investigation of the revenue management practices of accommodation establishments in turkey: an...

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Investigation of the revenue management practices of accommodation establishments in

Turkey: An exploratory study

Stanislav Ivanov *

Professor in Tourism Economics, Department of Tourism, Varna University of Management,

13A Oborishte Str., 9000 Varna, Bulgaria; tel: +359 52 300 680; email:

[email protected]

* Corresponding author

Çağakan Ayas

BA (Hons) International Hospitality Management programme graduate, Department of

Tourism, Varna University of Management, 13A Oborishte Str., 9000 Varna, Bulgaria; email:

[email protected]

Abstract:

This exploratory research paper investigates the revenue management practices of

accommodation establishments in Turkey through a survey of the managers of 105 hotels.

Specifically the paper looks at the revenue centres, revenue management tools, channel

management, performance metrics, revenue management team, software and forecasting

methods used by hoteliers. The findings indicate that respondent hoteliers put the emphasis on

price discrimination and room availability guarantee and are less likely to apply

overcontracting and overbooking. Most of the respondents do not have a revenue manager

and do not intend to hire one: revenue management is usually within the responsibilities of the

general manager, front office manager or the marketing manager. OTAs, hotel’s website, tour

operators and travel agents are the most important distribution channels. The size of the

property, its category, location and chain affiliation have significant impact on the degree of

application of the various revenue management practice. In general, revenue management is

mostly adopted by high category, chain affiliated, urban and seaside hotels with large number

of rooms. Managerial implications, limitations and future research directions are discussed as

well.

Key words: hotel revenue management, yield management, Turkey, revenue management

tools, revenue management system, revenue management process, forecasting, revenue

management team

mailto:[email protected]

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Introduction

Revenue management (RM) has been widely recognised as a key technique for matching

supply and demand in tourism and hospitality (Anderson and Xie, 2010; Cross et al., 2009;

Hayes and Miller, 2011; Ingold, McMahon-Beattie and Yeoman, 2001; Ivanov, 2014; Kimes,

2011; Legoherel, Poutier and Fyall, 2013; Mauri, 2012; Queenan et al., 2011; Tranter, Stuart-

Hill and Parker, 2008; Wang et al., 2015; Yeoman and McMahon-Beattie, 2004, 2011) and

other industries (Ng, 2009; Talluri and van Ryzin, 2005). Building on Kimes (1989) and

Kimes & Wirtz (2003), hotel revenue management can be defined as the constellation of tools

and actions dedicated toward the achievement of an optimal level of the hotel’s net revenues

and gross operating profit by offering the right product to the right customers via the right

distribution channel at the right time at the right price with the right communication. Its

arsenal includes various pricing, non-pricing and combined revenue management tools

(Ivanov and Zhechev, 2012; Ivanov, 2014) which hoteliers use to maximise the revenues and

the yield of their properties – price discrimination (Mauri, 2012), rate parity (Demirciftci et

al., 2010; Haynes and Egan, 2015), lowest price guarantee (Carvell and Quan, 2008), early

bird/last minute offers (Chen and Schwartz, 2013; Schwartz, 2008), overbookings (Hwang

and Wen, 2009; Ivanov, 2015), channel management, etc. Its adoption by hoteliers is largely

led by the argument that the application of revenue management contributes positively to the

financial results of the hotel although the exact contribution varies greatly by property

(Rannou and Melli, 2003).

While hotel revenue management has grown tremendously as a research field for the last 20

years and its application in developed economies is widely acknowledged and researched, its

application by the hotel industries in Central and Eastern Europe, Middle East, Latin America

and Africa, has not received much attention in the specialised academic literature with some

notable exceptions (Emeksiz et al., 2006; Gehrels and Blanar, 2012; Güler, 2012; Ivanov,

2014). This exploratory research paper aims at partially filling this gap by focusing on the

application of various RM practices by the accommodation establishments in Turkey. It

provides reality check by looking at whether the theoretical concepts in the field of hotel

revenue management have found their ways into the actual business practices of the

accommodation establishments in the country. Specifically the paper looks at the RM system

(revenue centres, RM tools, RM software, RM metric, RM team and RM process) of the

respondents and the external influences (such as competitive actions and customer

characteristics) on the revenue management decisions of hoteliers. It analyses the role of

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accommodation establishment’s characteristics (category, location, size and chain affiliation)

on the level of adoption of the various revenue management techniques by its managers.

Other problems that fall within the RM framework like value creation, segmentation, profiling

and targeting, information provision, customers’ perceptions of the fairness of the RM

practices, the links between RM and social media, and the measurement of the exact impact of

RM on hotel’s bottom line go beyond the scope of this exploratory research.

The rest of the paper is organised as follows. Next section provides focused literature review

on hotel revenue management; section III elaborated the methodology; section IV analyses

the findings while section V discusses the paper’s contribution, managerial implications,

limitations and future research directions.

Brief review of related literature

The vastness of hotel revenue management as a research field does not allow a comprehensive

review of all RM topics in short space. Extensive, in-depth and comprehensive critical

reviews of current debates in RM are provided by Wang et al. (2015), Mauri (2012), Ivanov

(2014), and Ivanov & Zhechev (2012). Here we shall briefly outline only some of the key

aspects of hotel revenue management that are relevant to this research.

INSERT FIGURE 1 AROUND HERE

INSERT FIGURE 2 AROUND HERE

Ivanov & Zhechev (2012) and Ivanov (2014) present the revenue management in a hotel as a

system that includes four structural elements (data and information, hotel revenue centres,

RM software and RM tools), the RM process and the RM team (see Figures 1 and 2). The RM

centres are those departments in the hotel that generate revenues. Besides the rooms division,

these may include F&B, parking, spa/fitness/sauna, golf course, casino, function rooms and

other services. The wider the scope of the revenue centres, the greater the possibilities for the

hotel to generate revenues and not to depend on one main source (i.e. rooms). The variety of

revenue centres is also one of the prerequisites for the application of cross-selling as a sales

technique. Usually research in regard to the RM centres focuses on a single hotel RM centre

or related hospitality industries like restaurants (Heo, 2013; Kimes & Thompson, 2004),

casinos (Kuyumcu, 2002), function rooms (Orkin, 2003), golf courses (Rasekh & Li, 2011),

spa centres (Kimes & Singh, 2009), although recent publications have advocated on total

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hotel revenue management (Buckhiester, 2012; Wang et al., 2015) that takes into

consideration all the revenues generated from the customer, not only the revenues from the

rooms.

The RM tools include a variety of instruments used by hoteliers to manage demand and

supply. Ivanov (2014) divides them into three groups. The first group is the pricing tools

includes the RM tools that concern only the price, namely: price discrimination (Mauri, 2012;

Shy, 2008; Tranter et al., 2008), dynamic pricing/early bird/last minute offers (Abrate et al.,

2012; Chen and Schwartz, 2013; Schwartz, 2008), rate parity (Demirciftci et al., 2010;

Haynes and Egan, 2015; Maier, 2011; 2013), lowest price guarantee (Carvell & Quan, 2008;

Demirciftci et al., 2010) and price framing and discounting (Croes and Semrad, 2012). The

pricing tools have direct impact on the price, and therefore, direct impact on the revenues of

the hotel and the fairness perceptions of the customers regarding the price they pay. The

second group is the non-pricing tools and as their name suggest include a set of techniques

that do not involve the price of the service. These tools include: inventory management

(overbooking and overcontracting: Hwang and Wen, 2009; Ivanov, 2006, 2015; Koide &

Ishii, 2005; Netessine & Shumsky, 2002), room availability guarantee, length-of-stay control

(Vinod, 2004), 100% satisfaction guarantee. Unlike pricing tools, the research on the non-

pricing tools is very scarce and focused predominantly on overbookings; room availability

guarantee, length-of-stay controls, 100% satisfaction guarantee have been largely neglected

(Ivanov, 2014). The third group of RM tools includes those techniques in the arsenal of

hoteliers that deal with the simultaneous manipulation of price and quantity, namely: channel

management (Choi & Kimes, 2002; Hadjinicola & Panayi, 1997) and optimal room-rate

allocation (El Gayar et al., 2011; Guadix et al., 2010). Proper channel management is of

utmost importance in order to avoid conflicts with the distributors (Ivanov et al., 2015) and

channel cannibalisation (Ivanov, 2007). In general, non-pricing and combined RM tools are

underresearched in the specialised literature and provide numerous future research

opportunities.

The RM process is the set and sequences of actions undertaken by revenue managers on

strategic, tactical and operational level in relation to managing the revenues of the hotel

(Ivanov, 2014: 34). Different authors propose different stages of the process. Emeksiz et al.

(2006) propose an RM model that includes five stages, namely: preparation, supply and

demand analysis, implementation of RM strategies, evaluation of RM activities and

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monitoring and amendment of the RM strategy. Tranter et al. (2008) identify eight steps in the

RM process – customer knowledge, market segmentation and selection, internal assessment,

competitive analysis, demand forecasting, channel analysis and selection, dynamic value-

based pricing, and channel and inventory management. Ivanov and Zhechev (2012) formulate

7 stages of the process: goal setting, information collection, analysis of data, forecasting,

decision, implementation and monitoring of the whole RM process. It is evident that despite

the different number and titles the authors include more or less the following stages within the

RM process – data collection, data analysis, forecasting, decision making, implementation of

the decision and control of the process. Data and forecasting are the basis of decision making.

That is why hoteliers are advised to collect data on various statistics and performance metrics

(occupancy, ADR, RevPAR, GOPPAR, etc.). Big data analytics currently is considered one of

the major challenges faced by revenue management because it allows better forecasting and

thus better managerial decision (Wang et al., 2015). Forecasting is essential for the proper

managerial decisions and various historical (time series), advance booking (additive and

multiplicative pick up) models, combined (regression, neural networks) and qualitative

(Delphi) methods are used (Chen and Kachani; 2007; Frechtling, 2001; Ivanov, 2014; Lim et

al., 2009; Phumchusri & Mongkolkul, 2012; Weatherford & Kimes, 2003).

The practical application of revenue management in the hotel is implemented by an RM team.

That is why human resources have been recognised as an essential factor in the planning and

design of the RM system and the proper implementation of the RM process (Aubke et al.,

2014; Beck et al., 2011; Selmi & Dornier, 2011; Zarraga-Oberty & Bonache, 2007). Their

actions actually determine whether the RM system in the hotel is successful and contributes

positively to the bottom line of the property. Although currently the RM software (Emeksiz et

al., 2006; Okumus, 2004) allows the automation of many decisions in the RM process,

especially related to pricing and overbooking, it is actually the revenue managers who often

need to confirm these decisions and take the responsibility for them. Therefore, human

resources are vital for the success of the revenue management in every hotel.

Empirical setting

In 2014 Turkey boasted 3131 accommodation establishments with 807316 beds that generated

130029917 overnights (Ministry of Culture and Tourism, 2015a, b); 2430 (77.61%) of them

with 671280 (83.15%) were classified as hotels. In terms of category structure, the high

category establishments prevail: 1270 establishments (constituting 49.30% of the categorised

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properties (hotels, holiday villages, thermal hotels and thermal holiday villages) or 40.56% of

all properties) and 612940 of the beds (81.05% of the beds in categorised properties or

75.92% of all beds) were categorised with 4 or 5 stars. In 2014 the 4- and 5-star hotels

generated 69.28% of all overnights in accommodation establishments in Turkey. The

accommodation establishments are highly geographically concentrated. Three regions

(Mediterranean, Aegean and Istanbul) concentrate 65.74% of all accommodation

establishments and 83.18% of the beds (Ministry of Culture and Tourism, 2015a). In 2014

23609016 international tourists stayed at accommodation establishments and spent 97581075

overnights (Ministry of Culture and Tourism, 2015b). The main international source markets

included Germany, the Russian Federation, United Kingdom, The Netherlands and France,

which contributed 61.07% of all overnights in the same year. The average stay was 3.18

overnights, but varies greatly by source market (1.88 for domestic tourists, 4.13 for

international tourists, 5.42 for tourists from Germany, while only 1.34 overnights for tourists

from the Republic of Korea).

Methodology

Research questions

The research questions followed the elements of the revenue management system and the

revenue management process, elaborated in Figures 1 and 2, respectively. Considering the

exploratory nature of this piece of research, the following 7 groups of research questions were

formulated:

RQ1: Revenue centres in the hotels

RQ1.1: Which are the revenue centres of hotels in Turkey?

RQ1.2: Which revenue centres have greatest potential for development?

RQ2: Pricing and non-pricing revenue management tools and sales techniques

RQ2.1: What is the level of application of the revenue management tools by hoteliers

in Turkey?

RQ2.2: What is the perceived level of importance of the revenue management tools by

hoteliers in Turkey?

RQ2.3: What is the impact of the revenue management tools on hotels’ sales?

RQ3: Channel management

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RQ3.1: What is the importance of the distribution channels used by hotels in Turkey?

RQ3.2: Which types of contract do hotels in Turkey mostly use in their relationships

with distributors?

RQ4: Revenue management team

RQ4: Who is in charge of revenue management implementation in hotels in Turkey?

RQ5: Revenue management software

RQ5: What are hotel managers’ perceptions about the specialised revenue

management software?

RQ6: Revenue management process

RQ6.1: How do hoteliers in Turkey measure the performance of their properties?

RQ6.2: How do hoteliers in Turkey forecast the future sales, revenues, occupancy?

RQ6.3: To what degree do hoteliers in Turkey consider customers’ characteristics and

preferences when applying various revenue management tools?

RQ6.4: How do hoteliers in Turkey react to competitor moves in prices?

RQ7: Factors, influencing the application of revenue management by hotels in Turkey

RQ7.1: Does the size of the hotel influence the application of specific revenue

management practices by its managers?

RQ7.2: Does the category of the hotel influence the application of specific revenue


RQ7.3: Does the location of the hotel influence the application of specific revenue


RQ7.4: Does the affiliation to a hotel chains influence the application of specific

revenue management practices by its managers?

Research approach

Usually the analysis of the revenue management practices of hotels and hotel chains is based

mostly on (qualitative single) case studies (e.g. El Haddad, 2015; Gehrels and Blanar, 2012;

Okumus, 2004; Pekgün et al., 2013) rather than quantitative surveys (e.g. Güler, 2012;

Ivanov, 2014; Kimes, 2009). While case studies allow the researcher to generate rich and in-

depth qualitative data about the RM practices of one or few hotels or chains, the results are

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not generalisable for the industry. Furthermore, single case studies (one hotel or chain) do not

allow the researcher to compare the RM practices of different groups of hotels/chains (e.g.

small- vs. large-sized hotels, independent vs. affiliated hotels, seaside vs. urban hotels, etc.)

which is possible with quantitative surveys. However, the quantitative surveys lack the

richness of the answers to the open questions, which is the main advantage of the qualitative

case studies. Considering the overall purpose of this exploratory research (i.e. to investigate

the practical application of various revenue management techniques by accommodation

establishments in Turkey) and the formulated research questions (especially RQ7), the authors

selected quantitative survey as research approach.

Data collection

Data collection took place in January-March 2015 through the distribution of an online

questionnaire to the managers of hotels in Turkey. The authors did not find a comprehensive

database with the contact details of the accommodation establishments in Turkey. That is why

they developed their own mailing list with the email addresses of 2080 properties through

extensive online search. The contacted accommodation establishments represent 66.43% of

all 3131 licenced accommodation establishments in Turkey in 2014 (Ministry of Culture and

Tourism, 2015a). An email with the link to the online questionnaire was sent to the managers

whose email addresses were available at the websites of the hotels – marketing managers,

revenue managers, general managers, etc. However, in very rare cases the website of the hotel

included more than one contact email address. As a norm, nearly all hotel websites included

only one general contact email address in the ‘Contact us’ section. That is why, the survey

participation invitation letter asked the recipients to forward the email to the person in charge

of revenue management in the hotel. The authors expected that the respondents would be

suspicious to the survey because they might fear that information about their responses might

leak to competitors and/or tax authorities, which would ultimately be reflected in low

response rate. In order to stimulate the response rate the authors undertook several actions.

First, the survey was completely anonymous and voluntary and no data that would identify the

respondents (e.g. name of the hotel, email address or else) were collected. The anonymity and

the voluntary participation were emphasised in the survey participation invitation letter in the

body of the email and in the introductory text of the online questionnaire. Second, the

questionnaire did not collect any financial data (revenues, costs, financial results) in absolute

values, but used scales instead. Third, following the recommendations of Illum et al. (2010),

the authors offered a link to a complimentary copy of a hotel revenue management e-book

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written by the first author to all respondents who completed the questionnaire. Due to

budgetary and ethical considerations, no monetary reward was offered as a means to stimulate

the participation of potential respondents. Fourth, reminder emails were sent twice (in

February and in March 2015). The authors restrained themselves from sending more

reminders in order to avoid their email addresses being classified as spam by the email servers

and accusations of aggressive email correspondence by the potential survey participants.

Finally, the authors called few of the hotels by phone asking them whether they had received

the email with the link to the questionnaire. The hoteliers confirmed receiving the email, but

most of them commented that they do not want to participate because of two reasons: either

their properties were too small and were not applying the revenue management practices

mentioned in the questionnaire, or they were afraid of breach of anonymity. A couple of

emails from other hoteliers with the same messages were also received. As a result the sample

included 105 completed questionnaires (5.05% response rate or 3.35% of all accommodation

establishments in Turkey) that were used in the analysis. Similar response rates were reported

in other surveys that focused on the hotel industries of other Balkan countries as well (e.g.

Ivanov, 2014; Ivanov et al., 2015). Sample’s characteristics are presented in Table 1.

INSERT TABLE 1 AROUND HERE

Instrument

The questionnaire was developed in English language and then translated into Turkish

language by the second author who is a native speaker in Turkish. The questionnaire included

several groups of questions. The first section collected demographic data about the

accommodation establishments – category (1, 2, 3, 4 or 5 stars), size (up to 50 rooms, 51-100

rooms, 101-150 rooms, 151 or more rooms), location (urban, seaside, mountain, countryside),

and chain affiliation (affiliated or independent). The second section asked respondents to

identify the revenue-generating services in their establishments. The third section

concentrated on revenue management tools applied at the hotel. Respondents were asked to

evaluate how often they apply specific RM tools (from 1-not applied to 5-very often), how

important is the application of a tool (from 1-extremely unimportant to 5-extremely

important) and what its impact is on hotel’s revenues (from 1-no impact to 5-very high

impact). The list of the RM tools was derived from prior research (Ivanov, 2014). The fourth,

fifth and sixth sections included questions regarding the RM team (whose responsibility

revenue management is, intentions to hire revenue manager), RM software (does/would

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specialised software help in better revenue management) and distribution channel

management (level of importance of different distribution channels, types of contracts with

distributors, release periods). The seventh and eighth sections included questions related to

the RM metrics used in the hotel and the respondents’ level of agreement with various

statements about the revenue management practices.

Data analysis

Kolmogorov-Smirnov z-test was adopted to check the normality of the distribution of

hoteliers’ responses. The results showed that the distribution of responses was statistically

different from normal. That is way the non-parametric tests were used to identify the role of

hotels’ characteristics in the degree of application of the revenue management techniques

(Baggio and Klobas, 2011). Specifically, Mann-Whitney U-test was used to identify the role

of chain affiliation, while Kruskal-Wallis χ2 test was used to identify the role of hotel’s size,

category and location on the application of the revenue management techniques. Paired

samples t-test was adopted to evaluate the differences in respondents’ answers to some

questions.

Findings

Revenue centres

Table 2 shows the revenue centres of respondent hotels and their potential for development

(RQ1 group of research questions), i.e. the services which the hoteliers charge their guests for.

A service that is offered free of charge by the hotel is not considered a revenue centre in this

research, although it increases the perceived value of the product by the customer and

contributes positively to hotel’s competitiveness (Ivanov, 2014). Besides rooms (generating

about 73% of respondents’ revenues – Table 2c), food- and beverage-related related services

(restaurant, bar/lobby bar, minibar) constitute the main revenue centres of the respondents

(Table 2a). These same revenue centres are also reported as having greatest potential for

development (Table 2b). Other services, like internet, parking and rentals are not significant

revenue centres and have least development potential – probably because there are either not

offered or offered free of charge to the guests.


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Respondents are not uniform in their responses. The managers of 4- and 5-star hotel, chain

affiliated properties and largest hotels (over 150 rooms) reported more revenue centres and

greater potential for their development and most of the differences are significant at p<0.01

(see Tables 2a, b, c). These results are expected, since higher category hotels offer more

services to their guests compared to lower category properties and, therefore, have more

revenue centres. Furthermore, hotel chains, in general, have well developed revenue

management and marketing strategies allowing them to charge their guests for some services

without sacrificing their competitiveness on the local market which independent hotels might

offer for free in order to attract demand. Finally, the sheer size of the large hotels makes some

services economically profitable to offer to the guests (e.g. fitness/spa/wellness,

golf/tennis/other sports) while these services would not be economically viable in the smaller

properties with less than 50 rooms. In terms of the location of the hotels, we find significant

differences in the answers of the respondents which are quite logical as well. For example,

urban properties rely less on sport facilities for their revenues than seaside properties

(χ2=16.815, p<0.01), rentals are offered more by seaside and mountain properties than urban

hotels (χ2=18.188, p<0.01), seaside hotels see greater potential for development of

fitness/spa/wellness revenue centre than urban and rural hotels (χ2=18.296, p<0.01), the

managers of seaside and mountain hotels see greater revenue potential of bar/lobby bar than

the managers of urban and rural hotels (χ2=25.578, p<0.01), etc.

Pricing and non-pricing revenue management tools and sales techniques

Table 3 presents the findings regarding the frequency of application, importance for the

industry and impact on property sales of various RM tools and sales techniques from the

arsenal of hoteliers in Turkey, which are related to the second group of research questions:

RQ2.


The most frequently used tools are room availability guarantee (m=2.94) and the price

discrimination (m=2.43), and the differences between their level of application and the

application of the other tools are significant (p<0.01 for all paired samples t-test values)

(RQ2.1). The overbooking (m=0.20) and overcontracting (m=0.39) are least applied tools.

Two major findings need to be emphasised. First, the respondents seem more eager to use

pricing tools (price discrimination, price parity, early bird rates and last minute offers, rather

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than non-pricing tools (overcontracting, overbooking, length-of-stay controls). A fast-forward

look at Table 7 provides further support of this conclusion: respondents reported that they

would decrease prices if the occupancy is low (m=4.43). A possible explanation for the

preference to use pricing rather than non-pricing tools could be the relative ease of price

changes, their intuitive nature and direct impact on demand, while overcontracting,

overbooking and length-of-stay controls require significant efforts by the hoteliers for their

implementation. The second important finding is that 80 out of 105 respondents (76.2%) who

mentioned giving room availability guarantee to their customers reported that they do not

apply overbooking, i.e. they do not perceive these two revenue management tools as

compatible.

The perceived importance of the revenue management tools (RQ2.2) mirrors closely their

level of application. Although the paired samples t-tests between the frequency/degree of

application and the perceived importance of the particular RM tool are significant at p<0.01

(excluding for the cross-selling sales technique, where the t-value is not significant), the

correlations between them are very high: ranging from ρ=0.763 (p<0.01) for the overbookings

to ρ=0.935 (p<0.01) for the price parity. Respondents consider as most important the room

availability guarantee (m=3.09) and price discrimination (m=2.273), while overcontracting

(m=0.85) and overbooking (m=0.59) are perceived as least important, i.e. we observe close

link between the perceived importance of the tool and its degree of application.

The perceived impact of the application of the various RM tools (RQ2.3) follows a different

pattern than the degree of application and the importance of the tools. What we find is that

those respondents that actually apply the specific tools perceive their impacts on property’s

sales as quite similar: with the exception of the higher impact of room availability guarantee

(m=3.24) and the low impact of overbooking (m=1.69) and upselling (m=1.52), the perceived

impact of the rest of the tools ranges from m=2.35 (length-of-stay controls) and m=2.83 (price

discrimination and cross-selling).

The characteristics of the accommodation establishments influence significantly the

application, importance and the impact of the RM tools. In general, the managers of chain

affiliated hotels perceive the respective tools as more important, with higher impact on sales

and apply them more frequently than the managers of independent properties and most of the

difference are significant at p<0.01 or p<0.05 (see Table 3). Only in regard to overbooking,

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lowest price guarantee and room availability guarantee the opinions of both groups of

respondents are uniform. These results suggest that the chain affiliated hotels have better

developed revenue management practices than the independent properties (RQ7.4). Not

surprisingly, we also find that the revenue management tools have greater acceptance in 4-

and 5-star hotels than in lower category properties (RQ7.2), in large (101-150 rooms) and

very large (over 150 rooms) hotels than in small (up to 50 rooms) and midsized (51-100

rooms) establishments (RQ7.1). Finally, again expectedly, the managers of seaside and urban

hotels have reported higher frequency, importance and impact of the revenue management

tools than the managers of mountain and rural properties and most of the differences are

significant at p<0.01 or p<0.05 (RQ7.3).

Channel management

The third group of research questions (RQ3) deal with channel management at hotels in

Turkey. Table 4 presents the level of importance of various distribution channel used by

hoteliers and the application of different types of contracts with the distributors. Findings

reveal that the hoteliers are very etourism-oriented: the online travel agencies (m=4.66) is the

single most important distribution channel (the paired samples t-tests with the levels of

importance of the other channels are all significant at p<0.01), followed by the direct sales via

the hotel’s website (m=4.12), the travel agents (m=4.09) and the tour operators (m=4.05).

GDSs (m=3.33), other direct sales (m=3.29) and the group buying websites (m=2.88) have

much less important for the revenues of the accommodation establishments (RQ3.1).


Results in Table 4 indicate quite diverse responses. First, the managers of chain affiliated

hotels report much higher importance of most distribution channels than the managers of

independent properties (p<0.01) excluding for the other direct sales (email, telephone) which

are more important for the independent hotels (p<0.05). The lower level of adoption of online

sales by independent hotels compared to the chain affiliated establishments and the preference

to bookings by email and telephone could be considered as a signal about the higher level of

hidden economic activities and undeclared revenues by the independent properties than by

chain hotels (see Vladimirov, 2015). Second, 4- and 5-star hotels and hotels with over 100

rooms rely more on sales via GDSs, OTAs, tour operators and travel agents than lower

category and smaller (less than 100 rooms) properties, which, on the other hand, rely more on

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email and phone bookings. These results are logical, because higher category and larger

properties are more attractive for distributors (Ivanov et al., 2015). Third, the managers of

seaside and mountain properties mention greater importance of GDSs (χ2=26.635, p<0.01),

tour operators (χ2=26.487, p<0.01), travel agents (χ2=27.525, p<0.01) and group buying

websites (χ2=27.525, p<0.01) than the managers of urban and rural properties, while the

managers of urban hotels show greater preferences to email and telephone bookings than the

managers of seaside and mountain properties (χ2=10.102, p<0.01). The reason for this finding

might be the fact that seaside and mountain properties serve organised tourists who book

through tour operators and agents, while urban hotels serve mostly individual tourists who

book either via OTAs or directly at the hotel by email/phone.

In regard to the types of contracts, allotment (m=3.30) and commitment (m=3.08) seem more

popular than upon request basis (m=2.55) and free sale (m=1.59), probably because the first

two types of contracts are preferred by the OTAs and the tour operators (RQ3.2).

Commitment is mostly used by seaside and mountain hotels (χ2=16.908, p<0.01), allotment is

used mostly by chain hotels, while free sale – by largest (over 150 rooms), chain affiliated,

high category and mountain hotels. No major differences were found regarding upon-request

contracts.

Revenue management team

In regard to research question RQ4 Table 5 presents the breakdown of respondents’ answers

about the revenue management responsibilities in their hotels. We see that a separate revenue

management department or a dedicated revenue manager position is reported by the managers

of chain affiliated hotels, 4- and 5-star hotels, and very large hotels with over 150 rooms. For

the rest of the hotels the revenue management responsibilities are part of the job description of

the marketing manager, the front office manager or the general manager of the hotel. These

findings are logical: hotel chains have well-developed revenue management practices that

include a separate revenue manager position or even a department (see also Legoherel et al.,

2013: 47-49; Mauri, 2012: 91). Furthermore, only large and high category properties could

generate enough revenues to justify a separate revenue manager position or a department.

Most of the respondents (48 out of 65 or 73.8%) that report not having a revenue manager or a

department do not intend to hire a revenue manager, probably because of their small size and

low category which make such position economically not viable. What is surprising is the

high share of respondents (40 out of 105 or 38.1%) who mentioned having a revenue manager

of 34

or even a department. However, considering the sample size we cannot generalise this share

for the whole hotel industry in Turkey, which is a limitation of this piece of research.


Revenue management software

The fifth research question RQ5 relates to the revenue management software and the findings

are reported in Table 6. The respondents were asked an open question to name the software

they were using for revenue management and 27 of them mentioned the software packages

elaborated in Table 6a. They include locally and internationally developed software

programmes for managing the revenues of the hotel. The respondents were not keen on using

specialised revenue management software (m=2.59) and only 38 show willingness to pay for

it. The managers of largest hotels (over 150 rooms), chain affiliated, seaside, 3-, 4- and 5-star

properties seem more inclined to use specialised licenced revenue management software,

probably because of the amount of revenues their properties generate, the multitude of

distribution channels used and the subsequent large (potential) benefits that such software

might contribute. In a similar manner, Mauri (2012) and Cleophas and Frank (2011)

emphasise that while specialised automated software contributes to the success of RM in large

companies, small hotels cannot afford and do not really need sophisticated software. Simple

spreadsheets may be enough to deal with the basic and small scale analysis, forecasting and

price optimisation small hotels perform.


Revenue management process

The sixth group of research questions relates to the revenue management process applied by

hoteliers in Turkey. In regard to RQ6.1 our findings show that the most frequently used

statistics include occupancy (104 respondents, or 99%), ADR (97, or 92.4%), and RevPAR

(74, or 70.5%). GOPPAR is reported by only 20 (19%) of the managers. Except for the higher

application of RevPAR by chain affiliated (χ2=31.884, df=1, p<0.01), 4- and 5-star (χ2=39.352,

df=4, p<0.01) and large hotels with over 100 rooms (χ2=28.160, df=3, p<0.01) no other

significant differences were found in the answers of the respondents. We can conclude that

the metrics that are easier to calculate and are more intuitive (occupancy, ADR, RevPAR)

of 34

have wider application, while the metric that requires more complex calculations and proper

measurement of room costs (GOPPAR) is less popular.

In regard to RQ6.2 74 of the respondents (70.47%) indicate that they use historical methods

for forecasting their future sales, revenues and occupancy, while 31 (29.53%) mentioned

statistical methods (regression analysis). This is probably due to the ease of application of the

historical methods, but the sample size does not allow us to generalise this finding for the

whole hotel industry in Turkey.


Table 7 gives further insights regarding the RM process. Results show that the hoteliers

consider each customer as equally important for the hotel (m=4.03), are eager to attract every

potential customer (m=4.49). The managers of seaside and mountain, large (over 100 rooms)

and chain affiliated hotels seem more selective and have lower levels of agreement with these

statements than the rest of the respondents, probably because they also tend to disagree with

the statement that the customers prefer lower prices to higher quality. Logically and

expectedly, hoteliers consider customers’ perceptions when setting prices and booking terms

(m=3.71) (RQ6.3). In general, if competitors decrease their prices the managers are less likely

to follow suit (m=3.52) than when prices are increased (m=4.21) and the difference is

significant (paired samples t-test: t=-4.985, p<0.01) (RQ6.4). This means that the hoteliers are

more elastic to increases and less inelastic to decreases of competitor’s prices. This is in line

with Enz et al. (2004) recommendation hotels to keep their rates at normal levels when

competitors are offering discounts. While it is logical to increase prices when competitors do

so, it is counterintuitive not to decrease your prices when competitors decrease theirs. At first

glance this may look as a strange result, considering that hoteliers are eager to decrease prices

if occupancy is low (m=4.43) and try to attract every potential customer. A closer look at the

differences between respondents’ answers reveals that the same group of managers that is

more selective about the customers they serve and relies on quality than price, is also less

likely to follow competitors’ price decreases. Therefore, we observe a clear division between

two groups of strategies of hoteliers in Turkey to attract customers – focusing on price

competition (small and midsized, low and mid category, independent, urban and rural hotels)

and focusing on quality (large size, high category, chain affiliated, seaside and mountain

properties), although the differences between the two groups are not always clear-cut. Finally,

of 34

the revenue management is considered as having positive impact on the revenues of the hotel

(m=3.94).

Conclusion

Contribution

The contribution of this paper is related to the empirical investigation of the real revenue

management practices applied by hotel managers in Turkey. While the RM theory identifies

and recommends the application of numerous techniques (pricing, non-pricing and

combined), forecasting methods, specialised software and others, it is the hoteliers who

actually apply them or not. Therefore, this paper provides a reality check and evaluates

whether hoteliers in Turkey apply in the properties they manage what hotel revenue

management theory recommends. Furthermore, to our best knowledge, this is one of the first

quantitative surveys of the revenue management practices of hotels in Turkey (the only other

one we are familiar with is Güler (2012)). Previous studies are based either on very small

number of respondents (the sample in Emeksiz et al. (2006) includes 3 five-star hotels in

Turkey), or narrow geographic coverage of the respondents (Güler (2012) focuses on hotels in

the Aegean region). Finally, this paper contributes to the advancement of knowledge by

analysing the impact of property’s category, size, location and chain affiliation on the level of

application of the various RM practices by its managers, which factors have not received

enough and systematic attention in the specialised literature.

Managerial implications

Table 8 summarises the specific answers to the research questions:


From managerial perspective findings show that the RM practices are not equally adopted by

hotel managers in Turkey. Findings reveal that respondent hoteliers are not eager to apply

tools like overcontracting and overbooking and put the emphasis on price discrimination and

room availability guarantee. Most of the respondents do not have a revenue manager and do

not intend to hire one. Revenue management is usually within the responsibilities of the

general manager, front office manager or the marketing manager. OTAs, hotel’s website, tour

operators and travel agents are the most important distribution channels, while the GDSs are

less significant for hotels’ revenues. The size of the property, its category, location and chain

of 34

affiliation have tangible impact on the degree of application of the various revenue

management practice. Not surprisingly, revenue management is mostly adopted by high

category, chain affiliated, urban and seaside hotels with large number of rooms. Similar

results are reported by Ivanov (2014) in the context Bulgaria.

The findings indicate that some of the respondents, particularly the managers of small and low

category properties, are probably afraid of or not quite familiar with the opportunities that

some of the RM practices provide to them, especially in regard to the non-pricing tools (e.g.

overbooking, length-of-stay controls). It is possible that the benefits of revenue management

are recognised but the hoteliers do not have the resources (financial, software, human) to

apply some of the practices. It is also likely that hoteliers, due to the small sales turnover of

their properties, do not consider the financial benefits of revenue management worth the

greater efforts required by them for its proper and full-scale application in the hotels they

manage. In this regard, executive hotel revenue management courses for hoteliers in Turkey

would be beneficial. Furthermore, revenue management could be included as a separate

module in the curricula of hospitality programmes in country’s universities so that future

hoteliers are well equipped with knowledge and skills in the field of revenue management.

Moreover, companies offering specialised RM software could organise seminars to emphasise

the benefits for the hotel not only of their own software, but of revenue management in

general.

Limitations and future research

The main limitation of this research is the sample size. Although the authors contacted 2080

potential respondents by email, sent two reminders, guaranteed the anonymity of the

participants, did not ask any financial and other sensitive questions that might dissuade

respondents from participating in the research, offered a complimentary e-copy of a hotel

revenue management book in order to stimulate participation, and called by phone some of

them, only about 5% (105) of the contacted managers actually volunteered to take part and

completed the survey. A much larger sample is reported by Güler (2012) in his study of the

revenue management success factors (460 hotel managers), but the author does not clarify

how many hotels these managers represent. The low response rate might be attributable to the

length of the questionnaire (it took about 15-20 minutes to complete), possible inappropriate

time-frame of the research (data collection took place in January-March 2015), the researchers

were not known among the local hotel industry representatives and could not fully leverage

of 34

industry contacts to stimulate participation, respondents probably feared that information

would leak to competitors and/or tax authorities or other reasons. Furthermore, as a

consequence of the voluntary participation in the research, the managers of 5-star hotels were

more willing to participate in the research and their properties were somewhat

overrepresented in the sample in deference to the managers of 3- and 4-star hotels which were

underrepresented compared to the category structure of the accommodation establishments in

Turkey. In this regard, the aggregated results of this research cannot be generalised for the

country as a whole, but the findings could give an indication about the differences of the

application of the various revenue management practices by category, chain affiliation,

location and size of the accommodation establishments.

Future research might try to overcome the limitation of this research. Future researchers could

find other appropriate ways to motivate contacted managers to participate in the research in

order to generate larger samples and more reliable results. A possible option is to offer a

financial reward in the form of a complementary/discount voucher for a service/product (e.g.

Amazon voucher) provided the budget of the research project allows this and it this reward

approach receives ethics clearance by the ethics committee of the researchers’ institution.

Future research may delve deeper into the channel management of hotels in Turkey and

elaborate the relationships between the parties, the conflicts that arise among them and the

ways they are solved. Furthermore, future research may shed light on the educational and

training needs of hotel managers in the area of revenue management. Additionally, future

research could focus on the links between revenue management practices of hotels in Turkey

and the customers’ perceptions of their fairness. Finally, future research could measure in

quantitative terms the impact of the application of revenue management on the performance

of hotels in Turkey.

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Table 1. Sample characteristics

Number of respondents Percent

Category 1 star 5 4.8

2 stars 14 13.3

3 stars 18 17.1

4 stars 16 15.2

5 stars 52 49.5

Size Up to 50 rooms 31 29.5

51-100 rooms 13 12.4

101-150 rooms 14 13.3

Over 150 rooms 47 44.8

Location Urban 61 58.1

Seaside 30 28.6

Mountain 9 8.6

Countryside 5 4.8

Chain affiliation Chain member 58 55.2

Independent 47 44.8

Total 105

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Table 2. Revenue management centres of accommodation establishments in Turkey

a) Revenue centres

Kruskal-Wallis χ2 test M-W U test

Revenue centre Yes No Category Size Location Chain affiliation

Restaurant 96 9 31.344*** 17.315*** 3.352 1154.5***

Bar/lobby bar 89 16 30.814*** 30.925*** 18.326*** 1056.5***

Minibar 72 33 13.533*** 5.053 14.020*** 1193.5

Fitness/spa/wellness 63 42 74.540*** 77.464*** 18.105*** 355***

Function rooms 60 45 75.456*** 60.309*** 7.287* 478***

Golf/tennis/other sports 42 63 40.201*** 46.935*** 16.815*** 586***

Internet/Wi-fi 41 64 16.163*** 20.301*** 4.105 1029.5**

Parking 25 80 23.938*** 26.745*** 10.404** 915***

Rentals 24 81 9.157* 3.526 18.188*** 1324

Note: ***Significant at 1% level; ** Significant at 5% level; *Significant at 10% level

b) Revenue centres’ potential for development


Revenue centre Mean Standard

deviation

Category Size Location Chain affiliation

Rooms 4.96 0.237 6.674 2.334 2.206 1328

Restaurant 3.55 1.352 26.875*** 20.104*** 5.390 830***

Bar/lobby bar 3.00 1.715 59.226*** 50.932*** 25.578*** 450***

Minibar 1.81 1.582 16.223*** 13.411*** 3.093 972.5***

Fitness/spa/wellness 2.10 1.886 58.913*** 59.046*** 18.296*** 360***

Function rooms 2.22 2.024 63.808*** 47.067*** 4.545 479.5***

Golf/tennis/other sports 1.39 1.684 45.739*** 53.199*** 25.433*** 448***

Internet/Wi-fi 0.97 1.404 12.474** 15.167*** 3.035 935***

Parking 0.40 1.034 13.230*** 13.031*** 9.893** 1020.5***

Rentals 0.78 1.270 7.082 3.138 21.541*** 1228.5

Notes: ***Significant at 1% level; ** Significant at 5% level; *Significant at 10% level; Coding: 1-very low, 5-

very high

c) Percentage of revenues from Rooms Division


Mean Standard

deviation

Category Size Location Chain

affiliation

Percentage of revenues

coming from Rooms

Division

73.00 12.279 49.155*** 38.198*** 21.404*** 565.5***

Note: ***Significant at 1% level; ** Significant at 5% level; *Significant at 10% level

of 34

Table 3. Revenue management tools of accommodation establishments in Turkey


N Mean Standard

deviation


Price discrimination

Frequency of application 105 2.43 1.008 30.163*** 28.471*** 5.818 788.5***

Importance for the industry 105 2.73 1.187 50.933*** 43.815*** 14.777*** 491***

Impact on property sales 81 2.83 0.755 10.276** 5.685 10.851** 543**

Price parity

Degree of application 105 2.02 1.337 23.535*** 18.920*** 6.759* 730***

Importance for the industry 105 2.18 1.350 19.930*** 12.708*** 7.013* 864.5***

Impact on property sales 70 2.70 0.768 3.931 3.523 1.807 438*

Last minute offers

Frequency of application 105 1.71 1.357 56.053*** 54.921*** 20.881*** 235.5***


Impact on property sales 68 2.68 1.043 14.258*** 10.261** 21.458*** 218.5**

Early bird rates

Frequency of application 105 1.97 1.471 51.682*** 42.028*** 14.661*** 341.5***


Impact on property sales 72 2.61 0.958 7.774* 6.976* 6.836* 345.5**

Overcontracting

Frequency of application 105 0.39 0.882 7.241 3.431 21.136*** 1136**

Importance for the industry 105 0.85 1.343 8.936* 3.020 18.394*** 1148*

Impact on property sales 27 2.63 1.079 7.726** 2.823 2.388 74.5

Percent of overcontracting 25 32.00 23.139 5.219* 8.178** 9.101** 14.5***

Overbooking

Frequency of application 105 0.20 0.508 7.855* 3.709 7.165* 1303

Importance for the industry 105 0.59 0.958 7.804* 4.844 14.653*** 1182

Impact on property sales 14 1.69 1.182 a a a a

Percent of overbooking 14 4.43 3.056 a a a a

Length-of-stay controls

Minimum length-of-stay control

Frequency of application 105 1.29 1.133 42.779*** 41.425*** 5.619 479.5***

Importance for the industry 105 1.55 1.232 37.925*** 35.292*** 10.725** 521***

Maximum length-of-stay control

Frequency of application 105 1.06 1.036 39.307*** 33.220*** 3.529 557***

Importance for the industry 105 1.49 1.241 39.833*** 36.080*** 8.109** 487.5***

Length-of-stay controls

Impact on property sales 60 2.35 0.777 2.648 4.311 17.928*** 250

Lowest price guarantee

Frequency of application 105 1.11 1.258 10.068** 4.593 10.764** 1154.5

Importance for the industry 105 1.43 1.322 12.208** 6.008 14.047*** 1084.5*

Impact on property sales 53 2.30 1.202 10.268** 6.200 12.737*** 263.5

Cross-selling

Frequency of application 105 1.71 1.405 57.777*** 49.352*** 21.990*** 129***


Impact on property sales 66 2.83 0.938 2.644 0.586 15.002*** 158**

Upselling

Frequency of application 105 1.10 1.097 51.985*** 33.232*** 9.338** 526***


Impact on property sales 62 1.52 0.936 22.042*** 11.131** 5.667 162**

Room availability guarantee

Frequency of application 105 2.94 1.413 6.824 0.371 2.886 1233

Importance for the industry 105 3.09 1.257 6.373 0.771 1313 873*

Impact on property sales 90 3.24 0.964 9.274* 4.050 6.909* 850

Note: ***Significant at 1% level; ** Significant at 5% level; *Significant at 10% level; a – not calculated due to

the small number of respondents who reported the percentage of overbooked rooms and its impact on sales;

Coding: Frequency of application: 1-not applied, 5-very often; Degree of application: 1-not applied, 5-very high;

Importance for the industry: 1-completely unimportant, 5-completely important; Impact on property sales: 1-no

impact at all, 5-very high impact

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Table 4. Distribution channel management

a) Level of importance of the various distribution channels


Mean Standard

deviation


GDSs 3.33 1.662 70.656*** 65.072*** 26.635*** 120.5***

OTAs 4.66 0.757 29.176*** 23.890*** 11.370*** 751.5***

Tour operators 4.05 1.121 28.018*** 19.735*** 26.487*** 699***

Travel agents 4.09 1.102 56.146*** 41.140*** 27.525*** 578***

Group buying websites 2.88 1.299 51.277*** 53.313*** 27.875*** 332.5***

Direct sales via the website 4.12 0.987 4.412 2.783 8.056** 1272.5

Other direct sales (email, phone) 3.29 1.072 12.917** 9.337** 10.102** 1033.5**

Note: ***Significant at 1% level; ** Significant at 5% level; *Significant at 10% level; Coding: 1-completely

unimportant, 5-completely important

b) Frequency of application of the different types of contracts with distributors


Mean Standard

deviation


Commitment 3.08 1.405 10.172** 6.784* 16.908*** 1100*

Allotment 3.30 1.186 10.365** 1.328 3.071 1059**

Free sale 1.59 0.840 19.076*** 12.407*** 36.369*** 876***

Upon-request 2.55 1.109 2.256 0.657 2.315 1170

Note: ***Significant at 1% level; ** Significant at 5% level; *Significant at 10% level; Coding: 1-not applied, 5-

very high

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Table 5. Revenue management responsibility

Revenue management responsibility

Revenue

management

department

Revenue

manager

Marketing

manager

Front office

manager

General

managers

Chain affiliation (Pearson χ2=30.795, df=4, p<0.01)

Independent hotels 0 5 5 14 23

Affiliated hotels 4 31 7 5 11

Category (Pearson χ2=54.787, df=16, p<0.01)

1 star 0 0 0 0 5

2 stars 0 0 0 5 9

3 stars 0 2 3 8 5

4 stars 1 5 5 2 3

5 stars 3 29 4 4 12

Location (Pearson χ2=24.285, df=12, p<0.05)

Urban 3 15 7 15 21

Seaside 1 13 5 3 8

Mountain 0 8 0 0 1

Rural 0 0 0 1 4

Size (Pearson χ2=78.510, df=12, p<0.01)

Up to 50 rooms 0 3 1 8 19

51-100 rooms 0 0 5 7 1

101-150 rooms 0 5 6 0 3

Over 150 rooms 4 28 0 4 11

Total 4 36 12 19 34

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Table 6. Revenue management software

a) Software used

b) Attitude towards specialised revenue management software


N Mean Standard

deviation

Category Size Location Chain

affiliation

Do you think that specialised

RM software helps/would

help you manage better the

revenues of your hotel? a

105 2.59 1.016 64.484*** 46.826*** 18.769*** 413***

How much money are you

willing to pay per year for

specialised RM software? b

105 295.24 477.60 18.618*** 13.749*** 6.932* 828***

Note: ***Significant at 1% level; ** Significant at 5% level; *Significant at 10% level; Coding: a 1-completely

disagree, 5-completely agree; b – amount of money in euros. Those not willing to pay (67 responses) coded as 0.

Frequency

Opera 4

Delphi 4

Trust 3

Elektra 2

Rategain 2

Reseliva 2

Erbasoft 1

Hotelrunner 1

Ideas 1

Amadeus RMS 1

Other 6

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Table 7. Level of agreement with various statements


Mean Standard

deviation


If occupancy is low it is best to

lower the prices

4.43 0.886 3.433 0.197 11.758*** 1283

Each customer is equally

important for the hotel

4.03 0.925 33.723*** 26.514*** 13.956*** 607***

We try to attract every potential

customer

4.49 0.652 15.787*** 13.768*** 24.351*** 941***

If competitor decrease prices we

decrease our prices too

3.52 1.084 18.844*** 19.853*** 11.074** 853***

If competitor increase prices we

increase our prices too

4.21 1.080 6.833 1.685 4.457 1134.5

Customers prefer lower prices

than higher quality

2.64 1.401 40.489*** 37.056*** 18.981*** 343***

Maintaining good relations with

the distributors is important for

property’s revenues

4.13 1.169 35.496*** 28.953*** 16.125*** 532***

Selling additional services is

important for property’s

revenues

3.69 0.964 29.314*** 18.570*** 14.054*** 697***

When we set the prices and

booking terms we consider

customers’ perception of these

3.71 0.805 12.394** 10.253** 1.961 1030***

In general the application of the

RM tools contributes positively

to the revenues of our property

3.94 1.336 27.457*** 21.640*** 7.721* 716***

Note: ***Significant at 1% level; ** Significant at 5% level; *Significant at 10% level; Coding: 1-completely

disagree, 5-completely agree

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Table 8. Summary answers to research questions

Research question Answer

RQ1: Revenue centres in the hotels

RQ1.1: Which are the revenue centres of hotels in

Turkey?

Main revenue centres rooms (73% of revenues),

restaurant, bar/lobby bar, minibar

RQ1.2: Which revenue centres have greatest

potential for development?

Rooms, restaurant, bar/lobby bar

RQ2: Revenue management tools

RQ2.1: What is the level of application of the

revenue management tools by hoteliers in Turkey?

Most applied tools are rooms availability guarantee and

price discrimination; least applied are overcontracting

and overbooking

RQ2.2: What is the perceived level of importance of

the revenue management tools by hoteliers in Turkey?

Most important tools are rooms availability guarantee

and price discrimination; least important are

overcontracting and overbooking

RQ2.3: What is the impact of the revenue

management tools on hotels’ sales?

Most tools have similar perceived impact on sales with

slight advantage of room availability guarantee

RQ3: Channel management

RQ3.1: What is the importance of the distribution

channels used by hotels in Turkey?

OTAs is the single most important distribution channel,

followed by the direct sales via the hotel’s website, the

travel agents and the tour operators

RQ3.2: Which types of contract do hotels in Turkey

mostly use in their relationships with distributors?

Commitment and allotment are most frequently used

contracts with the distributors

RQ4: Revenue management team

RQ4: Who is in charge of revenue management

implementation in hotels in Turkey?

A separate revenue management department or a revenue

manager position is reported by the chain affiliated, 4-

and 5-star, and very large (over 150 rooms) hotels

RQ5: Revenue management software

RQ5: What are hotel managers’ perceptions about

the specialised revenue management software?

The managers of largest hotels (over 150 rooms), chain

affiliated, seaside, 3-, 4- and 5-star properties seem

more inclined to use specialised licenced software

RQ6: Revenue management process

RQ6.1: How do hoteliers in Turkey measure the

performance of their properties?

Occupancy, ADR, RevPAR

RQ6.2: How do hoteliers in Turkey forecast the

future sales, revenues, occupancy?

Historical methods are more widely used than statistical

RQ6.3: To what degree do hoteliers in Turkey

consider customers’ characteristics and preferences

when applying various revenue management tools?

In general, customers’ characteristics and preferences are

taken into consideration

RQ6.4: How do hoteliers in Turkey react to

competitor moves in prices?

Increase prices if competitors increase their prices;

Hotels competing on price will decrease the prices if

competitors do so; hotels competing on quality are less

likely to do it.

RQ7: Factors, influencing the application of revenue

management by hotels in Turkey

RQ7.1: Does the size of the hotel influence the

application of specific revenue management practices by

its managers?

Yes, larger hotels have better developed revenue

management practices than smaller properties

RQ7.2: Does the category of the hotel influence the


its managers?

Yes, higher category hotels have better developed

revenue management practices than lower category

properties

RQ7.3: Does the location of the hotel influence the


its managers?

Yes, in general seaside and urban properties have better

developed revenue management practices than

mountain and rural properties, although the differences

depend on the specific practice.

RQ7.4: Does the affiliation to a hotel chains

influence the application of specific revenue


Yes, chain affiliated hotels have much better developed

revenue management practices than independent

properties in every aspect

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Figure 1. Hotel revenue management system (adapted from Ivanov & Zhechev, 2012)

Hotel booking request

RM process

Hotel booking elements

Data and

information

Hotel revenue

centres

RM software RM tools

Structural elements

Hotel revenue management system

Macroenvironment

Microenvironment

Impacts

Internal environment

Patronage intentions

Customer

RM team

Perceptions of RM

fairness

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Figure 2. Hotel revenue management process (adapted from Ivanov & Zhechev, 2012)

Goals

Monitoring

Implementation

Forecasting

Analysis

Information

Stage Content

RM metrics –RevPAR, ADR, occupancy, GOPPAR

Goals by revenue centres

Strategic, tactical and operational goals

Operational data and information provided by hotel’s marketing

information system

Analysis of demand and supply in the destination

Analysis of operational data and information

Forecasting demand and supply in the destination

Forecasting RM metrics on a daily basis

Forecasting methods

Pricing, non-pricing and combined RM tools

Optimisation process

Approaches for solving RM mathematical problems

Performance evaluation of taken decisions and the RM system as a

whole

Decision

Sales techniques

Human resource training

investigation of the revenue management practices of accommodation establishments in turkey: an...

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