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Price is fundamental in the competitive strategy of lodgings. Determining whether a company is setting its prices appropriately in relation to its main competitors and customer expectations is essential in the new digital age. Online reputation is a way of measuring customer ratings and, when shared on the Internet, it generates expectations for future users. On the other hand, websites specializing in tourism constantly provide updated information about the prices offered by lodgings. The purpose of this study is to establish whether there is a relationship between price and the main variables of online reputation (perceived value, added value and perceived quality of service) as well as the function that best suits considering the category of accommodation, using the information available on the website Booking.com. The methodology applied is regression analysis using different functions (linear, logarithmic, inverse, quadratic and cubic). In addition, 4- and 5-star lodgings are analysed separately from those with 3 stars or less, concluding that there are significant differences between the variables that best explain the price, as well as the functions that best achieve this fit. In 4 and 5-star accommodations, the average quality of service variable is the one most related to prices, whereas in 3-star accommodations or less, the added value is the variable most related to prices. The cubic, quadratic and logarithmic functions get the best adjustments. The results obtained are of great interest to the management of the accommodation as customer ratings are linked to price levels in a competitive environment. This methodology facilitates the definition of the strategy and tactics of prices on the basis of real and updated market data, indicating in the conclusions the direct implication in the future development of learning machines and artificial intelligence applied to tourism.

Analysing the Relationship between Price and Online Reputation by Lodging Category