Comparison Methods for Identifying Heavy Users

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 they 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 Methods and Measures that Profile Heavy Users The “heavy-half” concept generally suggests that 80% of the volume of a product is consumed by 20% of its consumers (Twedt 1964). Ever since the introduction of the “80/20 Rule,” many mangers of packaged goods have attempted to target consumers based on the volume they purchased (Haley 1968) or consumed (Wansink 1997a, 1998). Unfortunately, many efforts to identify characteristics of heavy users have been plagued with methodological or measurement problems that have limited the depth and usefulness of their conclusions. Many of these studies incorrectly concluded that heavy users are not especially different than light users (Clancy and Schulman 1993). This paper identifies methods and measures that can be most effective in differentiating heavy users. Two methods -the simple mean comparison method and the basic cluster method -have been used to examine heavy users. Both have their shortcomings. The commonly used simple mean comparison method can lead to over-generalized, non-diagnostic profiles. The basic cluster method can lead to overly specific profiles which are mathematically valid, but empirically nonexistent. What deserves to be examined is a hybrid technique which combines the best features of a mean comparison method with those of two-stage clustering. The question then turns to which measures of characteristics will most effectively differentiate heavy users from light users. Past marketing research has used demographics, psychographics, benefits, or behavioral variables to profile heavy user segments (Assael 1973; Assael and Poltrack 1994; Assael and Roscoe 1976; Bass et al. 1968; Goldsmith et al. 1994). Demographic data, however, have their limits in helping to generate insights about customers (Haley 1984), and psychographic data are costly to assemble and difficult to objectively interpret (Wells 1974). Likewise, the benefits a person seeks in a product are also limited in their ability to provide reliable profiles that are stable over time. While most of these