A Clustering Method for Categorical Data in Tourism Market Segmentation Research

Abstract

One challenge in tourism market segmentation research is finding a statistical clustering method that can use data from the commonly used qualitative (categorical scale) survey instrument. Current proven methods require the use of quantitative (ratio or interval scale) data. However, quantitative survey instruments are seldom used. Many quantitative clustering methods severely restrict the number of attributes measured despite the fact that segmentation analysis works best when it measures all the multistate attributes that visitors identify as influencing their tourist experience. This study demonstrated that multistate categorical survey data could be successfully used. Using data from a bed-and-breakfast survey (229 guests), a two-stage analysis method was employed. First, multiple correspondence analysis was used to spatially map each of the attributes, and then cluster analysis was used to identify market segments. It is believed this method can be more practical in the field of applied tourism research.