Model of Price Optimization as a Part of Hotel Revenue Management—Stochastic Approach

Abstract

The paper is focusing on the problem of price optimization in the area of accommodation services. The main aim is to propose a novel simulation-based methodology of price optimization based on the customer’s price acceptance. The authors create a model based on the known approaches but extended by the stochastic approach and optimization based on the coefficient of price elasticity. The whole model is created, the price is set and optimized in two steps. The first step makes segmentation and optimization (with the price elasticity approach). The second step then sets the price of the reservation—the final price for a customer. This reservation price is mainly determined by knowledge of the length of stay, occupancy and booking lead time. All those parameters are described in the text from the economic point of view and make the base for the whole and complex revenue management model.

Understanding customer's online booking intentions using hotel big data analysis

“…The attribute of willingness to pay would be necessary to compute (Heo and Hyun, 2015; Kang and Nicholls, 2021). The whole problem can be used in the complex optimisation model as a base of the revenue management system (for more information, see Petricek et al, 2021).…”

The current state of the application of revenue management activities in the SME segment: A case from the Czech Republic

“…For example, Lee et al (2020) focused on the length of stay control and its position in revenue management strategies. Other authors focused on overbooking strategies (Saito et al, 2019), pricing and room rates optimisation (Petricek et al, 2021;Pohland & Kesgin, 2018;Saito et al, 2019), customers choice behaviour (Saito et al, 2019), distribution channels and distribution strategies (Cirer-Costa, 2022), customer behaviour (Sirotnak & Ushakov, 2022), demand prediction and forecasting (Afrianto & Wasesa, 2020;Webb et al, 2020), the role competition in revenue management (Pohland & Kesgin, 2018), the application of revenue management systems in hotel operations (Dieguez et al, 2022), the effect of ewom on hotel performance (Han & Bai, 2022) or the position of personalisation of the prices and services in the online environment (Buyruk & Güner, 2022). The presented list is not finite but showcases a wide range of activities to be done when optimising hotel performance while respecting the needs of customers and their satisfaction.…”

Beyond accuracy: The advantages of the k-nearest neighbor algorithm for hotel revenue management forecasting

“…Revenue Management (RM) research continues to advance in the areas of forecasting (Fiori and Foroni, 2020, Webb et al, 2020) and optimization (Brunato and Battiti, 2020, Petricek et al, 2021) with the goal of improving pricing decisions. While these efforts are warranted, the fundamental components of the system remain masked, and as such, less useful to users.…”