Identifying Valuable Travelers and Their Next Foreign Destination by the Application of Data Mining Techniques

Abstract

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.

Keywords:

• data mining
• destination choice

Acknowledgment

This research was supported by research grant NSC-93-2416-H-130-004 from the National Science Council, Taiwan, Republic of China.

Additional information

Notes on contributors

Huei-Ju Chen

E-mail: [email protected]