Revenue management is interchangeably used with yield management. The term refers to the maximization of a company's revenue which is mainly brought about through optimization of the choice of customers to be served. To successfully manage yield and revenue, the selling procedures of any product must be timely, competitive in price and targeted to the right market niche. Yield management is vital in airline and hotel industry if they have to economically remain competitive since the business field is so much saturated (Zhao & Zheng 2000, p. 371).
It's the work of the yield manager to optimize revenue generated by each room or table allocated by ensuring that customers are well and timely placed, where the right pricing procedures are employed as well. It's imperative to understand why customers would choose a particular hotel over another and also to be determine group sales against the restaurants overall sales goals. According to Shy (2008, p. 126), those hotels whose aim is to increase occupancy would prefer hosting large groups at reduced rates while those whose objective is to optimize revenue may reject large group in preference to small parties whose revenue is high. These are some of the important things to be considered by every yield manager.
Revenue and yield management are vital in the airlines, hotels as well as car rental industries. For instance in restaurant, the restaurant managers may choose table allocation that accommodates the largest party per table in assumption that the larger the party size the more the total bill, hence increased revenue. It's vital to consider small parties as well when no large parties are expected in order to avoid empty tables. This raises the issue of managing the demand flow and optimizing tables' allocation among the customers. Revenue management in restaurants is vital as such efforts increases demand, and subsequently enhances the total restaurants performance levels (Philips 2005, p. 264).
Some restaurants rate their achievement using the perceptible capacity to accommodate many customers. This approach may not accurately best measure their revenue performance. One of the most common approach in revenue management in many hotels is the Revenue Per-every Seat (also referred to as RevPASH), normally availed as a performance metric. Other approaches include use of the capacity management science whereby the restaurant capacity and efficiency of various processes are assessed such that every production delivery and service process is monitored quantitatively with an aim to improve the satisfaction of the customers, workforce/employee as well as optimize on profit. RevPASH is basically built on two strategic levers namely the duration management and pricing approach which is usually demand-based. In this case, customers demand determines the prices to be set on a given service, and consequently influence the anticipated revenue (Lindvall 2003, p. 64).
Other approaches focus on minimizing the cost of operations, particularly the employees cost while still satisfying the diverse workforce needs. Other cost reduction procedures may include running of profit centers for each department, demand dispersion, reduction in activity duration/ hours of operation, and making operational process as well as other related procedures more efficient in order to decrease service time, thus increasing the number of clienteles served which translates to high revenues.
2.0 Customer Relationship Management (CRM)
All efforts to maximize on revenue/yield should be focused on effective CRM practices, where customer central-role is imperative in revenue generation. Customer is a valuable asset to any organization. In this case, companies should endeavor to carry out research and do analysis in target markets to enhance customer profiling which is basically segmentation and understanding of the various customers in terms of interest and needs. CRM enables a company to delineate and increase customers' value and motivation. A motivated customer exhibits unrelenting desires to purchase and thus boost firm's revenue. The main objective of CRM is to promote the customer-company relationship in order to maximize revenue, by perhaps allowing timely selling to the right clients via the right channels of distribution, and product positioning, among other factors.
Pricing strategy
According to Lindvall (2003, p. 43), customers pay different prices depending on location, size and hotels quality, authenticity or facilities offered and other services. This is called price dynamics. Hospitality companies must learn to tailor their pricing to meet the current demand and supply. Optimizing the pricing systems yields additional price gain that enables these companies to generate more revenue. These companies face pricing challenges since the business is very competitive and demand is constantly changing depending on economic conditions and other external/internal factors.
According to Philips (2005, p. 213), different models may be used in setting prices optimally. The challenge is determining if a given price at a predefined point fits a given situation and the customers' willingness to meet the consideration or pay for the services/products. This guides managers in setting differentials in the various products on offer to the clients. A common approach in pricing is the price gain approach where optimal price is determined through customers' willingness to pay for a particular brand, location, type of room, time of the year and payment conditions. Data on various variables is collected and statistically analyzed to draw meaningful information on customers' willingness to pay, with which one can improve on particular prices.
Discount allocation
According to Taljuli & Ryzin (2005, p. 561), strategies taken by any hospitality organization should be given thorough consideration to ensure that the entity does not gain-at the consumers' expense. This raises the use of discounts through menu adjustment, in the case of a restaurant, in order to add more value to customers. Discount allowance reduces on selling price and allows for competitiveness through menu engineering, this adds to revenue generation. It's a way of customers' compensation to counter any loss as a result of the restrictions imposed by the hotel. Substantial discount should be given to the customers in return for cancellation restrictions in order to collect that imbalance. Hotels can as well use restrictions to counterbalance any discount offered to the customers. However too strong restrictions upset the transactions balance but acceptable restrictions will enhance balance. For example, a moderate restriction applied on the minimum length of customers stay is acceptable.
Demand management strategy
Demand management and pricing are interrelated and should be coordinated if optimal revenue is to be achieved. Demand for a room in a restaurant is cyclic and follows a particular trend. In this case, the models used in revenue management ought to pinpoint demand by minimization of uncertainty and giving the best possible forecast. One basic concept to yield management is demand and supply principle. Fall in supply drives the prices up while a consequential rise in supply results to a drop in prices. Yield manager must learn to logically position their customers within the demand and supply spectrum (Philips 2005, p. 168).
Different segments should be used to meet the varying demand, where for instance, prices that are different can be used to segment the market and meet customers demand. To set the prices managers may rely on historical price performance in order to plan and balance future demand and supply levels. To remain in the stiff competition exhibited in this business, yield management techniques should be applied to the situations. As such, point-of-sales daily inventory channels can be used to respond appropriately to the buying patterns and supply/demand disruptions (Lindvall 2003, p. 28).
Capacity Management
This is a process of scaling the business down or up depending on clients needs or preference shifts. Proper demand management results a successful planning of business units and planned reduction on unnecessary surplus. Capacity management is not an easy task and it may cause marketing objectives to conflict with operational objectives. Under such considerations, prices variation should depend on demand and encouraging clients to use less crowded facilities or use them during less crowding seasons (Irene 2007, p. 120).
Challenges or problems in Revenue management
According to Yeoman & Beattie (2004, p. 187), techniques of revenue management and overbooking models applied aptly optimize revenue for the business in hospitality industry, but this goes hand-in-hand with some hindrances. The common obstacle includes revenue performance measurement as a major issue, occupancy rates and the yield measures which are influenced by external competition. As such hotels must segment their market and fix varying rates to accommodate various customers. The other challenge is pegged on the management bid to ensure that the revenue generated ought to remain at ideal levels. Additionally, differential pricing is said to be somewhat unfriendly to some customers. Based on this assumption, it's therefore fit for the business to strike a balance between short term revenue and enhancing customer loyalty. Notably, a challenge may come if the yield managers and other managers fail to strike a balance between revenue optimization and staff motivation.
Revenue Strategy
The goal of a revenue strategy is to achieve the optimal profitability level that can be achieved from a particular projected demand. This requires integration of the various departments involved in an entity. For example, in a hotel set-up various departments may include the kitchen, foodstuff-store, reservation and waiting room, among others. A successful revenue strategy ought to entail product definition, competitive benchmarking on fair pricing strategy, demand forecasting, business mix manipulation and distribution management procedures (Zhao & Zheng 2000, p. 378).
3.0: Revenue and Yield Management Basic models: The Case of hospitality Industry
There are various mathematical-based approaches that may be applied in sophisticating revenue optimization in the account of the expected time of waiting as well fair handling of customer needs in a bid to raise yield levels on daily basis. According to Yeoman & Beattie (2004, p. 193), models used in the optimization can be classified into two where the first class of models use dynamic programming, integer programming and stochastic programming methods. The second category uses the gradient algorithm in making decision, in regard to reservation acceptance. After deriving the arithmetic, deliberate efforts are instituted to incorporate these reservations in a model that is dynamic, analytical in order to effectively analyze customers' booking patterns. Any flaws are attended in a bid to boost reservation revenues/yields. The value of parameters used in these models is identified by use of best fit or regression approach to predict the anticipated customers' reservation and booking profiles.
3.10 Integer programming Approach
The model aims at maximizing the future expected revenue and control of waiting time expected in deciding where and when to allocate seat for each incoming customer, perhaps during peak season. Equal length of time period are used, the model assume that total revenue arising from each party increase as the party size increase. According to (Taljuli & Ryzin, 2005 p.346), better estimates of service time are made by breaking the service time and tracking the number of customers per phase. This model is similar to linear programming model used in revenue management in airline industry. This model use expected values information on the number of tables and their size, number of parties and their size, and the perceived duration of stay per customer or service-group considered. All these procedures are undertaken in order to calculate the expected revenue. Point of Sale (POS) software may be used in data collection as well as historical data on customers' arrival and generated revenues. For example, yield can be increased where POS electronic pad is basically used in tracking the customer phases in completing an order and notifying those people working in kitchen to attend to the customers' inflow. The pad is also useful to floor managers in estimating customers' progress in regard to their meals. This way, it's possible to use POS to estimate the average service durations, and possibly instigate possible measures to curb delays. Other important considerations include the waiting time and fairness in handling issues. To address the two issues, parameters like maximum waiting period (Max) are used as a trade-off between waiting time and generated revenue, this is however a probabilistic estimate and may change depending on the size of the party and their arrival time. As such it's paramount to separate appropriate values of Max.
This model however does not deny customer service explicitly but some parties may not be allocated table within the Max set. In this case, integer program model is formulated and solved for each client's arrival as well as departure time and from the optimal solution manager is able to determine how a particular party should be allocated tables. This way revenue can be maximized and fairness of issues and the waiting time be kept at reasonable levels. Exact numerical values can be determined through computational experimentation.
Constraints
According to Yeoman & Beattie (2004, p. 223), demand constraints are experienced in this model in that one should not use the model to seat more parties than expected. Another constraint to the model is seating capacity constraint, where each table size should be observed no to exceed the determined capacity. Fairness is also a constraint to this model in that, the last customer to arrive normally has a less waiting time than those who arrived early.
3.2 Stochastic Programming Model
Expected values of demand in the basic model are used as an indication of arrivals of customers in the future. Stochastic version of integer programming identifies different demand scenarios that can be used in integer programming model, and similarly to the arrivals simulation. These scenarios help to capture the characteristics of future demand more accurately. Additional data of modified parameters used in this model may involve the number of scenarios, the perceived probability of given scenario, and the expected number or even size of parties at a given time. The model has the same demand constraint as the case of integer programming. The Simulation of this model is similar to that of the basic model (Yeoman & Beattie 2004, p. 196).
3.3 Approximate Model of Dynamic Programming
This model determines the seating policy aimed at revenue maximizing for each customer. Various seating decision per customer are evaluated and the decision whose revenue if optimal is considered (Yeoman & Beattie 2004, p. 167).
3.4 Comparison Models
First-Come First-Serve
FCFS may be developed in various ways in order to compare revenue generated in the three mentioned models. For example, under FCFS customers should be allocated seats on their arrival order such that if customers arrive in large number but the tables available are meant for small sizes, the customers will seat in order of arrival, and those whose waiting time exceeds Max will leave the queue automatically (Barz 2007, p. 112).
3.5 Bid-Pricing Model
Bid-pricing heuristic model is commonly used in the airline revenue management and its run as a benchmark for performance. The models sets seating based on the disparity between estimated immediate revenue and total dual prices that correspond to the capacity utilized and the party's duration or stay period (Umenai & Iwasaki 1998, p. 73).
4.0 Conclusion and Recommendations
According to Bitran & Mondschein (1997, p. 56), optimization based models outperform the FCFS based models for all demand levels. Strategies which are optimization based do not affect the product and service quality adversely. For example, waiting times mostly remain unchanged or decrease and within the same sized parties the FCFS is maintained. The more sophisticated the model the more the revenue generated while the waiting time remain unchanged. Strategies which are optimization based play a great role in revenue management. There are however areas that should be given further research like the changing of a restaurant set up to accommodate the different levels of demand at each time. Or even any possibility of increasing business units in targeting large customers' base.
Some practices of yield management are more acceptable than others, and such procedures should be given more emphasis. These practices may include information on varying pricing options, substantial discount that should be offered to customers, restrictions imposed to counter balance the discount offered, and the different prices set for the various products availed. Unacceptable practices in yield management may include practices like offering benefits which are insufficient for the restrictions imposed, imposing of restrictions on given discounts, which are too severe, and failure of Hospitality Company to inform the customers of the changes in transactions. All these practices ought to be avoided in a bid to enhance yield and revenue generated. These practices mostly result from lack of professional yield/revenue managers (Shy 2008, p. 122).
Profitable revenue and yield management procedures ought to be undertaken in fair grounds. While firm's target to maximize revenue, caution should be taken where the firm may tend to emphasize on profitability at the expense of the consumers' welfare. Some of the common practices in hospitality industry are seen unacceptable by customers. Successful yield management must ensure that their practices enhance fair transactions since unfair practices risks customers' alienation. Though unfair practices indicate short term benefits, they turn to be unprofitable in long run (Zhao & Zheng 2000, p. 382). Fairness ought to be exhibited in all CRM procedures, in a view to build brand loyal consumers across cultures.
References
Barz, N. 2007. Risk-averse capacity control in revenue management. Springer Publishers: Chicago, IL.
Bitran, G.R. & Mondschein, S.V 1997. Periodic pricing of seasonal products in retailing. Management Science: San Jose Canada.
Irene, N. 2007.The pricing and revenue management of services: a strategic approach,Routledge Publishers, New York, United States.
Lindvall, M. V 2003.Yield management, Cengage Learning EMEA, Florence, KY.
Philips, R.L 2005. Pricing and revenue optimization, Stanford University Press: Palo Alto, CA.
Shy, O. 2008. How to Price: A Guide to Pricing Techniques and Yield Management, Cambridge University Press, Cambridge, UK.
Taljuli, K. & Ryzin, G 2005. The theory and practice of revenue management. Springer Publishers, Chicago, IL.
Umenai, R. & Iwasaki, S 1998. Yield management: subject property, Cornell University Press, Ithaca, NY.
Yeoman, I & Beattie, U.M 2004. Revenue management and pricing: case studies and applications, Cengage Learning EMEA, Florence, KY.
Zhao, K., & Zheng Y.S 2000. Optimal dynamic pricing for perishable assets with non-homogeneous demand. Management Science Press, San Jose, Canada.
