Revenue management

Revenue management (RM) is a widespread practice in service companies that sell non-storable products (transportation, accommodation, cultural and leisure activities, etc.). However, scientific literature does not offer a precise definition of this concept, which belongs to air transport economics, and of the related challenges. In addition, a survey of tourism and hospitality professionals (Meatchi, 2019) shows that tourism professionals often have a fragmented view of the RM. Some see RM as a simple tactic of increasing or decreasing prices based on supply and demand. Others refuse to use this technique considering it unfair for customers and risky for the company’s image (Sahut et al., 2016). However, numerous scientific studies show that RM is an interesting strategic approach allowing tourism businesses to maximise their occupancy rate and revenue by mobilising certain pricing levers and capacity management (quantity of services offered). This aim of this article is to propose an integrative conceptualisation of RM based on an analysis of the challenges of this practice, which is strategic for companies dealing with limited capacity and highly fluctuating demand.

To this end, a historical study of RM practices is carried out in order to identify the factors that have contributed to the emergence and development of this managerial practice. Then, based on academic literature and speeches by professionals, an integrative definition and a holistic conceptualisation are proposed. Finally, the challenges of RM for tourism businesses in general and for small and medium-sized enterprises (SMEs) in particular are analysed.

Origins of the concept of revenue management: from Yield Management in the 1980s to Total Revenue Management today

Revenue management was invented in the United States following the deregulation of air transport in 1978. American Airlines was behind the first revenue management techniques, known at the time as yield management. This technique (now called revenue management) was designed by American Airlines to optimise its marginal revenue through a flexible pricing system.  This allowed it to face competition from charter airlines that grew following the deregulation of the American air market in the late 1970s. Through revenue management (RM), American Airlines also increased its revenue by USD 1.4 million over three years (Kimes and Wirtz, 2015). In view of the results obtained by American Airlines using variable prices per unit sold, the other major airlines followed suit by creating their revenue management system. Over time, new levers were added, delivering increased revenue for service businesses. The term “yield management” (unit revenue management) had then given way to “revenue management” encompassing yield management and other strategic levers such as digital distribution, customer segment management and the optimisation of routes (for airlines) or of length of stay (for hotels).

Today, big data is used to forecast demand and these forecasts inform the decisions and actions of managers on a daily basis and guide the implementation of revenue optimisation levers. The practice of RM is also backed by highly advanced technological and IT tools (e.g., RMS), by robust probabilistic models (e.g., the EMSR model), and increasingly by artificial intelligence techniques (Meatchi and Camus, 2020). These models are necessary for a better knowledge of the consumer-customer, finer demand segmentation, real-time capacity adjustment in order to allocate the right price to the right customer at the right time (Abrate, Nicolau and Viglia, 2019). According to Legoherel and Poutier (2017), RM must be total and must be based on the search for revenue maximisation by combining cross-selling of different services within the same profit centre.

Since its beginnings, RM has relied on technological developments in information systems and the development of models to forecast demand and support decision-making in capacity allocation. The use of RM requires somewhat sophisticated technological tools (e.g., an Excel macro, RMS, machine learning tools, etc.) allowing a detailed analysis of past and future data (change in demand, a recurring yearly event, etc.) and accordingly the determination of a forecast allocation by fare class (Ill. 1).

RM is highly beneficial for service companies as it represents a vital tool to optimise the company’s overall profit. The practice of RM has now has gained strong momentum, suggesting a widespread recourse to this technique in service companies in general and in the tourism and hotel industry in particular.

Clarification and definition of different concepts: revenue management, yield management and pricing

In the scientific literature as well as in the lexicon of tourism professionals, yield management or revenue management are used without any real distinction in substance. The term yield was originally used to refer to revenue per mile and per seat in air transport. According to Talluri and van Ryzin, (2004), RM can be defined as a management practice using a systematic approach to overall revenue optimisation by setting prices and managing product availability in accordance with the behaviour of demand and customers’ willingness to pay a given price. According to Ng, Rouse and Harrison (2017), although this definition is clear, there is still no consensus on the meaning of the term revenue management or ‘RM’.

Many other definitions exist in the literature. Littlewood’s seminal work (1972) introduces the idea of maximising revenue for a given capacity rather than maximising the occupancy rate of an aircraft in the context of the liberalisation of the air market in the United States. Weatherford and Bodily (1992) restrict the meaning of RM to techniques used to determine the quantity of inventories (offers) to be made available to customers based on moments in a day, or days in a week, or seasons in a year, etc. Other researchers (e.g., Beluze and Guilloux, 2002; Talluri and van Ryzin, 2004) adopt broader definitions of RM including overbooking, forecasting, managing length of stays and itineraries, customising the product to customers, and managing business risks. It should also be noted that there are significant ambiguities between the terms yield management and revenue management, on the one hand, and between revenue management and pricing, on the other. Based on a systematic literature review and surveys of professionals conducted as part of Meatchi’s doctoral thesis (2019) the following definitions have been proposed:

• Revenue management is an overall management strategy that combines forecasting, inventory control and pricing in service companies with perishable assets and limited capacity (offers not scalable in the short term) and subject to fluctuating demand. In an article on revenue management taxonomy, Weatherford and Bodily (1992) analysed the characteristics common to all RM levers, namely capacity optimisation, overbooking and pricing. The authors combined all the areas of perishable asset management under one concept that they call Perishable-Asset Revenue Management (PARM). This conceptualisation gave birth to the term revenue management that has come to prevail in scientific literature and in the lexicon of professionals. Today, RM integrates new digital tools (channel management) as well as those from data sciences (data modelling) and artificial intelligence such as machine learning (Buckhiester, 2011).

Revenue management is an overall strategy for forecasting and inventory control, pricing, distribution and revenue in the services sector with limited capacity.

Buckhiestern, 2011

• Yield management is a component of revenue management. The aim of yield management (YM) is the management of unit revenues through optimal capacity allocation by fare class. In other words, yield management (or management of unit revenues) is defined as a tactic that seeks the best possible yield for each unit of product available (Autissier, 2000). Yield management is not synonymous to RM but a lever of RM. A summary of the definitions proposed by Selmi (2007) shows that yield management is mainly concerned with the management of unit revenues. The global vision of RM (forecasting, optimisation and control) combining pricing management and other levers such as distribution channels is less prevalent in the yield management approach. In view of current developments (total revenue management, revenue management integrity, etc.), RM and yield management should no longer be used as interchangeable concepts. Yield management should be considered as a component of RM. Yield management is mainly concerned with the management of unit revenues while RM deals with the overall and combined strategy of forecasting, optimisation, and control of the performance of actions linked to capacity and pricing management. Yield management deals with management in real time and in the very short term while RM is part of a global and strategic medium- and long-term vision.

The aim of yield management is the management of unit revenues through optimal capacity allocation by fare class.

Autissier, 2000; Guilloux, 2000

• Pricing is another component of revenue management. The aim of pricing is to create, organise and manage the pricing policy and pricing schedule according to the overall targets set within the framework of RM. In service companies with limited capacity, pricing (component of RM) is an alternative to traditional pricing methods such as cost-plus pricing or value-based pricing. According to Roquefort (in Legohérel and Poutier, 2017), pricing is the major profit lever of revenue management but it is also the most complicated to understand. It depends on large amounts of data and particularly on customers’ psychological factors. English-speaking researchers generally use revenue management pricing (RMP) terminology to talk about pricing or pricing policies in the context of revenue management. It is to be noted that RM is a strategy that goes beyond pricing and yield management, and encompasses other areas such as overbooking, distribution and business risk management.

The aim of pricing is to organise and manage the pricing policy and pricing schedule according to the overall targets set within the framework of RM.

Heo and Lee, 2011

RM challenges for service companies

Service companies with limited capacity (airlines, hotels, camp sites, ski resorts, theme parks, etc.) are usually characterised by high fixed costs (e.g., maintenance costs of an aircraft, hotel, cruise ship, ski resort, etc.) and relatively lower variable costs (Capiez, 2003). These businesses offer perishable and non-storable products. Demand is generally very erratic. It can vary greatly from one period to another, from one day to another or from one hour to another in the same day. A hotel may refuse customers on certain days (demand greater than supply) and lack customers on other days (supply greater than demand).

In addition, the turnover of a hotel located in a seaside town can increase two-fold depending on the season (high season versus low season). The occupancy rate of a train varies significantly according to the days of the week and even according to the hours of the same day. Faced with these various constraints, it is difficult to apply a rigid pricing system, as is done in the industrial and trading sectors.

But given the free pricing set out in the laws in force (e.g., Article L. 410-2 of the French Commercial Code), RM offers solutions that allow service companies with limited capacity to tackle the specific constraints of their market. RM has efficient tools that can be used to adjust inventories and prices according to demand and other factors such as competition, events scheduled in the year, weather, etc. In the tourism sector, a hotel can, for example, offer rooms at higher prices during the peak season in order to maximise revenue or to limit the demand it cannot meet. During the off-peak season, the same hotel can offer lower prices to stimulate demand. The practice of RM is therefore a crucial issue for hotel companies, which generally have high fixed costs, limited capacity and irregular customers. The adoption of RM has allowed many companies to substantially increase their revenue. According to a Wall Street Journal article, the adoption of RM by the American company Continental Airlines led to an increase in its profit from USD 50 to 100 million in the 2000s (Weatherford and Kimes, 2003).

Conclusion

RM is undoubtedly one of the most significant innovations in tourism management over the past decades. The objective of this article was to propose a conceptualisation and an integrative definition of the concept of revenue management (RM). A historical study of revenue management practices traced the origins of this practice and identified the factors that contributed to its development. From a theoretical point of view, based on a synthetic literature review, it has been possible to take stock of the knowledge on revenue management and propose an integrative definition of this concept. This new conceptualisation is important because it allows a more consensual definition of RM and highlights the links and differences with related concepts such as yield management and pricing.

The conceptual aspects of RM and its highly technical vocabulary are not much addressed in tourism research. Hence it is not always possible to understand the links and differences between the notions of revenue management, yield management and pricing. Defining and explaining these key concepts in tourism management makes them easier to understand. At a managerial level, this article offers professionals a more precise vocabulary by clarifying the different concepts of revenue management. It overcomes the misconceptions that RM is merely about increasing or decreasing prices in accordance with supply and demand. It is actually a very broad process that ranges from sales forecasts to performance monitoring, encompassing the optimisation of prices and capacity offered (airplane seats, hotel rooms, camping space, etc.). Revenue management is based on a set of levers and well-structured models (pricing according to days of the week, sales history, events scheduled in the year, etc.).

Sourou MEATCHI

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