Market Segmentation for Outdoor Recreation

Implicit in planning for a given recreational market is a full knowledge of the demand structure for that market. It follows that a precise determination of the demand priorities for any homogeneous sub-groups of participants (markets segments) in this market would lead to more accurate planning to fit the needs of the various market segments rather than a non-homogeneous aggregate market. This study presents a methodology (and an application of that methodology) for determining both the socioeconomic characteristics and activity preferences of selected market segments that might exist in an outdoor recreational market. Further analysis couples these market segments with their demand priorities for recreational activities.This methodology is based upon the measurement of relative differences between nine demand-related socioeconomic characteristics from a sample of recreational participants. After the differences are measured through the Mahalanobis D2 statistic, the maximizing hierarchical clustering technique is used to create clusters of respondents.This procedure yields groups of respondents having relatively homogeneous characteristics (maximized inter-group differences and minimized intra-group differences). The recreational demand priorities for these groups may then be analyzed to detect inter-group differences of value in future planning.In the analysis presented here, differences are noted in both the type of activities and the number of activities in which the members of different groups engage. The relationships of these differing activity priorities is linked to the demand-related socioeconomic characteristics of the participant groups. No one characteristic is observed to create these differences. Rather, the socioeconomic characteristics are related in differing combinations and in differing degrees to activity participation. This leads to the conclusion that proper planning for recreation must include a detailed analysis of the needs of various market segments. The methodology presented here allows the researcher to do this and gain maximum utility from the available information.