Introduction
In the hotel sector, “Revenue Management” refers to the numerous systems put in place to set up room rates for their benefit. Conventionally, revenue management (RM) is essential in providing direction to hotels rooms’ allocation to the demand with the aim of maximizing profits. While revenue management is still a relatively new concept, it has been effective in helping the hotel managers to maximize profits (Haley and Inge, 2004).
Why Measure Revenue Maximization?
Measuring revenue maximization is critical because it acts as an indicator as to how the hotel is performing. Since the very first days of RM, it has been credited with about 3% to 8% increase in revenue in the car rental, airline and hotel industries. Revenue maximization is necessary for the hotel sector specifically since there is high investment in the sector, fixed capacity and high fixed costs involved. Unlike in several other industries, supply of hotels services and facilities does not adjust quickly to the high demand because of the high investment cost required together with other barriers of entry to the market. Again, in the hotel industry, costs cannot be reduced drastically as the demand reduces. This means that the marginal cost of selling the facilities and the rooms is very low as compared to the amount of revenue obtained from the sales. When a day passes without a room being used, it goes to waste and can never be recovered. This means that the hotel rooms business is highly perishable (Boahen et al., 2013).
The revenue management method was only introduced in the early 1980s in the hotel industry, and the practice has received growth and today almost every major hotel uses the technique to help in the maximization of its profits. Every revenue management system has its own level of complexity. A number of hotels makes use of the daily occupancy forecasts to assist them in predicting the rate of availability, while other hotels uses the multiple arrival forecasts for length of stay, for combination rate and room type to optimize the allocation of inventory at every 15th booking.
The term revenue management was introduced by Bill Marriot in the hospitality industry. Bill Marriot first borrowed the term “yield management” from the airlines industry that is normally used to generate extra revenue from excess capacity, for instance when there is empty seats. Marriot used the American Airline as its lead study company, taking the detailed databases and the comprehensive computer systems and networks. He used these to predict and to monitor the demand of the passengers. He observed that targeted discounts were given to the market segments that were highly price-sensitive. Analysts who had the training studied variance, with the aim of allocating discounted seats with great accuracy. This yielded very high level of revenue to the airline at the end of the day.
In 2016, revenue management concept is already being used as a strategic tool and it is expected that it is going to be developed further and more sophisticated technologies are likely to arise from this trend. Initially, revenue management was specifically applied in the rooms-only allocations, but today, it is spreading to other sectors that are critical to hotel income stream, including areas like restaurants, spas, and the resort facilities like golfing. Revenue management is growing by the day and is assuming a more direct and greater role in hotel strategies, and is particularly being integrated in almost all aspects of hospitality management including operations, finance and marketing. Management of revenues is increasing becoming a critical area in hotel operations and it now supports almost all the hotel departments. The old metrics, for instance RevPAR (revenue per available room) may open way to a more profit-based measurements like GOPAR (gross operating profit per available room) or alternatively total revenue per available unit of area. This means that in the near future, the managers may need to create totally new analytical skills to maximize revenue even further.
Opportunities and Drawbacks
Computing the price elasticity, even at a highly accurate level was still not sufficient. IHG realized that when a competitor to a given hotel changes its pricing, the perception of the consumer of IHG’s won rates also changed (Tse and Poon, 2011). Meaning that, increasing the hotel rooms’ rate in the sector was now more transparent brining a convergence of pricing and positioning in the market. The IHG team used a third-party competitive data, through “Price Optimization” system and started to study the historical price and other related data to estimate the price elasticity in the local market and for changing period of stay. As a result, IHG was able to update the system that had the ability to measure the elasticity of the industry based on the time between the arrival date and the time of advanced booking by the consumer. The results were relative, with the RevPAR experiencing a 2.7% growth in only a few years. The Price Optimization system by IHG had the ability to evaluate the demand 24/7 all year round and at the same time compare the competitive rates in real time. The system continuously calculated an up-to-date data with high levels of accuracy while computing the accurate price elasticity to the clients, and giving the best prices that will maximize revenue for the business. IHG system was revolutionary, and this prompted other players in the industry like Marriot, SATRWOOD, Carlson, and others to change their systems to work like IHG’s system. This led to a revolutionary twist in revenue management (Haley and Inge, 2004).
Critical Analysis on existing RM measures
RevPAR
Revenue per available per room, abbreviated as RevPAR, has been widely implemented as the top indicators of hotel performance for several years. RevPAR expresses both occupancy and variables of rate elegantly in a single number (Guo and Xiao, 2011). A restaurant, for instance, might award incentive compensation to its top managers based on RevPAR instead of occupancy or rate, giving way for valuable comparisons across different time periods and properties (Chen, 2010). RePAR does not unnecessarily reward the employees unjustly (for increasing the level of occupancy by selling out rooms and other facilities at highly discounted rates or alternatively selling the only a small number of rooms at exorbitantly high rates). Still, it is expected that RevPAR may deviate away from its conventional purposes. In a more recent studies, only 18.6% of the participants reported that RevPAR would be used as a future measurement for performance. About 29.3% of the participants indicated that gross operating profit per available room (GOPPAR) would soon be the most preferred tool for measurement. Other performance measurement for the hospitality industry included total revenue per available room (TotRevPAR) with 20.5% and total revenue per available square foot (TotRevPASF) with 13.5% (Boahen et al., 2013).
RevPASH
RevPASH (Revenue per available seat-hour) systems is a metric adopted in the restaurant management including the operations of hotel F&B. Using RevPASH, F&B top managers can implement unique revenue management system to help in improving profitability in their businesses. The primary factor in using RevPASH is to accurately and correctly capture the combine seat capacity, revenue and time (STR Global, 2016). Conventionally, RevPASH was based on the period of time at which a check opened, while assuming the time used. A more accurate and precise approach would be to compute RevPASH depending on both close and open times. Such revised computation methods would naturally in a position to account for the restaurant seating demand for the entire period of the visitor meals, generating revenue for the hotel.
RevPATI
RevPATI (Revenue per available “time-based inventory unit) is a metric implemented in the airline, car rental, hotel and other business referred to as “time-based inventory units.” In the airline sector, RevPATI represents revenue per available seat-distance. In the car rental business, it refers to revenue per available car (deJong, 2009). In the restaurant sector, it refers to revenue per available seat. In the hotels sector, it refers to F&B outlets, and other facilities like spas. RevPATI is the average rape for every capacity utilization (Thompson and Sohn, 2008).
ADR and OCC
ADR is the Average Daily RATE and the OCC means Occupancy. OCC and ADR are basically the most popular statistics used by the hotel businesses. These metrics can be used individually, or to compute more sophisticated measurements. These systems have been integrated in the hotel systems are likely to stay for longer (Weatherford and Kimes, 2003).
RGI
Revenue generated index of RGI is a measurement of fair market share of hotels in their segment’s revenue per available room. The segment here means market, competitive set, and submarket, amongst others. For a hotel that is recording its fair market share, the index is likely to be 100. The RGI can be estimated either above or below 100 (Chen, 2010). The RGI measurement is often found in the well-established and well-known star reports (STR), given by Star Global where most hotels are members (Haley and Inge, 2004).
ARI
The Average Index Rate (ARI) also referred to as ADR index, in the estimation of the performance of a hotels in regards to the an aggregated grouping of hotels (for instance, market, submarket, and competitive set amongst others). An ARI of about 100 is the same as a fair share of ADR within the group. The ARI can be measured above or below 100. This estimation can also reside in the STR reports (star) (Boahen et al., 2013).
MPI
Market penetration index, appreciated as MPI and also known as OCC Index, is a share of a hotel segment demand (with the demand equating to the segment gain being competitive group, rooms sold, submarket, and market, amongst others) (Kimes, 2010). The formula is given by:
Hotel Occupancy ÷ Segment Occupation x 100.
MPI can be viewed as hotel’s “piece of pie” subject. For instance, if a hotel has 15% of all of its rooms within its group, and in a given period accounts for 15% of rooms nights sold; the hotel’s MPI for that period would be 100 percent. The MPI estimation can also be found in the STR report (star) (Boahen et al., 2013).
Flow Through
Flow through method estimates the relationship between the profits and revenues. For instance, if the income for the hotel is about $100,000, over an estimated GOP and budget of $70,000 over budget, then there is a flow-through of 70% (Ivanov and Zhechev, 2010). For example, 70% of the increased income goes to the bottom line. Surplus revenues for the higher ADR in most cases flows through the to the ADR because of the increased hotel variable costs, for example housekeeping and utilities costs. Analysis of the flow through is an important revenue management tool and will be in the vicinity for quite some time (Arps, 2015).
New Revenue Management Measures
ProfPAR
ProfPAR or Profit per available room is a metric used in the hotel industry that changes focus from the revenue stream to profitability (Hoisington, 2014). The metric of course shows a change in focus towards the consumers themselves, for instance, how much the customer spends at the restaurant besides the cost of room. The ProfPAR method is likely to experience growth as a significant revenue management graduates to the next advanced stages (STR Global, 2016).
RevPAC
RevPAC or revenue per available customer refers to total income or revenue from the hotel guests ÷ total number of guests. In short, RevPAC is a section of shift from the static and more conventional metrics towards a more complex and advanced method that focuses on individual customer profiles and profitability (Kimes, 2010). For example, how much a customer spends at the restaurant besides the cost of rooms? This technique is likely to grow in importance in the near future as the revenue management system moves to the next stages Kimes, 2010).
Performance Metrics Based on Available Space
Most participants in some recent research study shows that the meeting space or available function space will be the next important frontier in the revenue management stream. If this is true as it is alleged, then the only way forward is to start looking at things in the new ways. This will involve a lot of complexities (STR Global, 2016). In this regard, methods like RevPAF may be the leading metrics. Lastly, an expanded focus more on hotel revenue stream which will lead to changes in other metrics starting from RevPAR to metrics that constitutes all revenue (even plus the profits) (Weatherford and Kimes, 2003). These new developments will introduce new changes to the hotels and restaurants and reward RP (revenue performance). The new development will also have the capability to compare themselves with the competition in real time (Haley and Inge, 2004).
Dynamic Pricing
Dynamic pricing is a relatively new concept for pricing in the industry. The method gives the hotel the opportunity to maximize their respective RevPAR and also to generate profits by offering prices that reflects upon the current levels of occupancy and demand (Choi and Kimes, 2002). The consumers may pay different prices under this system, even when in the situations where the booking details are identical (type of rooms and length of stay, amongst others) all depending on the precise moment the customers made their bookings (Hoisington, 2014). The travelling public does not however approve the dynamic pricing model. However, the dynamic travelling metric can definitely increase profitability to the hotel. Hopefully as time goes by, it will be applied carefully, and accompanied with the correct and appropriate booking conditions and terms Kimes, 2010).
Conclusion and Recommendations
Revenue management is definitely an effective and efficient tool and has rapidly evolved over the years and will continue to evolve accordingly. The tool has been in the forefront in ensuring that the hotel industry, car rentals, restaurants and other similar businesses remains profitable all year round and for the long term. It is expected the term “revenue management” will evolve as time passes by to “revenue optimization” to involve other revenue sources besides rooms in the hotel industry Kimes, 2010). Chief revenue officer role will come forth in the future as a core person in the industry and all other revenue-producing departments will report to him or her, with the revenue managers directly reporting to this senior officer (Weatherford and Kimes, 2003).
References
Arps, T. (2015). How to Win in Hotel Revenue Management. Hotel News Now. Retrieved from http://www.hotelnewsnow.com/Article/15800/How-to-win-in-hotel-revenue-management
Aziz, H., Saleh, M., Rasmy, M. and ElShishiny, H. (2011). Dynamic room pricing model for hotel revenue management systems. Egyptian Informatics Journal 12, pp.177–183. Retrieved from http://www.sciencedirect.com/science/article/pii/S1110866511000375
Boahen, O., Quansah, K. and Sarpong, K. (2013). Assessing the Benefits of Yield Management in the Hospitality Industry in Kumasi Metropolis of Ghana. International Journal of Business and Social Research (IJBSR), Volume -3, No.-9, September, 2013.
Chen, I-Pei Claire (2010). Hotel revenue management: Investigating the interaction of information technology and judgmental forecasting. UNLV Theses, Dissertations, Professional Papers, Capstones. Paper 647. UNLV. Retrieved from digitalscholarship.unlv.edu/cgi/viewcontent.cgi?article=1648&context
Choi, S., and Kimes, S. E. (2002). Electronic distribution channels’ effect on hotel revenue management. Cornell Hotel and Restaurant Administration Quarterly, 43(3), pp.23-31. Retrieved from http://scholarship.sha.cornell.edu/articles/470/
Cross, R., Higbie, J. and Cross, D. (2009). Revenue Management’s Renaissance: A Rebirth of the Art and Science of Profitable Revenue Generation. Cornell Hospitality Quarterly, Vol. 50, No. 1, pp. 56–81. Retrieved from http://www.palgrave-journals.com/rpm/journal/v10/n1/full/rpm201039a.html
deJong, L. (2009). How Good Are You at Managing Profitability? In 2009, Operators Look to Flow-Through Reporting for Profitability Analysis. Hospitality Net. Retrieved from http://www.hospitalitynet.org/news/4040517.html
Guo, P., Xiao, B., Li, J. (2011). Unconstraining Methods in Revenue Management
Systems: Research Overview and Prospects. Hindawi Publishing Corporation.
Advances in Operations Research, Volume 2012, Article ID 270910. Retrieved from http://dx.doi.org/10.1155/2012/270910
Haley, M. and Inge, I. (2004). Revenue Management - It Really Should Be Called Profit Management. Hospitality Upgrade.com.
Hoisington, A. (2014). Revenue management best practices have failed. Hotel News Now. Retrieved from http://www.hotelnewsnow.com/Article/13762/Revenue-management-best-practices-have-failed%20target=
Ivanov, S. and Zhechev, V. (2015). Hotel revenue management – a critical literature review. Tourism Review, Vol. 60/ No. 2/ 2012/ 175 -197.
Kimes, S. (2010). The Future of Hotel Revenue Management. Cornell Hospitality Report, 10(14), pp.6-15. Retrieved from http://scholarship.sha.cornell.edu/chrpubs
Revenue Management: An Overview on Past, Present and Future (2015). Revenue Matters. http://revenuematters.com/revenue-management-an-overview-on-past-present-and-future/
STR Global (2016). Glossary. Retrieved from https://www.strglobal.com/resources/glossary
Thompson, G. and Sohn, H. (2008). Accurately Estimating Time-Based Restaurant Revenues Using Revenue per Available Seat-Hour. Cornell Hospitality Report, 8(9), pp.6-15. Retrieved from http://scholarship.sha.cornell.edu/chrpubs/144/
Tse, T. and Poon, Y. (2011). Revenue management: resolving a revenue optimization paradox International Journal of Contemporary Hospitality Management, Vol. 24 No. 4, 2012 pp. 507-521. Emerald Group Publishing Limited. Retrieved from www.emeraldinsight.com/0959-6119.htm
Weatherford, L. R., & Kimes, S. E. (2003). A comparison of forecasting methods for hotel revenue management. Cornell Hospitality Report. Retrieved from http://scholarship.sha.cornell.edu/articles/753
