The diploma thesis assesses the opportunity to implement an integrated approach to revenue management in order to meet the challenges of the competitive tourism environment, which are particularly stemming from the increasing importance of the internet. It is proposed that effective and efficient coordination of the disciplines of revenue management, marketing, sales and e-commerce leads to Revenue Management Integration (RMI), which has a positive effect on financial performance and competitiveness. The thesis aims at identifying key linkages among the disciplines and creates a guideline of how the integration may be achieved. Using multivariate regression, it is tested with 30 hotels, affiliated to eight of the largest hotel chains in Germany, whether Revenue Management Integration is positively related to financial performance. The study showed that integrated revenue management is positively related to RevPAR performance in hotel properties, while the relationship to other profitability measures, such as Return on Equity (ROE) and Operating Profit Margin (OPM) did not yield clear results.
Table of Contents
ABSTRACT
LIST OF FIGURES
LIST OF TABLES
1. INTRODUCTION
1.1. Research Questions
1.2. Research Contribution
1.3. General Objective
1.4. Specific Objectives
1.5. Hypotheses
1.6. Research Design
1.7. Research Methodology
1.7.1. Literature Review
1.7.2. Structured Interviews
1.7.3. Multivariate Regression Analysis
1.7.4. Secondary Analysis
1.7.5. Sampling Method
2. IMPACT OF THE MODERN TOURISM VALUE NET ON TOURISM INDUSTRY STRUCTURE
2.1. The Modern Tourism Value Net
2.2. Industry Analysis along the Five Competitive Forces
3. REVENUE MANAGEMENT INTEGRATION (RMI)
3.1. Integrated Revenue Management as a Competitive Advantage
3.2. Reengineering an Organization for Revenue Management Integration (RMI)
4. REVENUE MANAGEMENT PERSPECTIVE
4.1. EMSR Method and Demand Forecasting
4.2. Pricing and Price Elasticities
5. MARKETING PERSPECTIVE
5.1. Creating and Maintaining Customer Value
5.2. Blue Ocean Strategy and Value Innovation
5.3. Customer Segmentation and Differential Pricing
5.4. Integrating Customer Relationship Management
5.5. Willingness-to-pay for Customized Pricing
6. SALES PERSPECTIVE
6.1. Product Portfolio Optimization
6.2. Coordination and Goal Alignment
6.3. Dynamic Prices for Corporate Key Accounts
6.4. Sales and Marketing
7. E-COMMERCE PERSPECTIVE
7.1. Channel Analysis and Selection
7.2. Dynamic Pricing in Online Distribution
7.3. Active Channel Management
8. SHORT SUMMARY OF THE LITERATURE REVIEW
9. THE REVENUE MANAGEMENT INTEGRATION (RMI) STUDY
9.4. Research Questionnaire
9.1. Sample Selection
9.2. Financial Performance Measures
9.3. Individual and Aggregate Variables
9.5. Multivariate Regression for Statistical Analysis
9.6. Additional Control Variables
10. DATA ANALYSIS AND RESULTS
10.1. Multiple Regression Analysis with RevPAR
10.2. Reducing the Multiple Regression Model
10.3. Analysis of Reduced Model Results
10.4. Component Revenue Management Integration (RMI) Analysis
10.5. Multiple Regression with ROE
10.6. Multiple Regression with OPM
11. LIMITATIONS OF STUDY AND FURTHER RESEARCH
12. CONCLUSION
13. REFERENCES
14. APPENDICES
Appendix A: Questionnaire of Revenue Management Integration (RMI)
Appendix В: Amendment to the questionnaire
Appendix C: Formal letter for revenue managers
Appendix D: Hotels of the population for the study
Appendix E: SPSS data output of stepwise method
Appendix F: SPSS data output of ROE and OPM in full model
Abstract
The diploma thesis assesses the opportunity to implement an integrated approach to revenue management in order to meet the challenges of the competitive tourism environment, which are particularly stemming from the increasing importance of the internet. It is proposed that effective and efficient coordination of the disciplines of revenue management, marketing, sales and ecommerce leads to Revenue Management Integration (RMI), which has a positive effect on financial performance and competitiveness. The thesis aims at identifying key linkages among the disciplines and creates a guideline of how the integration may be achieved. Using multivariate regression, it is tested with 30 hotels, affiliated to eight of the largest hotel chains in Germany, whether Revenue Management Integration is positively related to financial performance. The study showed that integrated revenue management is positively related to RevPAR performance in hotel properties, while the relationship to other profitability measures, such as Return on Equity (ROE) and Operating Profit Margin (OPM) did not yield clear results.
Keywords: competitive tourism environment, internet, disciplines, Revenue Management Integration (RMI), financial performance, RevPAR
List of Figures
Figure 1: Visualized Course of Investigation
Figure 2: Traditional Tourism Value Chain
Figure 3: Modern Tourism Value Net
Figure 4: Influence of the Internet on the Competitive Forces
Figure 5: Four Decisions Set Model of Integrated Revenue Management
Figure 6: Departmental Approach to Revenue Management Integration
Figure 7: Kotter’s eight Steps to Transform an Organization
Figure 8: Mc-Kinsey 7-S Model
Figure 9: Diagram of Capacity Allocating Decisions
Figure 10: Demand Forecasting Data
Figure 11: Example of a Hotel Strategy Canvas
Figure 12: The Four Actions Framework
Figure 13: Impact of Blue Ocean Strategy on Revenues and Profits
Figure 14: Revenue potential covered by charging one price
Figure 15: Revenue potential covered by charging different prices
Figure 16: Revenue potential covered by charging many different prices
Figure 17: Factors influencing a differential pricing strategy
Figure 18: Segments according to profitability for choosing a CRM strategy
Figure 19: Classification of methods for eliciting willingness-to-pay
Figure 21: Online Distribution Environment
Figure 22: Optimization decisions in the booking horizon
Figure 23: Graphical Illustration of Revenue Management Integration (RMI)
List of Tables
Table 1: Stratified Sample of Hotel Chains
Table 2: Additional Independent Control Variables
Table 3: SPSS Data Output of Full Model Regression with RevPAR
Table 4: SPSS Data Output of Reduced Regression Model with RevPAR
Table 5: SPSS Data Output of Component RMI with RevPAR
Table 6: SPSS Data Output of Regression Model with ROE
Table 7: SPSS Data Output of Regression Model with OPM
1. Introduction
An integrated approach to Revenue Management (RM), which spans over various disciplines, has frequently been proposed for tourism companies providing perishable products of fixed capacity, such as hotel chains, to fully capture the potential of total profit optimization (Ng, Maull & Godsiff, 2007, McGuire & Pinchuk, 2009). Kimes and Wagner (2001) defined the general strategy of revenue management as the practice used by service firms with fixed capacity to match the supply of a perishable commodity with forecasted demand via strategies that manipulate price and time of consumption (as cited by O'Connor & Murphy, 2008). Furthermore, revenue management and pricing programs have commonly been praised to have the potential to increase revenues by 3 to 8 % which can in turn result in 50 to 100 % profit improvements (Skugge, 2007). This thesis proposes a departmental and functional integration of various activities to arrive at an integrated revenue management approach that is viewed as the necessary reaction to changing market environment conditions.
Information and communication technologies (ICTs) have been changing the tourism industry structure globally, while developing many new opportunities and threats (Buhalis & O'Connor, 2005). In this new environment, revenue management becomes as important and challenging as never before and to operate effectively, skills are required to combine several knowledge areas steadily and creatively to make profit from the process (Henriksson, 2005). Consequently, it is proposed that particularly due to the modern tourism environment, companies have to make use of strategic integrated revenue management to offset the threats and take advantage of opportunities that the modern tourism environment represents.
An integrated approach to revenue management spans the disciplines of yield management, pricing, marketing, sales and e-commerce distribution and seeks to identify linkages to effectively and efficiently coordinate activities in the different areas to arrive at total profit optimization (McGuire & Pinchuk, 2009). The thesis is designed to illustrate how Revenue Management Integration (RMI) could be achieved, and the research part aims to asses whether hotel chains that embrace a higher degree of Revenue Management Integration operate at higher profitability.
1.1. Research Questions
This thesis is designed to evaluate whether an integrated revenue management process provides benefits in terms of financial performance to tourism companies selling perishable inventory such as hotel chains, in particular in a changed and more competitive tourism environment. Consequently, the following research questions guide this thesis:
a. Does the integrated revenue management process have a positive impact on financial performance of hotels?
b. Is an integrated revenue management process crucial for competitiveness given the changed modern tourism industry environment?
1.2. Research Contribution
The research is useful since the topic of integrating revenue management achieved increasing interest in the academic field as well as in the practical application in recent years (Tranter et al. 2009, McGuire & Pinchuk, 2009, Pinchuk, 2007, Ng et al., 2007). According to Skugge (2007), future improvements of profitability for companies will be by filling gaps and optimizing current revenue management programs rather than investing in new more elaborate computer systems. As a result, companies have to optimize their revenue management procedures and processes within the company, and it is proposed that this can be achieved by taking a holistic view and integrating various disciplines to come to total profit optimization. Roll (2009) argued that “we only have limited knowledge about the impact of the pricing organization within a company on profitability” (p. 397). Thus, the research fills a gap that gives companies not only a guideline how the integration can be achieved but especially whether it has an impact on financial performance and should be pursued and invested in.
1.3. General Objective
The general objective is to find out whether it is worthwhile to invest into the implementation Revenue Management Integration (RMI) due to its contribution to financial performance and/or competitiveness.
1.4. Specific Objectives
1. Assessment how the tourism value net, with the internet at the centre, changes the tourism industry structure.
2. Assessment of how the integration of the revenue, marketing, sales and e-commerce departments is best achieved and identification of linkages and key challenges.
3. Assessment what companies may gain from an integrated revenue management process in terms of profitability.
1.5. Hypotheses
The following working hypotheses are derived for the thesis:
1. Revenue Management Integration (RMI) in hotels is positively related to financial performance.
a. Revenue Management Integration stands in positive relation to Revenue per Available Room (RevPAR).
b. Revenue Management Integration stands in positive relation to Return on Equity (ROE).
c. Revenue Management Integration stands in positive relation to Operating Profit Margin (OPM).
1.6. Research Design
The research design sets out the framework in which data is collected and analyzed for the purpose of testing the hypotheses. A cross-sectional design is chosen to determine the relationship between financial performance and Revenue Management Integration (RMI) of hotel chains. According to Bryman and Bell (2003), cross-sectional design is suited when the research is looking for variation between many cases and for that purpose, from each case observations on several variables are made. Olsen and St. George (2004) emphasized that the cross-sectional study design takes the observations at a single point in time, thus change in observations cannot be measured. In cross-sectional design it is crucial to have a standardized procedure based on quantitative data to measure the variation between cases, which also results in a study of high replicability (Bryman & Bell, 2003). Therefore, sound research methods are crucial for an effective study of cross-sectional design.
1.7. Research Methodology
Several research methods are used in the study. First of all, a literature review lays the theoretic foundation for the research and provides a comprehensive illustration of Revenue Management Integration (RMI). Additionally, questionnaires were used and structured telephone interviews conducted, as the primary means to collect relevant data for the study and statistical evaluation. The statistical method used for data evaluation is a multivariate regression analysis, designed to determine the relationship between financial performance and Revenue Management Integration. Secondary data is used for control variables of the statistical modeling and finally, sampling methods are considered to identify the appropriate population for the research study.
1.7.1. Literature Review.
First of all, the literature review gives an overview why integrated Revenue Management within an organization is regarded as more crucial then ever due to economic forces particularly triggered by the modern tourism value net with the internet technology, representing a new medium that is not to be underestimated in its influence. More importantly, the literature review then sets out the theoretic foundation for the research by illustrating the Revenue Management Integration (RMI) between various functions and departments. It will be assessed how the integration may be achieved and which major changes are necessary to reach true integration within the organization. From the theoretic illustration key indicators for the integration are derived and transferred into a questionnaire, intended to measure the Revenue Management Integration in hotel properties and across hotel chains.
1.7.2. Structured Interviews.
The questionnaire derived from the literature review was filled out by conducting structured telephone interviews, which were identified as being most suited for the crosssectional research design used. According to Bryman and Bell (2003), structured interviews are standardized whereby each interviewee receives the same questions in the same order, and the same way. Due to the standardized character, any differences in responses are due to real variation and not due to inconsistency of interviewer conduct. Consequently, a structured interviewing approach is best suited for quantitative research where responses are to be evaluated statistically. As Owens (2002) found, telephone interviews have the advantages of having relatively low costs, short data collection period, good response rates, and less influence of interviewer on responses. On the other hand disadvantages of telephone interviews are that persons without telephone are not able to participate, the interviewer cannot observe and is not sure to interview the right person, and also no visual aids can be used (Bryman & Bell, 2003). However, since the interviews took place with revenue managers in hotels, which are certainly available by phone and can be identified as the correct interviewee by their organizational title, the disadvantages may for the most part be neglected.
1.7.3. Multivariate Regression Analysis.
The statistical research method that is used to assess the relationship between financial performance and Revenue Management Integration (RMI) is the multivariate regression analysis. Regression analysis is commonly used to determine the relationship between a dependent and one or many predicting variables (Burrill, 1992). A multivariate regression is necessary to account for additional control variables that may have an impact on the profitability than Revenue Management Integration alone. Rubinfeld (2000) stated that multivariate regression is in general suited to the analysis of data where several possible explanations persist for a relationship, thus a single dependent variable as well as several explanatory variables are used to examine the theory. Algebraically the multivariate regression model is represented by the following equation according to Anderson, Sweeney, Williams, Freeman and Shoesmith (2006):
Abbildung in dieser Leseprobe nicht enthalten
In such a multiple regression model y represents the dependent variable, while x1, x2,...xp are the independent variables and ß1, ß2,...ßp are the parameters allocating weight to the independent variables. Finally, there is an error term s that accounts for variations in the model that do not result from the independent variables (Anderson et al., 2006).
Since one has to assume that many factors influence the profitability of hotels, it is necessary to include those in the form of additional independent variables, also called predictors, in the regression analysis. Sykes (n.d.) argued that even if the investigator is only interested in the effect of one particular independent variable, the multivariate regression is essential due to bias caused by simple regression. Consequently, even though Revenue Management Integration (RMI) and its effect on profitability is the actual independent variable examined, other variables expected to have an influence on profitability need to be included to have a complete unbiased explanation of financial performance. According to Burrill (1992), after running the regression with all independent variables the “full model” may be cut to a “reduced model” by excluding uninformative variables that do not have relevant significance for the explanation of the dependent variable. As a result, even though for the investigation many additional independent variables may be included at first, the model may be reduced in the later analysis.
1.7.4. Secondary Analysis.
The Business Dictionary defines secondary analysis as the evaluation and use of “existing primary data that was collected by someone else or for a purpose other than the current one” (Business Dictionary, 2010). Advantages of secondary data are the relative ease of obtaining it, with little resources devoted to the data collection because someone else has done it. In addition, there are large amounts of secondary data available and if retrieved from a professional source, it was done with expertise that ensures high quality of data. Disadvantages are that the data has not been collected for the actual research at hand and may, therefore, only be partly suitable. Since the data was not collected by the researcher, the analyst does not have control over the quality of execution and does know about problems that may have accompanied the data collection method (Boslaugh, 2007).
For the study at hand most of the information was retrieved from revenue managers in hotels through the questionnaire in the structured telephone interviews, which represents primary data. However, some secondary data is used, such as annual financial statements of the examined hotel chains to evaluate their financial performance, which is information originally gathered by the companies for their investors and tax authorities. Moreover, for the design of additional independent variables in the multivariate regression statistics from official institutions are used.
1.7.5. Sampling Method.
Sampling may be defined as selecting a subset of a total population that is examined in the research project and is representative for the entire population (Magnani, 1997). The sample size chosen for the study is a suggested minimum size, which is n=30. As found by Hauser and Griffin (1993), “an N of 30 reduces the probability of missing a perception with a 10 percent incidence to less than 5 percent” (as cited by Arnould & Epp, 2006, p. 104). Consequently, this standard n is regarded as being sufficient and at the same time convenient for the study conducted.
With respect to sampling methods, there are generally two categories which are probability and non-probability methods. Probability sampling methods are usually preferred because they result in a more representative sample, since every element of the basic population has a known chance of being selected that can be calculated. On the contrary, nonprobability sampling methods are more flexible and less time consuming at the expense of weaker evaluation results (Doherty, 1994). In the study, stratification as a common probability sampling method is used. According to Barreiro and Albandoz (2001), stratified sampling is a method in which the overall population of size n is further divided into k subpopulations, called strata. Stratified sampling in general provides good results when the strata are different between each other but homogeneous internally. Therefore, stratified sampling is useful to apply in the study since various hotels of different hotel chains, which represent the strata, will be interviewed and are expected to vary between each other, while hotels of the same chain are expected to display similar Revenue Management Integration (RMI). However, one major problem of stratification is the distribution of the sample size among the strata. Possibilities would be a proportional distribution to stratus size, proportional distribution to variability of parameter, or simply assignment of same size to each stratus (Barreiro and Albandoz, 2001). For the sake of balanced representation, a distribution that proportionally to the size of each stratus or Probability Proportional to Size (PPS) approach is chosen.
Visualized Course of Investigation
illustration not visible in this excerpt
Figure 1: Visualized Course of Investigation
2. Impact of the Modern Tourism Value Net on Tourism Industry Structure
2.1. The Modern Tourism Value Net
For the assessment of the potential effectiveness of Revenue Management Integration (RMI) and to meet all research objectives, the internal assessment of hotel properties by use of the questionnaire has to be complemented by an analysis of the external factors, thus the impact of the modern tourism environment on industry structure. Werthner and Klein (1999) pointed out that due to rapid development of information and communication technologies (ICTs), the way of doing business in the tourism industry has dramatically been affected and the traditional tourism value chain (Figure 2) was replaced by an emerging modern tourism value net (Figure 3) with the internet at the center (as cited by Henriksson, 2005). In the following part the modern tourism value net is assessed for the opportunities and threats it represents to companies such as hotel chains.
illustration not visible in this excerpt
Figure 2: Traditional Tourism Value Chain
(Werthner & Klein, 1999 as retrieved from Hedriksson, 2005)
illustration not visible in this excerpt
Figure 3: Modern Tourism Value Net
(Werthner & Klein, 1999 as retrieved from Hedriksson, 2005)
In strategic planning a company matches its capabilities with available opportunities and takes actions to minimize threats (Kotler, 2006). It is argued that two factors determine whether a company is able to create economic value, namely the competitiveness of the industry structure that determines average profitability of competitors and a sustainable competitive advantage that allows the company to outperform the average competitor (Porter, 2001). Since one of the objectives of this thesis is the assessment of how integrated revenue management may assist to be more prepared for the modern tourism environment it is regarded as being potentially that competitive advantage. Gratzer and Winiwarter (n.d.) found that to counteract the threats of the internet and to achieve competitive advantage a firm has to develop a strategy by using the opportunities of the internet. Since integrated revenue management bears a lot of potential to create competitive advantage and use online technology particular for distribution and pricing optimization, the development for competitive strategies should start with that concept.
2.2. Industry Analysis along the Five Competitive Forces
The analysis of the modern tourism environment represented by the tourism value net will be done using the five forces model by Michael E. Porter from Harvard Business School and is intended to identify the opportunities and threats that the internet represents for tourism product providers. The five forces model in general helps to shed light on the profitability and attractiveness of an industry and the stronger the five forces are, the less profitable the industry tends to be. Figure 4 on the next page illustrates how the five forces are influenced by the internet in general.
illustration not visible in this excerpt
Figure 4: Influence of the Internet on the Competitive Forces
(Porter, 2001)
First of all, the internet means a geographic opening of the market, and competition increases in number of companies that compete for the same customers online beyond international borders (Porter, 2001). Moreover, as Wirtz (2001) argued, the internet enables companies to leave out wholesalers or retailers in their supply chain and distribute directly to end customers, also known as disintermediation, which reduces barriers to entry the industry (as cited by Gratzer and Winiwartner, n.d.). For the same reason it is unnecessary, at least for small or medium sized enterprises (SMEs), to have an established sales force, thus barriers to entry are further reduced, leading to even more competition in the industry (Porter, 2001). The increased rivalry in the industry finally increases the pressure for price discounting. Consequently, hotel chains have to analyze in accordance to their revenue management strategy which channels are to be used for efficient distribution, and in how far the sales force may be substituted by the more cost efficient online opportunities or how it should be restructured. Further, it raises questions on how companies may differentiate themselves from the competition in terms of their product features, innovation and customer relationship management (CRM) in order not to be forced to engage in devastating price wars.
With respect to online pricing, there is another important factor to consider, namely reduced information asymmetry in the market. According to Gratzer and Winiwartner (n.d.), consumers are able to compare offerings of multiple providers on the internet for value and prices at low search cost. Consequently, bargaining power is shifted to the consumers and switching costs are reduced because the price becomes the most important decision criterion for the customers (Porter, 2001). The transparency of prices, due to the internet, represents issues to revenue managers in terms of price parity across channels and in the same respect opens opportunities such as dynamic pricing - all important aspects to be considered in an overall integrated revenue management strategy.
Henriksson (2005) suggested that opportunities of the internet are the provision of new and more efficient ways of customer relationship management and possibilities for interaction with customers to find out about their needs and preferences. Kung, Monroe and Cox (2002) found that the internet, through click stream analysis that tracks current online sessions or cookies which monitor buying histories, allows companies to research consumer purchasing behavior more effectively. As stated by Kung et al. (2002), online technologies can also be used to research pricing decisions on consumer purchases in real time at low cost and to experiment with innovative pricing strategies such as auctions. Consequently, online technologies may be used by companies to interact more closely with their customers, find out about their preferences, and create personalized offerings according to their needs and experiment with pricing strategies dynamically.
Knowledge about customers, their purchasing behavior and preferences, and their reaction to pricing policies can then lead to product or brand differentiation, which has a positive effect on the competitive positioning and reduces consumer’s sensitivity to price. Consequently, there are many opportunities for marketing tactics to elicit specific information of customers and to funnel that information in distinctive and innovative product development as well as revenue management and pricing strategies. Sahut and Hikkerova (2009) argued that in tailor-made, dynamic packaging the more elements of the package complement each other the more of a surplus can be given to the consumer and the less price is important for the final choice. Consequently, a company that engages in effective customer relationship management to find out about the needs, desires and preferences of its customers and combines it with innovative product and pricing strategies can offset the threat of price being the sole decision criterion and manage to build a competitive advantage.
As found by Kung et al. (2002), the research capabilities of the internet on customer preferences and price testing can also lead to the discovery of new segments that it would be worthwhile to pursue. In addition, Helgesen (2005) argued that management has two main goals in market orientation which is offering customers products according to their preferences and needs and secondly ensuring that activities are carried out to result in long term profitability. Therefore traditional ways of segmenting according to non-economic parameters should be combined with financially based segmentation techniques. As a result, companies should think over their segmentation and consider new ways of segmenting their customers which are more in sync with the challenges provided by the modern tourism environment, and allows for innovative targeting and pricing strategies.
The internet results in a reduction of distribution costs due to the opportunity of opening direct distribution channels to customers for which no commission and transaction costs have to be paid (Henriksson, 2005). In terms of the five competitive forces this means that the bargaining power of channels is reduced in favor of the tourism supplier (Porter, 2001). Furthermore, the internet provides many new types of intermediaries, so called cybermediaries that form electronic market places such as expedia.com, travelocity.com and others, and which partly replace the traditional intermediaries, also know as reintermediation (Gratzer and Winiwartner, n.d.). According to Tranter, Stuart-Hill and Parker (2009), the new electronic market places form the Internet Distribution System (IDS). The IDS means competition to the traditional electronic channels, like the Global Distribution Systems (GDS), thus takes bargaining power from them and allow companies to present their products in new, more independent ways and manage channels for profitability. The advent of an overwhelming amount of distribution channels online that serve different target markets, means that those e-commerce opportunities need to be evaluated, analyzed, and managed in a good revenue management sense.
The evaluation of the modern tourism industry structure and how it is influenced by the online technologies above gives a first broad overview of challenges, threats and opportunities that revenue managers face in order to ensure ongoing competitiveness of their organizations and develop effective pricing and revenue management strategies.
3. Revenue Management Integration (RMI)
3.1. Integrated Revenue Management as a Competitive Advantage
It has been shown that the tourism value net with the internet at the centre of the business network represents threats to companies but also many opportunities. In order to avoid the price eroding competition in an online- and e-commerce environment and take advantage of the opportunities, it is proposed that companies should react with the implementation of an integrated strategic revenue management process that enables company to differentiate from competitors via more effective pricing, product and distribution strategies and move to total profit optimization (Mc Guire & Pinchuk, 2009). Particularly through the increased demand information available through the internet, it becomes necessary to get a balanced view of revenue management; research and practice of revenue management becomes multi-disciplinary, and gets under pressure to widen its focus and to understand the linkages between the various disciplines (Ng et al. 2007). Consequently, the objective is to illustrate what Revenue Management Integration (RMI) means for hotel chains, how it is achieved, and whether it has a significant impact on overall financial performance.
On a rather abstract level Ng et al. (2007) suggested that the integrated revenue management process is comprised of four decision sets (Figure 5). The value set defines the attributes from which customers benefit and stands for the overall demand that can be generated with the current value propositions. The segmentation set is the grouping of customers according to the benefits received and pricing strategies reflecting the individual value perception of each segment, the sensitivity set further reflects how price influences the quantity and demand that can be realized, finally the allocation and forecasting set determines how much demand needs to be realized, given available capacity (Ng et al., 2007). This is a very basic framework of a revenue management process and the integrated revenue management system captures the four parts as interactive optimization areas by which more strategic tools can be developed for increasing revenues, allowing for creativity and innovation in product development and pricing approaches.
illustration not visible in this excerpt
Figure 5: Four Decisions Set Model of Integrated Revenue Management
(Ng et al., 2007)
In order to advance to a less abstract, more implantable and practicable set up of Revenue Management Integration, a departmental and more detailed functional view should be taken and the interactions and linkages between them examined. As suggested by Pinchuk (2007), revenue management, pricing, customer relationship management and distribution channel management should not be separated anymore but integrated in a total Profit Optimization System (POS) that is based on common goals, databases, analytical processes, rulers, and skill levels between the various departments. Cross, Higbie and Cross (2009) called the integrated revenue management process a “Renaissance of Revenue Management” that lets the discipline emerge with a more strategic role that encompasses marketing, sales, and channel strategy. Furthermore, Vinod (2007) emphasized that the new paradigm for the discipline is also to move to more customer- centric revenue management and that there are strong interdependencies that require a holistic view to understand business impacts.
In this respect, a departmental integration of revenue management can be derived that calls for a connection of the revenue department, the marketing department, the sales department, and the e-commerce department. Pinchuk (2007) suggested that the revenue department remains an inventory-focused department that allocates demand to existing capacity, while a Profit Optimization (PO) division combines the data of the separate areas in a newly created Central Profit Command Centre (CPCC) where it can be aggregated, planned, tracked, analyzed, and controlled from one system or interface. This would enable a coordinated and analytical approach to important optimization areas such as distribution decisions on what channel to open or shut, what marketing promotions or customer relationship management activities to start, and pricing decisions in different distribution channels to optimize profit on remaining inventory at any given point in time. In addition, the data may be useful for long-term planning, such as product development and refined market segmentation.
In this respect, the profit optimization function also overtakes pricing decisions and calculates prices according to all net effects given by the aggregated and analyzed data from the various departments. In addition, Kotler, Rackham and Krishnaswamy (2006) suggested to control more effectively the integration to appoint a Chief Revenue Officer (CRO) who is responsible to plan the generation of profitable and increasing revenues and controls the main rational for integrated revenue management, which is the common goal of profit optimization. A tight coordination finally leads to an optimization of all data aggregated in the system and actions to produce the optimal demand, which revenue management requires and a total profit optimization (Pinchuk, 2007). While the revenue department remains focused on inventory allocation, the profit optimization function is responsible for demand management working out pricing policies in coordination with the other relevant departments. Besides the common databases and analytical processes guaranteed by the Central Profit Command Centre (CPCC), Pinchuk (2009) found goal alignment and measurement as well as common skill sets to be crucial for true Revenue Management Integration. A visualization of the departmental and functional integration of all relevant departments is shown in the figure below (Figure 6).
illustration not visible in this excerpt
Figure 6: Departmental Approach to Revenue Management Integration
3.2. Reengineering an Organization for Revenue Management Integration (RMI)
“Some industry leaders are talking about a fully integrated RM function that overseas the marketing, sales, pricing and distribution functions of a hotel. Owing to the lack of individuals trained, and particularly, the need to change people’s mindsets, this integrated structure is still a long way away” (Milla & Shoemaker, 2007).
The quote above shows what organizational difficulties and challenges lay in the realization of an integrated revenue management approach. The integration has to be seen as an organizational reengineering and cultural shift that requires a well structured and executed change management process, complemented by extensive training programs. Depending on the state of integration of the various departments within a company and the ambition of the Integration effort, a minor continuous change may already have results in terms of financial performance. A major change that spans the entire organization may mean a derivation from the status quo and many employees, in particular in the affected departments, may not be comfortable with it. Therefore to ensure largest possible success of the effort, the major change should be conducted by following the eight steps of John P. Kotter (2000) the “guru of change” from Harvard Business School. The eight steps are displayed below (Figure 7):
illustration not visible in this excerpt
Figure 7: Kotter’s eight Steps to Transform an Organization
(Kotter, 2000)
With respect to the first step, urgency must be displayed why the company should move toward Revenue Management Integration (RMI). According to Kotter (1995), many organizations already fail at this first stage because employees resist to be forced out of their comfort zone of how things are done and display complacency. General sources of complacency are for example too many visible resources, low overall performance standards that give a false sense of success, measurement systems that focus on the wrong metrics, organizational structure that focus people on narrow functional goals without anybody looking at the overall end-to-end processes, or lack of feedback from external sources (Kotter, 1996).
Given the second step of forming a powerful guiding coalition, Kotter (1995) argued that major change efforts need the approval and support of senior management and, to have best chances for success, the coalition should include people of high power, expertise, and leadership skills, thereby the coalition is not necessarily limited to senior management, department managers, and employees but may include other interest groups and stakeholders. For building a effective coalition, it is particularly important to initially rise high sense of urgency among the managers of the affected departments, thus in the case of Revenue Management Integration, Revenue Management, Marketing, Sales, and E-commerce and jointly asses their opportunities for the integration and establish trust among them (Kotter, 1995).
In addition, a vision should be developed that illustrates how the company may be able to better serve the customers and optimize profits at the same time by the implementation of Revenue Management Integration. According to Kotter (1995), the vision has to illustrate a picture of how the future should look, which is attractive to customers, stockholders and employees, thereby establishes a direction in which the organization is intended to move (Kotter, 1995). Then the vision must be communicated through the organization, and employees should be empowered to act on the vision. For both steps, the Balanced Scorecard (BSC), developed by Kaplan and Norton in 1992, is the optimal tool since it used to translate an organization’s strategy and vision into concrete initiatives and performance measures. Executives use the BSC to control and communicate and as an information and learning system to align individuals, and cross-departmental efforts to achieve a common goal (Kaplan & Norton, 2007). Therefore, the Balanced Scorecard may be used to communicate and control Revenue Management Integration and align all employees and managers to it. The Balanced Scorecard measures organizational performance on four “balanced” measures, which are the customer perspective, the internal business process perspective, the learning and growth perspective and the financial perspective (Kaplan & Norton, 2007). The rational behind it is that company performance should not solely be measured on short-term objective measures such as financial performance but the achievement of more “soft” long-term objectives has to be considered as well. In this respect, it may be argued that the Balanced Scorecard is particular attractive as a tool to communicate and measure Revenue Management Integration since it follows a similar rational.
The integrated revenue management process aims at customer-centric revenue management, particularly due to the integration of Marketing, Customer Relationship Management and Sales. Consequently, an important objective is, to balance company profits and customer interests by being innovative in product development and pricing strategies and create lasting relationships with customers. Due to the balanced approach to revenue management that the integration aims at, there is a parallel to the Balanced Scorecard that renders it a useful tool to communicate Revenue Management Integration through the organization and to set out the specific initiatives and key performance indicators (KPIs) for the execution of the integrated revenue management process. The key performance indicators, thereby, should be chosen in a way to guarantee goal alignment among the departments and functions that are integrated.
Another crucial area for communicating the new vision and empowering employees are effective training programs. Skugge (2007) identified as a key issue that current revenue management systems have to be used to their potential by educating employees. Often the system is unnecessarily overridden because employees do not fully understand the underlying algorithms or mathematical assumptions on which the decisions rest. For companies, it will be crucial to have employees that hold or are able to develop skills to meet the changing market conditions and use the computer systems effectively. Beck, Knutson, Cha, and Kim (2009) identified skills such as communicating effectively, leading the revenue team, and managing daily activity as highly important. However, skills like developing effective revenue management strategies and analyzing trends were found to be most important and also to be those that require most training and are trained too little in most organizations. In addition, Pinchuk (2007) identified unified skill levels as crucial for an integrated revenue management process since it requires employees to work together and understand each others way of thinking and integrate action across different fields. Consequently, training needs to be aimed at creating skill sets that help people to become all-rounder and have at least some understanding of what is happening in other departments and functions.
The next step in a change process is the creation of short term wins. Kotter (1995) argued that even people that were formerly convinced about the transformation may become resisters to change if there are no wins to be observed within the first 12 to 24 months. Therefore, management should actively create wins by setting up clear short term goals, look for ways to improve performance and reward the employees involved with recognition, promotions, and incentives. When creating the reward systems, it has to be considered that they should be structured in a way to support the integration that is pursued. Lawler (1998) found that reward systems can reinforce and define organizational structure, particularly in terms of integration or differentiation. Thus, employees unite when they are rewarded in the same way or divide when they are paid differently. As a result, the reward systems have to be structured in a way that all employees of the various functions involved in Revenue Management Integration perceive to be rewarded the same way, since otherwise the integration may be hindered even if there are overall positive results.
The last two steps are crucial to make sure that the change effort will have persisting effect in how things are done in the organization. According to Kotter (1995), many managers “declare victory too soon” after the first major clear performance improvements are observed. However, management has to remain keeping an eye on the subjects under change and, in fact, should use the credibility achieved to tackle even bigger problems that are not yet consistent with the overall vision or transformation. That also leads to performance measurement of integrated revenue management. According to Skugge (2007), in the future it will be more important to identify proper metrics and track them to determine the contribution of the integrated revenue management activities to revenue and profit, and identify weaknesses in the process. Moreover, meetings should be hold with employees from all departments or functions that may be used to exchange best practice andjointly identify areas for improvement.
This section about change and organizational engineering illustrated how Revenue Management Integration goes far beyond mere processes that run in the background, but spans over all “hard” and “soft” factors of an organization that may be displayed by the McKinsey 7-S-Model (Figure 8). The model shows strategy, structure, and systems as hard elements which may be found in strategic statements or corporate plans and are therefore feasible and comparatively easy to influence and plan for. However, the soft elements such as shared values, skills, staff and style are under the surface, difficult to influence but may have great impact on the hard structures anyway (Recklies, 2001).
4. Revenue Management Perspective
For Revenue Management (RM) also the terms Yield Management (YM) or Perishable Asset Revenue Management (PARM) are used interchangeably and several definitions to the process of revenue management exist. Sheryl E. Kimes (2003), the guru of hospitality yield management, explains it as “the application of information systems and pricing strategies to allocate the right capacity to the right customer at the right price at the right time.” The definition of “right” in this case is to maximize revenue for the supplier while simultaneously providing sufficient value to ensure customer satisfaction (Kimes & Wirtz, 2003). Buhalis and O'Connor (2005) described revenue management as the coordination of 5Cs: calendar, clock, capacity, cost, and customer. According to them, levels of yield management are geared to match service timing and pricing to customers willingness to pay in relation to its timing and demand from other customers. Thereby, the concept of “cost” is introduced that has mostly been neglected in traditional revenue management applications where the focus was on revenue maximization through the right allocation of demand to existing capacity and aligned pricing strategies. However, also in practice it starts being recognized that costs have to be considered to move to total profit instead of only revenue optimization, particularly in scope of integrated revenue management.
Nonetheless, in practice revenue management may still be simply seen as price discrimination according to forecasted demand levels in a way that price-sensitive customers prepared to purchase in off peak times can do so for lower prices, while price-insensitive customers, who want to stay at peak times, are charged more according to their higher willingness-to-pay (Kimes & Wirtz, 2003). In the integrated revenue management process the revenue department’s core function is demand forecasting and allocating demand to existing capacity in a way to optimize business mix for profitability. Revenue Management experts in the profit optimization (PO) function overtake pricing responsibilities to set the right prices according to the net effects analyzed in the interface of the Central Profit Command Centre (CPCC) and in coordination with the other relevant departments (Pinchuk, 2009).
The definitions highlight some of the prerequisites that must be given to successfully apply revenue management. For revenue management to be implemented the product and the industry need to display certain characteristics that allow for the use of revenue management. According to IDeaS (2005) the following seven characteristics are necessary for an appropriate use of revenue management.
1. Segmentable Markets
- Demand for the service can be divided into clear market segments and sensitivity to prices varies among the market segments
2. Relatively Fixed Capacity
- The firm’s capacity is relatively fixed; it is expensive or impractical to add or subtract inventory in the short run, though there may be some ability to shift it.
3. Perishable Inventory
- There is a time dimension to the provision of the service; once that time has passed the inventory loses all of its value
4. High fixed costs, low variable costs
- The cost of selling an additional unit of the existing capacity is low relative to the price of the service
5. Advance Reservations
- There is an opportunity to evaluate and accept or reject order requests in advance of the performance of the service
6. Flexibility in pricing
- There is considerable flexibility to adjust prices quickly to reflect variations in the balance of supply and demand.
7. Time variable demand
- There a definite peaks and valleys in demand, which can be predicted, but not with a high degree of certainty.
(Kimes, 1989 , as cited by IDeaS, 2005)
4.1. EMSR Method and Demand Forecasting
The main revenue management function of allocating capacity to different segments in a way to maximize revenues can be illustrated on the standard method “Expected Marginal Seat Revenue” (EMSR,) which was developed 1987 by Peter Belobaba of Massachusetts Institute of Technology (MIT) (Mc Gill & Van Ryzin, 1999). The EMSR Method was originally designed for airlines and the allocation of airline seats but may just as well be applied to hotel rooms. The method reflects what revenue management does to manage and match capacity and demand. A basic assumption is that customers have different booking patterns and pace combined with different willingness-to-pay based on their value perceptions. For example, according to Tranter et al. (2009), leisure customers value to be sure that they can go on their booked holidays when they are off from work, thus book long time in advance. On the other hand, business travelers put more value on flexibility and book at short notice. However, business travelers have a much higher willingness-to-pay because the company is paying, while leisure travelers pay out of their own pocket and are more price-sensitive. As Netessine and Shumsky (1998) explained, from this relationship a trade-off develops for companies that may sell all rooms to early booking lower paying leisure travelers to secure revenue well in advance but then additional revenue from higher paying business travelers, who book later, is lost. However, if revenue management decides to arbitrarily keep rooms open for later booking business customer, it runs the risk to have rooms unoccupied at the end of the booking horizon and revenue from those rooms would completely be lost.
Therefore, as Tranter et al. (2009) explained, revenue management sets up demand forecasting for the various segments that is based on historical demand from previous years and a forward looking demand calendar that contains demand generators of a particular destination that draw visitors, such as conventions, trade fairs, sporting events, recreational activities, or special events. The information is then combined with statistical methods, such as exponential smoothing, moving averages or multivariate regression analysis to generate demand forecasts for allocation optimization. The demand forecasts in combination with the EMSR method are used to create booking limits i.e. protection levels for the total rooms which are derived from the relative size of the full and discounted prices and the forecasted demand for fully priced rooms (Netessine and Shumsky, 1998). The underlying theory is, basically, the core of revenue management and, therefore, it will be illustrated in an example derived from Netessine and Shumsky (1998). For a simplified problem a hotel with 210 rooms that takes reservations for a particular day is assumed. Further, two segments are considered with different willingness-to-pay and different value perception. Leisure travelers are more price- sensitive and book earlier while business travelers book later at higher prices. The discount price for leisure travelers is€105 while the full rate is €159 per night.
In the diagram below (Figure 9) the possible decisions along the booking period are displayed. It is considered that the protection level is at the level Q. If an additional room (Q +1) is sold to an early leisure traveler, 105 € in revenues is secured. However, at the same time the chance to sell that room to a higher paying business traveler is forfeited. On the contrary, if the additional room (Q +1) is protected as shown in the lower branch of the diagram, there is the chance to sell that room to business traveler before the booking horizon expires (1 - F(Q)). However, if no business traveler books a room anymore the room will go completely unsold (F(Q)) and no revenue at all could be secured.
illustration not visible in this excerpt
Figure 9: Diagram of Capacity Allocating Decisions
(according to Netessine & Shumsky, 1998)
This gives us the following expression for the calculation of protection levels:
illustration not visible in this excerpt
From the expressions, the following relationships can be derived; lowering the protection level from Q +1 to Q results in selling the room at a discount, securing the revenue of €105. On the contrary, to protect the room has the expected value as follows:
illustration not visible in this excerpt
Consequently, the protection level should be lowered to Q as long as the value of protecting is expected to be higher than the value of opening as shown below:
illustration not visible in this excerpt
This relationship may be transferred in the EMSR formula:
Where:
illustration not visible in this excerpt
The formula results in the following equation:
illustration not visible in this excerpt
Figure 10: Demand Forecasting Data for Full Priced Rooms (Business)
(Netessine & Shumsky, 1998)
When screening through the demand table (Figure 10), which represents the demand forecast for high rate demand, we find that the smallest Q with a cumulative probability greater or equal to our finding is 0.341 from which we can derive an optimal protection level of Q = 79. Consequently, the booking limit should be at 210 - 79 = 131rooms.
While the example of Netessine and Shumsky (1998) above illustrates in a simplified example how the capacity allocation of demand to different price classes on the basis of demand forecasting data works, in reality this is much more complex.
[...]
- Quote paper
- Malte Rücker (Author), 2010, Revenue Management Integration, Munich, GRIN Verlag, https://www.grin.com/document/187243
-
-
-
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X. -
-
Upload your own papers! Earn money and win an iPhone X.