Segmenting the Mature Travel Market with Data Mining Tools

Segmenting the Mature Travel Market with Data Mining Tools

Yawei Wang (Montclair State University, USA)
Copyright: © 2009 |Pages: 6
DOI: 10.4018/978-1-60566-010-3.ch268
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The graying of America is one of the most significant demographic changes to the present and future of the United States (Moisey & Bichis, 1999). As more baby boomers enter their 50s and 60s, the mature travel market becomes a fast-growing market segment and starts to attract attention from many tourism researchers and professionals. The significant increases in size and wealth of the older population make the mature travel market a strong component of the general travel market (Reece, 2004). Understanding the mature market as well as mature travelers’ motivations are vital to the success of the travel industry (Brewer, Poffley, & Pederson, 1995; Hsu, Cai, & Wong, 2007). Today’s mature travel market can be generalized as being “different, diverse and demanding” (Harssel, 1994, p. 376). Faranda and Schmidt (1999) suggest that mature tourism marketers must recognize three critical components: the aging process comprehended from multiple disciplines, the acknowledged “heterogeneity and dynamic nature” of the mature market, and the “necessity for sound segmentation methods” (p. 24). It is not a simple task for marketers to fully understand the mature travel market. In order to better understand and serve the diverse market, tourism professionals will have to use data mining (DM) tools and techniques to discover the hidden patterns and characteristics of the mature travel market. According to Pyo, Uysal, and Chang (2002), DM can be applied to many areas in tourism research. These areas include destination quality control and perceptions, environmental scanning and optimization, travel behavior, tourism forecasting, and market segmentation and positioning. Therefore, the purpose of this study is to review and analyze the segmentation methods reported in the literature during the past seven years on the mature travel market and to explore the application of DM tools regarding the segmentation of the mature travel market in the near future.
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A diversity of segmentation variables have been documented in the literature of the mature travel market. The segmentation efforts usually focus on socio-demographic variables (e.g., age, gender, and employment status) and psychographic variables (e.g., motivations and constraints). Demographic and behavioral profiles are then developed and compared based on subgroups or segments, with the help of data analytical tools.

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