Executive Summary
Park Hyatt Hotel is one of the leaders in Australia’s hospitality industry. Nevertheless, it has received some criticisms regarding its pricing strategy and saw a minor decline in profitability. To address the situation and retain competitive advantage, five revenue management strategies are suggested for implementation.
The strategies include redistribution of the marketing effort between the online and offline service distribution based on the yield of customer segment, the introduction of dynamic value-based prices, the expansion of the data collection process, partnering with a data analysis service to gain access to real-time updates for improved response time, and the increase is service transparency for customers through a dedicated platform. The combined effect of the said strategies is expected to include the diversification of the customer base, the increased loyalty of the existing clients, and the optimisation of resource allocation.
Introduction
Park Hyatt Sydney is a luxury five-star accommodation that is widely recognised as one of the leaders in Australia’s hospitality industry. The hotel offers high-grade services, a variety of rooms and suites, a gym, a spa, and numerous design solutions that indicate a persistent striving for top quality. The hotel enjoys favourable reviews from both the visitors and the press (Schlappig, 2015). Nevertheless, the latest report reveals a noticeable decline in revenues starting in 2014 (Hyatt Hotels Corporation, 2017).
In addition, the company received minor criticisms regarding its pricing policy oriented towards a higher-income segment of the population (Luxury Travel Expert, 2014). Such setting noticeably undermines the company’s reputation and can lead to its loss of competitive advantage in the long run (Van der Wagen & Goonetilleke, 2012). It is thus suggested to review the current revenue management approach and introduce several strategies that can improve the situation.
The current proposal contains five strategies that address the existing inconsistencies in the hotel’s policies and offer new tactics for enhancing customer satisfaction rates. The emphasis is made on distribution channels and pricing, with analytics, transparency enhancement, and forecasting assigned secondary priority. Distribution channel-oriented strategy provides additional means of diversifying the customer base as well as reaching the feasible new audiences. The pricing strategy focuses on the mechanisms of determining the costs of services and, in this particular case, adjusting them to the immediate conditions of the market (Armstrong, Kotler, Harker, & Brennan, 2015).
The analytics and forecast strategies outline the approach to data processing and utilisation and identify the necessary resources. Finally, the transparency strategy encompasses a broad range of tactics that collectively contribute to the improved trust among the stakeholders. The business proposal contains a brief description of five strategies which are interconnected and mutually supportive, outlines the task schedule and an approximate budget for their implementation, and addresses the most evident criticisms of the intervention.
Detailed Solution
In order to achieve the identified goals and minimise the adverse impact of the issue, five strategies have been devised by our team. The strategies address a wide array of operations and are thus expected to provide an overall improvement in quality, increase customer satisfaction, and, by extension, create a stronger, more loyal customer base.
Distribution Channel-Focused Strategy
Distribution of rooms within a hotel is among the factors that determine the sustainability of the revenue flow. A review of our current distribution channel approach revealed several minor shortcomings that have been addressed in the suggested strategy. First, it would be necessary to optimise the existing business mix to the distinct audiences that comprise our current customer base. In the broadest terms, two customer segments can be identified – high-yield and low-yield customers. Regardless of the proportion of each segment, several trends should be taken into account. For instance, low-yield customers tend to plan their recreational activities in advance.
Therefore, they book the rooms early. Importantly, they also keep a steady booking rate throughout the year. The high-income segment, on the other hand, often books their rooms late (down to several days before arrival) and often operates through online travel agencies. Finally, they tend to book the rooms closer to the hot travel season. By extension, it is possible to state that the former use offline distribution channels (e.g. wholesalers) while the latter are more likely to book through online ones (e.g. company’s booking service and online agencies).
With the current strong shift to online functionality, it is tempting to consider online distribution a perspective investment and gradually move away from its offline counterpart. Instead, we suggest redistributing the marketing effort by catering the offline distribution channels to the lower-yield segment. This can be done through a stronger emphasis on discounts, special offers, and bundled offers.
In addition, it would be reasonable to review the offline channel for suitability for an older population, since they are less likely to utilise online services (Leeflang, Verhoef, Dahlström, & Freundt, 2014). Simultaneously, a property management tool should be tested for compatibility with new requirements (e.g. the ability to book rooms based on the predicted customer activity and the availability of offers for both segments.
Market Research Strategy
In its current form, the data obtained from guests is the main source of business data. While it is a proven concept that allows for a reliable picture of customer perception of the quality of our services, it does not include an important segment of customers, namely those who considered booking a room and decided against the idea. In fact, this lost segment maybe even more important since it can offer insights into the ways of expanding our customer base.
Admittedly, the most probable reasons for their decision are the absence of rooms on a specific date and the high prices. However, it would be beneficial to match these reasons with the demographics of the customers. Therefore, an analytical suite should be developed and incorporated into our website and, if possible, combined with the tools used by our major partners. In this way, we would be able to correlate the demographics with the common decisions, estimate the losses associated with each group, and evaluate the viability of adding the lost customers to the business mix.
The data collection process can be done both through a streamlined, voluntary survey prompt introduced before leaving the website or through one of the available analytical platforms. The latter is less intrusive but requires a disclaimer to comply with the current privacy regulations. Importantly, the described strategy can be effectively combined with the distribution channel strategy described above. Specifically, the data obtained from the lost clients can be synchronised with the characteristics of low-yield and high-yield segments to verify and further refine the marketing strategies.
Pricing Strategy
As is common among the luxury hotels, Park Hyatt Sydney incorporates the value-based pricing model (Hyatt Hotels Corporation, 2017). While it provides significant revenue opportunities, especially considering the proportion of high-yield customers, it also compromises the system’s flexibility (Töytäri, Rajala, & Alejandro, 2015). Therefore, we suggest implementing a dynamic pricing strategy. Specifically, it is recommended to adjust the prices based on the combination of the value of the service and its current availability. One of the advantages of such a strategy would be the number of resources necessary for the transformation (Nagle, Hogan, & Zale, 2016).
The new strategy is noticeably similar to the value-based model currently adopted by Park Hyatt Sydney and would require relatively minor adjustments. At the same time, the factors determining the price of the offering would include the availability of rooms based on the major factors (e.g. the season and annual trends) and minor changes in customer behaviour (e.g. the aggregated demand of the last several days) (Anderson & Xie, 2016). Importantly the suggested dynamics have to be transparent to the customer in order to eliminate misinterpretation of changes in prices. This is especially relevant for the lower-yield segment that is more sensitive to the alterations in pricing (Tan & Dwyer, 2014).
Thus, they should be informed about the driving factors and, in the long run, presented with the opportunity to model the possible variations depending on the conditions via online functionality. It is important to understand that the described strategy requires significant technological investment due to the introduction of the analytical platform with data processing power sufficient for timely solutions (Law, Buhalis, & Cobanoglu, 2014). In addition, it is possible that some customers can be confused by the innovation, in the long-term perspective, the approach would create a self-regulating mechanism ensuring the optimisation of prices both to those offered by the competitors and to the expectations (and paying capacity) of the audience.
Forecasting Strategy
As was mentioned in the previous segment, the successful implementation of the dynamic pricing strategy requires the presence of a strong data analysis platform. The hotel’s current approach relies on the analysis of the available historical data combined with the standard informatics offered by the online solutions (Hyatt Hotels Corporation, 2017). However, in order to provide a reliable basis for the dynamically adjusted value of services, it would be necessary to introduce real-time updates.
The proposed forecasting strategy includes partnering with a data-crunching platform that can offer an integrated solution for multiple types of data (Li & Jiang, 2017). Importantly, the processed data must be readily available to the hotel workforce in a concise and accessible format. Therefore, the results should be selectively delivered in the form of personal notifications to the employees. This element would ensure responsiveness to the needs of the customers and, by extension, would improve customer retention rates (Chathoth, Ungson, Harrington, & Chan, 2016).
Besides, the strategy is expected to solve the incompatibility of certain types of data. For instance, the hotel management could identify minor trends in customer behaviours and make timely adjustments in staffing and resource supply.
Finally, such an approach could improve communication between hotel departments. In its current state, several of the hotel’s departments, such as operations, events, cleaning restoration, reservations, receptions, and foods and beverages receive updates from the accounting and HR departments, which can be complex enough to create a comprehension barrier (Jones, Hillier, & Comfort, 2016). A more approachable format can be developed as a part of the strategy, leading to fewer difficulties in understanding and, by extension, greater commitment among employees.
Service Transparency Strategy
Once the data delivery method described above is established, it becomes possible to introduce the customers to the system. While it would be unreasonable to expose them to the company data, some areas of operations can and should be available to the customers (Neirotti, Raguseo, & Paolucci, 2016). Thus, a strategy is recommended that would enable them to request, monitor, and leave feedback on the services offered by the hotel departments.
Specifically, tablet PCs can be installed throughout the hotel that would display information on the pending services. The tablets would be accessible for the customers, who would be able to leave requests on which the workers would receive notifications. In addition, the ability to rate the quality and leave valuable feedback can be incorporated into the platform. Admittedly, this strategy requires significant expenses both for equipment purchase and the staff training (Dhar, 2015).
However, once it is properly integrated into the hotel’s operations, it would improve trust on the part of the customers, enable communication between them and the company’s management, and make the entire process a seamless experience with a clearly observed process (Wang, Law, Hung, & Guillet, 2014). For the lower-yield segment, it will enhance the value of services by making the process more customer-oriented (Horner & Swarbrooke, 2016). For the higher-yield customers, it will ensure greater responsiveness to their individual needs. In addition, it can resolve certain issues with erroneous negative feedback, leading to a healthier workplace climate.
Benefits
As can be seen from the description, four of the five strategies are oriented primarily at the diversification of the customer base and more precise targeting of the marketing campaign. However, other improvements can be anticipated after their introduction. First, the services tracking platform would signal to the customers the readiness to report on the quality of the services, and, by extension, strengthen trust in the company.
Next, the dynamic pricing would decrease the losses of potential profits as well as open up the possibility to attain the customers from previously untapped areas. A more powerful analytical platform would ensure optimal allocation of resources, leading to reduced expenses and, to some extent, higher environmental friendliness. Finally, the employees of the company could benefit both from a more streamlined working process and the improved communication with the management.
Task Scheduler
Since the market research and forecasting strategies serve as sources of data necessary for the changes in pricing and distribution channels, they should be prioritised in the implementation process. Evidently, some time must be allocated for the accumulation of data sufficient for a definitive conclusion (George, Haas, & Pentland, 2014). Therefore, an estimated three-month timeframe would be necessary before the latter can be rolled out. Importantly, the purchase of equipment for service transparency, the development of software solutions for data collection, and the training of employees would require six weeks. After this, the pricing and distribution channel-focused strategies can be launched immediately.
Budget
The most resource-demanding elements of the strategies are the purchase of new equipment, the cost of services of data-crunching platform, and the employee training costs, followed by the development of the software solutions. Therefore, the total cost of implementation within a single hotel is estimated at $1.5 million including the training expenses.
Relevant Experience
The most complex parts of the analysis will be done externally. Therefore, the relevant experience is within the areas of technical proficiency, forecasting, and customer-centred strategies. The success of the implementation process would also depend on the leadership and communication skills of the intermediate management segment.
Anticipated Opposition
The most likely causes of opposition are the high total cost of the implementation from the top management and the concerns for additional complexities emerging from increased transparency from the hotel employees (Lozano, 2013). The former can be minimised with the modelling of the long-term revenue growth that is expected to mitigate the short-term losses. The latter should be addressed through the communication of advantages in the form of increased convenience of routine operations and, possibly, through the introduction of incentives for positive feedback obtained through the new quality control system.
Conclusion
The introduction of the new strategies is a challenging process both from a financial and technological point of view. In addition, it may lead to dissatisfaction of employees early in the course of implementation. However, once the main difficulties are resolved, the proposed innovation will yield a range of benefits. Specifically, the improved forecasting process is expected to reduce expenses associated with inadequate resource allocation whereas the increased service transparency will minimize human error possibility and appeal to the customers, facilitating greater trust. The dynamic pricing approach would expand the customer base while at the same time reducing the loss of potential profits. Finally, a more reliable analytical platform will allow for a better understanding of customers’ needs and expectations. In combination, the proposed strategies will expand customer base and increase customer satisfaction rate.
Appendix A
Appendix B
References
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