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Predicting Hotel Demand Using Destination Marketing Organization’s Web Traffic Data
Abstract and Figures
This study uses the web traffic volume data of a destination marketing organization (DMO) to predict hotel demand for the destination. The results show a significant improvement in the error reduction of ARMAX models, compared with their ARMA counterparts, for short-run forecasts of room nights sold by incorporating web traffic data as an explanatory variable. These empirical results demonstrate the significant value of website traffic data in predicting demand for hotel rooms at a destination, and potentially even local businesses' future revenue and performance. The implications for future research on using big data for forecasting hotel demand is also discussed.
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... Early research primarily focused on leveraging big data to enhance operational efficiency within the tourism and hospitality sector. For instance, Yang et al. (2014) demonstrated that web traffic data effectively reflects fluctuations in tourism demand. The subsequent investigation integrated multiple tourism big data sources (such as weather data) to precisely forecast weekly hotel occupancy rates at destinations through a time series model, showcasing the powerful potential of big data in predictive models (Pan & Yang, 2017). ...
... The study by Buhalis and Leung (2018) ranked fourth, proposing the construction of cloud-based hotel big data, enabling internal members to flexibly formulate revenue management schemes to enhance decisionmaking, strategic performance, and competitiveness. The fifth-ranked article uses network traffic data to predict hotel demand (Yang et al., 2014). The subsequent five publications have reached 75 citations or more, exploring accurate prediction of weekly hotel occupancy rates using multiple big data sources (Pan & Yang, 2017); understanding consumer sentiment toward hotels using Twitter data (Philander & Zhong, 2016); comparing the performance of different types of automatic classifiers with human raters (Kirilenko et al., 2018); investigating the cruise tourism demand through Twitter data (Park et al., 2016); and conducting a bibliometric analysis of customer experience (Kim & So, 2022). ...
The emergence of big data and its related technologies has brought about novel economic models, industry phenomena, and relational networks, instigating revolutionary changes with significant value for tourism sustainability. This study conducts a bibliometric analysis of 212 articles (2014-2024) on big data in tourism from the Web of Science (WoS) Core Collection database, aiming to create a knowledge map based on big data in tourism. This study utilizes VOSviewer software to carry out citation analysis, co-citation analysis, co-authorship analysis, and keyword co-occurrence analysis, revealing trends in publications, national contributions, influential journals and authors, author collaborations, as well as the conceptual structure and research trends in the field of big data in tourism. The findings indicate a concentration of research in seven areas: machine learning, social network analysis, sustainability, tourism demand forecasting, artificial intelligence, smart tourism, and text mining techniques. The research has focused on emerging hot topics since 2022, including destination image, COVID-19, topic modeling, and urban tourism. This study maps the knowledge of big data in tourism, elucidates the academic evolution in this field, and offers future research directions for scholars in the domain.
... The data obtained from the Trend Report were used in the analysis, and refer to: occupancy of hotel rooms, average daily rate (ADR) and revenue per available room (RevPAR). STR data on hotel performances have been used in research by a significant number of authors (Diaz & Koutra., 2013;Demirciftci et al., 2010;Enz et al., 2015;Yang et al., 2014). ...
Congress tourism or meetings industry is one of the fastest growing segments on the tourism market. Congress tourism has a positive effect on the development of the country's economy, providing additional economic effects, extending the season, and strengthening the image of destination. The aim of the paper is to analyze the impact of congress tourism on hotel performances, tourism performances, as well as the impact on macroeconomic indicators in the Republic of Serbia for the period 2015-2022. Regression and correlation analysis were used to check the hypotheses. The results showed a partial connection between congress and hotel performances, as well between congress and tourism performances, while no relation between congress and macroeconomic indicators were found. Based on the obtained results, it can be concluded that congress tourism in the Republic of Serbia has a significant impact on hotel and tourism performances, while there is no significant impact on macroeconomic indicators.
... Sites like internet travel companies [12] and tourist hotspots [46] are examples of this. The hotel sector is beginning to embrace UGC as a means to better comprehend research difficulties that have so far resisted conventional methodologies [47]. In this setting, the most often used BD approach for retrieval is text analytics [48], which frequently makes use of machine learning, statistical analysis, and computational linguistics. ...
... Furthermore, big data such as online customer reviews from social media have been investigated for solving various problems in hospitality and tourism (Xiang et al., 2017) and these data formats combined with the relative analytics approach, emphasize, and leverage the capacity to collect and analyze data with an unprecedented breadth, depth, and scale, to solve practical problems (Mehraliyev et al., 2020;Yang et al., 2014). Specifically for the hotel sector, Hu et al. (2017) suggested a novel text-based summarizing technique for the development of the hotel industry with online customer reviews, Handani et al. (2022) examined 8,441 textual customer reviews from seven luxury hotels in the Marina Bay area. ...
There is a generalized global market trend that tourists and consumers tend to pursue “themed” experiences closely related to their preferences or specific purposes. In the accommodation sector, the concept of “themed” is reflected by various theme hotels such as green hotels, family hotels, E-sports hotels, etc. In the era of Web 2.0, online information has been testified to be one of the most effective data sources for understanding customers and developing relative marketing strategies. Therefore, this research examined online customer reviews with both quantitative and qualitative methods to extract a range of variables reflecting customers’ experiences, and then, to explore their contribution to customers’ satisfaction/dissatisfaction with theme hotels; to understand theme hotels and develop novel insights for the industry, with new data sources. Moreover, a cross-cultural perspective was applied to examine the differences among different markets, so that more specific and targeted marketing strategies could be developed to develop and promote theme hotels. As a result, different clusters reflecting customers’ understanding of two areas have been applied. In addition, it was demonstrated that the connection between customer experience and satisfaction can be utilized to distinguish the differences between the two areas.
Purpose
This paper aims to systematically review the application of artificial intelligence (AI) in the tourism industry. By integrating human–computer interaction, machine learning, big data and other relevant technologies, the study establishes a comprehensive research framework that explores the systematic connections between AI and various facets of tourism.
Design/methodology/approach
This paper conducts a keyword co-occurrence analysis of 4,048 articles related to AI in tourism. The analysis identifies and classifies dominant topics, which are further refined through thematic literature review and manual coding for detailed discussion.
Findings
The analysis reveals five main topics: AI’s impact on tourist experience, AI in tourism marketing and prediction, AI in destination management, AI’s role in tourism enterprises and AI integration in strategic and regulatory framework. Each topic is reviewed to construct an integrated discussion that maps the current landscape and suggests directions for future research.
Originality/value
This paper transcends the fragmented discourse commonly found in the literature by establishing a unified framework that not only enhances understanding of the existing methodologies, theories and applications of AI in tourism but also identifies critical areas for breakthroughs, aiming to inspire a more humane and sustainable integration of AI in the tourism industry.
This paper describes early work trying to predict stock market indicators such as Dow Jones, NASDAQ and S&P 500 by analyzing Twitter posts. We collected the twitter feeds for six months and got a randomized subsample of about one hundredth of the full volume of all tweets. We measured collective hope and fear on each day and analyzed the correlation between these indices and the stock market indicators. We found that emotional tweet percentage significantly negatively correlated with Dow Jones, NASDAQ and S&P 500, but displayed significant positive correlation to VIX. It therefore seems that just checking on twitter for emotional outbursts of any kind gives a predictor of how the stock market will be doing the next day.
The paper provides a comprehensive review of the recent development of revenue management in different industries. We discuss research on different revenue management strategies including pricing, auctions, capacity control, overbooking and forecasting. Related issues such as economic concerns, customer perception, competition and consolidation, implementation, performance evaluation, and common techniques and approaches used for solving revenue management problems are also discussed. Finally, we give our suggestion on some important areas that warrant further research.
Tourism demand is the foundation on which all tourism-related business decisions ultimately rest. Governments and companies such as airlines, tour operators, hotels, cruise ship lines, and recreation facility providers are interested in the demand for their products by tourists. The success of many businesses depends largely or totally on the state of tourism demand, and ultimate management failure is quite often due to the failure to meet market demand. This book introduces students, researchers and practitioners to the modern developments in advanced econometric methodology within the context of tourism demand analysis, and illustrates these developments with actual tourism applications. The concepts and computations of modern advanced econometric modelling methodologies are introduced at a level that is accessible to specialists and non-specialists alike. The methodologies introduced include general-to-specific modelling, cointegration, vector autoregression, time varying parameter modelling, panel data analysis and the almost ideal demand system (AIDS). In order to help the reader understand the various methodologies, extensive tourism demand examples are provided throughout the volume.
Smith Travel Research's estimates of lodging occupancy in Virginia Beach have improved over time, most likely because its sampling accuracy has increased.
