Pricing and revenue management (RM) techniques have become a popular field of research in hotel management literature. The sector’s background framework and evolution and the widespread use of new technologies have allowed a customer-oriented approach to be taken to pricing and the development of RM tools, while also contributing to better processes in hotel management performance at individual hotel level. Thus, price optimization (PO) methods that seek to maximize hotel revenue are based on inventory scarcity, customer segmentation and pricing. In the hotel sector, as in the airline industry, different pricing policies have a greater impact than competition measurement effects. This is mainly as differentiation strategies and specific policies at hotels can reduce the pressure of a competitive environment. The main contributions of the article are the presentation, description and classification of the principal RM and PO techniques in hotel sector literature.

Pricing is a basic strategic tool in hotel revenue management (RM). This study proposes a particular demand function model for resort hotels for measuring their own-price elasticities, along with the different seasonal demands, and across the booking horizons. The model is applied to the online transient demand for two hotels in Majorca – a well-known, mature mass tourism destination – in order to estimate and compare different elasticities, which could be used by RM departments to correctly manage prices in the short run and establish optimum pricing strategies (over the medium and long run). The results show that the two hotels display completely different own-price elasticities during high season, while during low season, demand is quite inelastic at both hotels; secondly, common price variations among seasons or hotels may sometimes be an erroneous pricing strategy, such as the common early booking strategy. The model is easily adaptable to different hotels.

Online customer behavior in terms of price elasticity of demand and the effect of time along the booking horizon are key requirements for the price optimization process that allows hotels to maximize their revenues. In this vein, this study adapts the online transient hotel demand functions to deterministic and stochastic dynamic models—two extended optimal pricing methods existing in the literature—in order to determine the prices that maximize the revenues of two resort hotels located in Majorca. The main findings indicate that (1) seasonality, the number of rooms available, the hotel location, and the tourist profile affect dynamic pricing (DP); (2) the booking horizon limitation leads to larger revenue decreases under elastic demand; (3) higher levels in demand elasticities generally produce lower levels of prices; and (4) the distribution of elasticities across the booking horizon and the natural variability of demand have an impact on DP. Implication for industry revenue managers is that they have to consider the booking horizon duration together with the demand price sensitivity in order to maximize the hotel revenues.

The present study uses data on seven 4-star hotels belonging to the same multinational hotel chain located in different Spanish regions. The objective is to estimate the dynamic prices that allow the hotel revenue maximization during high season. The study includes the demand functions of seven resort hotels and implements a dynamic pricing deterministic model to estimate the prices that will maximize the hotel revenue for each date of stay. The results point out general revenue management implications, mainly that hotels located in the same destination should follow individualized pricing policies, more focused in the specific hotel and tourists’ characteristics; while in practice, hotel companies apply similar pricing policies to hotels located in the same destination. Furthermore, the deterministic model performs well with the data available on seven different hotels with different customer profiles and hotel characteristics.