Executive Summary
The paper analyzes the extant academic literature on revenue and hospitality management in order to develop five tactics that can be used by a revenue manager of Swissotel Sydney for improving the company’s bottom line. The first section of the paper overviews the subject of revenue management in hospitality. It is followed by a discussion of a distribution channel-based strategy. The third section presents an innovative pricing strategy.
The next section discusses the implementation of a strategy based on the use of big data. The fifth section elaborates on a solution for optimizing OTA commission fees. A section that follows presents market basket analysis as a strategy for effective price promotion. The final section underscores the importance of revenue management and recounts proposed strategies.
Introduction
Revenue management (RM) in the hospitality sector is a recent trend, which is associated with forward-thinking organizations that are willing to generate greater returns on investment, thereby gaining a competitive edge in a business environment that is characterized by a high level of rivalry. RM refers to a wide range of practices aimed at the prediction of consumer behavior and maximizing revenue growth (Hayes & Miller, 2011).
A large body of research, generated over the past ten years, shows that RM in the hotel and lodging sector is at a more advanced stage when compared with the airline and spa industries (Guillet & Mohammed, 2015). The majority of the hospitality RM studies is focused on pricing and customers’ reaction to RM activities, which indicates that customer-centric RM is essential for the creation of long-term value for companies.
The aim of this paper is to present a business proposal for improving RM in Swissotel Sydney, which is “a luxury five-star hotel” that offers its visitors “369 well-appointed rooms and suites” (Swissotel, n.d.b, para. 1). The hotel’s revenue centers include room service, business center, meeting rooms, dry cleaning, laundry service, minibar, restaurant, fitness center with gym, pool, bar, airport transportation, conference facilities, hot tub, and spa (TripAdvisor, n.d., para. 4). The paper will outline the following RM strategies: distribution channel-focused strategy, pricing strategy, big data strategy, strategy for optimizing OTA commission fees, and market basket analysis.
Strategies
Distribution Channel-Focused Strategy
Hoteliers recognize that a value message can be communicated to their customers through a variety of distribution channels, which can be defined as “a source of business customers or the vehicle to communicate with a source of customers” (Hayes & Miller, 2011, p. 111). The growing popularity of Internet-based business solutions has resulted in the transformation of distribution channel strategies in the hospitality industry (Law, Leung, Lo, Leung, & Fong, 2015).
Swissotel Sydney has not been an exception to the trend. The company increasingly relies on online distribution, which is more efficient in comparison with traditional approaches to distribution. Furthermore, with the widespread use of mobile devices, the company has launched a mobile hotel booking (MBH) app, which is known to for its ability to attract new customers and engender favorable attitude towards the brand (Swissotel, n.d.a; Ozturk, Nusair, Okumus, & Singh, 2017).
Unfortunately, the company’s presence on major social media is limited to three platforms: Facebook, Twitter, and Instagram (Swissotel, n.d.a). Considering the importance of social media in the decision-making process of modern travelers and a comparatively low cost of each channel, it is necessary to improve Swissotel Sydney’s customer reach by broadening its distribution landscape. To this end, the company has to extend its online presence to the following social media platforms: LinkedIn, Pinterest, YouTube, and Yelp. Also, the company needs to optimize its booking conversion rate by better positioning content on its online platforms (Duetto, n.d.; Leung, Law, Hoof, & Buhalis, 2013).
There is ample evidence suggesting that hotel booking decisions are influenced to a great extent by hotel reviews (Casalo, Flavian, Guinaliu, & Ekinci, 2015; Mauri & Minazzi, 2013). It means that the main benefit of the solution is the positive effect on the hotel’s popularity, which will translate into higher revenues.
Also, the use of social media channels is an effective method for reaching target customers. The strategy can be met with opposition based on the necessity to hire a social marketing manager. However, this objection can be neutralized by showing that unlike traditional approaches to marketing, social marketing campaigns can be conducted on a budget that ranges from $2, 000 to $12 000 per month (Jade, 2016). It has to do with the fact that the management of social media distribution channels can be outsourced to third parties.
Given that the revenue manager has worked with companies specializing in the customization of content, it will be easier to find a suitable contractor for outsourcing social media marketing efforts. Furthermore, taking into consideration the revenue manager’s experience with using Sprout Social, which is a tool for organizing social media platforms, it is possible to cut the budget for strengthening the company’s online presence to $5 000 per month.
The money will be used for purchasing a subscription for social media management package, Raven Tools, which costs $249 per month (Raven Tools, 2017). The package will allow tracking online conversations related to Swissotel Sydney by analyzing keywords, which is essential for maintaining a positive image on social media. The rest of the budget will cover content marketing, which will be outsourced to Wpromote. The company will help to create “a content-rich experience for the user” (Wpromote, n.d., para. 4), thereby making sure that Swissotel Sydney receives more online traction and additional transactions. Appendix A shows the task schedule for the implementation of the social media project.
Pricing Strategy
During the last decade, many hoteliers have switched to dynamic pricing strategies in an attempt to maximize profits. However, despite the fact that such strategies are based on both strategic and tactical dimensions of the business, they are not capable of taking into consideration ongoing market conditions with a high degree of efficiency (Abrate & Viglia, 2016). Therefore, Swissotel Sydney should modify its dynamic pricing strategy with the help of an automated competition-based approach.
Currently, the hospitality industry utilizes the following approaches to pricing strategies: “demand forecast, the elasticity of demand in the market segment, and competitive rates” (Noone, 2016, p. 4). As is shown in Figure 1, even though there are substantial variations between engagement in the demand-based and competition-based RM between different star-level hotels, almost all of them make use of competition information.
Unfortunately, the analysis of competitive rates is often highly inefficient; therefore, Swissotel Sydney should utilize a ‘best-response’ pricing algorithm proposed by Fisher, Gallino, and Li (2016). The algorithm relies on observational data that can be obtained from competitors’ websites with the help of simple software applications. After testing their approach to dynamic, competition-based pricing, researchers argue that the use of the algorithm is associated with 11 percent revenue improvement (Fisher et al., 2016). Given the efficiency of the algorithm, there should not be any objections to its use.
However, if the revenue manager faces any opposition, it is necessary to point to the fact that properly managed idiosyncratic price movements are associated with better hotel performance (Kim, Lee, & Roehl, 2016). Figure 2 shows that there is a substantial difference between the expected profit (Y*) under the condition of high price variability (Pa*-Pb*) and the expected profit (Y) under the condition of low price variability. (Pa-Pb).
The findings of recent research can also be used to modify the company’s pricing strategy. Abrate and Viglia (2016) suggest that tangible attributes such as the number of rooms and the room size can become a tactical instrument in determining price under a dynamic pricing model. It means that whenever almost all Easy Life rooms, which is a standard option in Swissotel Sydney, are occupied, the hotel might remove the availability of the rest of them in favor of Premier, Swiss Advantage, and Pinnacle rooms. This is an extremely profitable strategy since the difference in room prices exceeds 50 percent (Swissotel, n.d.c).
The revenue manager does not have relevant experience for the implementation of the solution; therefore, an external contractor should be hired. Based on the calculation of cost estimates conducted with the help of an online Leap Platform application, a monthly budget for the project should not exceed $910 (HMWMSC, n.d.).
Big Data Strategy
Swissotel Sydney should make use of big data in order to facilitate its RM decision-making process. The development of big data analysis methods makes the process of competitor data collection and processing much easier, thereby rendering previous techniques such as the STAR report obsolete (Haynes, 2016). The output of big data analysis can be used to transform competitor data into actionable RM knowledge. Unfortunately, many revenue managers admit that “they are only scratching the surface in terms of exploiting the available big data” (Tracey, 2015, p. 6).
A cloud-based software solution Demand360, provided by TravelClick, is capable of transforming traditional methods of RM analysis that are based on the evaluation of “the long-term dynamics of RevPAR, ADR, occupancy, GOPPAR, and other RM metrics” (Ivanov, 2014, p. 72). Demand360 allows revenue managers to take a holistic approach to the data analysis process, which is essential for smart RM.
The solution relies on information that is regularly collected from more than 22, 000 hotels located in 176 countries (TravelClick, 2017a). Moreover, a recent collaboration with Duetto has provided TravelClick with access to an additional pool of historical reservation data, which can be used by the revenue manager to better understand competitiveness in their market (Frenkel, 2017). The data generated by the software solution are grouped into the following categories: “group, transients, retail, discount negotiated, wholesale, and qualified rate” (TravelClick, 2017b, para. 5).
Demand360 uses the following channels for data collection: web, Central Reservations System (CRS), Global Distribution System (GDS), an online travel agency (OTA) reservations. Furthermore, valuable information is also gathered from travel websites such as Expedia, Orbitz, and Hotwire, among others.
Despite the fact that the revenue manager is not familiar with Demand360, they have experience working with data analytics (DA) technologies. Therefore, no difficulties should be encountered during the process of implementing the solution. A budget for the implementation of the project is in line with the cost of other business intelligence solutions and will range from $1 500 to $5 000 per month (Software Advice, 2017). Given the low budget of the project and the excellent reputation of TravelClick, no one should oppose the solution.
Optimization of OTA Commission Fees
The rapid advancement of information technology (IT) has introduced booking through OTAs. Many hotels establish cooperation with OTAs in an attempt to attract more customers (Ling, Guo, & Yang, 2014). However, taking into consideration the realities of the highly competitive market, some OTAs resort to charging a premium for top slot placement, which is associated with higher booking rates (Guo, Zheng, Ling, & Yang, 2014). Figure 3 shows the relationship between a position on a search page and a propensity of booking.
It is clear that each position on a search list is associated with a certain click-through rate (CTR), which translates into monetary value for a hotel. However, it is not an easy task to determine an optimal maximum commission fee (MCF) per search engine improvement. Swissotel Sydney also uses the services of numerous OTAs such as Priceline, Booking, OneTravel, and Expedia. The effective collaboration with such intermediaries presupposes commission fees for sold rooms.
The findings of a recent study can provide the revenue manager with an effective tool for optimizing OTA commission fees. Rest et al. (2016) argue that it is possible to establish a numerical relationship between search position placement and booking probability. The researchers have developed a method, which is based on conjoint analysis, that can be used for effective calculations of MCF per search slot. The revenue manager can use the method not only to determine how much Swissotel Sydney should pay for better placement but also to assess “how much a lower room rate can compensate for a higher slot commission” (Rest et al., 2016, p. 179). This innovative approach will allow generating higher access demand.
If the hotel’s management raises objections against this RM strategy, it is necessary to show them that the solution can be realized on a minimum budget by establishing synergy between the conjoint analysis and analytic capabilities of Demand360, which have been discussed in the previous section of the paper. The revenue manager has relevant experience of working with DA technologies and can implement the solution. Appendix B shows the task schedule for the implementation of the social media project.
Market Basket Analysis
It is extremely important for the luxury hotel industry to sustain its value proposition. In an attempt to attract more customers, Swissotel Sydney resorts to the use of price promotion through Priceline, Hotwire, and other discount websites. There is ample evidence suggesting that such a strategy harms the quality perception of luxury services, thereby diminishing revenues (Yang & Mattila, 2016; Yang, Zhang, & Mattila, 2016).
It has to do with the fact that consumers with a different need for status (NFS) indexes have opposite perceptions of luxury characteristics of a hotel when they encounter price promotions. Namely, customers with a high NFS index associate luxury hotels with their identity; therefore, they do not like discounting practices.
It would be unreasonable to entirely abandon the strategy of price promotions; therefore, it has to be modified. The proposition is to apply Market Basket Analysis and clickstream DA to discounting practices of Swissotel Sydney. These analyses are commonly used by retailers to predict customer choices (Alswiti, Alqatawna, Al-Shboul, Faris, & Hakh, 2016; Solnet, Boztug, & Dolnicar, 2016).
Similarly, they can be used by the hotel not only to predict additional products and services that can be offered to its customers but also to recognize those clients who should not be offered price discounts. It will allow attracting more consumers with low NFS indexes while not engaging those individuals whose high NFS indexes might prevent them from booking a room. In addition to Market Basket Analysis and clickstream DA, socio-demographic and geographic data can be used to optimize price promotion practices of the hotel.
The revenue manager does not have relevant experience in order to implement the project; therefore, an external contractor should be hired. A budget for the project is approximately $990 per month (Dubois, n.d.). In order to eliminate objections against the use of these DA technics, it is necessary to mention that both of them were successfully applied in a hospitality context (Solnet et al., 2016).
Conclusion
This proposal will become an invaluable instrument in a hotelier’s arsenal and will allow them to increase both the net revenue and the gross operating profit of their business. The paper has outlined the following RM strategies, which can be effectively used by Swissotel Sydney to improve its standing in the highly competitive market: distribution channel-based strategy, pricing strategy, big data strategy, OTA commission fees optimization strategy, and market basket analysis.
Appendices
Appendix A
Task Schedule for Distribution Channel-Focused Strategy
Appendix B
Task Schedule for Optimization of OTA Commission Fees
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