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- Algarve hotel price determinants: a hedonic pricing modelPublication . Soler, Ismael P.; Gemar, German; Correia, Marisol B.; Serra, FranciscoThis study sought to assess customers' willingness to pay for a wide variety of characteristics and attributes of hotels in Portugal's Algarve region. After collecting nearly all the information available on TripAdvisor for hotels in this region, a hedonic pricing model was developed using a database of 9992 cases. The results suggest that - after standardisation - the most important variable shaping Algarve hotel room rates is the previous day's prices. When associated with a family-friendly hotel, star category and services have a greater value than beaches or golf courses do. Customers also appreciate some types of hotels, such as boutique, quaint or trendy hotels, but view others negatively, such as family-friendly or business hotels. Only the specific location of Falesia Beach adds value, although the Algarve is a desirable destination overall. Both destination and hotel managers can use the proposed method to analyse data for their region on customers' propensity to pay.
- Guest reputation indexes to analyze hotel’s online reputation using data extracted from OTAsPublication . Choupinha, R.; Correia, Marisol B.; Ramos, Célia M. Q.; Martins, Daniel; Serra, FranciscoNowadays many travelers use online travel agency (OTAs) to book flights, hotel rooms, rent-a-cars, cruises or entire vacation packages. Usually OTAs allow their users to give scores and to write reviews about what was used. Each OTA defines the terms and conditions for guest rating or review score and hoteliers are giving increasing importance to the scores and reviews their guests do in OTAs. This paper proposes two guest reputation index to help hoteliers to monitorize their presence in OTAs. The Aggregated Guest Reputation Index (AGRI), which shows the positioning of a hotel in different OTAs and it is calculated from the scores obtained by the hotels in those OTAs. Another one, the Semantic Guest Reputation Index (SGRI), which incorporates the social reputation of a hotel and that can be visualized through the development of word clouds or tag clouds. Examples of usage of these indexes are given with data extracted from 5-stars hotels in the Algarve, south region of Portugal, that are available on Booking and Expedia.
- Big data warehouse framework for smart revenue managementPublication . Ramos, Célia M. Q.; Correia, Marisol B.; Rodrigues, J. M. F.; Martins, Daniel; Serra, FranciscoRevenue Management’s most cited definitions is probably “to sell the right accommodation to the right customer, at the right time and the right price, with optimal satisfaction for customers and hoteliers”. Smart Revenue Management (SRM) is a project, which aims the development of smart automatic techniques for an efficient optimization of occupancy and rates of hotel accommodations, commonly referred to, as revenue management. One of the objectives of this project is to demonstrate that the collection of Big Data, followed by an appropriate assembly of functionalities, will make possible to generate a Data Warehouse necessary to produce high quality business intelligence and analytics. This will be achieved through the collection of data extracted from a variety of sources, including from the web. This paper proposes a three stage framework to develop the Big Data Warehouse for the SRM. Namely, the compilation of all available information, in the present case, it was focus only the extraction of information from the web by a web crawler – raw data. The storing of that raw data in a primary NoSQL database, and from that data the conception of a set of functionalities, rules, principles and semantics to select, combine and store in a secondary relational database the meaningful information for the Revenue Management (Big Data Warehouse). The last stage will be the principal focus of the paper. In this context, clues will also be giving how to compile information for Business Intelligence. All these functionalities contribute to a holistic framework that, in the future, will make it possible to anticipate customers and competitor’s behavior, fundamental elements to fulfill the Revenue Management