The revenue management refers to the application of business analytics methods to the consumer behavior at the micro-level and optimizes the product type, price and availability so that the revenue of the organization is maximized. The primary aim of revenue management is to assure that the right product is sold at the right moment to the right customer. The analytical methods are applied to understand the perception of the customers regarding the product value and using it to align with the price, availability and the placement of the product service. The business organization often faces questions regarding different consumer product such as how to sell them and when to sell them. The revenue management uses the data driven and analytical methods to maximize the organization’s revenue. The revenue management encompasses data mining and operational research so that the customer behavior can be identified and associated with the sales procedure of the organization. There are four basic elements of the revenue management, namely, data collection, segmentation, forecasting and optimization. The revenue management process begins with the collection of relevant data. The organization should focus on collecting relevant, accurate and actionable information. The revenue management system collects information regarding the inventory, price and demand of the product. After the analysis of the data, customer segmentation is achieved so that profit can be maximized and the prices are competitive and in accordance to the market conditions (Verret, 2008). The success of the business organization depends on identifying the price responsiveness of the customers towards different products in accordance to the time and place. Later, forecasting is conducted to analyze the customer demands, market share and the inventory ability of the organization. In the last phase, the organization conducts optimization in its operations so that it responds to market demands.
In the hotel industry, the revenue management is considered as an important part of the strategic management as it can boost the occupancy of the independent property and is crucial for the organization’s success. The scenario in the hospitality industry is changing and independent owners are focusing on taking active roles in the management of the organization. The revenue management is a unique industrial practice which analyzes the guest trends and the booking patterns to reduce the overall cost of the organization. In the hotel industry, the best rate to sell the room at a specific day will be quite different from the optimal rate at any other day. As a result, a dynamic pricing strategy can be formed so that the prices of the rooms can be determined according to the inflow of the consumer or the demand of the consumers. The revenue management is also essential in the hotel industry to increase the profitability as selling rooms at low rates or paying heavy commissions can result in the loss of large amount of money even when the occupancy is highest in the hotel (Zangador, 2014). The proper utilization of the revenue management scheme can increase the bottom line of the organization. The dynamic pricing strategy of the organization improves the property’s occupancy and increases the bottom line of the hotel. It also ensures that pricing methods improve the property’s occupancy and the hotel is selling the rooms at the highest possible price and generating the maximum revenue it can achieve at a time.
The revenue managers or the revenue management are vital in operating a revenue management system. In the hotel industry, the revenue management can be the responsibility of a single person or an entire team depending upon the financial resources or a separate revenue management team. Most commonly, the revenue management responsibility are performed by the general manager, marketing manager or the front office manager of the organization. Only, large business organizations can afford a separate revenue manager. However, it is important to implement a revenue manager in the front line or the bottom line for accurate judgment of the consumer needs and expectations. Regardless of whether there is separate department for the revenue management or not, the revenue management technique must be understood by the front line managers in order to boost the sales or revenues of the organization. The front line managers are integral in upselling and cross-selling of the products which are crucial for increasing the sales of the products. In the tourism industry, the upselling refers to selling higher prices products and cross-selling of the products which refers to additional products which can be sold by other departments in the hotel (McGuire, 2015). The front office of the hotel is responsible for informing them guests during the check-ins about different offers in the restaurant.
The front line officers should have all the knowledge regarding the services offered by the hotel. They must also be trained in the sales technique. The front line managers of the organization should also be trained in the marketing, finance and forecasting of the organization. The front line managers are crucial in the revenue management as they are responsible for making reservations and sales of the organization. The front line managers are responsible for capacity management, overbooking control and upgrading and up-selling as part of the revenue management responsibilities. The capacity management refers to selling a fixed perishable service in a specific time period by putting limit to the availability of the product. In the capacity management of the organization, there are two important components, namely, average room rate and occupancy rate through which the capacity of the hotel is maintained. It also involves demand and supply forecasting. The demand forecasting is the process in which the organization obtains the detailed data regarding the past demand patterns of the market and forecast the currnt demand for the organization.
The overbooking control process is conducted so that reimbursement to cancellations and no-shows can be controlled. The over sales refers to overbooking in which an un-accommodated guest has to walk out. It creates negative organization image and reduces the customer base of the organization.
The fit of revenue management in an organization is dependent upon the type of industry and structure of the company. Several business organizations integrate the revenue management with the marketing teams as the marketing teams and revenue teams work for the unified goal of attracting maximum number of customers towards the organization. The other business organizations give the revenue management responsibility to the finance department due to its huge implications on the bottom line of the organization. Today, a large number of business organizations are implementing revenue management principles in the supply chain management of the organizations. It is because both of the disciples have common synergies. The supply chain management is targeted at fulfilling the existing and the forecasted demand patterns of the organization while minimizing the cost to the organization. On the other hand, the revenue management presumes that the costs and the capacity of the organization are fixed; however, look to create flexible pricing so that the revenue of the organization is maximized (Rucker, 2012). The companies which have attained excellence in supply chain management as well as revenue management have immense opportunities as they can link the operational focus of each segment to achieve better efficiency and working expertise.
The business intelligence organizations are also using revenue management process to increase their efficiency. The business intelligence platforms are driven by the data mining processes technology. The technology can be used to deliver business intelligence solutions as historical reporting and analytics can be used to explain the events of the past and result in optimized decision making. The business intelligence platforms create proactive forecasting for creating actionable information for business.
In hospitality cooperation and groups, revenue management is applied through different software and demand and optimization system. In revenue management, there is difference between the decision system and the recommendation system. The decision system automatically takes action according to the obtained information. In contrast to it, the recommendation system provides recommendation on the basis of information. Therefore, it can be critiqued that despite the advent of technology and intervention of the computer system, the use of human judgment is still essential in establishment of revenue management system. In the hospitality industry, the decision and the viewpoint of managers and the computer system interact and the recommendations and predictions of computer systems are evaluated. The hotel companies also use some basic software for creating spreadsheet for creating historical data and forecasting demand (Ivanova, Ivanov & Magnini, 2016). The revenue management system composes of difference software modules which can use different configuration and use different IT configurations or using spreadsheets for the data collection or forecasting demand.
The objective of revenue management or the yield management strategies is to increase the occupancy rate of hotels and revenue generated from the occupancy. In the revenue management cycle, forecasting demand is the foremost step. It is the process of making an estimate of the expected demand of the consumers of a specific product or service. The compilation of the forecast data requires thorough analysis of the research and accumulation of sales record from the previous records, studying of the current market, examining the prevailing economic conditions and the strategies and business of the competitors. The business can use demand forecasting to examine at what times of the year, the demand rises and at what times of the year, the demand falls. The information assists a business in examining the factors which increase the demand of the year at certain times of the year. The business can predict the annual drivers of drivers of sales and alter their business operations accordingly. The forecasting concept is commonly used to shift the prices of products or services higher when the demand of the product increases (Enz, 2010).
The shelf life of a single hotel room is only one day; therefore, the managers need to maximize its value. The forecasting is the most important principle in taking the managerial decisions regarding the prices or inventory management. However, inaccurate forecast results in reduced profits and revenues for the organization. There are several benefits of adopting forecasting methods in the hotel industry. Firstly, the forecasting strategy of the organizations is beneficial in executing its marketing strategy (Haensel, 2012). The forecasting mechanisms can be used to pan the promotional campaigns of the organization. There is high correlation between forecasting and marketing strategies as targeted customer base and offers are designed according to the forecasting patterns of the organization. The sales and marketing department of the organization can use the forecasting patterns to create demand for the rooms. In addition to it, offers and promotions can be used to create combined effect of the promotional campaign. The forecast related to the revenue generated with each department is associated with the financial forecast of the organization (O’Fallon & Rutherford, 2011). The forecasting can also assist in the expansion decisions of the organization as the demand forecast of the unconstraint demand can be used in the expansion plan of the organization. It also contributes to decisions related to hiring new staff members. The organizations can hire more staff during the peak season. During the low occupancy forecasted time, the hotel can plan its maintenance. Therefore, it can be critiqued that reliable forecast can result in periodic maintenance and positively relates with the bottom line operations of the organization (Pizam, 2012).
A large number of business organizations all across the world use demand forecasting to increase the price of the product or services during demand times of the year.
In the hotel industry, the forecasting methods fall into any of the three categories, namely, historical booking models, advanced booking models and combines models. The historical booking models emphasize on total number of booked rooms or guest arrival at a particular night. The historical data only contains the information regarding the overall bookings at a particular night. The advanced booking models consider the final number of rooms booked at a particular period of time for staying at a certain night. Traditional forecasting methods include exponential forecasting, regression methods and Bayesian methods. These traditional forecasting methods are used to derive forecasts on the historical arrivals in the organization. The hotel companies generally use Holt-Winters exponential smoothing methods to estimate the long-term forecasting of the organization. The advanced booking models are additive or multiplicative methods to arrive at more accurate results. The additive models presume that the number of reservations on a particular day is independent of the final rooms sold at that day. On the other hand, multiplicative model assume that number of reservations is dependent upon the current number of reservations (Weatherford & Kimes, 2003).
Overbooking is a common concept in the hotel industry. All the business organizations which make advance booking or accept reservations have to address the issue of no-shows. It refers to the problem of the customers not showing up after making reservations. It results in revenue loss and inability to use the product to its full potential. The hotels can protect themselves from revenue loss and no-shows by overbooking the place. However, the hotels have to strategically overbook so that there are minimum customers who needed to return due to full capacity of the hotel. The overbooking can be defined as the process in the hotel industry in which the hotels confirm more rooms than the available capacity of the organization. The optimal level of overbooking balances the loss of revenue with the penalties or financial compensation, loss of customer goodwill when there is higher demand than the available rooms.
The optimal overbooking room calculation requires demand forecasting, non-linear cost of overbooking, variation of capacity and dynamic booking. The most common method of overbooking is standard expected marginal revenue technique. According to this method, the optimal level of overbooking is when the cost associated with financial penalty or reputation loss is equal to marginal revenue generated from the booked room.
The overbooking is a management process which can be defined as the use of managerial techniques used with continuous planning, reservation and control to maximize the revenue generated after confirming more rooms than the capacity of accommodation establishments. The overbooking of an organization involves two set of activities, identifying and establishing the overbooking for each date, especially on the dates wherein the inflow of the customers is higher. The organization should also establish measures to change the overbooking number according to the market demands, booking patterns and the demand on that particular day. The overbooking concept also encompasses activities associated with managerial decisions and operational activities associated with walking guest out or directing them with overbooking.
There are several reasons regarding why overbooking is commonly used in the hotel industry. The main reason is that all the hotel managers have knowledge that all the bookings on a particular date will be actually used. There are several reasons due to which several guests do not show or arrive at the hotel. Other than that, there are several bookings confirmed at the last minute which reduces the number of guests. The percentage of unsold rooms in this manner is much higher in unguaranteed reservations rather than the guaranteed reservations. If these hotels limit their booking to the actual set of rooms available in the hotel, several of the rooms will remain unoccupied and the organization will not be able to achieve its goal of revenue maximization (Haensel, 2012). In addition to it, the hotel services are perishable and cannot be moved or stored for later use. The revenue lost from unsold rooms is lost permanently; therefore, it is essential to implement overbooking principles.
Conclusion
It can be concluded that revenue management is a significant aspect of the hotel operations. The front line managers or employees are directly associated with the revenue management operations of the organization. It is due to the fact that the front line employees are directly associated with the revenue management of the organization. Revenue management or yield management refers to the process of maximizing the revenue by implementing dynamic pricing models. In the hotel industry, there are several days when the inflow of customers is higher than the normal days. The hotels can hike their price per room on these days to increase their revenue. The implementation of a dynamic pricing model is important for the organization as the rooms are perishable goods and the value once lost cannot be retained. In the revenue management, the foremost step is forecasting. The organizations in the hotel industry make extensive efforts to identify the customer patterns and interest. With the help of forecasting, the companies can determine their pricing strategy. Moreover, forecasting is also essential in the marketing strategy and future expansion plans of the organization. In order to maximize revenue, the business organization can also use overbooking concept. It is a concept in which the instructor can book the number of rooms more than the capacity of the hotel. It is due to the realization that a significant number of customers do not show up after making the booking. It results in loss of revenue of the organization. While overbooking, the company has to determine an ideal number so that the loss incurred by walking out the customers is equal to the benefits from overbooking. The hotel industry is unique and requires several practices from overbooking.
References
O’Fallon, M.J., & & Rutherford, D.G. (2011). Hotel Management and Operations. John Wiley & Sons.
Ivanov, S. (2014). Hotel Revenue Management: From Theory to Practice. Zangador.
Sumarjan, N. et al. (2013). Hospitality and Tourism: Synergizing Creativity and Innovation in Research. CRC Press.
Ivanova, M., Ivanov, S., & Magnini, V.P. (2016). The Routledge Handbook of Hotel Chain Management. Routledge.
Weatherford, L.R., & Kimes, S.E. (2003). . A comparison of forecasting
methods for hotel revenue management. International Journal of Forecasting, 19(3).
Verret, C. (2008). Hotel Sales and Revenue Management Book 2.0. iUniverse.
Zangador, S. (2014). Hotel Revenue Management: From Theory to Practice. Zangador.
McGuire, K.A. (2015). Hotel Pricing in a Social World: Driving Value in the Digital Economy. John Wiley & Sons.
Rucker, M. (2012). Revenue Management Integration: The Financial Performance Contribution of an Integrated Revenue Management Process for Hotels. diplom.de.
O’Fallon, M.J., & Rutherford, D.G. (2011). Hotel Management and Operations. John Wiley & Sons.
Enz, C.A. (2010). The Cornell School of Hotel Administration Handbook of Applied Hospitality Strategy. SAGE.
Pizam, A. (2012). International Encyclopedia of Hospitality Management 2nd edition. Routledge.
Haensel, A. (2012). Choice-set Demand in Revenue Management: Unconstraining, Forecasting and Optimization. Alwin Haensel.
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