Abstract
Sabre has developed a new decision support prototype for proactive pricing and price leadership to help airlines achieve their overall marketing objectives. The strategic pricing business process described in this article is recognized as important and introduces a new paradigm for establishing fare levels for airline pricing departments. Our proposed approach is analogous to revenue opportunity modeling but applied to airline pricing. It involves perfect hindsight review of price and sales information from prior seasons and applying methods such as price elasticity, game theory and optimization models to estimate the price-level changes needed to further improve revenue, share or yields. By knowing the direction and magnitude of recommended changes in previous seasons, these hindsight review tools help pricing managers make better price-setting decisions for future seasons.
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Notes
The Demo example data are intended to be as realistic as possible. Unfortunately actual airline pricing and sales history could not be used in this article for confidentiality reasons.
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Acknowledgements
The authors wish to thank numerous individuals for their collaboration in our research efforts over the years and helpful assistance in preparing these materials including the following: Gene Bartholf, Christie Cratty, Ross Darrow, Guillermo Gallego, Thibaut Gardien, Jeff Glenn, Joakim Kalvenes, Yingying Kang, Mohammed Liaee, Brent Overbeek, Alexandre Poisson, John Reavy, Norbert Remenyi, Roman Shevchuk, Michael Sultan and Dieter Westermann. Special thanks to Doak Jones for his excellent work in building the prototype.
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APPENDIX
APPENDIX
Alternative regression models for demand estimation
In addition to the log-log model, the authors considered several other, commonly used demand models to estimate price elasticity from the available airline data sample. The following is a list of the methods considered and their functional forms along with observed issues in practice.
Linear demand model
For our purposes, the basic problem with the linear demand model is that the E p estimates are relatively more elastic (that is, larger magnitude) for the high fares and relatively less elastic for the low fares. By inspection, Bp/q results in larger values when the fares are high and the sales quantities are low. That direction is counterintuitive for airline pricing analysts.
Log-linear (semi-log) demand model
For our purposes, the log-linear model suffers from the same E p directional problem as the linear demand model. By inspection, B/q results in larger values when the sales quantities are low (that is, when fares are high), thus the higher fare values are more price elastic. That direction is also counterintuitive for airline pricing analysts.
Exponential demand model
From the second equation, it is apparent that this demand model is just another log-linear function in disguise. Although it is amenable to linear regression to easily estimate the parameters, this model has the problem of increasing E p as fares increase (and vice-versa).
Logistic demand model
The logistic function is one form of the well-known ‘S-curve’. It has the helpful property of limiting demand estimates at extreme fare values (either high or low) to prevent overextrapolation. For the sample data we tested, we tried a three-parameter version of the logistic function, and the estimated demands were a very good fit to the observed historical data. Also, the E p estimates seemed generally reasonable and in the correct direction. Unfortunately for our purposes, the logistic model parameters were difficult to estimate (requiring complex non-linear regression methods).
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Ratliff, R., Vinod, B. An applied process for airline strategic fare optimization. J Revenue Pricing Manag 15, 320–333 (2016). https://doi.org/10.1057/rpm.2016.32
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DOI: https://doi.org/10.1057/rpm.2016.32