Hotel Revenue Management

Revenue (yield) management is an essential instrument for matching supply and demand by dividing customers into different segments based on their purchase intentions and allocating capacity of the different segments in a way that maximizes a particular firm’s revenue (El Haddad, Roper and Jones, 2008).

Revenue management can be profitably applied in hotels, shopping malls, golf courses, restaurants, telephone operators, conference centers and airlines (Ivanov and Zhechev, 2012).

Revenue management is commonly used in the hotel industry to help hotel decide on room rate and allocation. Hotel revenue management is perceived as a managerial tool for room revenue maximization, i.e. for attempting to sell each room to the customer who is willing to pay the highest price so as to achieve the highest revenue (El Gayar et al, 2008).

From these points of view, to put hotel revenue management in practice seems to be very important for every hotel. In this paper the main concepts of hotel revenue management will be discussed.

The main aspects which managers of the hotels should have to take into account when making decisions are: which are the revenue centers of the hotel and what type of data and information are available for them?

Revenue centers

Hotel revenue centers are all potential sources of revenue for the hotel, (rooms, casino and gabling facilities, spa & fitness facilities, golf courses and other additional services), and the ability of the hotel to actively use pricing as a revenue generation tool (Ivanov and Zhechev, 2012). Concerning this fact, the managers of the hotels should take in account all revenue centers of the hotel, not only the rooms in the decision making process.

Data and Information

In the process of making decisions, managers of the hotels use a lot of different data and information which is stored by the hotel. There are many different examples for data and information which is gathered by hotel. El Gayar et al, provide some examples: Historical arrivals – this is the final number of quests that arrive in the hotel at a certain time in the past. Reservation records – detailed version of reservation data. Booking matrix – this is compact version of reservation data (El Gayar et al, 2011).

Revenue management tools

In the process of revenue management every manager is able to use many different tools by which he or she can influence the revenues that hotels can get from their customers. The revenue management tools can be widely divided into pricing and non-pricing tools (Ivanov and Zhechev, 2012). According to them pricing tools include price discrimination, the erection of rate fences, dynamic and behavioural pricing, lowest price guarantee and other techniques that directly influence hotel’s prices. Non-pricing tools are all tools that do not influence pricing directly and relate to inventory (capacity management, overbookings, length of stay control, room availability guarantee) and channel management.

Revenue management software

Revenue management software is one of the important aspects of the hotel revenue management because it gives to the hotel’s managers some advantages. Revenue management software helps managers by giving suggestions on price amendments, inventory control and channel management (Ivanov and Zhechev, 2012).

Revenue management process

Revenue management process starts with the goals setting by the revenue manager with specific strategic (several years), tactical (week/months) and operational (days) time horizon (Ivanov and Zhechev, 2012).

The goal of forecasting in the hotel industry is to get an estimate of the future demand for rooms, based on past and current customer bookings (Chen and Kachani, 2007).

Forecasting involves the application of different forecasting methods in order to provide the revenue manager with prognoses about the future development of revenue management metrics, demand and supply (Ivanov and Zhechev, 2012).

Weatherford and Kimes (2003) divided the methods to historical, advanced booking and combined methods. Their advantage is the relatively easy application and low data requirements. Advanced booking models forecast the numbers of booked rooms on particular arrival day on the basis of the number of the booked rooms on a previous day (Ivanov and Zhechev, 2012). As combined methods Weatheford and Kimes (2003) identify regression models and weighted average between historical and advanced booking forecasts (Chen and Kachani, 2007).