The 80 20 rule targets what user group?

Journal of Advertising Research Advertising Research Foundation 641 Lexington Ave., New York, NY 10022; (212) 751-5656

August/September 2000

Brian Wansink
and
Sea Bum Park
University of Illinois

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 managers of packaged goods have attempted to target consumers based on the volume they purchased (Haley, 1968; Wansink, Kent, and Hoch, 1998) or consumed (Wansink, 1994, 1997a, 1998a). 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 from 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 overgeneralized, nondiagnostic 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 that 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 psycho-graphic 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 variables seem to be indeterminant, by integrating them we can better examine the common factors they might represent (Sheth, 1974). In summary, this research examines two questions:

1. Which segmentation method generates the most diagnostic heavy-user profiles?
2. Which characteristics best differentiate heavy users?

To answer these questions, we first review previous research on the methods and measures used to profile heavy users. Second, we report results from a national survey to show how different segmentation methods can profile the same user groups quite differently. The methods and measures we test show that a hybrid cluster method provides distinctly different profiles than the more commonly used mean comparison model. We also show that personality variables are more differentiating than lifestyle variables. Following this, the implications for identifying heavy users are discussed along with how this information can be used to investigate whether media preferences and shopping baskets ('affinity marketing') can be used to profile heavy users of a particular product.

DIFFERENTIATING HEAVY USERS

What segmentation method generates the most diagnostic heavy-user profiles?

The performance of most marketing programs is determined by its effectiveness and efficiency. To this end, identifying and profiling heavy users has been a 'Holy Grail' to some brand managers. Unfortunately, most of these studies have generated only modest descriptive and explanatory power (Goldsmith et al., 1994; Singh, 1990). More disconcerting, however, is that other studies have generated inconsistent findings. For example, some studies found that heavy users are more deal prone, but other studies found quite oppositely that they are innovators or early adopters (Hackleman and Duker, 1980; Morgan, 1970). Why the inconsistencies? They may come from variations in measurement and methodologies. While some efforts captured differences between the segments, others found only noise.

Two basic techniques that have been used to cluster heavy users have been the simple mean-comparison method and the basic clustering method. The commonly used mean-comparison method simply divides 'heavy half' consumers from the 'light half' and evaluates the mean differences across these two groups. While a simple mean-comparison method of profiling heavy users has often been seen as too crude, the basic clustering method has also met with skepticism (Frank and Green, 1968; Wells, 1975). From a methodological viewpoint, cluster analysis groups consumers together based on a set of relevant variables, such as personality and lifestyle, without any prior assumptions about important differences that might differentiate usage. Unless usage segments are very different, however, homogeneous groups identified from the cluster analysis can appear to be heterogeneous because researchers can arbitrarily choose the number of clusters.

To solve the ad hoc nature of the basic clustering method, Punj and Stewart (1983) suggested a two-stage cluster analysis that helps enhance external validity as well as internal reliability. Figure 1 describes how this hybrid two-stage cluster analysis can be used to profile heavy users.

Considering that both mean comparisons and the two-stage cluster analysis have their own advantages and disadvantages we believe that combining the two methods can generate more diagnostic segment profiles based on the same population (Punj and Stewart, 1983) as described in in Table 1, we expect that the hybrid two-stage cluster analysis may resolve some of the fundamental problems found with both other methods.

TABLE 1: THREE METHODS TO PROFILE HEAVY USERS

Despite its robustness, any method will be ineffective if it collapses too many distinct segments of consumers together. Not all heavy users are created equal, and aggregating across two different groups of heavy users can give a blurry profile, because the result is a hybrid customer � perhaps one that does not even exist. For example, while one might be a heavy user for convenience, the other might be for price. Or one might be a heavy user of a store brand while the other a heavy user of that same category�s premium brands.

A two-stage cluster analysis enables different profiles to emerge as two- and three-cluster solutions are used. As has been found, when multiple clusters are examined, the first cluster is maximally different from subsequent clusters. That is, the second and third clusters are often more likely to show overlaps in their profiles. A recent study looking at soup preferences (Wansink and Park, 2000) indicated that while the first cluster profiles very clearly differentiate soup lovers of various soup flavors (tomato, vegetable, chili, and so on), the second clusters were more similar than different (see Figure 2). This means that while multiple cluster solutions are important for differentiating heavy users, their real value comes more in their ability to provide distinct, highly differentiated first clusters than to provide highly differentiated second and third clusters.

Which characteristics best differentiate heavy users?

While some researchers believe demographic profiles of heavy users provide safe surrogates for psychographic profiles (Assael and Poltrack, 1994; Grenhaug and Zaltman, 1981), several researchers have expressed their skepticism about the use of demographic and psychographic data as a basis for market segmentation (Frank et al., 1972; Kassarjian, 1971). Indeed, demographic variables have been shown to be poor predictors of brand-choice behavior, partly because of narrowing differences in income, education, and occupational status in an affluent, mass consumption society (Sheth, 1974). Even when demographics discriminate heavy users of products, they still have their limits in helping generate insights about customer segments (Clancy and Schulman, 1994).

However, lifestyle and personality characteristics that are specific to certain consumers and product categories must be defined and measured in order to be useful to marketers. For instance, it would be relevant for a food company to identify a health-conscious segment or for a clothing company to identify a fashion-conscious segment. In other words, personality and lifestyle characteristics, in contrast to demographics, need to be defined by researcher�s objectives. Even if marketers get managerial insights from the psycho-graphics of consumers that are consistent over time, they are still costly and lack objectivity. For example, the Value and Lifestyle (VALS) typology provides rich description about a unique way of life and values (Mitchell, 1981), but the pieces may or may not have any relevance for a certain brand. This also holds true for benefit segmentation, which has been considered the most preferred segmentation methodology for almost two decades (Haley, 1968, 1984). If two competing brands provide an identical benefit that consumers want, benefit segmentation may not fully discriminate the target consumer segment for one brand from another.

When focusing on heavy users, however, the universe of potentially discriminating variables decreases. Heavy-user studies are typically focused on food and fast-moving consumer packaged goods (Wansink and Gilmore, 1999), and issues of fashion leadership and aesthetic taste become less important.

To better begin developing a standard for personality and lifestyle characteristics that differentiate heavy users of foods and packaged goods, a collection of personality and lifestyle characteristics were collected from past studies (see Wansink and Sudman, forthcoming 2001). These variables were subsequently modified and combined through factor analysis to arrive at 14 lifestyle and 20 personality variables that have been effective in differentiating heavy users of various foods and packaged goods. A factor analysis of these variables was completed and the dimensions shown in Table 2 were found to be significant in differentiating heavy users.

The factor analyses of these variables showed that there were six lifestyle factors that differentiated heavy users (active lifestyle, family-spirited, homebody, intellectually stimulating pastimes, TV lover, and pet lover) and five personality characteristics that tended to differentiate heavy users (mentally alert, social, athletic, carefree, and stubborn).

Of particular interest is that there were a number of variables that have been used in commercial studies but not in academic studies. These revolved around lifestyles and interests and include the extent to which a person is a pet lover, workaholic, good cook, churchgoer, TV addict, book lover, technology whiz, or world traveler.

METHOD

One key objective of this study is to determine how accurately the segments obtained through a mean-comparison method and a hybrid two-stage cluster analysis differentiates heavy users. American adults from 18 to 72 years old (mean of 47) were surveyed by telephone in June of 1999. The survey collected the key personality and lifestyle traits outlined in Table 2, along with demographic information and behaviors and preferences related to soup and soup consumption. A total of 1,003 interviews were completed among 602 women and 401 men.

To create user profiles, two basic segmentation methods were used: a simple mean-comparison and a hybrid two-stage cluster analysis. In the mean-comparison method, consumers were categorized into three user groups, heavy, light, and non-users, and comparisons of each user group�s average responses were made. (This was conducted on lifestyle characteristics and on personality characteristics.) In the hybrid two-stage cluster analysis, we modified the two-stage cluster analysis method proposed by Punj and Stewart (1983) by conducting three separate two-stage cluster analyses for heavy, light, and nonusers.�

Which segmentation method generates more diagnostic heavy-user profiles?

Consumers were categorized into three groups: heavy users (38% ate soup more than once a week), light users (47% ate any soup more than once a month), and nonusers (15%). Our results indicate that a simple mean-comparison method provided somewhat distinctive but limited profiles of heavy users. Heavy users are socially active, creative, optimistic, witty, and less stubborn than light- and nonusers. Yet even though heavy and light users were compared across 34 characteristics, only 3 characteristics differentiated the two groups.

To allow more discrimination between profiles, hybrid two-stage cluster analyses were conducted across lifestyle and personality characteristics. As indicated in Figure 3, both personality and lifestyle hybrid two-stage clustering methods generated more diagnostic heavy-user profiles than the mean-comparison method. While the mean-comparison method provided the least differentiation across light and heavy users (only 9% of characteristics differentiated the user groups), the clustering methods differentiate among 36% of the lifestyle characteristics and 85% of the personality characteristics. Figure 3 illustrates these levels of differentiation and Appendices 1 and 2 provide additional detail.

In contrast to the mean-comparison method, both the lifestyle clustering method and the personality clustering method provide richer descriptions of heavy users. Both demonstrate that heavy users are not all the same. Furthermore, both show, without further separating heavy users on the basis of an a priori variable (such as benefits sought, health consciousness, and so on), the simple mean-comparison method inaccurately combines distinct types of heavy users together into one generic profile.

TABLE 2: LIFESTYLE AND PERSONALITY VARIABLES THAT DIFFERENTIATE USERS AND PREFERENCE

Which characteristics best differentiate heavy users?

Do all heavy users fit the same profile? As discussed earlier, our major concern is that not all heavy users are alike. One heavy user of soup might be a heavy user for convenience, while another might be one for price. These two might have very different profiles. For example, even if our results indicate that heavy users are socially active, creative, optimistic, and witty, we believe there may be significant differences among heavy users that we have overlooked.

To examine which variables � personality characteristics or lifestyle characteristics � better profiled heavy users, we compared the numbers of variables that showed statistically significant differences in means between soup users in segment (cluster) 1 and segment (cluster) 2. As indicated in Table 4, personality segment number 1 showed that light users were 100% different from nonusers and heavy users were 85% different from light users in terms of their personality, while the two lifestyle segments showed 71% and 36%, respectively.

Consider the two personality segments of heavy users (Table 3). One segment might be classified as 'traditionalists' while the other group might be classified as dynamos.' The traditionalists enjoy home and family magazines but are not affectionate, sophisticated, competitive, trend-setting, intellectual, nutrition conscious, fun loving, sarcastic, or stubborn. This segment is in dramatic contrast to the second segment. The 'dynamo' segment likes reading sports magazines and is adventurous, creative, outgoing, athletic, optimistic, fun at parties, witty, spontaneous, detail-oriented, and down-to-earth. While it is interesting that the heavy user of soup segments could be so dramatically different, this is because both groups eat soup for different reasons. The 'traditionalist' segment eats soup because it is an 'inexpensive meal solution' that 'goes with other foods.' The 'dynamo' segment eats soup because it is 'quick and convenient' and perceived as 'healthy.'

All heavy users may not be uniformly considered the same customer segment, and there may be overlapping clusters among heavy, light, and nonuser segments (recall Figure 2). The hybrid two-stage cluster analyses for personality and for lifestyle characteristics supported the notion that the heavy users could be differentiated from each other because they were not all alike. While the distinctiveness between Segment1 and Segment2 depends on whether we use lifestyle or personality variables, Segment1 appears to be more differentiated. This is consistent with the pattern shown in Figure 2. Interestingly, the new lifestyle variables that were tested - pet lover, TV addict, book lover, and churchgoer � differentiated between heavy and light users and between the two segments of heavy users.

SUMMARY AND DISCUSSION

Implications for profiling heavy users

This research showed how two general segmentation methods, a simple mean comparison and a hybrid two-stage cluster analysis, can lead to very different lifestyle and personality profiles of heavy users. When the results of the two methods are compared, the hybrid two-stage cluster analysis � which combines the simple mean-comparison method and basic cluster analysis�is the more diagnostic method for building heavy-user profiles.

TABLE 3: LIFESTYLE AND PERSONALITY PROFILES OF SOUP USERS: MEAN COMPARISONS VS. CLUSTER ANALYSES

TABLE 4: PERSONALITY PROFILES DIFFERENTIATE BETTER THAN LIFESTYLE  PROFILES

Second, these results provide an important benchmark by specifying the characteristics that differentiate heavy from light users. Although personality variables generate more distinctive and unique heavy-user profiles than the lifestyle and demographic variables, all help build rich heavy-user profiles. In particular, it was found that there were six lifestyle factors that differentiated heavy users (active lifestyle, family-spirited, homebody, intellectually stimulating pastimes, TV lover, and pet lover) and five personality characteristics that differentiated heavy users (mentally alert, social, athletic, carefree, and stubborn). Of particular interest is that there were a number of variables that have been used in commercial studies but have not been used in academic studies until now. These include the extent to which a person is a pet lover, workaholic, good cook, churchgoer, TV addict, book lover, technology whiz, or world traveler.

Last, this study provides useful guide-lines and methodological implications for future research on heavy users as well as customer database management. Future research on heavy users needs to adopt a rigorous segmentation approach in order to provide accurate profiles of heavy users. One way to do this is by using consumer profiles generated from one method that need to be supported by another method, such as customer prototyping techniques (Wansink, 1999). Managers of customer databases need to take a holistic attitude in building and managing their customer database because any single variable alone, such as personal lifestyle, demographics, or media preference, cannot provide full descriptions of customers.

Can media preferences differentiate heavy users?

The result of these differences in magazine readership suggest that consumers preferences across other products might also be differentiated across heavy, light, and nonusers. 'Affinity marketing' is of significant interest to e-commerce and to database marketing, because it suggests a bundle or a metaphorical 'shopping basket' of products that an ideal target market might be attracted to (Wansink and Ray, 1992, 1996). This provides new justification for marketers to try building their customer prototypes based on their preference for certain products or brands along with building customer prototypes based on personality and lifestyle data.

CONCLUSION

We identified the methods and measures that can be used to effectively profile heavy users. The most effective market segmentation generates the most accurate, detailed, diagnostic, and in-depth profiles of heavy users. This article shows the shortcomings of the commonly used mean-comparison method of heavy-user segmentation, and it outlines a clustering method that effectively differentiates different types of heavy users from light users. The various characteristics that differentiated heavy users from light users 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.

APPENDIX 1: THE MEAN COMPARISON METHOD OF PROFILING SOUP USERS

   APPENDIX 2: CLUSTERING METHODS AND MEASURES THAT DIFFERENTIATE SOUP USERS

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ENDNOTES

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What is the form of relationship between product and consumer?

Which marketing philosophy emphasizes interacting with customers on a regular basis and giving them reasons to maintain a bond with a company's brands over time?

Are statistics that measure observable aspects of population?

When a product helps to establish the user's identity the user is said to have an type of relationship with the product?