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Scheller College of Business

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Author: Ferguson, Mark E. × Author: Crystal, Carolyn Roberts ×

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Now showing 1 - 5 of 5
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    The "Killer Application" of Revenue Management: Harrah’s Cherokee Casino & Hotel (ed. 2)
    ( 2008-01-11) Ferguson, Mark E. ; Metters, Richard ; Crystal, Carolyn Roberts
    Harrah’s Cherokee Casino and Hotel is an extreme and unusual example of revenue management techniques. Typical revenue management installations yield revenue enhancements of 3-7%. Harrah’s, chainwide, has seen 15% improvements, with Harrah’s Cherokee Casino and Hotel perhaps the most excessive beneficiary, despite serving no alcohol and having no traditional table games. Further, many traditional revenue management techniques are turned on their heads: For example, pricing decisions and customer segmentation rules are different for casinos than in virtually any other revenue management application.
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    A Comparison of Unconstraining Methods to Improve Revenue Management Systems (ed.3)
    (Georgia Institute of Technology, 2007-02-16) Ferguson, Mark E. ; Crystal, Carolyn Roberts ; Higbie, Jon ; Kapoor, Rohit
    A successful revenue management system requires accurate demand forecasts for each customer segment. The forecasts are used to set booking limits for lower value customers to ensure an adequate supply for higher value customers. The very use of booking limits, however, constrains the historical demand data needed for an accurate forecast. Ignoring this interaction leads to substantial penalties in a firm's potential revenues. We review existing unconstraining methods and propose a new method that includes some attractive properties not found in the existing methods. We evaluate several of the common unconstraining methods against our proposed method by testing them on intentionally constrained simulated data. Results show our proposed method outperform other methods in two out of three data sets. We also test the revenue impact of our proposed method, EM, and "no unconstraining" actual booking data from a hotel/casino. We show that performance varies with the initial starting protection limits and a lack of unconstraining leads to significant revenue losses.
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    The "Killer Application" of Revenue Management: Harrah’s Cherokee Casino & Hotel (ed.1)
    ( 2006-09-16) Ferguson, Mark E. ; Metters, Richard ; Crystal, Carolyn Roberts
    Harrah’s Cherokee Casino and Hotel is an extreme example of revenue management techniques. Typical revenue management installations yield revenue enhancements of 3-7%. Harrah’s, chainwide, has seen 15% improvements, with Harrah’s Cherokee Casino and Hotel perhaps the most excessive beneficiary, despite serving no alcohol and having no table games. We investigate what drives this phenomenal success.
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    A Comparison of Unconstraining Methods to Improve Revenue Management Systems (ed.2)
    (Georgia Institute of Technology, 2006-05-31) Ferguson, Mark E. ; Crystal, Carolyn Roberts ; Higbie, Jon ; Kapoor, Rohit
    A successful revenue management system requires accurate demand forecasts for each customer segment. The forecasts are used to set booking limits for lower value customers to ensure an adequate supply for higher value customers. The very use of booking limits, however, constrains the historical demand data needed for an accurate forecast. Ignoring this interaction leads to substantial penalties in a firm's potential revenues. We review existing unconstraining methods and propose a new method that includes some attractive properties not found in the existing methods. We evaluate several of the common unconstraining methods against our proposed method by testing them on intentionally constrained simulated data. Results show our proposed method along with the Expectation Maximization (EM) method perform the best. We also test the revenue impact of our proposed method, EM, and "no unconstraining" on actual booking data from a hotel/casino. We show that performance varies with the initial starting protection limits and a lack of unconstraining leads to significant revenue losses.
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    A Comparison of Unconstraining Methods to Improve Revenue Management Systems (ed.1)
    ( 2005-12-07) Ferguson, Mark E. ; Crystal, Carolyn Roberts ; Higbie, Jon ; Kapoor, Rohit
    A successful revenue management system requires accurate demand forecasts for each customer segment. These forecasts are used to set booking limits for lower value customers to ensure an adequate supply for higher value customers. The very use of booking limits, however, constrains the historical demand data needed for an accurate forecast. Ignoring this interaction leads to substantial penalties in a firm's potential revenues. We review existing unconstraining methods and propose a new method that includes some attractive properties not found in the existing methods. We evaluate several of the common methods used to unconstrain historical demand data against our proposed method by testing them on intentionally constrained simulated data. Results show our proposed method along with the Expectation Maximization (EM) method perform the best. We also test the revenue impact of our proposed method, EM, and “no unconstraining” on actual booking data from a hotel/casino. We show that performance varies with the initial starting protection limits and a lack of unconstraining leads to significant revenue losses.