Online Travel Agency Channel Pricing Policy based on Dynamic Pricing Model to Maximize Sales Profit Using Nonlinear Integer Programming Approach

Abstract

A dynamic pricing strategy on the cooperation between a hotel and an online travel agency (OTA) is commonly applied to build a pricing policy. The purpose of this study is to propose a pricing policy according to the dynamic pricing model on a single online travel agency channel. The paper provides a dynamic pricing model adjusted to hotel problems with multiple room types. The study consists of two stages. First, we apply a revenue management tool that is dynamic pricing to model the effect of price on demand. The price dynamically changes based on the parameter of demand model. Second, we use a nonlinear integer programming approach to maximize the profit by substituting the demand model which has the lowest root mean square error. The parameter of the demand model is estimated by using the historical sales-price data from one of the hotels in Bandung, West Java, Indonesia. Our results propose a pricing policy of each room types that able to increase 18.54% from the historical sales profit. The proposed pricing policy completes the gap of the method in the existing pricing policy. Moreover, the findings provide an optimal room rate to the front office manager along the planning horizon. Introduction In the last few decades, many hotels have collaborated with online travel agency (OTA) to book hotel rooms in the tourism industry [1]. The challenges facing for the hotel is to utilize the OTA channel to maximize revenue by managing hotel room information, such as the offered room rate. To obtain the optimal rate, the hotels generally develop a pricing policy by using the tools on revenue management [2]. Revenue management has commonly applied for hotel industry to achieve an optimal level of net revenue that mostly generated from the room sales [3]. Particularly, hotel revenue management defines as an essential instrument for selling the right room to the right customer, the right time, the right price, and the right distribution channel with the best commission efficiency [3,4]. The tools used in the concept of revenue management to manage the price strategy called as pricing tools [3]. A few researchers have developed pricing policy with the pricing tools such as price discrimination, dynamic pricing, price presentation, price parity, and lowest price guarantee. However, in the last few years, dynamic pricing has increasingly adopted and successfully operated in terms of evolving pricing policy in hotel industry [2,5]. Dynamic pricing is defined as a strategy to model the effect between the price for a product or service on the specific period and price along the planning horizon or known as demand model [6]. Previous researches have addressed dynamic pricing for hotel revenue management. For example, dynamic pricing approach is based on price multipliers that use Monte Carlo Simulation as an optimization algorithm [7]; structure of dynamic pricing depending on the type of customer, star rating, and number of suppliers with available rooms that using panel data analysis [8]; and proposed 1st International Conference on Engineering and Management in Industrial System (ICOEMIS 2019) Copyright © 2019, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Intelligent Systems Research, volume 171