Identifying Valuable Travelers and Their Next Foreign Destination by the Application of Data Mining Techniques
Bibliographic Data
| ID | 12462199 |
|---|---|
| Authors | Jehn-Yih Wong, Jehn‐Yih Wong (0000-0003-3942-812X, Ming Chuan University, corresponding author), Huei-Ju Chen, Huei‐Ju Chen (0000-0001-5095-8821, National Kaohsiung University of Hospitality and Tourism), Pi-Heng Chung (Ming Chuan University), Nai-Ching Kao (Ming Chuan University) |
| Year | 2006 |
| Volume | 11 |
| Issue | 4 |
| Pages | 355-373 |
| Publication date | 2006-11-09 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Asia Pacific Journal of Tourism Research (JOURNAL) |
| Journal identifiers | ISSN: 1094-1665 • E-ISSN: 1741-6507 |
| Publisher | Routledge (PUBLISHER • GB) |
| DOI | 10.1080/10941660600931200 |
| OpenAlex | W2107584601 |
| Language | EN |
| Citations received | 3 |
| References cited | 17 |
The purpose of this research is to identify “valuable travelers” from Northern Taiwan and to predict their “next foreign destination” by applying data mining techniques. Three data mining procedures were used in this research. First of all, researchers used the RFM (the recent year of traveling abroad, the frequency of traveling abroad and the monetary value spent on traveling abroad) model to identify valuable travelers. A C4.5 decision tree was then applied to discover the tourist characteristics of valuable travelers, such as demographics, buying and decision-making behavior patterns, and destinations visited. A market basket analysis then mined the travelers' preferred destination for the next possible foreign destination via cross-selling. The results of the C4.5 decision tree indicated several tourist characteristics that will be the criteria for distinguishing high value travelers from low value travelers. The results of the market basket analysis suggest that Asian countries are the preferred cross-selling destination for Taiwanese outbound travelers
Advertising · Business · Data mining · Decision tree · Demographics · Destinations · Geography · Tourism · Value (mathematics · Computer Science · Customer Service Quality and Loyalty · Diverse Aspects of Tourism Research · Wine Industry and Tourism · Marketing
| Unique citing works | 3 |
|---|---|
| Citations per year | 0.2 |
| Citation span | 2011 - 2022 (12) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 3 |