استراتژی بهینه قیمت گذاری بر اساس تقسیم بندی بازار برای محصولات خدمات با استفاده از سیستم های رزرو آنلاین : درخواست برای اتاق های هتل ها
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|22596||2014||8 صفحه PDF||سفارش دهید||6430 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : International Journal of Hospitality Management, Volume 35, December 2013, Pages 274–281
As an effective policy which brings the service providers high occupancy rate and generates more profit than fixed pricing, the dynamic pricing strategy is extensively used in the online distribution channel. This paper studies the optimal dynamic pricing strategy based on market segmentation for service products in the online distribution channel taking hotel rooms as an example. Firstly, the pricing model is built to maximize the hotel profit through a dynamic process. Then the solution methodologies based on Chebyshev's Sum Inequality and dynamic programming are provided for the linear demand case and non-linear demand case, respectively. The optimal number of segments and optimal boundaries can be obtained. The results suggest that an appropriate policy of market segmentation in using of online reservation systems is benefit for the service suppliers as well as the consumers. Finally, an illustration based on a 300-room hotel is provided for the more realistic non-linear case.
In the service industries, numerous service providers are confronted with the dilemma that only a small fraction of products are sold on a given time and given capacity, while the unsold part cannot be kept in inventory for future use when the market demand surpasses the available capacity (Stolarz, 1994). For instance, in the hotel industry, the unbooked rooms in the low demand season cannot be inventoried to the high demand season for sale. As further evidence, the unsold seats of a plane cannot be retained to a future fight. Furthermore, the marginal profit of each sold product (such as a hotel room and an airplane ticket) is very considerable, while the unit variable cost is much lower than the high fixed cost (Ladany, 1996). Therefore, how to achieve the full utilization of the high margin and zero-salvage product capacity becomes a significant issue for the service providers. Fortunately, the profits can be increased considerably with a proper pricing strategy provided by Market Segmentation ( Ladany, 1996), which is “one of the most important strategic concepts contributed by the marketing discipline to business firms and other types of organizations” ( Myers, 1996). For example, in the e-tourism era, the online reservation system (ORS) is widely used in the marketing of hospitality industries and makes it possible for e-consumers to reserve hotel rooms at anywhere and anytime with access to the Internet. Consequently, different segments for hotel rooms can be achieved by ORS with a dynamic pricing strategy respecting to the lead time of the reservations. Furthermore, there are many hotels that adopt this kind of dynamic pricing strategies for their consumers. Abrate et al. (2012) collect the dynamic pricing data “from almost 1000 hotels in eight European capital cities”, which implies that there are so many hotels using dynamic pricing strategy in their revenue management. For instance in practice (all the examples are selected and verified July 2012), Marriott International, Inc. (https://www.marriott.com) offers a 25–50% discount to the consumers who book rooms 30 days earlier through the Internet. Similar concession occurs at Hilton Hotels (http://www.hilton.com). Compared with a float discount strategy above, Hotel ICON (http://www.hotel-icon.com/), which established by School of Hotel and Tourism Management Hong Kong Polytechnic University, will provide a 20% fixed discount to her consumers who book rooms at least 14 days before their arrival date. All these successful dynamic pricing strategies are operating on the hotels’ websites through their online reservation systems. The dynamic pricing strategy segments the market of service products into different parts by the length of the lead time to the end of the horizon. Take hotel rooms as an example, a higher rate for business travelers who reserve room on the target day or one or two days earlier; a medium rate for tourists who reserve rooms a longer time before the target day, like a week; and a lower rate for consumers who book rooms more than two weeks earlier before the target day. Due to the price concession, the pricing policy attracts more consumers for the service providers and most products are sold in advance of the end of the horizon. However, once all of the service products are booked before a long time of the horizon finished, the consumers who want to purchase the service near the target day will be declined due to the finite capacity. This may incur an opportunity loss of the profits to the service providers, because the margin is higher if the purchasing time is closer to the end of the horizon. Consequently, how to determine the optimal dynamic pricing strategy, i.e., the optimal segmentations and the corresponding sale prices are the key issues in revenue management of the service providers in using of online reservation systems in the e-commerce era. Taking the hotel industry as an example, in this paper, we build a pricing model to describe the dynamic pricing process for the service providers. The efficient solution methodologies are outlined for both the linear demand function case and the non-linear case. Finally, the optimal solution of segmentations and the corresponding number of hotel rooms and price in the non-linear case are given by a numerical example with a 300-room hotel. To the best of our knowledge, this paper may be the first attempt to determine the optimal dynamic pricing strategy in using of ORS in service industries. The remainder of this paper is organized as follows. After reviewing the related literature about dynamic pricing and market segmentation in service industries in Section 2, Section 3 presents the dynamic programming model of the pricing strategy based on market segmentation. In Section 4, we provide the solution methodology for the pricing model. A numerical example for a 300-room hotel is presented in Section 5. And finally, in Section 6 we discuss the management insights of our model and provide some research directions for further study in this field.
نتیجه گیری انگلیسی
This paper studies the dynamic pricing strategy in using of online reservation systems for the service products. With a hotel as an example, both the linear case and non-linear case of the demand functions are analyzed. In the linear case, we give the solution under the help of Chebyshev's Sum Inequality. While for the non-linear demand scenario, a dynamic programming model is adopted to solve the problem, which may more practical for the service providers. The results suggest that the market segmentation is benefit for the profit of the service providers, and there is a unique optimal segment-number which leads to the maximal profit. The numerical results in the non-linear case illustrate that if the number of segments is larger than the optimal one (e.g., n* = 7 in the base example), the total profit decreases. This is because the additional profit brought by the added segment cannot cover the additional operational cost for this segment. The dynamic pricing strategy based on market segment, which operates through online reservation systems, is a win–win policy that benefits for both service suppliers and also the consumers. The numerical results tell that the pricing strategy can bring the service providers full utilization of their service capacities and provide the consumers considerable price discounts. Table 2 shows that the pricing policy can bring more than treble profit for the hotel, and Table 3 and Table 4 show that the consumers may get a 50% discount if he made a reservation two weeks before the target day.