Pricing Decisions Associated with Hotel Booking

Topics: Decision theory, Decision making, Marketing Pages: 22 (8158 words) Published: March 10, 2007
This paper demonstrates a particular model for making the pricing decisions associated with hotel booking. Implementing such pricing decisions that are designed to optimize the profitability of the hotel forms part of a policy commonly referred to as yield management. The model utilizes fore casts of demand in individual market segments to capitalize on the willingness of people in one segment to pay more than people in another segment. The procedure for doing this is necessarily time-based since the market segments are differ entiated also by the timing of bookings relative to a rental date. The procedure for making the pricing decisions is de scribed and an example is given. Unlike the commonly in voked marginal revenue models, this model is optimal and requires fewer assumptions about the demand process. It is shown that the procedure has rather modest information re quirements and is based on data that is typically available through market research. We also show that the procedure demands minimal amounts of CPU time making it applicable even in small hotels. INTRODUCTION

The term "yield management" is used to label many approaches to maximizing the profitability of a hotel through manipulation of its pricing and booking policies. The goal of a yield-management system is to consistently maintain the highest possible revenue from a given amount of room capacity. To achieve this goal, the yield-management process includes determining policies for overbooking and allocating hotel capacity to customers of different revenue-generating potential through discriminatory pricing. Ideally, both of these policies should be concurrently incorporated in a hotel's reservation system. However, it is beyond the scope of this paper to prescribe an optimal policy for simultane ously planning both functions in a coordinated manner. The objective of this paper is to present a model developed for the pricing policy. The overbooking policy deals with the likelihood of cancellations and no-shows and the consequent lost revenue. Balancing the expected lost revenue due to unoccupied rooms against the loss of goodwill caused by not honoring overbooked reservations is the essential consideration in determining an overbooking policy. The pricing problem can be identified in two forms. First, there is the revealed-price (RP) case in which a customer calling in a reservation is assumed to be able to identify his/her customer class, thereby receiving a certain rate. An example of such a situation would be the case of discount rates allowed for attendees to a convention or vacationers who have been given a special group rate. The booking policy that must be determined is usually expressed in the form of booking limits for each customer class. Inasmuch as this problem deals with demand from different market segments with a different price charged to each segment, one can view determining a room allocation to each segment as a pricing decision. The hidden price (HP) problem, as we define it here, is characterized by the inability of the hotel's reservation system to identify the market segment to which a customer belongs at the time that a reservation is made. A simple example of two market segments that are indistinguishable by the booking process would be business people who are traveling to a pre-scheduled meeting and salespeople who are making unscheduled, discretionary calls on customers. In such situations, it is not possible to set booking limits for different customer classes explicitly. However, the room rates that customers are willing to pay is likely to be different in these two market segments. We refer to the maximum rate that a customer is willing to pay for a room as the threshold price. Only by setting a price which excludes one of these market segments can the hotel's reservation system distinguish them. Therefore, the pricing policy regulates the sales to different customer classes through the screening of some...

References: Brumelle, S., McGill, J., Oum, T., Sawaki, K., Tretheway, M. (1990), Allocation of airline seats between stochastically dependent demands. TRANSPORTATION SCIENCE 24, 183-192.
Curry, R
Glover, F., Glover R., Lorenzo, J., McMillan, C. (1982), The passenger mix problem in the scheduled airlines. INTERFACES 12, 73-79.
Kimes, S
Ladany, S. (1976), Dynamic operating rules for motel reser- vations. DECISION SCIENCES 7, 829-840.
Lee, A
Lieberman, V., Yechialli, U. (1978), On the hotel overbook- ing problem: an inventory problem with stochastic cancellations. MANAGEMENT SCIENCE 24, 1117-1126.
Orkin, E
Pfeifer, P. (1989), The airline discount fare allocation problem. DECISION SCIENCES 20, 149-157.
Relihan, W
Rothstein, M. (1971), An airline overbooking model. TRANS- PORTATION SCIENCE 5, 180-192.
Rothstein, M
Rothstein, M. (1985), Operations research and the airline overbooking problem. OPERATIONS RESEARCH 33, 237-248.
Thompson, H
Williams, F. (1977), Decision theory and the innkeeper: an approach for setting hotel reservation policy. INTERFACES 7, 18-30.
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