Methods and Measures That Profile Heavy Users

Abstract

Heavy users can be a critical segment for packaged-goods marketers to target. Yet many attempts to profile heavy users have proven to be unsuccessful because of methodological and measurement problems. This article shows the diagnostic shortcomings of the commonly used mean comparison method of heavy-user segmentation, and it presents a clustering method that effectively differentiates different types of heavy users from light users. Characteristics that differentiate heavy users from light users were collected from academic and commercial studies and are shown to relate to five basic lifestyle factors and six personality factors. While providing a key starting point for studying heavy users, they also show the dominant role that personality characteristics (versus lifestyle or demographic characteristics) play in differentiating heavy users from light users.