Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/19782
Title: Predicting browsers and purchasers of hotel websites : a weight-of-evidence grouping approach
Authors: Wu, EHC
Law, R 
Jiang, B
Keywords: Hotel website
Prediction
Information technology
Data mining
Travel behavior
Weight of evidence
Issue Date: 2013
Publisher: SAGE Publications
Source: Cornell hospitality quarterly, 2013, v. 54, no. 1, p. 38-48 How to cite?
Journal: Cornell hospitality quarterly 
Abstract: A study of the online browsing and purchasing habits of some 1,400 outbound travelers in Hong Kong demonstrates the analytical power of weight-of-evidence (WOE) data mining. The WOE approach allows analysts to identify and transform the variables with the most predictive power regarding the likelihood of tourists' online preferences and decisions. The study found that just over one-third of the respondents browsed hotel-related websites, and about half of those browsers had booked a room on those sites. Browsers in Hong Kong tended to be young, well educated, and well traveled. Those who used the hotel websites for purchases were, of course, part of the browser group, and were likewise relatively well educated. However, one unexpected variable set off those who used the websites for a hotel purchase, the length of their most recent trip. One possible reason is that long-haul tourists want to be sure of their accommodations, or this may reflect hotels' free-night offers. The convenient use of model-based customer segmentation and decision rules would help hospitality practitioners effectively manage their marketing resources and activities, and enhance information-based marketing strategies to attract target customers.
URI: http://hdl.handle.net/10397/19782
ISSN: 1938-9655
EISSN: 1938-9663
DOI: 10.1177/1938965512468225
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