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dc.contributor.author
Neochoritis, Vasilis
en
dc.date.accessioned
2020-06-09T13:33:40Z
dc.date.available
2020-06-10T00:00:46Z
dc.date.issued
2020-06-09
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/29531
dc.rights
Default License
dc.subject
Tourism industry
en
dc.subject
Customer analytics
en
dc.title
Customer Analytics for the Hospitality Industry
en
heal.type
masterThesis
en_US
heal.creatorID.email
v.neochoritis@ihu.edu.gr
heal.generalDescription
This dissertation was written as a part of the MSc in e-Business & Digital Marketing at the International Hellenic University. Travel and tourism industry has changed rapidly over the years as customers are using digital channels to make their bookings online. Hotel managers are making efforts to reach possible customers to this new digital market, where the competition is fiercer than ever. One of the most important and effective strategies is the online reputation management. Online reputation consists of the electronic word of mouth created by online reviews and ratings provided by customers.
en
heal.contributorName
Neochoritis, Vasilis
en
heal.language
en
en_US
heal.access
free
en_US
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Science and Technology, MSc in e-Business and Digital Marketing
en_US
heal.publicationDate
2020-05-22
heal.abstract
This dissertation was written as a part of the MSc in e-Business & Digital Marketing at the International Hellenic University. Travel and tourism industry has changed rapidly over the years as customers are using digital channels to make their bookings online. Hotel managers are making efforts to reach possible customers to this new digital market, where the competition is fiercer than ever. One of the most important and effective strategies is the online reputation management. Online reputation consists of the electronic word of mouth created by online reviews and ratings provided by customers. The first part of this paper examines the impact online reputation has on the decisions of the consumers, as well as the importance of online review and ratings on the performance of a hotel. By investigating previous research, it will explore the ways hotel managers can use the online generated content to improve their hotel’s performance and the effective ways they can should interact with it to improve their brand’s online presence. On the second part we will perform deep analysis of a dataset containing reviews from Booking.com customers, to discover new patterns and insights. Finally, we will build machine learning explanatory models to determine the most important factors affecting the scores provided by reviewers.
en
heal.advisorName
Thomaidis, Nikolaos
en
heal.committeeMemberName
Thomaidis, Nikolaos
en
heal.committeeMemberName
Stavrinides, Stavros
el
heal.committeeMemberName
Baltatzis, Dimitrios
el
heal.academicPublisher
IHU
en
heal.academicPublisherID
ihu
en_US


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