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Scheller College of Business
Scheller College of Business
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ItemThe "Killer Application" of Revenue Management: Harrah’s Cherokee Casino & Hotel (ed. 2)( 2008-01-11) Ferguson, Mark E. ; Metters, Richard ; Crystal, Carolyn RobertsHarrah’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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ItemRevenue Management Performance Drivers: An Empirical Analysis in the Hotel Industry(Georgia Institute of Technology, 2007-06-22) Crystal, Carolyn RobertsRevenue Management (RM) is an important tool for matching supply and demand by segmenting customers into different segments based on their willingness-to-pay and allocating scarce capacity to the different segments in a way that maximizes firm revenues. The benefits of RM are well accepted in the hospitality industry, and the technical aspects of RM form a rich analytical research stream. However, the research is missing a holistic examination of important elements of effective RM. The literature shows that market segmentation, pricing, forecasting, capacity allocation, IT use, organizational focus, aligned incentives, organizational structure, and education and training contribute to effective RM. We group these elements into two concepts: RM technical capability and RM social support capability and propose that these nine elements positively impact RM performance. We develop scales to measure our constructs and collect responses in the hotel industry. Our survey yields interesting results. In line with expectations, we find evidence that forecasting and organizational focus positively impact RM performance. On the other hand, the results show evidence that improved organizational structure negatively impacts RM performance. We provide a few explanations for this non-intuitive result and proposals for future research.
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ItemA 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, RohitA 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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ItemThe "Killer Application" of Revenue Management: Harrah’s Cherokee Casino & Hotel (ed.1)( 2006-09-16) Ferguson, Mark E. ; Metters, Richard ; Crystal, Carolyn RobertsHarrah’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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ItemA 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, RohitA 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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ItemA Comparison of Unconstraining Methods to Improve Revenue Management Systems (ed.1)( 2005-12-07) Ferguson, Mark E. ; Crystal, Carolyn Roberts ; Higbie, Jon ; Kapoor, RohitA 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.