Giovanni Angelini , Michele Costa , Andrea Guizzardi
{"title":"Complex data in tourism analysis: A stochastic approach to price competition","authors":"Giovanni Angelini , Michele Costa , Andrea Guizzardi","doi":"10.1016/j.bdr.2025.100520","DOIUrl":null,"url":null,"abstract":"<div><div>This study examines pricing strategies and decision-making processes in the hospitality industry by analyzing “ask” prices on online travel agencies (i.e., the rates at which hoteliers are willing to sell their rooms). We face the challenge of modeling a continuous flow of big data organized as “time series of time series,” where daily seasonality and advance bookings intersect. Our research combines insights from tourism, quantitative methods, and big data to improve pricing strategies, contributing to both theory and practice in revenue management. Focusing on Venice, we analyze price competition as a multivariate stochastic process using a Structural Vector Autoregressive (SVAR) approach, aligning with modern dynamic pricing algorithms.</div><div>The findings show that time-based pricing strategies, which adjust based on the day of arrival and booking, are more important than room features in setting hotel prices. We also find that price changes have a non-linear and decreasing effect as the booking date approaches. These insights suggest that hotels could create more advanced pricing strategies, and policymakers should consider these factors when addressing the challenges related to overtourism.</div><div>We study the complex competitive relationships among heterogeneous service providers with an approach applicable to any market where consumption is delayed relative to purchase time. However, we highlight that the quality and accessibility of information in the tourism sector are key aspects to be considered when using big data in this industry.</div></div>","PeriodicalId":56017,"journal":{"name":"Big Data Research","volume":"40 ","pages":"Article 100520"},"PeriodicalIF":3.5000,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data Research","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214579625000152","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
This study examines pricing strategies and decision-making processes in the hospitality industry by analyzing “ask” prices on online travel agencies (i.e., the rates at which hoteliers are willing to sell their rooms). We face the challenge of modeling a continuous flow of big data organized as “time series of time series,” where daily seasonality and advance bookings intersect. Our research combines insights from tourism, quantitative methods, and big data to improve pricing strategies, contributing to both theory and practice in revenue management. Focusing on Venice, we analyze price competition as a multivariate stochastic process using a Structural Vector Autoregressive (SVAR) approach, aligning with modern dynamic pricing algorithms.
The findings show that time-based pricing strategies, which adjust based on the day of arrival and booking, are more important than room features in setting hotel prices. We also find that price changes have a non-linear and decreasing effect as the booking date approaches. These insights suggest that hotels could create more advanced pricing strategies, and policymakers should consider these factors when addressing the challenges related to overtourism.
We study the complex competitive relationships among heterogeneous service providers with an approach applicable to any market where consumption is delayed relative to purchase time. However, we highlight that the quality and accessibility of information in the tourism sector are key aspects to be considered when using big data in this industry.
期刊介绍:
The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic.
The journal will accept papers on foundational aspects in dealing with big data, as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered. Occasionally the journal may publish whitepapers on policies, standards and best practices.