Classifying Tourists into Market Segments

Abstract

Marker segments in travel and tourism like elsewhere are defined either predetermined criteria (a priori segmentation) or result from a clustering process (a posteriori segmentation). Each method requires searching for additional descriptive attributes in order to streamline selective market operation. These descriptors should be much easier to measure than those the segments originate from. For a posteriori segments, in particular, it is desirable to predict segment membership from a set of easily determined consumer attributes without having to process a larger number of psychographics. Prediction of segment membership often employs discriminate analysis. Neural network models are also capable of classifying tourists. Having been trained on a set of input variables (descriptors) and output data (segment membership), they may excel discriminate analysis in determining the correct segment affiliation.

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