By Zheng Xiang, Daniel R. Fesenmaier
This e-book provides leading edge learn at the improvement of analytics in trip and tourism. It introduces new conceptual frameworks and dimension instruments, in addition to purposes and case reviews for vacation spot advertising and administration. it truly is divided into 5 components: half one on commute call for analytics makes a speciality of conceptualizing and imposing go back and forth call for modeling utilizing colossal info. It illustrates new how one can establish, generate and make the most of huge amounts of information in tourism call for forecasting and modeling. half specializes in analytics in trip and way of life, proposing contemporary advancements in wearable pcs and physiological size units, and the results for our figuring out of on-the-go tourists and tourism layout. half 3 embraces tourism geoanalytics, correlating social media and geo-based information with tourism information. half 4 discusses web-based and social media analytics and provides the newest advancements in using user-generated content material on the net to appreciate a few managerial difficulties. the ultimate half is a suite of case reviews utilizing web-based and social media analytics, with examples from the Sochi Olympics on Twitter, leveraging on-line stories within the inn undefined, and comparing vacation spot communications and industry intelligence with on-line inn experiences. The chapters during this part jointly describe a number of assorted techniques to knowing marketplace dynamics in tourism and hospitality.
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Extra resources for Analytics in Smart Tourism Design: Concepts and Methods
2 What Is Tourism Big Data? The tourism industry thrives on information (Benckendorff, Sheldon, & Fesenmaier, 2014; Poon, 1988). The vast new big data repositories of information—far greater than what is captured in standard databases—can provide researchers, managers, and policymakers with the data-driven evidence needed to make decisions on the basis of numbers and analysis rather than anecdotes, guesswork, intuition, or past experience (Frederiksen, 2012). The bounty of tourism big data has the potential to deliver new and more highly informed inferences about human activity and behavior that will give the tourism industry a big boost and benefit not only customers but also those who participate in the tourism industry (Fuchs, H€ opken, & Lexhagen, 2014).
Travel is a fast-paced industry, and this drives the need for speedy data analytics and quick 20 H. Song and H. Liu decisions. In tourism, any demand needs to be addressed instantly in order to remain relevant to travelers, and this is what makes big data so important. With the vigorous growth of the amount and applications of big data, traditional tourism data and methods are going to be interfacing with the novel data and methodologies: for example, call centers are going to be interfacing with online consumer reviews; loyalty programs are going to be linking with booking histories; and “property preferences” are going to be combined with social media chatter.
Forecasting Chinese tourist volume with search engine data. Tourism Management, 46, 386–397. , & Song, H. (2014). Predicting hotel demand using destination marketing organization’s web traffic data. Journal of Travel Research, 53(4), 433–447. Travel Demand Modeling with Behavioral Data Juan L. Nicolau 1 Introduction Today’s travelers demand personalized and comprehensive experiences, and guided by their personal motivations, they try to back their decisions on recommendations expressed on the Internet.
Analytics in Smart Tourism Design: Concepts and Methods by Zheng Xiang, Daniel R. Fesenmaier