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Prediction of Hotel Customers’ Revisit Behavior by Determining the Appropriate Marketing Mix Using Customer Review Analysis

Marandi, Ali Akbar | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53226 (44)
  4. University: Sharif University of Technology
  5. Department: Management and Economics
  6. Advisor(s): Najmi, Manoochehr; Tasavori, Misagh
  7. Abstract:
  8. In recent years, the tourism industry has become one of the most influential industries in the income generation of countries and has attracted the attention of researchers. Identifying the important features of the hotel from the users' point of view is one of the areas that have been considered, while the segmentation of hotel customers based on the extracted features has been less in the focus of researchers and the need for more research in this field has been felt by experts.Given that the desire to return to the hotel by travelers has always been one of the factors affecting the financial performance of hotels, the factors affecting it are of great importance. It can be very valuable to know better the features of the hotel that users think will increase the desire to return to the hotel. Also, recognizing groups of hotel customers that different sets of these features together increase their willingness to return can play an important role in developing a hotel marketing strategy.In this study, based on text analysis methods, first, those comments from users from one hundred selected hotels around the world who had indicated their desire to return were isolated in the text of their comments. The important features of the hotels are then extracted from the perspective of these users, and finally, these comments are divided into fifteen different groups.In this research, seventy-one important features have been extracted from the users' point of view, which are topped by rooms, staff, hotel location and food. Segmentation also divides returning customers into fifteen groups. This segmentation shows how different groups of these travelers differ in terms of the different characteristics grouped according to the marketing mix
  9. Keywords:
  10. Segmentation ; Feature Extraction ; Marketing Mix ; Marketing Strategy ; Text Mining ; User Generated Content ; Tourism

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