Information and Information Processing Requirements of Yield Management in Capacity Constrained Service Firms

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1 Information and Information Processing Requirements of Yield Management in Capacity Constrained Service Firms Klaus Weiermair Christine Mathies Center for Tourism and Service Economics University of Innsbruck, Austria {klaus.weiermair; Abstract Capacity constrained service enterprises attempt to maximise revenues by applying Yield Management techniques. This paper discusses the information and information processing requirements for the successful application of revenue management in the service industry, especially in the airline and accommodation industry. The main part of the paper investigates the possible (mis)use of yield management tools in various other capacity constrained tourism sub-branches and specifies special requirements. The findings will then be applied to a tourism destination as a whole to indicate how Yield Management may contribute to the complex price and product decisions in a destination. Keywords: Yield Management, Tourism Sub-Branches, Information Processing Requirements 1 Introduction A growing number of tourism enterprises rely on information and communication technologies to encounter problems associated with quality standards and capacity utilisation (La and Kandampully 2002). Capacity constrained service enterprises which display high fixed cost, for example, typically attempt to maximise revenues by applying different strategies of demand and capacity management. Yield Management, which originated after the deregulation of the airline industry in the late 70ies, is one of the most popular decision making tools for revenue maximisation and encompasses reservation control and pricing of perishable capacity units (Baker and Collier 1999). Kimes (1989) characterises yield management as a method to sell the right inventory unit to the right type of customer, at the right time, and for the right price, thereby arriving at maximum capacity profitability. Airlines and hotels with Yield Management systems have been able to achieve up to 5% increase in revenues by implementing efficient overbooking policies and by properly allocating capacities among various rate categories (Weatherford 1995).

2 Early Yield Management solutions were based on simple heuristics such as Threshold control, and First in First Served approaches. More sophisticated techniques formed nested booking categories, attached a booking limit to each nest/bucket, and found optimal allocation with the help of linear programming. However these solutions could not be applied to multiple leg flights within an airline alliance network, and for hotels assumed one night stays only. More recent Yield Management algorithms allow allocating seats across a large network of flight routes, and the application of these new algorithms to the hotel industry allows accounting for the possibility of multiple night stays. 2 Basic Information and Information Processing Requirements Yield Management is an excellent showcase of how information technologies help to realise solutions for complex management problems in tourism enterprises. There are a number of market conditions and associated data requirements that need to be fulfilled in order to apply Yield Management techniques. First of all, customers have to be clearly segmented according to their price elasticity in order to apply price and product discrimination strategies that lie at the heart of Yield Management. The airline industry, for example, distinguishes highly pricesensitive leisure travellers who tend to book well in advance, and the corporate market showing low price sensitivity. The accommodation sector acknowledged duration of stay as an additional segmentation variable and thus requires a more detailed market segmentation that considers at least three basic types of customers: tourists, business travellers, and groups (Britan and Mondschein 1995). Secondly, fluctuating yet predictable demand patterns are a prerequisite for the strategic pricing and allocation decisions of revenue management. Service enterprises face the challenge of predicting variations in demand for different times, such as weekends, peak and low season, and peak hours during the day, by collecting and processing information on reservation and demand histories for all available rate classes (Kimes 1989). Major hotel chains, for example, use linear-programmingbased models that require detailed forecasts by day of arrival, length of stay, and rate category. Airlines face a similar challenge with the forecasting of origin-destination (Kimes 2001). It is evident that IT support is crucial to collect and process data on past demand patterns in order to allow accurate forecasts. Thirdly, Yield Management tools can only be applied if the majority of customers book services in advance. The time-span between reservation and consumption varies across different customer segments, with price sensitive customers expected to book further in advance (Desiraju and Shugan 1999). Reservation control heuristics in the context of Yield Management, however, also have to consider cancellations, noshows and overbooking policies when systematically accepting or rejecting reservation requests. For example, simple threshold curve methods compute the number of acceptable reservations for any point of the pre-booking phase for the

3 different fare categories (Baker and Collier 1999). More complex heuristics, such as Nested by Deterministic Model Shadow Prices and Bidprice Method, apply linear programming or even accurate integer programs to obtain shadow prices for each reservation request. It is thus not surprising that the implementation of a comprehensive information system is considered the most important management task to use capacity efficiently. Availability and accuracy of data is the biggest problem in applying yield management, and thus capable information systems on an operating and a strategic level are crucial. 3 The role of customer satisfaction and pricing decisions Current Yield Management computing solutions focus on allocation and pricing decisions for identified customer categories, although the common practice of separating customer satisfaction and cost orientation in the service industry is highly counterproductive (Carù and Cugini 1998). Customer satisfaction and pricing decisions are two sides of the same coin and should therefore be considered together. Customers perception of fair prices, or good value for money, derives either from an increase in received service quality, or from lower prices. Varki and Colgate (2001) found that customers price perceptions have an even stronger effect on value than quality. Yield Management performs customer segmentation based on price elasticity and can therefore exploit this price relationship with two different concepts of discrimination, namely price and product discrimination. Both create better value for a wider range of customers. In addition, yield management is meant to indirectly maximise revenues, as increased quality levels and higher customer value, and subsequently higher customer satisfaction, allows service providers to charge higher prices (Capiez and Kaya 2002). However, only recent research has attempted to explore customers reactions to yield management policies and the resulting price differences. Kimes (2002) for example investigated the perceived fairness of yield management in the airline and hotel industry and found that fairness also depends on the price of so-called reference transactions. Reference prices are based on market prices, advertised prices, and prices previously paid to the same provider. As customers expect the value to the firm to be equal to the value to the customer, price increases can only be justified if additional services/products complement the purchase service, or if customers are deceived by a higher rack rate and resulting higher discounts. The issue of perceived price fairness in the context of Yield Management applications now raises the question as in how far information technologies can help to determine and influence customers price perception. Effective Yield Management must go beyond revenue maximisation per capacity unit at any given time, and include issues of customer satisfaction in relation to Yield Management for allocation decisions.

4 A more comprehensive Yield Management system which considers both short-term yield maximisation and long-term interrelations of customer satisfaction and pricing decisions poses additional data and information requirements. First of all, the transparency of markets and prices due to information technologies facilitates the determination of customers reference transactions, i.e. reference prices. It can also be argued that the collection of customer history data gives the service provider a comprehensive overview of prices previously paid for the same or similar services, e.g. flights, hotel rooms, restaurant meals or movie tickets. Both information sources are invaluable to understand the building of reference prices by consumers. 4 Possibilities and limits of yield management in selected tourism sub-branches In this section, the application of Yield Management in a few selected tourism subbranches will be illustrated in order to determine in how far information technologies can help to collect relevant data and perform decision rules. The results will be summarised in Figure 2 at the end of this chapter. Even though an increasing number of other capacity constrained services in other industry settings are beginning to adapt yield management concepts, research has in the main only addressed the airline and accommodation industry (Kimes 1997). Yield Management has first been developed for the aviation industry, and hotels constitute the second largest area of application. Despite a few striking similarities, the airlines concept of Yield Management can however not be easily transferred to the market for hotels. A hotel is simply not an aeroplane without wings (Kong 1993), and therefore more complex problem solving information processing and decision-making algorithms are required. 4.1 Accommodation Industry The main difference between flights and hotel rooms is the possibility of multiple day stays. While seats on an aeroplane on a specific day are either sold or vacant, yield decisions in a hotel also have to consider the occupancy rate for the next few days. For example, it might be better to reject a lower fare customer who requests a room for four days even if the room remains vacant for the current night, if occupancy for the next three days is already high (Bitran and Mondschein 1995). Weatherford (1995) thus introduced a decision algorithm that nested booking requests according to their overall value, which is a combination of rate and length of stay. He proved that heuristic decision rules that account for length of stay increase hotel revenues by 0.5 to 1.5% compared to older and more simpler rules such as the dynamic Expected Marginal Revenue Model (Belobaba 1992). Also, in the airline industry there are only a few major competitors, while hotel guests can usually choose from an abundance of different providers. Thus the barrier

5 to change hotels even a few days into the stay is much lower (Kimes 2002). As a result, price discrimination has to be applied much more carefully. The particularity and usefulness of yield management in various tourism sub-branches could provide valuable insights for its advancement and further development in tourism related industry settings other than airlines and hotels. Both application and applicability outside the aviation and accommodation industry will in the main depend on two industry characteristics, i.e. the predictability of duration of services and the ability to vary prices according to customers willingness to pay. Fixed Price Variable Duration Unpredictable Predictable Movies Stadiums and Arenas Convention Centres Restaurants Golf Courses Internet-Service Provider Airlines Hotels Rental Cars Cruise Lines Continuing Care Hospitals Source: Kimes (2001) Fig. 1. Typology of Revenue Management 4.2 Restaurants Yield management in restaurants differs substantially from its use in other industries as the duration of service consumption now becomes a very important aspect in the allocation of inventory units, i.e. tables. While in traditional yield management applications duration is clearly determined, time spent in a restaurant depends on and

6 varies with individual customer characteristics and situational variables, all of which are difficult to predict and control. Moreover, restaurants have so far been reluctant to openly apply price discrimination strategies. Restaurant managers are afraid of dissatisfying customers if they were to apply demand-based pricing that goes beyond promotion offers such as happy hours. A survey conducted by Kimes and Wirtz (2002) validated managements objections to yield management. Restaurant patrons are likely to accept only different prices for different times of the day, and lunch-versus-dinner pricing as fair. Price differences for weekdays and weekends however are perceived as unfair and can therefore create dissatisfaction. The use of information technologies for revenue management in restaurants is very limited. Minimum data requirements include arrival time, meal-duration, and revenue data on a day-part basis. In order to understand and exploit demand and revenue patterns, data should be collected per hour. Data can either stem from the restaurant s Point of Sale system, actual observations, or customer surveys. While information technologies are essential to collect and process this data, restaurants do not apply computing for decision rules. Instead, management uses the data to decide on the timing of happy hours, and to shorten meal times during peak hours (Kimes, Barrash, Alexander 1999). 4.3 Holiday Retail Shopping Coulter (1999) suggest that yield management can also be applied to holiday retail shopping, although in this case the capacity, i.e. product stocks, cannot be considered as fixed and perishable in the same way as seats on an airplane or hotel rooms. Nevertheless, the decision on which amount and type of inventory to carry, and at what prices, might be solved with yield management techniques, as shopping patterns are subject to seasonal fluctuations (e.g. Christmas shopping), and certain inventory decreases in value after a certain time (e.g. outdated fashion and gloves in summer). In contrast to the airline and hotel industry, price discount decisions are reverse, as the price-sensitive customers are more likely to buy at the end of a shopping season and wait for discount sales, while full prices can be achieved earlier (Reverse demand threshold curve). Data collected is similar to restaurants, and comprises items purchased, and value of each purchase, subdivided by hour of the day. Revenue management in retail shopping is limited to decisions on type and amount of stock, and to determine the starting point for sales. In addition, linear programming solutions help to decide how much store display should be dedicated to which type of merchandise.

7 4.4 Yield management in a Tourist Destination Tourist destinations present the most complex service networks in tourism, comprising of a number of different service components offered by many different producers. Given that yield management at the individual firm level has to deal with various problems depending on the respective tourism industry sub-segment, it is questionable whether revenue management can be extended toward the level of the tourist destination. The tourism product consists of transport, accommodation and hospitality services and a variety of tourist activities. This implies that individual capacities in a destination have to be harmonised. For example, the maximum capacity of air and rail carriers must not exceed the number of hotel beds in a destination. Similarly, the average number of tourists should have a vital impact on the capacity planning of facilities such as cable cars and movie theatres., which have to be related to the overall destination capacity. Within this overall destination capacity, transport businesses can apply their established yield management techniques. Accommodation providers within a destination should join their resources, however a sophisticated revenue sharing model has to compensate individual hotels which accept a less favourable booking in order to maximise yield of the destination. Different facilities within a destination, such as ski lifts, spa bath, etc. charging individual entrance fees can similarly use yield management tools. In order to comprehensively apply capacity management techniques for the destination as a whole, information requirements are somewhat more complex: Additional information needs include information on (1) the destinations competitive attributes, (2) customer preferences for attributes within the destination and (3) the timing of these preferences. This implies that destination capacity management requires not only yield management tools, but has to go one step further and employ conjoint analysis of customer preferences regarding possible price-product combinations (Pullmann and Norre 1998). In order to accommodate for the more complex pricing and product decisions (considering multiple customer segments, multiple product/price configurations and capacity aggregation to destination level) required on the destination level, it becomes crucial to introduce one ticket that gives the visitor access to all amenities during their period of stay. This can be done with the newly invented electronic guest cards, which track the visitors purchasing behaviour. Although electronic guest card provide destination managers with an abundance of data, there are certain limitations. For example, it can not be traced whether a customer waited without being served or did not even line up for a certain activity.

8 Sub-Branch Transport Hotels Current YM Applications Supply Side Customer Segments prevalent in mass transportation such as airlines, railways, ferry boats less frequently used for cable car transportation and car rentals Standardised capacity units Homogenous providers Two main segments: business and leisure frequently used by large hotel chains in large destinations and by global hotel chains less frequently applied by small independent hotels Less standardised capacity units Heterogeneous providers Three main segments: business, leisure, groups; Often further refined Demand Variable demand Variable demand Data Sources Decision Rules Prospects PMS Reservation System Sophisticated methods due to network focus Heuristics and optimal decision rules Rapid rate of industry wide diffusion Complexity * Multiple-day stays PMS Reservation System Simple approaches, e.g. Threshold Curve Method Sophisticated methods, e.g. Nested Models using linear programming Mainly heuristics Limited to larger sized hotel chains and integrated independent firms * Increasing complexity of yield management associated with respect to increasing variance in products, qualities and suppliers and the bundling of service products Fig. 2a. YM in Tourism Sub-branches

9 Sub-Branch Restaurant Destination Current YM Applications Supply Side Customer Segments Demand Data Sources Decision Rules Prospects Very limited use Less standardised capacity units Heterogeneous providers First attempt to distinguish segments Variable demand Variable duration POS Observations Surveys Management s reaction on identified peak and low hours Increasingly used by larger restaurants and chain restaurants Complexity * used when tourist destinations are managed like a single corporation Complex bundle of different products and services by different providers Comparable to accommodation Variable demand Variable combinations POS PMS Sophisticated methods + Conjoint Analysis Inreasingly used with the introduction of single destination management companies Fig. 2b. YM in Tourism Sub-branches (cont.) 5 Conclusion Acknowledging the important interrelationship of revenue management based on price and/or product discrimination and customer satisfaction, this paper identified the existing data requirements, decision algorithms, and limitations of current yield management tools. The specification of additional information processing requirements can however only serve as a starting point for further research. Based on the findings of this work, an assessment has to be made as to how far the deficiencies can be corrected by combining yield management with other technology-based industry solutions, such as client data management systems. References Baker, T. & Collier D. (1999): A Comparative Revenue Analysis of Hotel Yield Management Heuristics. In: Decision Science, Vol. 30(1), pp

10 Belobaba, P.P (1992) Optimal vs. heuristic methods for nested seat allocation. In: Proceedings of AGIFORS Reservations and Yield Management Study Group, Brussels, pp Britan, G. & Mondschein, S. (1995): An Application of Yield Management to the Hotel Industry Considering Multiple Day Stays. In: Operations Research, Vol. 43(3), pp Capiez, A. & Kaya, A. (2002): Yield Management, Customer Satisfaction and Performance in the Hotel Industry. Paper presented at the Fifth Biennial Conference Tourism in Asia: Development, Marketing & Sustainability. May 2002, Hong Kong SAR. Carù, A. & Cugini, A. (1998): Profitability and customer satisfaction in services: an integrated perspective between marketing and cost management analysis. Paper presented at the 5 th International Research Seminar in Service Management on Marketing, Strategy, Economics, Operations and Human Resources Insights on Service Activities, Aix-en Provence Coulter, K. (1999): The application of airline yield management techniques to a holiday retail shopping setting. In: The Journal of Product and Brand Management, Vol. 8(1), pp Desiraju, R. & Shugan, S. (1999): Strategic Service Pricing and Yield Management. In: Journal of Marketing, Vol. 63 (Jan. 1999), pp Kimes, S. (1989): Yield Management: A Tool for Capacity-Constrained Service Firms. In: Journal of Operations Management, Vol. 8(4), pp Kimes, S. (1997): A strategic approach to yield management. In: Yeoman, I. & Ingold, A. (eds) (1997): Yield Management Strategies for the Service Industries. London: Cassel. Kimes, S. (2001): Forecasting for Hotel Revenue Management Testing Aggregation Against Disaggregation. In: Cornell Hotel and Administration Quarterly, August 2001, pp Kimes, S. (2002): Perceived Fairness of Yield Management. In: Cornell Hotel and Restaurant Administration Quarterly, Vol. 35(1), pp Kimes, S.; Barrash, D.; Alexander, J. (1999): Developing a Restaurant Revenue-management strategy. In: Cornell Hotel and Restaurant Administration Quarterly, October 1999, pp Kimes, S. & Wirtz, J. (2002): Perceived fairness of demand-based pricing for restaurants. In: Cornell Hotel and Restaurant Administration Quarterly, Vol. 43(1), pp. 31. Kong, N. (1993): Front Office Management, 3rd ed. Leeuwarden. La K.V. & Kandampully J. (2002): Electronic retailing and distribution of services: cyber intermediaries that serve customers and service providers. In: Managing Service Quality, Vol. 12(2), pp Pullmann, M. & Norre, W. (1998): Service Capacity Planning with Conjoint Analysis: Combining Marketing and Operations Perspectives for Profit Maximization. Paper presented at the 5 th International Research Seminar in Service Management on Marketing, Strategy, Economics, Operations and Human Resources Insights on Service Activities, Aix-en Provence Varki, S. & Colgate, M. (2001): The Role of Price Perceptions in an Integrated Model of Behavioral Intentions. In: Journal of Service Research, Vol. 3(3), pp Weatherford, L. (1995): Length of Stay Heuristics. Do They Really Make a Difference? In: Cornell Hotel and Restaurant Administration Quarterly, December 1995, pp