurement errors and contradictory model results [ 7 ]. In addition, the predicted association between contingency factors studied and the performance of organizations has not been adequately explained [ 59 ]. As an example, the following factors have not been considered as probable amplifications of substantial associations, government support, risk-averse managers, high profit businesses, and a tendency to use what others liquidate. The associations are believed to be direct, while effects are said to equal, while some connections could also be curvilinear when several proportions of efficiency contingencies are considered [ 59 ].

An absence of replications of such investigations of diverse settings such as the hotel sector in developing countries and of a focus on present facets of RM practices restrict the ability to generalize and revive contingency theory through other major academic domains [ 5 ]. An analysis of related literature shows that it is essential to discover how contingency factors impact RM practices that have not been researched within the hotel sector. Regardless of the limitations of the theory of contingency raised, it is still a credible theory for pursuing a considerate association flanked by determinants of RM practices and hotel performance in the profoundly vibrant hotel industry.

It is thus hypothesized that a connection exists between contingency factors, practices of revenue management, and the financial performance of hotels. Three significant propositions based on existing ideas have been developed. The first suggests that contingency factors (internal and external determinants) affect revenue management practices. The second suggests that RM practices affect the financial performance of hotels. The third suggests that practices of revenue management mediate the relationship between hotel contingency factors and the financial performance of hotels.

Research implications and contributions

Generally, a theory aims at guiding practices and research of a particular filed; practice authorizes hypothesis testing, which produces investigations; these investigations enhance the building of hypotheses and selecting rules for practice; hence, hypothesis development and research interlink to create data for other fields [ 58 ]. RM researchers and hotel practitioners have been making constant calls for empirical investigations of RM practices; such studies are contributing to the growth of academic literature [ 10 , 11 , 12 ]. Nevertheless, studies focusing on RM practices and their influence on the financial performance of Kenyan hotels are scarce, and information on a theoretical framework for the same is limited. A theoretical framework is a structure that holds or strengthens a hypothesis that should be investigated and that illuminates why related research should be carried out [ 60 ].

Additionally, it is crucial to develop a theoretical framework that gives direction to subsequent investigations of RM practices used in hotels and to advance sound RM theorization on hotel management. To fill this gap, this paper proposes a theoretical framework. Further, based on contingency theory, which contemplates internal and external determinants, the paper contributes to the development of a standard and adaptable RM theory, RM practices, and the financial performance of hotels. The developed theoretical framework may fundamentally reinforce future investigations by supporting critical assessments of the theoretical suppositions, correlating the research with prevailing information, and enunciating the hypothetical foundations for study. The proposed framework can provide answers to “why and how” questions and identify the limitations associated with proposed generalizations. Further, the presented theoretical framework postulates that contingency determinants have a significant impact on the RM practices and financial performance of Kenyan hotels, illustrating appropriate hypotheses and propositions, and what should be measured.

Based on the arguments of contingency theory, the proposed hypothetical framework illuminates the impacts of contingency determinants on the revenue management and financial performance of Kenyan hotels. Past theoretical justifications and empirical studies of hotel management give direction in identifying the current theoretical framework. Highlights and validations for using the theory of contingency and its rare applications to studies on revenue management have been provided. RM practice and financial performance metrics have been adopted from past researches, modified, and accustomed as proposed. Three main propositions are made. The present work advances a theoretical framework that creates opportunities for future study. The paper does not present a completely new framework for RM practices of Kenyan hotels; rather, it proposes a theoretical framework that can guide practice and the development of hypotheses, and research on the RM practices of Kenyan hotels. Based on the literature from other academic disciplines, the paper strengthens the collective evidence for the conceptualization and description of revenue management and financial performance. This work presents an empirical study applying the proposed model through the use of a large cross-sectional survey. The proposed theoretical framework can help conceptualize and advance future studies on revenue management in hotels.

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Abbreviations

average daily rate

available room

gross operating profit per available room

Kenya National Bureau of Statistics

knowledge, skills, and abilities

revenue per available room

revenue per occupied room

revenue management system

return on investment

tourism regulatory authority

total revenue per available room

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Michael Murimi, is a student pursuing PhD in Hospitality Management at the Maseno University, Kenya, He holds a Bachelor of Science in Eco-tourism and Hospitality management degree from Egerton University, Kenya; a Master of science in Hospitality Management from Mount Kenya University. He is currently an assistant lecturer in Hospitality Management at Gretsa University, Kenya.

Billy Wadongo, is a senior lecturer in Maseno University, Kenya. He is also Managing Partner at SwiftCheck Kenya Consulting Ltd. He holds PhD in performance Management and Evaluation from University of Bedfordshire, MSc in Hospitality Management, and a BSc (First Class Hons) from Maseno University, Kenya, as well as a Diploma in Community Development and Project Planning and Management. Furthermore, he has previously worked as a lecturer in the UK and Kenya and published several peer-reviewed articles on performance management in international journals. Over the years, he has gained experience as performance management and measurement consultant and a training facilitator for corporates, publics, and non-profits in Kenya and the UK. In research and training, he is an active researcher in performance management and measurement, non-profit management, management accounting, qualitative, quantitative and mixed methods research designs, qualitative and quantitative data analysis, and training facilitation. He has gained vast experience in data collection and analysis. In particular, he has practical experience in sampling techniques (including LQAS), developing data collection instruments using Qualtrics Snap Surveys, Survey Monkey, KOBO; data collection and coding. He is excellent in qualitative data analysis (thematic and framework analysis) using QSR-NVIVO software and quantitative data analysis including factor analysis, multivariate statistics, time series, and structural equation modelling using IBM SPSS for windows, IBM-AMOS, STATA, Eviews, R software, Smart PLS, and EPInfo. Finally, he has demonstrated valuable experience in writing technical, research and evaluation reports as well as publications. More recently, Billy has gained certification in polygraph testing and analysis, EYEDetect systems, and fraud risk management.

Tom Olielo, is a senior lecturer Maseno University, Kenya. He holds a PhD Degree, Foods, Nutrition and Dietetics, Kenyatta University, MSc Degree, Food Science and Technology, Research, Nairobi University, MSc Degree, Food Science and Technology course, Zurich ETH Technical University, and BSc Degree, Agriculture, Nairobi University. He has published refereed journals and has served as a lecturer in senior management position in universities and public sector in Kenya.

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Murimi, M., Wadongo, B. & Olielo, T. Determinants of revenue management practices and their impacts on the financial performance of hotels in Kenya: a proposed theoretical framework. Futur Bus J 7 , 2 (2021). https://doi.org/10.1186/s43093-020-00050-9

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• Contingency theory
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• Hotels in Kenya
• Revenue management practices
• Financial performance of Kenyan hotels
• Revenue management

Airline revenue management with segmented continuous pricing: methods and competitive effects

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• Volume 23 , pages 14–27, ( 2024 )

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With the introduction of IATA’s New Distribution Capability (NDC), airlines will no longer be limited to discrete fare classes for their fare product distribution but could show fare quotes from continuous ranges to booking requests. NDC will also allow airlines to present different fare quotes to passengers from different demand segments as identified by the airline. In theory, airlines can better extract passenger willingness to pay, and thus, see gains in revenue, by offering segmented continuous fare quotes to different passengers requesting to book. This paper describes the revenue management (RM) methods for Segmented Continuous Pricing and examines their potential effects on airlines’ revenue through simulations in the Passenger Origin–Destination Simulator (PODS). We describe a class-based algorithm for continuous pricing, a straightforward extension from the traditional methods used with existing RM systems. Our simulation results show that in a calibrated scenario in which only one airline adopts Segmented Continuous Pricing and has an 80% accuracy in identifying business versus leisure passenger booking requests, the first-mover airline can see as much as a 17% revenue gain, at the expense of competitors. The revenue gains come primarily from the leisure passenger segment by offering lower fares than competitors closer to departure. The first-mover airline loses bookings but does not see losses in revenue from the business passenger segment. We also explore potential response strategies by the competing airlines. We discover that competitors can reverse the first-mover’s revenue gain by removing their fare restrictions while still using traditional RM methods. We conclude that although adopting Segmented Continuous Pricing is promising in theory, its gains in practice will depend heavily on the competitive situation and the responses made by competing airlines.

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Introduction

Most airline revenue management systems still rely on fare classes associated with fixed price points for flight itineraries. The New Distribution Capability (NDC) will allow airlines to adopt pricing methods that are no longer constrained by fixed price points and to offer fare quotes from a continuous range. With continuous pricing, it will also be possible for airlines to charge different prices to business and leisure passengers if the airline is able to distinguish booking requests from each demand segment. The combination of customer segmentation and continuous pricing is the topic of this paper: Segmented Continuous Pricing.

Previous studies by Papen ( 2020 ) and Liotta ( 2019 ) focused on testing the effects of continuous pricing mechanisms without customer segmentation. In those studies, all passengers are quoted a single fare from a continuous range. With NDC enabling airlines to generate segmented fare quotes, it is useful to extend the research to examine the combined effect of continuous pricing and customer segmentation and explore the competitive effects in realistic settings.

The main contribution of this paper is to explain the underlying mechanisms and provide an analysis of the potential effects of Segmented Continuous Pricing, in which airlines offer different continuous fare quotes to business and leisure passengers, respectively. First, we present a framework for continuous pricing methods and the mathematics behind the pricing models. The key differences between Segmented and Unsegmented continuous pricing are highlighted. Then, the Segmented Continuous Pricing methods are tested in the Passenger Origin–Destination Simulator (PODS). The effects of Segmented Continuous Pricing are examined under asymmetric competition (where one airline takes the role of a first mover). We also explore some possible counter measures by competitors using traditional revenue management methods, using an asymmetric competitive scenario that is calibrated in Long ( 2022 ).

Literature review

The term “dynamic pricing” has recently become popular in the airline industry, but its definition often varies. Wittman and Belobaba ( 2018 ) offered a general definition of dynamic pricing: “Firms practice dynamic pricing when they charge different customers different prices for the same product, as a function of an observable state of nature.” Wittman and Belobaba ( 2019 ) constructed a definitional framework for dynamic pricing and described three mechanisms for its implementation: assortment optimization, dynamic price adjustment, and continuous pricing.

In assortment optimization, firms decide which subset of a finite set of price points is made available to the customer at different times over the booking process. With dynamic price adjustment, airlines can choose to quote fares that deviate from the fixed price points to certain segments of passengers based on additional transactional information they receive. Wittman ( 2018 ) developed the Probabilistic Fare-Based Dynamic Adjustment (PFDynA) method for dynamic price adjustment, and Wittman and Belobaba ( 2018 ) demonstrated in simulations that airlines can gain revenues when dynamic price adjustment is deployed. A working group organized by the Airline Tariff Publishing Company (ATPCO) proposed a set of new specifications for “Dynamic Pricing Engines” that allow airlines to mark up or mark down their pre-filed fares for certain booking requests (Dezelak and Ratliff 2018 ).

The third mechanism in this dynamic pricing framework is continuous pricing, where firms can choose a price from a continuous range without having any pre-filed menu of discrete price points. Continuous pricing requires significant changes to existing revenue management and distribution tools for its implementation (Wittman and Belobaba 2019 ). Westermann ( 2006 ) suggested a continuous pricing mechanism in which revenue-maximizing fares are calculated based on passenger WTP, competitor fares and other contextual information. Westermann ( 2013 ) explained how continuous pricing can be realized in an NDC environment.

Details of practical implementations have been presented by Bala ( 2014 ), who discussed the potential benefits and risks of implementing continuous pricing and proposed an “automated fare filing” process, in which filed fares are updated continuously to approximate the effect of continuous pricing. More recently, Lufthansa Group ( 2020 ) described the implementation of a limited form of continuous pricing applied to existing fare structures and used in direct distribution channels.

There has also been substantial research devoted to the theoretical concepts required for possible future implementations. Liotta ( 2019 ) explained several algorithms for generating continuous fare quotes and simulated the revenue benefit from continuous pricing over traditional airline revenue management. Papen ( 2020 ) also showed in simulations that airlines can see increases in revenues with Unsegmented continuous pricing, where airlines offer a single fare quote from a continuous range to all passengers. Szymanski et al. ( 2021 ) explored different algorithms for determining unsegmented continuous fare quotes, both class-based and classless approaches.

NDC enables the use of contextual information in determining the offered price and allows airlines to use the information to distribute personalized offers (Westermann 2013 ). Belobaba ( 2016 ) suggested that airlines can segment the market demand by trip purpose, price sensitivity, and time sensitivity. Teichert et al. ( 2008 ) used behavioral and social-demographic factors to identify passenger segments and argued for segmenting passengers into more than just two categories (i.e., business and leisure travelers), as they do not sufficiently capture the passenger heterogeneity. Bruning et al. ( 2009 ) studied segmentation of passengers in NAFTA countries (USA, Canada, and Mexico), and identified five passenger segments through a cluster analysis. Similar studies have also been conducted on passengers in Serbia (Kuljanin and Kalić 2015 ) and Greater China (Chen and Chao 2015 ; Pan and Truong 2020 ), and different discriminant factors were identified.

Although there have been studies done on continuous pricing and passenger segmentation respectively, little research has been done on the combination of both elements in the context of airline revenue management. This paper provides a comprehensive analysis of the combined effect of passenger segmentation and continuous pricing in airline revenue management through simulations in PODS.

Methods and models for continuous pricing

We first introduce a high-level framework for continuous pricing methods to illustrate the distinctions between two types of continuous pricing algorithms: “Class-Based Continuous Pricing” (CBC) and “Classless Continuous Pricing.” In this paper, we focus on the formulation and simulated performance of the CBC algorithm. The segmented fare quotation process for continuous pricing is described, and the implementation of Segmented Continuous Pricing is explained.

Class-based vs. classless continuous pricing

There are two types of continuous pricing algorithms: Class-Based Continuous Pricing (CBC) and Classless Continuous Pricing. Both methods generate continuous fare quotes to passengers, but CBC still requires airlines to keep historical data of bookings and fare values by fare class while Classless Continuous Pricing does not. Figure  1 provides an overview of the main differences between traditional class-based RM, Class-Based Continuous Pricing, and Classless Continuous Pricing.

Differences between Traditional RM, CBC, and Classless Continuous Pricing. (Papen 2020 )

The difference between CBC and traditional class-based RM lies in the fare quote generation. As illustrated in Fig.  1 , CBC relies on booking and revenue data by fare class just like traditional RM, and thus, it can use the same forecasters and optimizers. In bid price control for traditional RM, the airline determines its fare class availabilities by comparing the adjusted fare values of the requested itinerary with the sum of the bid prices over the flight legs to be traversed. This is equivalent to comparing the unadjusted nominal fare values and the sum of the traversed bid prices plus a marginal revenue fare modifier that reflects the optimizer fare decrement from the fare adjustment process.

Assuming a negative exponential demand model, the fare modifier is calculated by the following equation:

where \(f_{q}\) is the fare value of the lowest fare class, and \(FRAT5_{t}\) is the airline’s estimate of the median conditional willingness to pay (WTP) of total (unsegmented) demand relative to the lowest fare class during timeframe \(t\) in the booking horizon. Specifically, each FRAT5 value is the fare ratio to which 50% of demand is expected to sell up—a FRAT5 equal to 2.5 with lowest fare $100 reflects a median WTP estimate of $250.

In Class-Based Continuous Pricing, the airline offers a continuous fare quote that equals the sum of traversed flight leg bid prices ( \(BP_{l}\) ) and the fare modifier. Both the bid prices and FRAT5 values are for the current timeframe, as both will change over the remainder of the booking horizon. For simplicity, in the remainder of this paper we omit the subscript t, which is implied. To prevent extreme fares, the generated fare quote can be limited to be between the lowest filed fare \(f_{Q}\) . and the highest filed fare \(f_{Y}\) . In the PODS simulations below, these constraints on the continuous fare quotes are always applied. The offered fare is, thus, calculated by

Figure  2 provides a detailed illustration comparing the processes of CBC and traditional RM that are used in this paper. In this example, Q-forecasting and discrete fare adjustment is used since there is no differentiation between the fare quotes for different passengers. The adjusted fares and re-partitioned forecasts are then fed into the same traditional class-based ProBP optimizer to calculate the bid prices. The processes only diverge in the last step after the bid price calculation.

RM processes for traditional ProBP and class-based continuous ProBP

Classless Continuous Pricing, on the other hand, does not depend on any information from the pre-determined fare classes. Classless Continuous Pricing optimizes over booking timeframes instead of fare classes. Q-forecasting is used as the forecaster, but the observed bookings are recorded by timeframes only rather than by both fare classes and timeframes. For more details on the Classless Continuous Pricing method and its revenue effects, see Liotta ( 2019 ) and Papen ( 2020 ).

Unsegmented vs. segmented continuous pricing

With the continuous pricing method described above, a single fare quote is generated at each point in the booking horizon and offered to all passengers shopping for a ticket. Although the fare quote generation process does consider the combined willingness to pay of all passengers arriving at each timeframe through the FRAT5 inputs, it does not consider the differences in willingness to pay between different types of passengers. Furthermore, the continuous fare quotes are not differentiated through restrictions or advance purchase requirements. Passengers cannot self-select into different segments like they do with differentiated fares in traditional class-based revenue management.

With Segmented Continuous Pricing, an airline can generate different continuous fare quotes for distinct passenger groups with various levels of willingness to pay, if the airline can identify passenger requests from each segment with a certain identification accuracy. We assume two segments of passenger demand: business and leisure, with business passengers having higher willingness to pay than leisure passengers.

The difference between Unsegmented Continuous Pricing and Segmented Continuous Pricing lies in the fare quotation step only. Instead of adding a single unsegmented MR modifier to the bid prices, different MR modifiers are used to generate the segmented fare quotes for business and leisure passengers, respectively. The segmented MR modifiers are calculated by:

where \(SegWTP_{B}\) and \(SegWTP_{L}\) are the segmented willingness to pay estimates for business and leisure passengers, respectively, at any given timeframe in the booking horizon. Similar to the FRAT5 values, these segmented WTP values represent the airline’s estimates of the median conditional WTP of the passengers, relative to the lowest fare class, in each demand segment. It is important to note that these segmented willingness to pay estimates are used for calculating the segmented MR modifiers only. The previously described aggregated FRAT5 values are still used for forecasting, fare adjustment, and bid price calculations.

Each passenger shopping for a ticket is identified as a business or leisure passenger at the time of booking request and is shown only one fare quote according to the identification result. The differences between the fare generation processes for Unsegmented and Segmented Continuous Pricing are highlighted in Fig.  3

Comparison between Unsegmented and Segmented Continuous Pricing

Passenger segment identification accuracy

In Segmented Continuous Pricing, the airline is assumed to have some ability to correctly identify the passenger segment of an incoming booking request. In practice, characteristics of the booking request can help airlines distinguish between business and leisure travel requests. For example, requests made far in advance of departure (e.g., 30 days), for round-trip travel involving a longer stay at the destination (e.g., 6 days or over a weekend), for multiple passengers on the same itinerary (e.g., 4) are much more likely to be for leisure than business travel.

However, such an identification process can have imperfect accuracy and lead to misidentification of booking requests. In the PODS simulations below, scenarios with both perfect and imperfect identification accuracies are tested. In cases where the identification has imperfect accuracy, misidentification is assumed to be equally likely to happen to both business and leisure passengers. For example, with an assumed 80% identification accuracy, 20% of the business passengers from the total business demand will be identified to be leisure passengers, and vice versa. The misidentified passengers will be quoted the continuous fare generated for the other segment.

Simulation study and results

The potential benefits and competitive effects of implementing Segmented Continuous Pricing were tested with the Passenger Origin–Destination Simulator (PODS). In this section, we first provide a brief overview on the PODS simulation mechanism, followed by descriptions of the experiments where only one airline in a competitive network implements Segmented Class-Based Continuous (CBC) Pricing. We focus on testing the Class-Based method for Segmented Continuous Pricing. We believe the CBC results are more relevant for airlines looking to invest in Continuous Pricing as they are more likely to start with a Class-Based method rather than a Classless method that requires extensive changes to their current RM systems. Lastly, we present the simulation results with detailed analyses and discussion on the competitive effects of Segmented Continuous Pricing. For analysis of effects of Unsegmented Continuous Pricing, see Papen ( 2020 ) and Szymański et al. ( 2021 ).

Passenger origin–destination simulator

The Passenger Origin–Destination Simulator (PODS) replicates the real-world interactions between passengers and airline RM systems and provides an integrated platform for testing different RM methods. We present here the details of the simulation environment most relevant to the current work. For a more comprehensive overview of PODS, see Fiig et al. ( 2010 ) or Wittman ( 2018 ).

Fundamentally, the PODS software consists of two main building blocks: passenger choice model and airline RM systems. As illustrated in Fig.  4 , the software iteratively simulates the interactions between passengers and airlines and reports detailed performance metrics to provide insights on the effects of the tested RM methods.

Schematic of interactions between passengers and airlines in PODS (Wittman 2018 )

The airline RM system in PODS has two major components: a forecaster and an optimizer. The forecaster uses the recorded historic booking data from previous simulated departures to forecast the demand by itinerary (path) and fare class for each future departure date. Given the forecasted demand, the optimizer calculates bid prices and/or fare quotes to maximize the expected revenue for the airline. Like airline RM systems in the real world, the RM systems in PODS do not know the true underlying passenger demand characteristics: the PODS RM systems only use estimates of passenger WTP (i.e., FRAT5 values) as inputs. The underlying WTP of business and leisure passengers generated in the PODS simulation does not change over different timeframes in the booking horizon. However, the mix of business and leisure passengers in the total demand does change from timeframe to timeframe. Thus, the aggregate WTP (as well as FRAT5 values) will increase over the booking horizon.

The simulation tests reported in this paper were performed in Network U10. As illustrated in Fig.  5 , Network U10 is an international network with four competing airlines. Airline 3 is intended to represent a low-cost carrier (LCC). It is smaller than the other three competitors as it only serves domestic O-D markets. Each airline operates from a hub city and serves both the local O-D markets as well as the coast-to-coast O-D markets through connections at the hub. There are 572 O-D markets served in Network U10 with a total of 40 spoke cities in addition to the 4 hubs.

Airline flight networks in PODS Network U10

In each O-D market, every airline offers the same 10-class fare structure in an all-economy cabin. In Network U10, there are three different fare products (FP) for several types of O-D markets. In domestic O-D markets, the airlines use either FP1 or FP2. In markets where Airline 3 is present, the less differentiated FP2 fare structure is used to simulate the effect of competition from an LCC with a simplified fare structure. In domestic markets without the presence of Airline 3, the more restricted and more differentiated FP1 fare structure is applied. The restricted FP3 fare structure is used in all international O-D markets.

The details on fare structures and restrictions for the three fare products are provided in Table 1 . R1, R2, R3, and R4 represent the various restrictions that are associated with each of the fare classes. Among the four restrictions, R1 is the strongest restriction that adds the most disutility to the fare, followed by R3, R4, and R2 in the order of decreasing associated disutilities. The average lowest fares of the three fare products are $166, $157, and $476, while the average fare values of the highest FCL1 are $669, $520, and $1444, respectively, resulting in high-to-low fare ratios of about 3.5:1.

Simulation setup: RM methods

Probabilistic Bid Price (ProBP) is the network RM optimizer used in our simulations, and the class-based continuous pricing optimizers are derived from traditional ProBP. Developed by Bratu ( 1998 ), ProBP is an iterative convergence algorithm that determines the bid price of each flight leg in an airline’s network. The bid price control logic is then applied to determine the fare class availabilities for each itinerary request. To update the bid prices in the simulation tests, ProBP is executed daily after decrementing the forecast input by the number of forecasted bookings for that day.

Q-forecasting and fare adjustment are used in conjunction with the continuous pricing optimizers since the continuous fare quotes are not differentiated through restrictions or advance purchase requirements. In Q-forecasting, the observed bookings are converted to an equivalent demand at the lowest filed fare in each timeframe before departure. (Hopperstad and Belobaba 2004 ). The equivalent Q-demand for the timeframe is re-partitioned back into the higher fare classes to generate forecasts for each of the fare classes, using estimates of sell-up from the lower fare class. The re-partitioned timeframe forecasts from all booking timeframes are then summed to generate a total demand-to-come forecast by path/class for the RM optimizer.

To account for potential buy-down to the lowest available fare class, fare adjustment in the optimizer is needed in addition to Q-forecasting (Fiig et al. 2010 ). The RM optimizers are fed with decremented fare values for lower fare classes to account for the opportunity costs (i.e., potential lost revenue) associated with passenger buy-down.

FRAT5 estimates of WTP for total demand at each timeframe are used in both Q-forecasting and fare adjustment. It is important to note that the FRAT5 values fed as inputs to the Q-forecasting and fare adjustment algorithms represent the airline’s estimation of passengers’ median conditional WTP but not the actual underlying true WTP of the passengers in the simulation.

We first establish a baseline scenario in which all airlines in PODS Network U10 use traditional RM methods and the standard restricted fare structure shown in Table 1 . The RM settings for the baseline experiment are listed in Table 2 . The airlines use a set of pre-defined FRAT5 values (FRAT5 C) as their conditional WTP estimates as shown in Fig.  6 .

Airline estimates of FRAT5 values by timeframe in the FRAT5 C curve

Simulation results

We focus here on the scenario with AL1 implementing Segmented Class-Based Continuous Pricing and compare the results to the baseline scenario where all airlines use traditional class-based RM. We simulated two sets of segmented WTP estimates as inputs—constant and sloped-segmented WTP estimates. Long ( 2022 ) found that while the use of constant WTP estimates over the booking horizon can lead to revenue gains, even greater gains were possible by using sloped WTP estimates for both leisure and business demand segments.

The segmented WTP estimate and passenger identification accuracy settings for the first-mover airline (AL1) are summarized in Table 3 . The segmented WTP estimate curves used by AL1 for continuous fare quote generation, along with the FRAT5 C curve used for forecasting and fare adjustment, are shown in Fig.  7 .

Segmented WTP estimate curves and FRA5 C sell-up estimates used by AL1

The airlines’ revenues and load factors are presented in Figs. 8 and 9 . Compared to the baseline scenario, AL1 sees + 16.8% more revenue from asymmetrically using Segmented Continuous Pricing with sloped business and leisure WTP estimate curves. The competing airlines with traditional RM systems see revenue losses of 1 to 4%. AL1 also sees a substantial increase in its load factor while the international network carrier competitors (AL2 and AL4) see lower load factors than in the baseline.

Airline revenues with AL1 asymmetrically using Segmented Continuous Pricing

Airline load factors with AL1 asymmetrically using Segmented Continuous Pricing

Table 4 shows AL1’s changes in bookings and revenues in each passenger segment from the baseline. AL1’s + 16.8% revenue gain with sloped WTP estimate curves is much larger than the + 3.4% it saw using constant segmented WTP estimate curves (B = 3.0, L = 1.2). With constant-segmented WTP estimates, AL1 sees its revenue gains mainly from undercutting competitors in the leisure segment with its low-segmented leisure care quotes. However, AL1 loses bookings and revenue in the business segment with its high segmented business fare quotes, especially in the early timeframes. With sloped segmented WTP estimates, AL1’s low business fare quotes remain attractive to the business passengers in early timeframes and AL1 sees large recoveries in bookings and revenue from the business segment. At the same time, AL1 still sees large increases in bookings and revenue from the leisure passengers by offering less expensive, unrestricted fare quotes targeted at leisure passengers.

Table 5 shows the changes in bookings and revenues for each passenger segment of AL2, the largest competing airline with traditional RM. Contrary to AL1’s results, the competitor AL2 sees losses in both bookings and revenues in the leisure segment as it loses leisure passengers to AL1. On the other hand, AL2 sees gains in business revenue and bookings as it accommodates some of the business passengers that are rejected by AL1’s more expensive segmented business fare quotes. Overall, the competitor AL2 sees small losses in total revenue and bookings.

Since AL1 sees higher total revenue without seeing losses in revenue from the business passenger segment in the sloped WTP estimate scenario, we assume that the first-mover airline would be more likely to use sloped WTP estimate curves with its asymmetric adoption of Segmented Continuous Pricing.

Competitors respond by removing restrictions

With asymmetric Segmented Continuous Pricing, AL1 offers continuous fare quotes that are free of fare restrictions. This gives AL1 an advantage over the competitors as these unrestricted fares have no restriction disutilities and, thus, can be very attractive to the passengers. The competitors with traditional RM systems may choose to remove the restrictions associated with their fares as a response, allowing them to also offer fares with no restriction disutilities.

In this test, AL2/3/4 remove all the restrictions in their fares while keeping the advance purchase requirements, as presented in Table 6 . All four airlines continue to use the same RM settings as in the above Sloped WTP Estimate Scenario.

The airlines’ revenues are shown in Fig.  10 . With the competitors’ removal of fare restrictions, AL1 is not able to maintain its revenue gains from asymmetric adoption of Segmented Continuous Pricing and sees a large − 17.2% total revenue loss as compared to the baseline scenario in which it uses traditional RM. On the other hand, the competitors see full revenue recoveries and see small revenue gains compared to the baseline scenario.

Airline revenue changes with AL2/3/4 removing non-AP restrictions

These changes in airlines’ total revenues are directly related to the changes in the airlines’ load factors, as shown in Fig.  11 . AL1 sees a large drop in its load factor as it loses passengers to the competitors who now offer fares without restrictions. At the same time, the competitors see much higher load factors that contribute to their revenue recovery.

Airline load factors with AL2/3/4 removing non-AP restrictions

To further explain the competitive impacts, we focus on analyzing the results of AL2, the largest competing airline using traditional RM. Table 7 summarizes AL2’s revenue and booking changes in each passenger segment as it removes fare restrictions along with the other traditional RM competitors. Since business passengers are more sensitive to fare restrictions, AL2 sees a large increase in business bookings, and consequently a much higher load factor, compared to both the Traditional RM baseline and the Sloped WTP Estimate Scenario. Despite the buy-down in the business segment, the increase in business bookings leads to a further gain in AL2’s business revenue that contributes to its full recovery in total revenue. Although AL2 also sees some small recovery in leisure bookings, it does not see recovery in leisure revenue with the leisure passengers paying less on average due to buy-down.

With the traditional RM airlines regaining their revenues from the business segment, AL1 sees the opposite changes: it sees large losses in both bookings and revenues in the business segment. Since the competitors also offer unrestricted fares, AL1’s expensive segmented business continuous fare quotes become even less attractive to the business passengers.

Table 8 summarizes AL1’s revenue and booking changes in each passenger segment with the traditional RM competitors removing their fare restrictions. AL1 sees enormous losses in business bookings and revenue compared to the traditional RM baseline, as it only retains about half of the bookings and revenue from business passengers. AL1’s asymmetric continuous pricing still offers some advantage in attracting leisure bookings. However, the relatively small gains in the leisure segment cannot offset AL1’s dramatic losses in the business segment. Overall, AL1 still sees a large drop in total revenue and load factor.

These test results show that AL1 cannot maintain its revenue benefit from asymmetric Segmented Continuous Pricing and sees a large revenue loss compared to the baseline when the competitors respond by removing their fare restrictions. (AL1’s relatively expensive but unrestricted business segmented fare quotes become highly undesirable to the business passengers when compared to the lower and now unrestricted fares offered by the competitors.) In Long ( 2022 ), tests were also conducted on a scenario where competitors remove their AP requirements but not the non-AP restrictions as the competitive response. The results show that it is an unhelpful strategy, causing the competitors to see further revenue losses with AL1 still maintaining a large revenue advantage over the other airlines.

In this paper, we simulated and analyzed the potential competitive impacts of Segmented Continuous Pricing in the airline industry through experiments in the Passenger Origin–Destination Simulator (PODS). We examined the effects of asymmetric use of Segmented Continuous Pricing where one airline moves ahead of others in adopting the method. In initial asymmetric tests with constant segmented WTP estimate inputs, the first-mover airline sees revenue gains that came mainly from undercutting the competitors in the leisure segment by offering inexpensive, unrestricted fare quotes to late-arriving leisure demand, which could lead to poor competitive stability. AL1 also saw large booking and revenue losses in the business segment, as it offers much higher segmented fare quotes to the business passengers. Such losses in the business segment would be undesirable for real-world network airlines.

In an attempt to mitigate these problems, we also tested the idea of using sloped segmented WTP estimate curves with asymmetric Segmented Continuous Pricing. The test results show that AL1 can see significant booking and revenue recovery in the business segment using sloped business WTP estimate curves that have lower values in the early timeframes. The extent of undercutting in the leisure segment can also be reduced with AL1 using sloped leisure WTP estimate curves that have higher values in the late timeframes. With sloped WTP estimate curves, AL1 sees a large revenue gain of about 17% from the baseline, while the competitors see revenue losses of about 1% to 4%. It is important to note that the various WTP estimates in our simulations were parametric and not actually estimated from historical booking data. In a simulation world, the tested FRAT5 and SegWTP estimated led to very good revenue gains for Segmented Continuous Pricing but could well over-estimate both the gains achievable in the real world and, in turn, the magnitude of competitive feedback.

We explored a potential response strategy by the competing airlines with traditional RM systems and assessed their effectiveness against the first-mover airline using Segmented Continuous Pricing with sloped WTP estimates. Since the first-mover airline offers continuous fare quotes that are restriction free, it has an advantage over the competitors as the unrestricted fares can be highly attractive to the passengers. To respond, competitors with traditional RM could choose to remove the restrictions associated with their fares, while keeping advance purchase rules. Our test results show that this can be an effective response strategy: the competitors can see full revenue recoveries, while the first-mover airline loses all the revenue benefit from asymmetric Segmented Continuous Pricing and sees a large revenue loss of about 17% compared to the traditional RM baseline. These simulation results suggest that the main advantage of asymmetric adoption of Segmented Continuous Pricing comes from the unrestricted continuous fare quotes rather than from the better pricing granularities, and the first-mover airline may suffer from large revenue losses if the competitors respond by reducing the restrictions in their fare structures.

Although the simulation results suggest that using Segmented Continuous Pricing could lead to revenue benefits in theory, we cannot overlook the potential competitive vulnerabilities of asymmetric Segmented Continuous Pricing to competitor responses. The potential revenue benefits from Segmented Continuous Pricing appear to be highly dependent on competitors’ decisions, raising concerns about competitive stability in the long run. When the competitors try to recover their revenue losses by also offering unrestricted fares, the first mover could lose its revenue leverage from Segmented Continuous Pricing and see revenue loss instead. Consequently, the first-mover airline may need to explore ways to recover its losses, and such consecutive competitive responses could eventually lead to a spiral-down effect and negatively affect the revenues of all airlines competing in the same markets.

Directions for future research

As we found that asymmetric adoption of Segmented Continuous Pricing can be highly vulnerable to competitors’ responses, future work could be done on finding potential ways to incorporate competitive information in the continuous pricing methods. For instance, such competitor-aware methods could use information on competitors’ lowest available fares to model the probability of spilling passenger to competitors, and thus, allows the first-mover airline to adjust its prices accordingly. This could potentially improve the robustness of the Segmented Continuous Pricing methods in competitive situations.

Furthermore, in the Segmented Continuous Pricing algorithms presented in this paper, the segmentation process occurs in the fare quote generation step by using different segmented WTP estimate values for business/leisure passengers. The demand forecaster, however, does not distinguish the bookings from the two segments and uses aggregated WTP estimates (the FRAT5 values) to generate the demand forecasts that are subsequently fed into the optimizer. It could be useful to develop a new forecaster that incorporates the segmented WTP information in the demand forecasting step and generates separate demand forecasts for each passenger segment, as it may lead to more accurate estimates on the overall passenger demand, and potentially greater revenue advantage.

In addition to fare quote segmentation, we could also explore ways to combine product differentiation with the continuous pricing methods. In the tests conducted in this paper, airlines adopting Segmented Continuous Pricing offer one continuous fare quote to each passenger. While business and leisure passengers are offered different prices, the fares are not differentiated from each other by fare restrictions or ancillary services. With product differentiation, each passenger could be offered multiple fare options at booking request. The fare options are differentiated from each other by having different restrictions and/or ancillary services and passengers can choose their most desired fare option. Future research work could also be done on new RM algorithms that integrate the continuous pricing methods with dynamic offer generation (DOG) to generate such fare options.

Data availability

Not applicable.

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Long, Y., Belobaba, P. Airline revenue management with segmented continuous pricing: methods and competitive effects. J Revenue Pricing Manag 23 , 14–27 (2024). https://doi.org/10.1057/s41272-023-00462-6

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Victor M. Mukhin was born in 1946 in the town of Orsk, Russia. In 1970 he graduated the Technological Institute in Leningrad. Victor M. Mukhin was directed to work to the scientific-industrial organization "Neorganika" (Elektrostal, Moscow region) where he is working during 47 years, at present as the head of the laboratory of carbon sorbents.     Victor M. Mukhin defended a Ph. D. thesis and a doctoral thesis at the Mendeleev University of Chemical Technology of Russia (in 1979 and 1997 accordingly). Professor of Mendeleev University of Chemical Technology of Russia. Scientific interests: production, investigation and application of active carbons, technological and ecological carbon-adsorptive processes, environmental protection, production of ecologically clean food.   

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