PRICE POLICY FORMATION FOR HOTEL AND RESTAURANT COMPLEXES USING INFORMATION TECHNOLOGY
Abstract
The paper explores the tools of travel distribution information systems for decision-making concerning pricing of hotel and restaurant complex (HRC), the purpose of which is to use open data to form the optimal price of hotel rooms. The development of the tour program takes into account many factors (travel route, list of partner travel companies, list and composition of services provided during the tour, range of entertainment activities, etc.) developed for tourists using such information as systems Saber, AMADEUS, Galileo, Worldspan. By modeling pricing through the means of RStudio software tool for hotel and restaurant complexes using the open data of the Booking and g.port system, it was obtained that a better pricing forecast for the Zaliznyy Port HRC allows the resource g.port, which is more popular in this market segment due to more attractive financial conditions for the owners of HRC. Substantial price differentiation on Booking worsens the quality of the price forecasting. The coefficient of determination for pricing model on Booking is 33%, while on g.port is 71%. Among the main influencing factors that are statistically significant are the distance to the sea, the comfort of the room and the availability of a swimming pool. Estimated average room price with and without kitchen and confidence interval for the forecast price based on open data for Booking and g.port. Prepared recommendations for the formation of pricing taking into account statistically significant factors. Price of vacation package will decrease on 1,34 UAH if at the distance to the sea increase on 1 meter. Price of vacation package will increase on 1641 UAH if there are conditions in the room. Dummy variable (the presence of a swimming pool) will increase on about 966 UAH if hotel includes swimming pool. Average room price will be 9,537 UAH for a room with a kitchen and 6,228 UAH for a room without a kitchen. With a probability of 95% price confidence interval will vary from 7750 UAH up to 11636 UAH for g.port for a room with a kitchen and from 4979 UAH up to 7792 UAH for a room without a kitchen. Using Booking.com Analytics or other open data, we can access big data that reflect sales level in the hotel. This data can help to form the optimal price per room.
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