Continuous pricing algorithms for airline RM: revenue gains and competitive impacts
Traditionally, airlines have been limited to a set of fixed fare classes and, in turn, price points, to distribute their fare products. The advent of IATA’s new distribution capability (NDC) will soon enable airlines to quote any fare from a continuous range. In theory, such continuous pricing could...
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| Published in | Journal of revenue and pricing management Vol. 20; no. 6; pp. 669 - 688 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Palgrave Macmillan UK
01.12.2021
Palgrave Macmillan |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1476-6930 1477-657X |
| DOI | 10.1057/s41272-021-00350-x |
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| Abstract | Traditionally, airlines have been limited to a set of fixed fare classes and, in turn, price points, to distribute their fare products. The advent of IATA’s new distribution capability (NDC) will soon enable airlines to quote any fare from a continuous range. In theory, such
continuous pricing
could increase revenues by extracting more of the consumer surplus, through its ability to offer more granular fares, closer to the customer’s willingness-to-pay (WTP). In this article, we describe several algorithms that lead to the quotation of a single fare from a continuous range. These algorithms either rely on traditional fare classes for the purpose of forecasting and optimization (
class-based
), or completely abandon the notion of fare classes, instead assuming different WTP distributions within each booking period prior to departure (
classless
). We describe how these algorithms build upon and differ from their traditional RM counterparts. Performance of these heuristics is then benchmarked against traditional class-based RM, and competitive impacts are analyzed when continuous pricing is adopted by one airline asymmetrically or both airlines symmetrically in a hypothetical 2-carrier network in the passenger origin–destination simulator (PODS). We find that continuous pricing is generally revenue-positive, and the revenue gains can be as high as 2.0% for the first-mover and reach up to 1.2% when both airlines adopt the new method. In addition, we show that these gains depend on the number of fare classes in the traditional fare structure used as a baseline, and that they are smaller under lower demand-to-capacity ratios. |
|---|---|
| AbstractList | Traditionally, airlines have been limited to a set of fixed fare classes and, in turn, price points, to distribute their fare products. The advent of IATA’s new distribution capability (NDC) will soon enable airlines to quote any fare from a continuous range. In theory, such
continuous pricing
could increase revenues by extracting more of the consumer surplus, through its ability to offer more granular fares, closer to the customer’s willingness-to-pay (WTP). In this article, we describe several algorithms that lead to the quotation of a single fare from a continuous range. These algorithms either rely on traditional fare classes for the purpose of forecasting and optimization (
class-based
), or completely abandon the notion of fare classes, instead assuming different WTP distributions within each booking period prior to departure (
classless
). We describe how these algorithms build upon and differ from their traditional RM counterparts. Performance of these heuristics is then benchmarked against traditional class-based RM, and competitive impacts are analyzed when continuous pricing is adopted by one airline asymmetrically or both airlines symmetrically in a hypothetical 2-carrier network in the passenger origin–destination simulator (PODS). We find that continuous pricing is generally revenue-positive, and the revenue gains can be as high as 2.0% for the first-mover and reach up to 1.2% when both airlines adopt the new method. In addition, we show that these gains depend on the number of fare classes in the traditional fare structure used as a baseline, and that they are smaller under lower demand-to-capacity ratios. Traditionally, airlines have been limited to a set of fixed fare classes and, in turn, price points, to distribute their fare products. The advent of IATA’s new distribution capability (NDC) will soon enable airlines to quote any fare from a continuous range. In theory, such continuous pricing could increase revenues by extracting more of the consumer surplus, through its ability to offer more granular fares, closer to the customer’s willingness-to-pay (WTP). In this article, we describe several algorithms that lead to the quotation of a single fare from a continuous range. These algorithms either rely on traditional fare classes for the purpose of forecasting and optimization (class-based), or completely abandon the notion of fare classes, instead assuming different WTP distributions within each booking period prior to departure (classless). We describe how these algorithms build upon and differ from their traditional RM counterparts. Performance of these heuristics is then benchmarked against traditional class-based RM, and competitive impacts are analyzed when continuous pricing is adopted by one airline asymmetrically or both airlines symmetrically in a hypothetical 2-carrier network in the passenger origin–destination simulator (PODS). We find that continuous pricing is generally revenue-positive, and the revenue gains can be as high as 2.0% for the first-mover and reach up to 1.2% when both airlines adopt the new method. In addition, we show that these gains depend on the number of fare classes in the traditional fare structure used as a baseline, and that they are smaller under lower demand-to-capacity ratios. |
| Author | Szymański, Bazyli Papen, Alexander Belobaba, Peter P. |
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| References | CR2 CR4 CR3 Gallego, van Ryzin (CR8) 1994; 40 CR18 CR17 Belobaba (CR1) 1989; 37 Lautenbacher, Stidham Jr. (CR10) 1999; 33 CR13 Fiig, Goyons, Adelving, Smith (CR7) 2016; 15 CR11 Wittman, Belobaba (CR19) 2019; 18 Chen, Chen (CR5) 2015; 24 Walczak, Mardan, Kallesen (CR15) 2010; 9 Westermann (CR16) 2013; 12 Liu, van Ryzin (CR12) 2008; 10 Fiig, Isler, Hopperstad, Belobaba (CR6) 2010; 9 Talluri, van Ryzin (CR14) 2004; 50 Gallego, van Ryzin (CR9) 1997; 45 Zhang, Zhaosong (CR20) 2013; 25 Qian Liu (350_CR12) 2008; 10 Dan Zhang (350_CR20) 2013; 25 Thomas Fiig (350_CR7) 2016; 15 350_CR11 Darius Walczak (350_CR15) 2010; 9 Dieter Westermann (350_CR16) 2013; 12 350_CR4 350_CR13 Peter P Belobaba (350_CR1) 1989; 37 350_CR3 350_CR2 350_CR18 350_CR17 Thomas Fiig (350_CR6) 2010; 9 Guillermo Gallego (350_CR8) 1994; 40 Ming Chen (350_CR5) 2015; 24 Kalyan Talluri (350_CR14) 2004; 50 Conrad J Lautenbacher (350_CR10) 1999; 33 Michael D Wittman (350_CR19) 2019; 18 Guillermo Gallego (350_CR9) 1997; 45 |
| References_xml | – volume: 25 start-page: 102 issue: 1 year: 2013 end-page: 115 ident: CR20 article-title: Assessing the value of dynamic pricing in network revenue management publication-title: INFORMS Journal on Computing doi: 10.1287/ijoc.1110.0488 – volume: 37 start-page: 183 issue: 2 year: 1989 end-page: 197 ident: CR1 article-title: Application of a probabilistic decision model to airline seat inventory control publication-title: Operations Research doi: 10.1287/opre.37.2.183 – volume: 40 start-page: 999 issue: 8 year: 1994 end-page: 1020 ident: CR8 article-title: Optimal dynamic pricing of inventories with stochastic demand over finite horizons publication-title: Management Science doi: 10.1287/mnsc.40.8.999 – ident: CR18 – ident: CR3 – ident: CR4 – ident: CR2 – volume: 15 start-page: 360 issue: 5 year: 2016 end-page: 379 ident: CR7 article-title: Dynamic pricing—The next revolution in RM? publication-title: Journal of Revenue and Pricing Management doi: 10.1057/rpm.2016.28 – 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| SubjectTerms | Air fares Airline industry Airlines Algorithms Business and Management Pricing policies Research Article |
| Title | Continuous pricing algorithms for airline RM: revenue gains and competitive impacts |
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