Measuring the impact of Revenue Management Delfo Melli Bernard Rannou Optims / Revenue Management Division dmelli@optims.com, brannou@optims.com Abstract Revenue Management vendors in the hospitality industry are prone to put forward the fact that successful Revenue Management programs will result in substantial revenue increases. Decision-makers in hotels or hotel chains are also looking for measurement tools that can justify the investment they are considering to make or have made in such programs. This paper presents a method, which aims at scientifically measuring the performance of any Revenue Management program. It includes operational results obtained in leading hotels of Western Europe and suggests further enhancements for improving the performance measurement of a Revenue Management program. Keywords: Yield Management (YM) Revenue Management (RM) Performance Measurement Return on Investment Introduction How do I measure if Revenue Management earns me money? This is a question we are often asked, as professionals within the Revenue Management industry. Indeed in a survey sponsored by The American Hospitality Institute (AHI Management seminar series, 1998), this question is listed first among the 8 most often cited questions, as raised by revenue managers from various backgrounds (airlines, hospitality, cruiselines, car rentals). Even forecast quality, O&D or Length of stay controls and systems integration do not rank as high as this one concern: am I making the money I should be?
1 Isolating the actual impact of a YM Program Such indicators as RevPAR and comparison with competition are not relevant to estimate the impact of a Revenue Management implementation, as they do not permit to isolate the actual impact of Revenue Management itself. These indicators may increase or decrease due to positive or to negative market conditions, independently or with limited impact of the Revenue Management program in place. They may also show a decrease due to negative market conditions whereas the Revenue Management system (RMS) has indeed helped to generate significant additional revenues. Although event independent performance measurement has long been recognized as a necessity in the realm of Revenue Management, the formal proof of value which is called for in (Battagia, 1996) has so far been missing. The method that Optims has developed in a new suite of performance measurement products, allows to evaluate revenue enhancement which is solely due to the Revenue Management program and which is also independent of: Market conditions (price and volume) Changes to other sales systems Reservation procedures Distribution strategy Actual use of a Revenue Management system recommendations It is important to understand this last point as the actual impact depends on the choice of the hotel management to implement the recommendations provided by the RMS in place. Sometimes, organisation and staffing issues may limit the use of the RMS. As a consequence, if the system is not fully used, its actual impact will not show the full potential of the system. 2 The concept of performance In general, two types of methods are used to evaluate the impact of a Revenue Management installation: The first methods are based on the comparison of two periods, one period without using the RMS, or before the RMS has been implemented, and the other period after the completion of the program. The second method is based on simulating different control strategies for the same period of time The first method does not allow one to isolate the impact of the RMS. For that reason, some RMS vendors introduce, when using these methods, a third period of time, just before system implementation, in order to evaluate the impact of external factors. The Optims performance measurement program is a method of the second category. It concentrates on one period of time, usually the past two or three months, sometimes the whole past year. It focuses on the comparison of revenues generated by different control strategies for the same period of time.
The impact of these control strategies is simulated on the basis of the observed flow of reservation requests. The following table lists the different control strategies that can be measured in order to assess the performance of a Revenue Management program. Table 1 Control Strategy No Control Definition No control is applied to the flow of reservation requests. Reservation requests are then accepted on a first-come firstserved basis until the level of the capacity has been reached for the inventory. The pricing rules are those defined by the user. User Control This strategy consists in applying the usual rules in practice at the hotel, before the RMS implementation, to the flow of reservation requests on a daily basis. These rules (pricing and availability) are formalised with the hotels Revenue Managers during an initial audit performed within the hotel property. Actual Control Actual control is measured taking into account actual (real) production results. Production results may differ from the recommendations given by the Revenue Management system, as these recommendations may be overriden by the revenue analysts. RMS Control This strategy consists in applying the Revenue Management system daily recommendations to the flow of reservation on a daily basis. Perfect Control Optimal controls (price and availability) are applied afterwards on the basis of the perfect knowledge of demand. This is an ideal situation, where the forecast and the optimisation are 100% correct, and the recommendations of the RMS are forced into the various production systems of the hotel / hotel chain. In other words, the no control situation corresponds to a theoretical situation, which hopefully never happens. The user control situation is the prevailing one, whenever no RMS is in place. The actual control is the observed situation, after a few months of correct system utilisation. The RMS control situation is rarely seen, as it pre-supposes a full and unconstrained application of optimisation principles to a real life situation. As for the perfect control situation, it is an ideal situation, which, for sure, has never been encountered. This situation could only happen using some additional knowledge which is external to a Revenue Management system.
The purpose of this paper is to explain in greater detail the methodology used by Optims to estimate both the no control and the perfect control situations, and to illustrate how these control estimations are used to assess the impact of a Revenue Management program. Each of the five preceding control strategies leads to a given revenue level. Revenue R P R R R A R U R N No Control User Control Actual Control RMS RMS Control Control Control Levels Fig. 1. Control levels Fig. 1 Helps understand important issues concerning Performance Measurement : The following ratio, [R U R N ] / [R P R N ] (1) gives an index allowing the estimation of how good the Revenue Management rules of thumb are, before a system has been implemented. The controls that are measured by R U are those, which are directly actionned in the Property Management System (PMS) of the hotel or in the Central Reservation System (CRS) of the hotel chain, without referring to an external yield system. Usually overbooking and some simple rate controls can be enforced by the user directly into the PMS or CRS of the hotel. The second indicator, [R P R U ] (2) gives an estimate of the maximum potential improvement. It is worth noting that this figure is lower when R U is higher, which means that the potential of revenue enhancement is higher when the user practices are primitive and much lower when the user is already applying good and sound Revenue Management principles.
The efficiency of the Revenue Management System (RMS) can be measured by the following ratio: [R R R N ] / [R P R N ] (3) This ratio is called RMS Business Opportunity Assessment (rboa), as it measures the percentage of optimisation opportunities that have been detected by the system, with respect to overall opportunities. Likewise, two similar ratios can be defined to measure the efficiency of (i) the actual yield program in place within the hotel and (ii) the user efficiency prior to the installation of a Revenue Management program. (i) Reflects the performance of the Revenue Manager and system integration. It is called actual business opportunity assessment (aboa) and is calculated as follows: aboa = [R A R N ] / [R P R N ] (4) (ii), also called user Business Opportunity Assessment (uboa), can be defined as follows uboa = [R U R N ] / [R P R N ] (5) The percentage of gain obtained with the RMS can be measured by: [R R R U ] / R U (6)* While the actual percentage of gain achieved by the user using the RMS, is [R A R U ] / R U (7)* *(6) and (7) will be used to estimate the performance of the Revenue Management program. 3 Defining the User Control Strategies The rules applied by the user to control reservations will be formalized during an initial audit and they will be used to simulate the R U revenue. To that end, there is a need to define the following concepts: Room Class: a Room Class is a group of room types (singles, doubles, queens, etc) with equivalent price levels (standard rooms, suites, deluxe, rooms with view ). Room Classes are inventories for which a maximum booking limit is calculated, taking into account overbooking, upsell opportunities and eventual upgrades. The Revenue Management System will calculate a booking limit on a daily basis for each hotel and for each Room Class, as well as a bid-price and associated bid-price gradient, as explained in (Belobaba and Williamson, 1988).
Yield Class: a Yield Class is a group of bookings defined as a combination of market segments, rate codes and rate levels, channel of distribution and point of sale (origin of the booking). Yield Classes are the elementary cells that serve as a basis for forecasting and enforcing control strategies (open/close, maximum booking limit, length of stay restrictions). The RMS control is derived from the recommended bid-price, open/close statuses, etc by date of arrival and by length of stay. For each Room Class and Yield Class, the current strategies applied by the yield manager (before a system is in place), will be defined as follows : Booking limits by date of occupancy / hotel / Room Class Booking limits by date of occupancy / hotel / Yield Class Minimum prices by date of occupancy / hotel / Room Class Minimum prices by date of occupancy / hotel / Yield Class Open/close by date of arrival / hotel / Yield Class / length of stay Each strategy may be defined using one or several of the following conditions: Period of application (effective date, discontinue date) Day of week or date when the strategy is applicable Days before occupancy Occupancy (on the books) per hotel, Room Class or Yield Class Occupancy by type of bookings Transient occupancy Group occupancy 4 Method of Evaluation of the Yield Performance The efficiency of the Revenue Management System can be evaluated by the rboa ratio (3), while the user + system efficiency can be evaluated by the aboa (4). They measure the additional revenue due to the application of Revenue Management (respectively to user & Revenue Management system), as a percentage of the total revenue increase that would be achieved in a perfect world. The rboa and uboa indicators compare two revenue calculations (R N and R P ) against the actual turnover generated, R A, and the turnover that would be generated, R R if the potential of the RMS system were fully used. The first calculation is based on the potential revenue generated assuming that no Revenue Management is taking place. In this case, rooms would be sold on a first come first served basis until the hotel capacity is sold out. This calculation gives the minimum `base from which to assess Revenue Gains, and it is named MiR for Minimum Revenue. Therefore, R N = MiR
The second calculation is based on the potential revenue generated using a fully integrated and operational Revenue Management System. It corresponds to the perfect world situation mentioned hereabove, where the forecast is 100% accurate and where the hotel reservations are sold strictly according to value. This calculation gives a maximum potential of Revenue Gain, named MaR (Maximum Revenue). Therefore, R P = MaR These two factors are then compared to the actual turnover realized over a given period, to compute the actual gain. This method enables the assessment of the actual impact of the Revenue Management System on the revenue generated. 4.1 MiR: Minimum revenue The MiR revenue corresponds to the revenue obtained without any Revenue Management. In this case, the principle of first come, first served prevails. Calculation method. Starting from the reading date, or milestone, which is the first date on which a reservation is recorded for a given date of future occupancy, one applies the booking pick up profiles that have been built statistically over the past. These profiles are unconstrained, that is, they represent the booking behavior that one would observe in the absence of capacity or price constraints. They are built for each Yield Class. In this process, the following rules are applied: As long as the reservations that are on-the-books have not reached hotel capacity, all reservations are accepted irrespective of their contribution As soon as reservations become in excess of capacity, all new requests for reservation are rejected If cancellations occur, then new reservations will be accepted, until the number of valid i.e. not cancelled, reservations on the books again reaches hotel capacity The statistical unconstrained booking up profiles are used to simulate the reservation activity, including the chronological order in which reservation requests at different rate levels are made. The MiR revenue corresponds to the sum of the revenues coming from all the reservations that have been accepted at the time of check in, as illustrated in the following figure.
60 Reading date Rd X 50 Capacity n3 n2 X1 X2 40 30 20 X3 X1 X2 Capacity n1 10 0 D-16 D-14 D-12 D-10 D-8 D-6 D-4 D-2 D 0 Fig. 2. MiR calculation The curves, which have been drawn in Fig. 2, are unconstrained booking pick up curves for three Yield Classes, respectively X1, X2 and X3. Hotel capacity is reached at reading date Rd. As there are no cancellations, the MiR is the revenue generated by the reservations held at Rd, that is: MiR = n1 * revenue(x1) + n2 * revenue(x2) + n3 * revenue(x3) 4.2 MaR : Maximum Revenue The MaR corresponds to the maximum potential revenue that the hotel could get if one had a perfect knowledge of demand and of the chronological order in which the reservation process takes place. This ideal revenue can be calculated a posteriori, that is, once the date of occupancy is over and the exact break down of reservations is known. Calculation method. At day D 0, the reservations by Yield Class are sorted by order of decreasing revenues. One only keeps those with the highest revenues; as long as the sum of their unconstrained demand remains lower than capacity (the demand corresponding to the last Yield Class may have to be truncated so that the overall sum remains below capacity). The MaR is obtained by summing up the corresponding revenues.
60 X3 50 Capacity X X1 n'2 n'1 40 30 20 X3 X1 X2 Capacity 10 0 D-16 D-14 D-12 D-10 D-8 D-6 D-4 D-2 D 0 Fig. 3. MaR Calculation The curves, which have been drawn in Fig. 3, are the unconstrained booking pick up profiles of three different Yield Classes, named respectively X1, X2 and X3. X1 is assumed to be the highest revenue Yield Class, followed by, respectively, X2 and then X3. The occupancy figures at D0 are unconstrained occupancy figures. If the Yield Class has not been constrained, then the figures that serve in the MaR calculation are the actual in-house guests for this Yield Class, while if it has been constrained, a projected unconstrained occupancy is used, using the booking up profile of the Yield Class. At D 0, only the reservations of the highest Yield Classes are kept until they exceed capacity. The MaR is the revenue that is generated with such reservations. With the highest revenue Yield Class being X1, one takes the associated reservations into consideration, and, as they remain below capacity, X2 reservations are also considered. X2 reservations are considered only until they reach the capacity limitation. No X3 reservation can be considered in the MaR calculation, as with X1 and part of X2, the capacity is reached. Therefore, MaR = n 1*revenu(X1) + n 2 * revenu(x2) 4.3 MiR_OVB: Determining the Impact of Overbooking The impact of an overbooking policy can be assessed using the MiR calculation described above. The capacity line which serves to eliminate the reservations that exceed capacity at one time in the reservation process can indeed be positioned at a higher level, to account for the overbooking process. This results into a new MiR, called MiR_OVB, greater in value as the overbooking level is higher. The impact of overbooking is: MiR_OVB - MiR
5 Results of the Performance Measurement Program Optims has applied theses concepts of Performance Measurement to a number of hotels for more than one year. The experience has been interesting and mutually enriching, both for the users that have applied the program and who have learnt a lot about their business and its potential and for the initiators of the program themselves, who have seen concrete and sometimes unthought of results emerge out of their calculations. The following results have been observed on a sample of twenty 3 or 4* hotels, over a 12-month period. The number of observations is therefore 240. The first observation is that R U proves to be non negligible, although very disparate from one property to the other. With an average of 28% the uboa ratio was found to culminate at 86% for one hotel on a one-month period (one observation). The second result is the confirmation of what existing literature holds for true, that Revenue Management accounts for 4 to 6% of additional revenues. Indeed, the performance of the RMS was found to be 6.7% (ratio (6)) and that of the actual yield program a little above 4.2% (ratio (7)). The surprise, though, that this Performance Measurement has uncovered is the large disparity among hotels, some averaging 0.4 to 0.1% of additional revenues while others have achieved as high as a 20% actual increase for an extended period of time (over one and sometimes two months). The least performing hotels were found in the airport hotel categories. The hotels showing the highest revenue increases were resort type hotels or city hotels with a good mix of business and leisure markets (hotels in Rome, Italy, Cannes, France or Vienna, Austria). Business hotels had an average performance, less than leisure hotels. There is a direct link between performance and the occupancy factor which can be summarised as follows: - Below 40%, the actual performance has been in the range of 0-2% of additional revenues, - Between 40 and 60%, this performance has been in the range of 3-5 % of additional revenues, - Between 60 and 70%, the average performance has proved to be in the 4-8% range, but a couple of hotels with respectively 66% and 65% have indeed recorded an 11% and a 15% revenue increases for more than three months, - Beyond 70%, the performance has climbed up to 10-12% of revenue increase. One hotel has recorded a 20% increase for two consecutive months, with an average occupancy of 73%. Overbooking accounts, on average, for 40% of the additional revenues. Usually, the more proficient the users are with Revenue Management, the lesser this percentage tends to be, as proficient Revenue Managers will use many more levers than simply overbooking.
The measurement of the seized business opportunities does not variate as much as the additional revenues, most hotels achieved between 60% and 70% of actual seized opportunities (aboa), once a RMS program was in place. A few hotels do reach 90+% and, when this is combined with great demand, it accounts for the largest revenue increases Few hotels do use the full potential of the RM system. The R A revenue has been found to be on average 2.4% lower than the revenue that it was possible to achieve with the system (R R ). Fig. 4. Example of BOA results Fig. 4 is an example of a BOA performance report. The left scale is expressed in revenue for the Yield Impact, Mir, Mar and AR while the right scale is expressed in percentage (for the BOA).
Conclusion The impact or Return on Investment for a Revenue Management program depends on three key criteria, which can be summarized as follows: The current Revenue Management practice in place before the RMS program is implemented. The more sophisticated this manual practice is, the less benefit will come from the implementation of an automated system. But few hotels can indeed invest in dedicated resources to make sure that manual practice remains at an adequate and good level. The level of demand the hotel is faced with. Increasing revenues by, say, 4%, pre-supposes that demand can fill the hotel up to at least 50% of its actual capacities The ability of the users to seize the revenue maximisation opportunities. In this respect, the more integrated the system is with the hotel marketing policies, practices and systems, the closer the actual revenues are to RMS revenues. This Performance Measurement program will be going through further enhancements in the very near future. On the one hand, there is a need in the hotel industry to resort to a more predictive set of analyses, with which the results set forth in this paper would be evaluated before a RMS is actually in place at the hotel property. This would help hotel decision makers take the right decisions and software vendors become even more convincing at equipping hotels with such optimisation programs. On the other hand, it is important both for the software vendor and for the hotel revenue managers to better understand why the potential of a Revenue Management System is not always used completely. Better user support and services are probably one part of the solution, but customisation and PMS evolution issues are certainly another part of it. References AHI MANAGEMENT SEMINAR SERIES EUROPE 98 86 th Edition Hotel Yield Management Seminar pp 31-36 Presented by Horand Vogel & Associates BATTAGGIA, F. (1996) Selling Revenue Management to Skeptics. IATA s 8 th International Revenue Management Conference and Exhibition, 12-14 November 1996 BELOBABA, P.P., WILLIAMSON, E.L. (1988) - Optimization Techniques for Seat Inventory Control. Proceedings of the Annual AGIFORS Symposium, October 1988.