Tourism in the Digital Age: E-booking Perspective

Digitization in the tourism sector beside hospitality information systems application as its integral part has also led to the application of on-line booking systems, which enable the booking of the desired tourist arrangement via the Internet. Consequently, the emergence of the e-booking concept has made it possible reducing administrative and operational costs, since the e-booking system is also used on smartphones with appropriate applicative support. This paper aims to point out the importance of e-booking in the digital age of tourism, especially from monitoring the most common destinations, customer preferences and performing predictive analytics by collecting large amounts of data. In this way, the implementation of big data and cloud computing concept enhances tourism services, since it is possible to analyse destination history and tourist potential of the client via the Internet. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</p

Optimizing B2B Product Offers with Machine Learning, Mixed Logit, and Nonlinear Programming

In B2B markets, value-based pricing and selling has become an important alternative to discounting. This study outlines a modeling method that uses customer data (product offers made to each current or potential customer, features, discounts, and customer purchase decisions) to estimate a mixed logit choice model. The model is estimated via hierarchical Bayes and machine learning, delivering customer-level parameter estimates. Customer-level estimates are input into a nonlinear programming next-offer maximization problem to select optimal features and discount level for customer segments, where segments are based on loyalty and discount elasticity. The mixed logit model is integrated with economic theory (the random utility model), and it predicts both customer perceived value for and response to alternative future sales offers. The methodology can be implemented to support value-based pricing and selling efforts. Contributions to the literature include: (a) the use of customer-level parameter estimates from a mixed logit model, delivered via a hierarchical Bayes estimation procedure, to support value-based pricing decisions; (b) validation that mixed logit customer-level modeling can deliver strong predictive accuracy, not as high as random forest but comparing favorably; and (c) a nonlinear programming problem that uses customer-level mixed logit estimates to select optimal features and discounts

A review of revenue management : recent generalizations and advances in industry applications

Originating from passenger air transport, revenue management has evolved into a general and indispensable methodological framework over the last decades, comprising techniques to manage demand actively and to further improve companies’ profits in many different industries. This article is the second and final part of a paper series surveying the scientific developments and achievements in revenue management over the past 15 years. The first part focused on the general methodological advances regarding choice-based theory and methods of availability control over time. In this second part, we discuss some of the most important generalizations of the standard revenue management setting: product innovations (opaque products and flexible products), upgrading, overbooking, personalization, and risk-aversion. Furthermore, to demonstrate the broad use of revenue management, we survey important industry applications beyond passenger air transportation that have received scientific attention over the years, covering air cargo, hotel, car rental, attended home delivery, and manufacturing. We work out the specific revenue management-related challenges of each industry and portray the key contributions from the literature. We conclude the paper with some directions for future research

Sistema web para la reserva de habitaciones en el Hospedaje Tony, 2022

La presente investigación titulada “Sistema web para la reserva de habitaciones en el hospedaje Tony, 2022” tuvo como objetivo principal determinar de qué manera influyó el sistema web en la reserva de habitaciones en el hospedaje Tony. El tipo de investigación que se utilizó fue aplicado con diseño experimental del tipo preexperimental. La muestra estuvo compuesta por 28 fichas de registro de reservas generados por la empresa “Hospedaje Tony”. El sistema web con redes neuronales fue desarrollado con la metodología XP, Django, Vue.js y Scikit-learn. La técnica que se ejecutó para medir la reserva de habitaciones, fue la observación y el instrumento fue una ficha de observación en un periodo de 28 días. La investigación comprobó que la implementación de un Sistema web mejoro la reserva de habitaciones en la empresa hospedaje Tony; reflejado en la mejora de los indicadores, para el primer indicador Ingreso de habitaciones disponibles (RevPAR) incremento en un S/.1.2497 y el segundo indicador porcentaje de ocupación incremento en un 1.587 %

Data set for "Application of Online Booking Data to Hotel Revenue Management"

This is a dataset used in Section 3.2 "Estimation" of "Application of Online Booking Data to Hotel Revenue Management". The dataset is created based on raw data by the procedure described in Section 3.1 "Data Set"