Hotel_yield

Hotel yield management using different reservation modes

Jean-Franc ?ois Sanchez and Ahmet Satir

Department of Decision Sciences and MIS,John Molson School of Business,

Concordia University,Montreal,Canada

Abstract

Purpose –This paper explores the implementation of yield management using different reservation modes at a global hotel network (referred to as the “Group”).

Design/methodology/approach –The Group operates close to half-a-million rooms in about 4,000hotels world-wide.Following an overview of yield management in hotel industry,the two reservation modes used in the Group are presented.The performance of Group’s online and off-line reservation modes globally over a two-year period is then discussed in terms of three yield management performance measures,namely:average price (AP),occupancy rate,and average revenue per available room.

Findings –The ?ndings indicate that the online mode outperforms the off-line mode with respect to performance measures of AP and average revenue.Further to a global-based comparison,a localized evaluation of these two modes is also presented for two sub-groups of hotels clustered in a given region.Statistical analysis of ?ndings is provided,pointing to a substantial revenue increase for the hotel sub-group that switched from the off-line to the online reservation mode,compared with the hotel sub-group that continued to operate off-line.The paper concludes with a brief discussion on the strengths,weaknesses,opportunities and threats associated with the online reservation mode.

Research limitations/implications –Future research could look into the impact of speci?c macro and micro economic conditions on the three yield management performance measures de?ned.

Originality/value –The research reported is of value to hotel executives who want to pursue online reservations.

Keywords Yield management,Performance measures,Rooms,Hotel and catering industry

Paper type Case study Yield management in the hotel industry Yield management (also referred to as,the “revenue management”)is an integrated and systematic approach to revenue maximization via manipulating the rates offered to the customers in light of forecasted demand and supply patterns.It is geared towards selling the right inventory unit to the right customer at the right time for the right price.Hence,market segmentation,timing (demand and supply management)and pricing are the three pillars of yield management.Its successful implementation should bene?t all the stakeholders.The supplier of the service bene?ts in terms of increased turnover and thus higher revenues,whereas the end-user has the option of taking advantage of reduced rates at non-peak times for the same service at the same quality.The airlines industry pioneered the technique in the early 1980s and the leading players in the hotel industry adopted it about a decade later.The global hotel network (referred as the “Group”,hereafter)studied in this paper had developed an interest in yield management during the late 1990s with some solid results in recent years.An overview of yield management is provided in Donaghy et al.(1995).Organizational and ?nancial aspects of yield management are discussed in

The Emerald Research Register for this journal is available at

The current issue and full text archive of this journal is available at https://www.wendangku.net/doc/1b2582719.html,/researchregister https://www.wendangku.net/doc/1b2582719.html,/0959-6119.htm IJCHM

17,2136

International Journal of

Contemporary Hospitality

Management

Vol.17No.2,2005

pp.136-146

q Emerald Group Publishing Limited

0959-6119

DOI 10.1108/09596110510582332

Donaghy et al.(1997)and Burgess and Bryant(2001),respectively.Upchurch et al. (2002)compare usage and competency factors of yield management principles within mid-sized,upper market lodging operations in United States.Based on the study’s ?ndings as to the factors comprising the yield management process,the authors conclude that there is a perceived difference between one’s usage and comfort in applying various yield management principles.

Quantitative and knowledge-based modeling techniques are applied to yield management.Among the former,Badinelli(2000)discusses the implementation of dynamic programming for hotel yield management.Choi and Cho(2000)uses a probabilistic rule-based framework in knowledge discovery technique as decision support tool in hotel yield management.The practical aspects of yield management in Las Vegas casino hotels are presented in Norman and Mayer(1997).

Yield management requires the gathering,processing and storing substantial amount of information.Jauncey et al.(1995)argue that executing a good yield management within the context of hotel industry requires:

(1)demand forecasting based on historical demand analysis and emerging

business patterns;

(2)supply analysis in terms of room availability provided by a property

management system,taking into consideration refurbishments,periodic cleaning and other tasks;

(3)market segmentation;

(4)non-arrival and cancellation analysis by period and market segment;

(5)scenario analysis with respect to implementing different rates,while

considering various business restrictions by market segment;and

(6)providing advice to yield management team as to the most appropriate rate mix

and restrictions by period and market segment.

Such advises may then be accepted or modi?ed by the team.Rate mix and restrictions are then electronically communicated to the front of?ce and central reservation of?ce employees responsible for reservations.

Policies relating to the following levers of yield management also need to be developed,as argued by RevDev Consulting(https://www.wendangku.net/doc/1b2582719.html,):

(1)Impact of events,local attractions,holidays,as well as the more general

in?uence of social,political and economic variables on the hotel’s occupancy must be considered.

(2)Policies such as overbooking and upgrading should be an integral part of room

management with capacity constraints.

(3)The rate structure must be set so that there is enough?exibility to adapt the

offer to?uctuating demand,while allowing preferential rates for regular or exceptional guests.

(4)The volume generated by the groups and the rates granted must be weighted

against the revenue generated by individual reservations during the same period.Controlling the length of stay may be manipulated through assigning a higher priority for a longer term stay spanning over both high and low occupancy days over a one night stay during high occupancy period.

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137

(5)Potential revenues (generated by dining,bar services,telephone calls,room service,

banqueting)should also be incorporated into the market segment analysis.

(6)A shared inventory of rooms among several properties scattered around a limited area may be a substantial lever depending on the potential for transferring reservations from one hotel to another.

Yield management in the “group”

The hotel network studied in this paper operates globally with a total room capacity close to half-a-million rooms scattered over about 4,000hotels.The bulk of these properties are located in North America and Europe.There are several hotel brands in the Group,each focusing on a given market segment (business,leisure and economy).Yield management practices are exercised at business and leisure clientele focused hotels,whereas the economy hotels in the Group are not subject to yield management.The success of the economy lodging model has largely been attributed to a clear and in?exible pricing policy,where the customer books at an economy lodging property because of the well-known product standard and the low (more or less)?xed rate.This price inelasticity hinders the application of yield management practices in those Group hotels operating in the economy market segment.

Yield management by area

The Group implements yield management by grouping hotels in a limited area and assigning an area yield manager to execute the practice.The “area”is de?ned as a geographic entity where the customers’demand is considered to be relatively well established over the years,naturally subject to seasonality and emerging trends in the industry.Each area houses numerous Group properties.Generally,a large city would qualify as an area,whereas big metropolitan centers would have “sub-areas”.

The practice of area yield management provides the Group with an advantage over its competitors.The Group bene?ts from the presence of various brands in a given area that favors the possibility of transferring reservations from one property to another.More than being an additional lever of yield management,the opportunity to shift the ?ow of customers at a negligible cost facilitates the practice of overbooking and reduces the risk of losing the customer to competition.An area is managed like a single unit with the objective of selling the maximum number of rooms at the optimal price.For example,a fully booked hotel is able to propose a fair rate to a customer willing to stay in a sister hotel with same or higher level of comfort.Such yield management practices require establishing synergy that enhances coherence in terms of pricing and brand policies among the area properties.The presence of an area yield manager who supervises and maintains such a synergy is crucial in this business model.

Yield management practices in an area are coordinated by an area yield manager,who largely oversees the implementation of the overall strategy and acts as a consultant to individual hotel’s management.Over and above possessing a thorough understanding of yield management practices in hotel industry,the yield manager is a functional specialist with a knowledge of the area geographically and an understanding of the speci?c features offered by the various brand hotels in the area.This position requires leadership and negotiation skills in order to convince a hotel management as to the validity and soundness of a given advice.IJCHM 17,2138

Central reservation system

The Group launched its in-house developed central reservation system during late 1990s.The system,called the group global reservation system(GGRS),soon became the pillar of yield management.GGRS is the central reservation tool developed in house by the Group in order to allow group hotels to be sold by the reservation service pools. GGRS has four applications,namely:

(1)“database”,where all the hotel and rates information are entered into;

(2)“reservation”,where all consultation(hotel availability information)and

bookings,cancellations and modi?cations are done;

(3)“reporting”,where statistical reports on reservations,cancellations,

modi?cations and rates are provided by period,by distribution channel and by client segment;and

(4)“commissions”,where commissions owned to various distribution channels are

calculated and settled.

Distribution channels served by GGRS include travel agencies,tour operators, internet-based reservation systems,call centers,voice servers and sales force. Reservation modes

For strategic and operational reasons,the group hotels have been rolled out in one of the following two modes:

(1)GGRS online.The online version of the reservation system operates on the

virtual private network of the Group.The reservation system is interfaced with the property management system of the hotel.This makes it possible for room bookings coming from external reservation channels to be automatically downloaded into the property management system.

(2)GGRS off-line.Room reservations in these hotels are executed by traditional

means,such as faxes and telephone calls.As such,these properties function more like isolated,individual business units without taking advantage of the ?exibilities offered by yield management in terms of inter-hotel room and rate availabilities,as provided by the online operational mode.

Initial roll out to online reservation mode started in the early2000.As of1January 2004,about2,100hotels(out of4,000hotels)in more than20countries were operating in this mode.This study aims to study the impact of going online in reservations using three yield management measures.These measures will be de?ned in the next section. The time span studied covers a24-month period from January2002to December2003. The use of a wide range of properties allows to attenuate the impact of factors such as geographic location or rooms unsold due to refurbishment.Furthermore,two years of data provide a relatively large sample size to mitigate some potential biases,such as local seasonal changes,in?ation rates,as well as the overall global economic situation.

Yield management performance measures

Three yield management measures are used in this study to evaluate the two reservation modes.These are:

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139

(1)average price(AP);

(2)occupancy rate(OC);and

(3)average revenue per available room(RR).

A brief de?nition of measures follows.

(1)AP:This is a revenue related global?nancial measure given by:

AP?eglobal monthly revenueT=erooms sold in the monthT

(2)OC:This measure indicates the utilization of global physical capacity available.

It is given by:

OC?erooms sold in the monthT=erooms available in the monthT

(3)RR:RR in a given month is a function of AP and OC.It is calculated by dividing

the total monthly room revenue by the number of rooms available for sale in a given month.

RR?eglobal monthly revenueT=erooms available in the monthT

The values for the above three performance measures over the24month period studied are presented in Figures1-3,respectively.Readers should note that in order to protect the con?dentiality of the data,each performance measure is presented as a percentage of monthly change compared to the same period in previous year.

The online reservation mode provided a consistently better AP for the Group compared to off-line reservation mode over the2002-2003period,as illustrated in Figure1.After April2002,the online mode provided a positive AP measure(in the range of from1.7to6.2percent)in terms of monthly change from one year to the next. During the same period,the off-line mode stayed frequently in negative territory, especially after May2003.

Analysis of OC performance measure results in a somewhat different result.Figure2 shows that the online reservation mode is mostly below that of the off-line mode and

Figure1. Evolution of

AP

IJCHM 17,2 140

also below the 0percent threshold until July 2003.The decrease in OC up to mid-2003is not surprising due to macro economic and global security issues.However,other factors may also have been instrumental in OC evolution as depicted in Figure 2.The initial hotels to be rolled out in online mode were chosen according to their high revenue potential.Since these properties had already a high OC,their potential to improve on this particular performance measure was somewhat limited.Another contributing factor could be the higher AP for online reservations as shown in Figure 1.Many properties tend to close their prices in advance based on their OC forecasts.There may be a negative impact on this measure if some of these properties do not re-open their prices when the reservation portfolio does not materialize as expected.

Figure 3.

Evolution of

RR

Figure 2.

Evolution of

OC

Hotel yield management 141

The third performance measure used for yield management is the RR,which is a function of the ?rst two performance measures.Figure 3shows that the online reservation mode outperforms the off-line mode almost every time on this measure.The former mode hardly goes below the threshold point of 0percent,whereas the latter mode is below that threshold point quite often.The relatively similar patterns of RR for both modes indicate that this measure is largely impacted by the more macro concerns

such as the global economic situation and security concerns,the online mode being superior to the off-line mode for the same global environment in a given month.It is important from a yield management perspective to be able to see the relation between the AP and the OC,which ultimately determines the average revenue per room.Figures 4and 5show this relation for the online and the off-line modes,respectively.The negative correlation that naturally exists between these two measures are more readily observed for the online reservation mode,as shown in Figure 4.However,the periods when both AP and OC measures are evolved in the positive territory point out to the speci?c yield management practices which proved to

Figure 4.

AP vs occupancy rate for

the online reservation

mode

Figure 5.

AP vs occupancy rate for

the off-line reservation

mode

IJCHM 17,2142

be the most effective.On the other hand,the off-line mode in Figure5offers only few such instances where both measures are in the positive territory.This observation further enforces the importance of using online reservation mode over the off-line mode within the yield management efforts of the Group.

A focused study

The purpose of the work in this section is to verify the work done in previous section by providing a focused study for two subgroups within the Group.The yield management performance measure is used in the RR.The?rst group labeled as“Hotel A”comprises400properties with a total of56,045rooms.These hotels operated off-line in1999and2000and went online in2001.The second group labeled as“Hotel B”has 335properties with a total of41,200rooms.This group operated off-line from1999to 2002.The room revenues for a given group are aggregated in order to come up with RR value for years1999,2000and2002.Since Hotel A was rolled out in2001into the online mode,the transition year2001was discarded from the study in order not to bias the ?ndings.The RR(in US dollars)for both subgroups are given in Table I.The difference between the RR values for the two subgroups over a24-month period(years1999and 2000)when both subgroups were operating in the same reservation mode(off-line)are then subjected to statistical analysis.Distribution parameters and con?dence intervals obtained from this analysis are presented in Table II.The statistical values obtained form a base for comparative evaluation of the two subgroups for2002,when Hotel A switched to online reservation mode.

It is evident in Table I that the RR?gures were in decline for the year2002 compared to year2000.This can be attributed to overall macro economic situation and global security concerns following the events of9/11.However,the important observation to note is the rate of decrease in RR(1.08percent)was lower in Hotel A than it was for Hotel B(7.26percent).

Table II reveals that the difference in RR values over a24month period in years1999 and2000varies in the range($12.41-$17.69)with a mean of$15.05.This difference is exclusive of the reservation mode used during this period since both subgroups operated off-line.When online reservations became fully operational for Hotel A in2002,the mean difference in RR?gures for the year shows a$2.10increase($17.15vs$15.05)

199920002002 Hotel Number of rooms Status RR Status RR2001Status RR

A56,045Off-line54.43Off-line59.21Roll out of Hotel A Online58.57 B41,200Off-line38.87Off-line44.66Off-line41.42

Table I. RR(in$)for Hotels A

and B

Con?dence intervals

Distribution moments Parameter Estimate Lower CI Upper CI1-Alpha

Mean15.05

SD 6.25Mean15.0512.4117.690.95 Std Err Mean 1.27Std Dev 6.25 4.858.76

N24

Table II. Statistical analysis for differences in RR for Hotels A and B(years

1999and2000)

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for Hotel A.Over a calendar year,this is equivalent to an average increase of $42,958,492

in Hotel A’s revenues (($2.10/room)￡(56,045rooms)￡(365days)).Again,although it is not the sole factor that contributed to the relatively superior performance of Hotel A over Hotel B,the Group’s top management believes the implementation of online reservations was a major contributing factor in this regard.

Use of e-distribution channels for reservations

The growth of e-distribution channels and particularly the launch of Group’s web site during the mid-2000had an impact on the reservation portfolio.The share in reservations (in percent)for global distribution systems (GDS)of travel agencies and web channels are given in Figure 6for both online and off-line reservation modes.The GDS,to a large extent,is the channel used by the business traveler,whereas the web is the channel largely used by the leisure traveler.One immediate observation from the ?gure is the upward trend for the web originated online reservations and a downward trend for the GDS originated off-line reservations over the 24-month period studied.This results in a widening gap between the online and off-line reservation modes for both GDS and web channels.This ?nding is in line with the increasing use of internet-based technologies and practices over the recent years.Another ?nding to note is that towards late 2003,web originated online reservations surpassed the GDS originated off-line reservations for the ?rst time.The extensive use of internet implies profound changes in customer behavior and hence forces the marketing department to be more proactive in using the e-distribution channels.Yield management favors bookings made through e-distribution channels,since the transaction does not require the involvement of the hotel team,thus resulting in lower transaction costs.In the absence of online reservation mode,hotels cannot acknowledge new promotions in real time and Figure 6.

Reservation shares for

travel agencies’GDS and

web-based

reservations

IJCHM 17,2144

hence unable to rearrange their prices.This results for off-line hotels in a lack of awareness and responsiveness to evolving market conditions compared to the online hotels.However,one should also point out a potential de?ciency when using an online reservation system.If the system in use processes a booking request by only providing a“Yes(open)”or a“No(close)”response without providing alternative options to the potential customer,then an opportunity cost in terms of a lost customer incurs.In an off-line system,the human touch can provide the?exibility required to mitigate such opportunity costs,by referring the customer to a nearby sister hotel or even lowering the price quoted if the individual is a frequent user of the facility.Therefore,the yield management systems and hence the online reservation systems would improve their effectiveness in proportion to the level of ?exibility they could offer to the potential customer.

Conclusion

Effective use of yield management techniques are known to have a signi?cant impact on the?nancial performance.In this paper,the use of yield management in the context of a central reservation system in a global hotel group was discussed.The system, called the GGRS,incorporated two modes of reservations:online and off-line.These reservation modes are comparatively evaluated through three yield management performance measures over a two-year period.There is strong evidence that the online mode facilitated a higher RR.This performance was attributed to better management of AP when this mode is utilized,rather than a superior OC performance.The?ndings arrived at the global level were then veri?ed at a micro level,where the performance of two subgroup hotels was evaluated.

A SWOT analysis carried out on the implementation of the GGRS online implementation revealed the following major strengths,weaknesses,threats and opportunities,as speci?ed by the Group users through extensive interviews.A fundamental strength was the signi?cantly improved understanding of the Group’s revenue management practices and the reservation distribution channels.Group’s global reservation system online mode enables a fully booked property to propose an alternative to the client in a nearby Group’s hotel.The database application makes it possible to react faster by allowing the hotels to manage their prices in real time. The reservation application allows the identi?cation of brand cardholders and referenced clients through a common?le.The reporting application permits a complete check for the origins of reservations and facilitates extensive reservation statistics https://www.wendangku.net/doc/1b2582719.html,mission processing is of better quality in online mode and the commissions payable to travel agencies are processed automatically.A weakness for the online reservation mode is the long response time when searching for information. Although not frequent,the network can freeze for minutes and in some cases for hours. The tool incorporates many functionalities of which only about one-third is fully utilized.

In terms of opportunities,the evolving technology of GGRS online allows to integrate additional systems such as data mining,an important tool in yield management.One other crucial opportunity is the potential of establishing regional call centers and eventually a global call center where the GGRS is the pivotal facilitator. On the threat side,one possible threat to consider is how to preserve network security in terms of information con?dentiality.

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