Jul 3, 1996 - Audience segmentation is widely regarded as essential to effective health communica- tion campaign efforts. Nonetheless, its practice is ...
Theory and Method in Health Audience Segmentation MICHAEL D. SLATER Department of Technical Journalism Colorado State University, Fort Collins, Colorado, USA Audience segmentation is widely regarded as essential to effective health communication campaign efforts. Nonetheless, its practice is typically ad hoc. The conceptual history and theoretical bases for audience segmentation are reviewed, and typical audience segmentation strategies for health communication efforts are described and critiqued. An analogy is drawn between the methodological problems associated with audience segmentation and those of multivariate classication and taxonomy in botany and zoology. Cluster analytic techniques responsive to these issues are described, as are applications of these techniques for analysis of health communication campaign audiences. Approaches that would permit widespread use of such segmentation strategies are discussed, and recommendations for such efforts are made.
Audience segmentation is widely acknowledged as essential to creating effective communication efforts (Atkin & Freimuth, 1989; Donahew, 1990; Grunig, 1989). Audience segmentation, communication authorities continually point out, is the necessary prerequisite to creating messages that are responsive to the concerns, needs, and perspectives of specic populations. Similarly, segmentation provides the basis for selecting the media, community, organizational, or interpersonal channels most appropriate to such populations (e.g., Cutlip, Center, & Broom, 1985; Wells, Burnett, & Moriarty, 1989). Unfortunately, as is detailed in this article, typical strategies for segmenting audiences are often ad hoc, crude, or based on typologies more appropriate for theory development than for campaign design. Campaign and intervention planners usually recognize the necessity for formative research on both audience attitudes and audience assessments of intended messages or interventions. What may be less well recognized are the implicit assumptions made in conducting such formative research. When, as is increasingly standard practice, focus groups are the primary tool for audience research, the planner implicitly segments the audience a priori. For example, in a smoking cessation intervention, the focus groups may be recruited on the basis of age, gender, and ethnicity—whether or not these are in fact the most appropriate or useful segmentation variables. Even when a knowledge– attitude–behavior survey is conducted, results are typically described in terms of mean levels for some various a priori groups— such as young African-American women or middle-aged White men. If these a priori choices turn out to be inaccurate, or less than optimal, the resulting message or intervention design and dissemination decisions will suffer. In other words, if one is to communicate, one must begin with an implicit or explicit de nition of who one’s audiences or interlocutors are. Segmentation is, at its core, a systematic and explicit process for arriving at such a de nition. This article includes a brief review and critique of the concept and practice of audience segmentation. I also offer an alternative conceptualization of audience segmentation as a research activity and suggest a model for conducting audience segmentation in support of public health communication and other similar behavior change efforts. Address correspondence to Michael D. Slater, Department of Technical Journalism, Colorado State University, Fort Collins, CO 80523, USA.
267 Journal of Health Communication, Volume 1, pp. 267–283, 1996 Copyright © 1996 Taylor & Francis 1081-0730/96 $12.00 + .00
268
M. D. Slater
Dening Audience Segmentation Audience segmentation has its roots in the history of social sciences and social philosophy over the last 75 years. In the early years of the century, theories of mass society and crowd psychology were highly in uential as thinkers grappled with the phenomena of industrial society and rising totalitarianism (see Bauer & Bauer, 1960; McQuail, 1983, for reviews). Other theorists, including John Dewey (1927), more concerned with understanding the social processes characterizing heterogeneous, pluralistic societies, took a very different view. Dewey introduced the notion of publics into the study of public opinion. Publics were subgroups or subpopulations that shared similar values or interests with respect to a given issue; public opinions was larged shaped, in Dewey’s view, by the formation and the activities of such publics. Focus also shifted in other social sciences to identifying and describing typologies of people, as reected in the rise of personality research in psychology (e.g., Allport & Odbert, 1936) and in the use of theoretical typologies in sociology, which have their roots in Weber’s (1949) model of “ideal-typing.” These concepts moved, in time, into the more applied social science disciplines. The term segmentation was introduced in the marketing eld by Smith (1956). Smith pointed out that marketers typically increased market share by product differentiation—attempting to increase demand by creating a supply of a product unique in some respect. Smith advocated, instead, market segmentation— identifying promising subgroups of consumers, learning what their needs and desires were, and developing products tailored to those subgroups. Segmentation became one of the central strategies taught and utilized in the marketing eld (e.g., Bonoma & Shapiro, 1983; Kotler & Andreason, 1987; Weinstein, 1987). The advantages of segmentation were clear: Marketing and promotion efforts could generate a higher return by creating products and promotions tailored to the desired segment. Audience segmentation has also been widely accepted as essential in conducting communication campaigns intended to in uence health and other socially relevant behaviors (e.g., Atkin & Freimuth, 1989). The purpose of audience segmentation is to make communication efforts more effective and ef cient. Effectiveness and ef ciency are de ned with respect to intended communication outcomes: typically, to change levels of knowledge or concern about some topic or to shift valence or increase accessibility of relevant attitudes and ultimately to reshape behaviors, such as dietary patterns, sexual practices, or substance use (Rogers & Storey, 1987). Nonetheless, there remains a certain ambiguity to the segmentation concept. As Grunig (1989) pointed out, the basic idea of segmentation is simple: divide a population, market, or audience into groups whose members are more like each other than members of other segments. (p. 202) Certainly, breaking up a heterogeneous audience into relatively more homogeneous audiences is the essence of segmentation. But what criteria, what discriminators are properly used to segment an audience or market? Grunig (1989) summarized criteria described in the marketing literature: In general, segments must be de nable, mutually exclusive, measurable, accessible, pertinent to an organization’s mission, reachable with communication in an affordable way, and large enough to be substantial and to service economically. (p. 203)
Health Audience Segmentation
269
These criteria certainly would characterize segments useful to health communicators. They provide little guidance, however, for how to actually identify such segments. In fact, what has emerged is a smorgasbord of segmentation techniques and strategies, with little but personal preference as a guide for selecting a preferred strategy. Before critically reviewing those strategies, however, it is important to develop more explicit and prescriptive criteria for distinguishing audience segments.
Behavior Determinants: The Criteria for a Segmentation Strategy The most straightforward model for in uencing health or other behaviors through communication proposes that if people will attend to, understand, accept, and remember relevant information, they will change attitudes and nally their behavior (Fishbein & Middlestat, 1989; McGuire, 1989). Other approaches include in uencing perceptions of relevant social norms (Ajzen & Fishbein, 1980), providing appropriate behavioral models and skill training (Bandura, 1986; Maibach & Flora, 1993), increasing salience of attitudes supportive of the desired behavior (Vincent & Fazio, 1992), increasing salience of and cognitive involvement with the behavior (Chaffee & Roser, 1986), and mobilizing the support of in uential individuals and groups (Finnegan, Bracht, & Viswanath, 1989). Few campaigns have the resources to use every strategy with the entire intended audience. Nor would such an effort be very ef cient or sensible. It is very unlikely that relevant behavior of everyone in the intended audience is equally in uenced by each of the possible determinants of that behavior. What is needed, clearly, is to identify subgroups that have in common similar determinants of the behavior in question. In other words, segments should be homogeneous with respect to patterns of variables (and values on those variables) determining the attitudes and behaviors targeted by a communication effort. If such subgroups or segments can be identi ed, common messages or intervention strategies can be designed for them. Any segmentation strategy for a communication effort designed to in uence knowledge, attitudes, or behavior in a given domain, then, should proceed as follows. The rst step is to identify from existing research, as completely as possible, the known determinants of knowledge, attitudes, and behavior in that domain. The second step is to identify audience segments on the basis of distinctive patterns of determinants, each of which can be addressed through tailored communications and related activities. The remaining problem, of course, is how to go about identifying patterns of determinants. With most health behaviors, it would not be dif cult to identify a dozen theoretical perspectives that would suggest behavioral determinants. Although some theories overlap to a degree, one may still expect to be looking at two dozen or more variables. Given that each segment may best be distinguished by almost any combination of two or more of those variables, the practical problem of identifying patterns from such multivariate data may seem daunting. Later, I discuss some of the conceptual and methodological problems posed by such data, some appropriate solutions, and some examples of audience segmentation efforts utilizing these solutions. First, however, it is useful to review existing segmentation strategies in light of the position just outlined.
Segmentation Methods: A Brief Review The broad array of strategies and methods used to segment audiences— including demographics, surveys in which demographics are crosstabbed with media use and some key psychosocial variables such as involvement and behavior, focus group discussions with
270
M. D. Slater
key target groups, and commercially available segmentation schemes such as Values and Lifestyles (VALS)—have led to characterizations of segmentation as being more art than science. There are, however, clear conceptual distinctions and implicit hierarchies in segmentation strategies. Bonoma and Shapiro (1983) argued that segmentation strategies form a nested hierarchy (an argument further developed and interpreted by Grunig, 1989). The most general segmentation schemes discriminate between audience or market groups on the basis of easily accessed variables, such as demographics (education, race, income, gender) and geographic location. More sophisticated schemes use inferred variables to discriminate segments— variables that more precisely describe differences between groups of people and that therefore yield more informative segments, but that require more resources to identify and access. In other words, it is easy to de ne low-income, urban African-American adolescent boys as an audience segment. Such a segment is only truly useful to a campaign designer insofar as it provides a basis for campaign design. If it turns out that there are some very different patterns of beliefs, behavior, and value in this population, this demographically based segment would prove inadequate. For example, consider the following hypothetical situation: Some members of this demographically de ned group are anomic, poorly connected to school, family, or any social institution except perhaps law enforcement; others are characterized by much closer ties to family and community institutions such as the church; and still others are torn between family and community values and the norms of more anomic peer groups. Clearly, a single message or channel strategy directed to all of these demographic members would probably be counterproductive. A more productive strategy would include some inferred, psychosocial variables that serve to identify these differences and that could be addressed in message design. The problem with the demographic segmentation suggested here also describes a aw besetting much focus group and other qualitative audience research methods. Focus groups involve in-depth discussions with individuals recruited to represent some particular target group. The representativeness problems in such recruitment are well recognized. A more insidious problem involves the initial identi cation of the target group itself. Most often, the target group is de ned demographically. If demographics are not a good basis for segmentation for the topic or issue in question, the focus group will not describe a segment. Instead, it will simply alert the researcher that the segment seems unexpectedly heterogeneous in its concerns and motivations—if the recruitment was fortunate in reaching the range of people who in fact compose that demographic group. Focus groups and other forms of qualitative research are often most effective when a rigorous, quantitatively based segmentation strategy has helped de ne the segmentation scheme. Then, individuals can be recruited who in fact t the de nition of the segment, and rich qualitative data can be obtained and be more meaningfully and usefully interpreted. The problem is exacerbated when one is concerned with in uencing behaviors that may be poorly described by demographics. Crack cocaine use, for example, may be closely associated with demographics, but demographics alone are of more limited use in describing cigarette smokers or alcohol abusers. A variety of psychosocial and behavioral variables must be tapped to reasonably differentiate between types of audiences. Problems such as these are most acute in industrialized countries or in urban areas in developing countries, in which there is considerable heterogeneity with respect to cultural norms and values even within demographically similar groups. In national or regional environments that are marked by long-standing, relatively intact and homogeneous cul-
Health Audience Segmentation
271
tures and traditions, the segmentation problem is less complex. However, such cases are increasingly becoming the exception rather than the rule. This returns us to the quandary discussed earlier: How does one develop segmentation schemes when dealing with many variables simultaneously? A typical compromise strategy— and one, given limited research resources, that has much to recommend it—is as follows. Demographic categories known or reasonably suspected to be at least somewhat associated with the target health behavior become a starting point. One then reviews the literature for what might plausibly be crucial psychosocial variables— self-ef cacy and peer or family support for cigarette smoking cessation, for example. These are then used to further segment demographic categories. This approach has obvious and not-so-obvious drawbacks. First, it assumes that there are in fact just a few crucial variables that dwarf others in explanatory power. This may be true for some health behaviors; it certainly is not true for all or even most. Second, it assumes that one has correctly identi ed those variables. Third, even if the rst two assumptions hold, one may well miss additional, less dominant but useful distinctions among segments. Such subtle distinctions are precisely what permit careful and creative targeting of messages. 1 The segmentation process, nonetheless, usually stops there. The reason it does is, in part, embedded in the standard training and socialization of academic social scientists (though not, typically, of market researchers). Empirical, academic social science develops, tests, and applies theoretical models. Such models emphasize parsimony—explanation of a given process or phenomenon using a minimum number of variables (e.g., Babbie, 1983; Boltzmann, 1960). The statistical models used to do hypothesis testing quickly become cumbersome to analyze and interpret when overloaded with variables. The value of parsimony in theory building and testing and their associated statistical methodologies is directly opposed to the need to deal with many, even dozens, of variables simultaneously to optimally segment audiences with respect to health behavior. There are alternative statistical methodologies, detailed later in this article, intended precisely to create typologies based on patterns across multivariate data. Before reviewing these methods, it is important to establish and further clarify the theoretical justi cation for their use, as an alternative to more conventional approaches to typology in the academic social sciences. Social Science and Typology Construction
Segmentation as normally conducted is, from a methodological point of view, a process of creating typologies. Typologies have a familiar and respected role in the construction of social theory. As McKinney (1966) argued, social scienti c inquiry seeks to identify uniform, predictable patterns or to impose conceptual order on the apparent disarray of the social world, and typologies have an important role in such inquiry: “The constructed type is a means of reducing diversities and complexities of phenomena to a generally coherent
1 In making this and following critiques, I outline what is an ideally preferable set of approaches and strategies. I recognize that in reality compromises often have to be made. Given that the principal purpose of segmentation is to alert campaign planners and message designers to audience distinctions, even relatively ad hoc approaches are preferable to no segmentation efforts at all. Some suggestions for segmentation choices under conditions of limited resources are outlined in Slater (1995).
272
M. D. Slater
level … a manifest function of types is to identify and simplify” (pp. 5–6). This process of identi cation and simplication is an important step in the theory building process: “The constructed type as a conceptual device represents an attempt to advance concept formation in the social sciences from the stage of description and empirical generalization to the construction of theoretical systems” (McKinney, 1966, p. 7). A construct type, as a building block of theory, exists primarily “for predictive rather than descriptive purposes” (McKinney, 1966, p. 6). Segmentation typologies, in contrast, are intended primarily to provide a basis for obtaining descriptive information concerning the beliefs held, the behaviors enacted, and the constraints faced by group members so that appropriate message design and communication strategies can be developed to in uence attitudes and behaviors. It is essential that segments be predictive of the targeted behavior— if there is no association between segment membership and the behavior of interest, the segment will have little or no value to the campaign designer or health educator. To better guide channel selection and intervention design decisions, the segments should also be predictive of distinctive patterns of media use or reliance on different organizational, community, or interpersonal channels. The heuristic value of audience segments, however, is as much or more to describe, so as to inform message and intervention strategy design, as it is to predict. A typology in the theoretical context is a variable representing a single, nominal construct (or, in more complex cases, two or even three nominal variables in a matrix, creating four, six, or eight different types) intended to impose predictable order on empirical data. For example, one of the best-known such theoretically based typologies used for segmentation purposes is Grunig’s (1983) situational theory. Three variables— problem recognition, issue involvement, and constraint recognition— have been demonstrated to predict information seeking behavior and attention to information with respect to a given issue (e.g., Grunig, 1983). They are dichotomized and matrixed to create eight segmentation categories, which are then collapsed to identify four modal segments. The strength of this strategy is that it provides a theoretically sound typology for understanding both information-related behavior and for processes—such as public opinion formation— in which such behavior is central (e.g., VanLeuven & Slater, 1991). However, such a typology is of limited use in identifying segments that can guide a communicator in developing the content for a series of messages or in trying to select speci c channels for disseminating those messages. The situational theory variables are very useful but alone are insuf cient to create adequately informative segments, in particular for behavior change campaigns that need to identify and address determinants of behavior. For example, knowing that some group recognizes lifestyle-related heart disease risks, but is not especially involved with or concerned about that risk, is useful. However, one needs to know a great deal more about the nature of group members’ beliefs regarding risks and the proposed behavior change, the possible reasons or context for their apparent lack of involvement as well as their overall orientation to their health, and the extent of their actual risk for this information to be most effectively used in designing a health communication effort. In fact, this group may prove to be several groups with very different patterns. One group might be distinguished by beliefs that lifestyle changes have limited ef cacy and are relatively noxious, and another might be characterized primarily by limited concern with their health and a resulting lack of interest in health-related behavioral changes. Obviously, message content or interventions targeted at these two groups would have different content. Segments, on the other hand, are constructed post hoc from empirical data, using a variety of constructs and variables— the more the better, in terms of providing insight to
Health Audience Segmentation
273
the campaign or education planner. A segment is not intended to operationalize an underlying concept—it can only be summarized by synthesizing and interpreting the variables that uniquely identify it. Nonetheless, social science researchers conducting audience segmentation research are most likely to construct typologies in the manner most familiar to them—and most respected by their colleagues and most publishable in journals. Typologies will most likely be based, as described earlier, on one or more conceptually based variables, converted to a nominal variable, and perhaps crossed with some demographic variables of interest. Theory-based typological research has an important role to play in audience segmentation—by identifying additional variables that should be incorporated in segmentation analyses. As a segmentation technique, such approaches, though useful, are not optimal. Typing Audience Segmentatio n Strategies
Grunig (1989) adopted Bonoma and Shapiro’s (1983) concept of inferred segmentation variables nested within objective variables and proposed a nested hierarchy of segmentation strategies. This hierarchy is based on a continuum of lesser to greater speci city and utility. Visualized as nested boxes, the outermost box is the undifferentiated mass audience. The next box inside the mass audience represents demographic segmentation schemes. Within that are geodemographic schemes (e.g., PRIZM, which identi es dozens of demographic types located by census tract—Claritas, Inc., 1985; Winkelman, 1987), followed by boilerplate lifestyle or psychographic schemes, such as VALS (Mitchell, 1983), which characterize segments in a richly multivariate way but seek to apply the segmentation strategy to virtually all American consumers and a very wide range of different behaviors. The other nested boxes, in order, are communities, publics (de ned as individuals sharing similar communication behaviors with respect to a given issue), and nally individuals themselves. This nested hierarchy usefully highlights the advantages of focusing on the more specic, versus the more general segmentation strategies; with increasing speci city comes increasing detail and increased ability to focus the communication to the audience. Grunig’s (1989) approach, however, is in some respects debatable. His proposed hierarchy is based on an interesting assumption concerning the intent of segmentation efforts. He argued that theories or approaches guiding segmentation efforts should “help communication planners divide a population into segments of people who will communicate similarly about the topic of a campaign” (Grunig, 1989, p. 208). This focus on communication behavior led to his emphasizing, as criteria for identifying segments, those variables that predict amount of media and channel use and that predict attention to information encountered on given topics. Such variables are very important for one of the two functions that must be served by a segmentation scheme: identifying appropriate communication channels. However, they are less useful for providing the insight necessary to create the messages or the content of intervention strategies designed to in uence a given behavior. These insights, as argued earlier, must be based on knowledge about the factors that determine existing beliefs, attitudes, and behaviors. Some critics (e.g., Cameron & Yang, 1991) have pointed out that Grunig’s (1989) assumptions about segmentation exclude attitudinal variables as fundamental as the valence of people’s attitudes about a given topic. Grunig’s (1989) nested approach makes useful points. He emphasized that the preferred inner boxes reect situational, issue- or behavior-speci c variables, though he
274
M. D. Slater
approached these in terms of theoretical typologies rather than multivariate analysis. Demographics are demographics, regardless of whether the topic is weight loss or crack cocaine use. More sophisticated segmentation approaches identify variables that are less generic and more likely to distinguish individuals with respect to the issue at hand. However, Grunig’s hierarchical continuum emphasizes the increasing speci city of the segmentation strategy with respect to the size of the unit of analysis (e.g., gender or ethnicity groups, communities, publics, then individuals), rather than simultaneously increasing speci city and comprehensiveness with respect to the variables known to determine a target attitude or behavior. One can then usefully recon gure Grunig’s (1989) nested hierarchy along a slightly different axis. In Figure 1, segmentation strategies are organized in terms of the degree of correspondence between segmentation variables and the variables that in fact are known to shape or determine the attitudes or behaviors of interest. The outer box remains the same: the mass audience, which is a univariate concept that does not vary. The next two remain demographics and geodemographics. The separate category for lifestyle and psychographic methods has been dropped. Such methods are in fact multivariate classication strategies. The implementations of such strategies (e.g., VALS) discussed by Grunig (1989) involve classi cation schemes that are based in overarching models and assumptions about American lifestyles and values and not on variables that predict a speci c behavior (except, perhaps, the purchase of certain categories of durable goods). As such, they do not t the de nition of multivariate classication strategies as proposed here.
Figure 1. Segmentation strategies nested according to correspondence with determinants of target attitudes or behaviors.
Health Audience Segmentation
275
The next box is labeled theoretical typologies. These include publics, in the sense used by Grunig: segments that are homogeneous with respect to values on one or several variables theoretically linked to the targeted issue or behavior. The penultimate box is labeled multivariate classication: segments discriminated by, as proposed earlier, distinctive patterns of the known attitude or behavior determinants. The nal box is labeled taxonomies. What remains to be explored are the conceptual issues and the methods involved in multivariate classication and taxonomic approaches to segmentation.
Multivariate Classication and Taxonomy The central argument of this article thus far has been that audience segmentation is fundamentally a problem in nding systematic patterns for the variables (and the values on those variables) that determine a target behavior. This problem is closely analogous to what was for centuries the central task of biology, and which still remains an important concern—the organization and classication of the tremendous variety of living organisms, the enormous intellectual enterprise of taxonomy. Both audience segmentation and biological taxonomy are problems in multivariate classication. Recent approaches and developments in biological taxonomy provides some useful insights and methodological models for audience segmentation. Certainly, consideration of biological taxonomy underscores the importance of conceptualizing audience segmentation, from a methodological point of view, as a process of multivariate classication. Abbott, Bisby, and Rogers’s (1985) comments about biological taxonomy are equally applicable to audience segmentation: It seems increasingly clear that modern biologists must give up the idea that there will ever be a single, ideal classication for any group. Instead, the special purposes of any classication should be clearly stated and the data input and methods of analysis be made as explicit as possible …. classication techniques need to be understood by anyone doing research with comparative biological data, especially where large numbers of variables must be considered simultaneously. (p. 7)
Biological Taxonomy
It is rst useful to brie y review some current issues and nomenclature in the eld of biological taxonomy. Taxonomy may be traced back to Aristotle’s early efforts to classify animals; its present form first began to take shape with Linnaeus and his proposed hierarchical ordering— taxa—of kingdom, class, order, genus, and species (see Pellegrin, 1986). Generations of taxonomists in the centuries since have sought to classify the seemingly endless variety of plant, animals, insects, and microorganisms into sets of categories. Assiduous data collection permitted grouping together those specimens that were relatively invariant on some set of characteristics, and further distinguishing them from specimens better identi ed using some still different characteristics (Abbott et al., 1985; Mayr, 1964; Panchen, 1992). Two developments led to considerable growth and controversy in biological taxonomy. First, as Panchen (1992) pointed out, the theory of evolution (as against the theory of
276
M. D. Slater
natural selection) really was a theory about taxonomy. Darwin’s argument was that taxonomic classications in fact reected patterns of common ancestry. This eventually gave rise to taxonomic schools—the phyleticists or cladists—who attempt to organize taxonomies to reect what they deduce to be phylogenetic or evolutionary history (Hennig, 1979; Scott-Ram, 1990). A second and related development was the growing use of computers and mathematical algorithms for the computational analysis of multivariate classications, or numerical taxonomy (Clifford & Stephenson, 1975; Sneath & Sokal, 1973). These mathematical approaches were adopted both by the phyleticists and by pheneticists —taxonomists who argued that it was not possible to reliably deduce evolutionary families and that therefore taxonomy was better practiced in terms of similarities and dissimilarities of currently available data (Abbott et al., 1985). The phenetic approach provides the closest analogy to the problem of audience segmentation. At present, at least, there is no theory of audiences comparable to evolutionary theory, and there is neither a prospect nor an obvious pressing need for such a theory. Moreover, the differences between the cladists, especially the more recent transformed or pattern cladists (e.g., Scott-Ram, 1990) and the pheneticists are far fewer than the similarities in their use of numerical taxonomy. 2 The following discussion, then, reects primarily the somewhat simpler phenetic perspective. Numerical Taxonomy and Cluster Analytic Approaches to Classication
Multivariate data, of course, is typically represented in a matrix. Arrayed on one axis of the matrix are the units of observation— people, in the case of audience segmentation, biological specimens in the case of taxonomy. Arrayed on the other axis are the relevant characteristics of the units of observation— determinants of a target attitude or behavior in the case of audience segmentation, morphological or biochemical features in biological taxonomy. Such a matrix can also be represented spatially. The relationship between the units of observation and a single variable can, of course, by represented in a two-dimensional Cartesian coordinate graph. The relationships between units of observation and many variables can be conceived of as an n-dimensional plotting of points, with as many dimensions as one has variables measured. 3 Earlier, the question was posed as to how one might discern complex patterns of relationships between many variables representing determinants of an attitude or behavior simultaneously. If there is no pattern, if the population is uniformly homogeneous, all points would tend to be equidistant from one another. To the extent that patterns exist— that is, to the extent that a sizable subgroup is characterized by similar values on some variables, points will tend to cluster in n-dimensional space accordingly. 4 2 Some cladist techniques for classi cation, including weighting of speci c characteristics for classi cation purposes (Scott-Ram, 1990), might be adapted to audience segmentation strategies. The use of numerical taxonomic techniques in audience segmentation has a long way to go before such issues become salient. 3 An excellent discussion of these multivariate methods, including a review of various relevant variants, is given in Aldenderfer and Blash eld (1984) and Abbott et al. (1985); the discussion here brie y summarizes points elaborated in those two sources. 4 Another approach gaining increasing acceptance in the market research community is commonly known as Chi-Squared Automatic Interaction Detection (CHAID) (Kass, 1980; Magidson, 1993), which uses categorical variables (or collapses ordinal scales into categorical variables) to generate segments; CHAID is based, like regression or discriminant analysis, on prediction of a speci c criterion variable and does provide signi cance tests of that predictive relationship.
Health Audience Segmentation
277
Such cluster analytic techniques have been commonly used by marketers as well as by numerical taxonomists in biology to conduct what are commonly called lifestyle or psychographic studies (Weinstein, 1987; Wells, 1974). It is dif cult to ascertain the extent to which such studies conform to the proposed model of building segmentation speci cally on variables demonstrated to determine the target behavior, given that most are proprietary and not accessible to critical review. They certainly do not easily generalize to other domains, such as health behaviors. Several studies in the health education eld are available as models, such as Slater and Flora’s (1991) segmentation based on determinants of cardiovascular risk behaviors, and Morris, Tabak, and Olins’ (1992) segmentation based on determinants of prescription-drug information seeking by elderly persons. A few examples from these studies may make the concept of multivariate classication intuitively clearer. In fact, one of the advantages of such classication is that the resulting segments tend to be intuitively intelligible and of obvious heuristic value, despite the mathematical and conceptual complexity underlying them. Slater and Flora’s (1991) study identi ed seven principal segments with respect to cardiovascular disease risk in a population sampled from several central California cities. Two of the segments are especially instructive, given their demographic similarities. Both segments were mostly White, middle class, middle income. One was the lowest risk segment of all: Diets were considerably better than the overall mean, and segment members engaged in moderate though not vigorous exercise, tended not to smoke, believed cardiovascular disease was preventable, were willing and believed themselves able to change their health behaviors, and were attentive to health information. In contrast, the other group included persons with poor diet, smoking, and alcohol consumption habits, low self-ef cacy with respect to diet change, and peers with poor diet and smoking habits. They perceived themselves as being at risk for disease but had little intention to reduce such risks, and they avoided exposure to health information (Slater & Flora, 1991). Morris et al. (1992) identi ed four segments describing older persons’ motives for seeking information about drugs. These segments distinguished between, to take two examples, elderly persons who were risk avoiders (relatively assiduous in accessing information from reliable sources, heavily reliant on health professionals for care, and high in both actual and perceived knowledge) and elderly persons who were more inclined to practice self-care, were low in knowledge and perceived knowledge, and relied more on advertisements and less on more credible information. Morris et al.’s study is particularly useful as a procedural model—unlike in Slater and Flora’s (1991) study, instrumentation and measurement were tailored for the research question rather than being based on secondary data analysis. Slater and Flora’s (1991) research highlights another issue: that of validation. Multivariate classification— be it biologic al taxonomies or audience segmentation schemes—is a tool for collapsing complex data and rendering it interpretable and meaningful, not unlike traditional factor analysis. Like factor analysis, multivariate classication is much less often used to test hypotheses— rather, it is used to provide effective research tools. And, as is true of factor analysis, one can use techniques to assess reliability, such as hold-out analyses (comparable to split-half reliability tests) and can use predictive tests to examine construct validity (Aldenderfer & Blash eld, 1984). Taxonomy and Hierarchical Segmentation Strategies
Biological taxonomies, however, go further than do existing multivariate audience classication efforts. Biological taxonomies are hierarchical. The problem of creating an ef -
278
M. D. Slater
cient information-storage-and-retrieval framework for a vast array of categories is managed by the use of the familiar levels of subspecies, species, genus, phyla, and so on. At every level, the taxa are constructed so that every specimen is included in one and only one category, the categories being distinguished in increasingly gross ways (Abbott et al., 1985; Panchen, 1992). The question arises as to whether or not it is both desirable and possible to construct such hierarchic taxonomies for purposes of audience segmentation. The answer, I believe, is clearly yes on both counts. Hierarchical audience segmentation taxonomies would be desirable for two pragmatic reasons. First, communication campaigns use a variety of channels ranging from national media efforts to sponsoring interpersonal interactions between, for example, health care providers and patients. Obviously, the messages disseminated through national media cannot be nearly so closely tailored to speci c audiences as can messages disseminated on a community basis. The useful level of differentiation of segments is therefore quite different: The planner of a national public service announcement (PSA) effort will be interested in a segmentation scheme at a different level of precision and detail than would the planner of a community intervention in a given city. Second, campaigns vary in the types and variety of people they are trying to reach. Campaigns intended to change cardiovascular risk factors may try to reach a very wide variety of persons, and the breadth of a segmentation scheme is crucial. A campaign aimed at reducing crack cocaine use, or the sharing of possibly AIDS-contaminated syringes among drug users, is obviously far more targeted. Audience segmentation efforts in such a case would be very concerned with identifying useful if even seemingly minor differences between members of the target audience. After all, given the dif culty of in uencing such audiences, no variation that might provide insight and permit development of a more effective, better targeted communication or intervention should be overlooked. The problem of how to do such hierarchical segmentation schemes is more dif cult. Numerical taxonomists in biology use a range of techniques based not only on different algorithms but also on different conceptual models. Although cluster analyses of the type described above may be the most common approach, other approaches are based on set theory and information theory models that have yet to be applied to segmentation studies (Abbott et al., 1985; Clifford & Stephenson, 1975). Moreover, cluster analyses in numerical taxonomy are typically translated into hierarchical representations such as dendrograms, tree-branching diagrams that show hierarchical relationships. The problems and potential in using such approaches remain, so far as I am aware, to be explored in audience segmentation. It would not be dif cult, however, to develop hierarchical segmentation models using the more commonplace cluster analytic techniques, given a large and well-constructed sample population. One would simply need to develop an initial, more general set of segments or clusters and then proceed to analyze each into smaller components or clusters. This could be iterated as far as sample sizes allowed and as the task demanded. Whatever the benefits of multivariate classification approaches are for audience segmentation, it is clear that they are complex and sophisticated approac hes. Considerable resources are needed— the time and funds to collect necessary data, and the expertise to analyze and interpret such data. How practical, how useful, are such approaches, given the time and money limitations faced in most communication campaign contexts?
Health Audience Segmentation
279
Implem enting Multivariate Classication Segmentation for Health Communication Conducting multiple classication research to segment audiences clearly is not inexpensive. Any population survey involving random sampling and requiring reasonably high response rates involves substantial costs. (Response rates need to be good because some segments may also be characterized by lower levels of accessibility to or cooperation with researchers and thus could easily be minimized or missed. As is discussed further below, oversampling of those demographic groups likely to have low response rates can help.) Instruments tend to be lengthy, given the large number of psychosocial, knowledge, and behavior items that must be measured (see Slater & Flora, 1994). Quali ed researchers must be available to conduct classication analyses and interpret the resulting data. Given these costs, it seems that only the largest, best-funded communication efforts can indulge in multiple classication segmentation efforts. In fact, this is only the case if the audience segmentation task is approached inef ciently. Currently, very considerable resources are being devoted to audience segmentation efforts when summed across hundreds of extant health campaigns that may take place in the United States or other industrialized countries or the dozens that may take place in less developed countries. Typically, these efforts are to a greater or lesser extent ad hoc . It is not unusual, as discussed earlier, to rely on crude demographic distinctions to identify segments for the apparently more useful and pertinent focus group discussions and message evaluation sessions. At best, surveys may be conducted that crosstabulate one or two relevant theoretical variables with the demographic data. An alternative, and far more ef cient, approach is to create on a regional or national basis audience segmentation categories for each major health behavior of interest to supplement (not to replace) local audience research efforts. The procedures for doing so are relatively straightforward. An instrument could be constructed on the basis of thorough literature review to identify variables that have been shown to help determine health behaviors of interest. One of the major research decisions, in fact, will be determining which behaviors are suf ciently similar to permit construction of a single instrument and allow data collection in a single pass. For example, several health behaviors, such as diet, exercise, and smoking, are sufciently similar that it may be possible to get all necessary items onto a single, albeit lengthy, instrument, at considerable savings. The next issue concerns sample selection and design. The basic model for a largescale segmentation effort is the national probability sample. For some behaviors, such as dietary practices, all members of a national population may be of nearly equal interest. However, in most cases there are subpopulations of particular interest. For example, researchers, educators, and public health of cials concerned with smoking, alcohol abuse, or drug use obviously need to learn more about the substance users and abusers and about populations, such as young people, who are particularly at risk for becoming substance abusers. In addition, it may be important to focus additional attention on racial and ethnic minorities traditionally underserved or at risk. The sampling strategy, then, would ideally involve systematic efforts to oversample on the basis of relevant behavioral or demographic criteria. This does not contradict the principles of multivariate classication: The segments may still not be dominated by behavioral or ethnic differences if the sample is properly weighted. The additional data on such populations, though, will permit more ne-grained analysis of the segments, includ-
280
M. D. Slater
ing potentially the development of hierarchical segmentation strategies or creation of segmentation schemes focused on particular populations. After all, a campaign targeted speci cally at inner-city African-Americans could not make use of the regional or national segmentation tools unless enough of an oversample existed to provide speci c segmentation insights that would distinguish usefully among the intended audience. The issue of the utility of regional or national segmentation efforts for communitybased campaigns and interventions is especially important. National education campaigns may be more visible, but community-based efforts are arguably more important and certainly in total represent the bulk of health communication and intervention activity. To be useful at the local level, segmentation information must be ne-grained enough to provide useful insight for message or program design in the community. Communication or intervention planners in a community must also have tools that allow them to easily and inexpensively identify the size, makeup, and communication channel use of the relevant segments in their own community. Analyses of regional or national survey data, then, should include several steps beyond creation of an initial set of segments and associated validation procedures. Where appropriate (e.g., in segments that identify relatively high-risk populations), further efforts should be made to develop subsegments, using one or more of the approaches to hierarchical segmentation discussed earlier. Such subsegments would be especially useful to communication and intervention design at the community level. Even more important, discriminant analyses (e.g., Abbott et al., 1985; Klecka, 1980) and other statistical analyses should be carried out to identify a small subset of survey items (e.g., 25–30 items) that can be used with reasonable accuracy to determine segment membership. These items, with instructions for their use, could be provided to local health planners and communication designers. Relatively brief, inexpensive surveys could then be conducted locally to obtain the necessary segmentation information. Even if small-scale random surveys are not possible, these short instruments could be used selectively to help qualify and sort participants recruited for focus group research. Finally, the national task should also incorporate a qualitative research component. Once segments and important subsegments have been identi ed, it becomes a relatively straightforward task to recruit a small number of members of each segment for focus group, in-depth interview, or ethnographic research. This would considerably enrich the insights made available through the segmentation study. This broad-based approach to segmentation analysis is, of course, super cially not dissimilar from some of the marketing segmentation schemes discussed earlier, such as VALS and PRIZM. Large-scale health segmentation studies would share the virtues of ef ciency in use of research resources, as well as helping provide a common vocabulary for campaign and intervention planners and designers. Unlike such commercial schemes, they would be explicitly rooted in analysis of the variables demonstrated to determine relevant health behaviors and in the sampling of those populations of greatest concern with respect to a given health behavior. Though not inexpensive, they would represent a more cost-effective use of funds that are expended presently on attempting to solve, inadequately, the same segmentation problems over and over again. Two substantive efforts to develop national health audience segmentation schemes have been conducted to date in the United States (Maibach, Max eld, Radin, & Slater, in press; Patterson, Haines, & Popkin, 1994). Both have identi ed somewhat comparable segments and were well validated, with the clusters predicting various criterion health behaviors in most cases substantially better than do regressions using demographic vari-
Health Audience Segmentation
281
ables; Maibach et al’s (in press) study also provides a much more detailed psychosocial pro le of cluster members. To date, however, neither segmentation effort has published targeted subsample segmentation descriptions or associated qualitative studies or has made available reduced item sets for cluster identi cation, though in at least one case such efforts are currently nearing completion (A. Max eld, personal communication, March 19, 1996). A search of the literature also suggests that similar efforts have not been made elsewhere than in the United States. The health behavior problems confronted in the United States and in an increasing number of other countries around the world—AIDS and safer sex; tobacco, alcohol, and other substance abuse; cardiovascular and cancer disease risk—are urgent and costly. Given the dif culty of in uencing such behavior through communication, health promotion, and interventions, it is important not to compromise such efforts through impromptu and inadequate audience segmentation strategies. The development and renement of sophisticated health audience segmentation tools is long overdue.
References Abbott, L. A., Bisby, F. A., & Rogers, D. J. (1985). Taxonomic analysis in biology. New York: Columbia University Press. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall. Aldenderfer, M. S., & Blash eld, R. K. (1984). Cluster analysis. Beverly Hills, CA: Sage. Allport, G., & Odbert, H. S. (1936). Trait names: A psycho-lexic al study. Psychological Monographs, 47 (No. 1). Atkin, C. K. & Freimuth, V. (1989). Formative evaluation research in campaign design. In R. E. Rice & C. K. Atkin (Eds.), Public communication campaigns (2nd ed., pp. 131–150). Newbury Park, CA: Sage. Babbie, E. (1983). The practice of social research. Belmont, CA: Wadsworth. Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ: Prentice-Hall. Bauer, R. A., & Bauer, A. (1960). America, mass society, and mass media. Journal of Social Issues, 10(3), 3–66. Boltzmann, L. (1960). Theories as representations. In A. Danto & S. Morgenbesser (Eds.), Philosophy of science (pp. 245–252). Cleveland, OH: World. Bonoma, T. V., & Shapiro, B. P. (1983). Segmenting the industrial market. Lexington, MA: Lexington Books. Cameron, G. T., & Yang, G. (1991). Effect of support and personal distance on the de nition of key publics for the issue of AIDS. Journalism Quarterly, 68, 620–637. Chaffee, S. H., & Roser, C. (1986). Involvement and the consistency of knowledge, attitudes, and behaviors. Communication Research, 13(3), 373–399. Claritas, Inc. (1985). PRIZM: The integrated marketing solution. Alexandria, VA: Author. Clifford, H. T., & Stephenson, W. (1975). An introduction to numerical classication. New York: Academic Press. Cutlip, S. M., Center, A. H., & Broom, G. M. (1985). Effective public relations (6th ed.). Englewood Cliffs, NJ: Prentice-Hall. Dewey, J. (1927). The public and its problems. New York: Holt, Rinehart. Donahew, L. (1990). Public health campaigns: Individual message strategy. In E. B. Ray & L. Donahew (Eds.), Communication and health (pp. 136–152). Hillsdale, NJ: Erlbaum. Finnegan, J. R., Bracht, N., & Viswanath, K. (1989). Community power and leadership analysis in lifestyle campaigns. In C. T. Salmon (Ed.), Information campaigns (pp. 19–53). Newbury Park, CA: Sage.
282
M. D. Slater
Fishbein, M., & Middlestat, S. (1989). Using the theory of reasoned action as a framework for understanding and changing AIDS-related behaviors. In V. M. Mays, G. W. Albee, & S. F. Schneider (Eds.), Primary prevention of AIDS (pp. 93–110). Newbury Park, CA: Sage. Grunig, J. (1983). Communication behaviors and the attitudes of environmental publics: Two studies. Journalism Monographs, 81. Grunig, J. (1989). Publics, audiences, and market segments: Segmentation principles for campaigns. In C. Salmon (Ed.), Information campaigns: Balancing social values and social change (pp. 199–228). Newbury Park, CA: Sage. Hennig, W. (1979). Phylogenetic systematics (D. D. Davis & R. Zangrel, Trans.). Chicago: University of Illinois Press. Kass, G. (1980). An exploratory technique for investigating large quantities of categorical data. Applied Statistics, 29, 119–127. Klecka, W. R. (1980). Discriminant analysis. Newbury Park, CA: Sage. Kotler, P., & Andreason, A. R. (1987). Strategic marketing for non-prot organizations (3rd. ed). Englewood Cliffs, NJ: Prentice-Hall. Magidson, J. (1993). SPSS for Windows CHAID Release 6. Englewood Cliffs, NJ: Prentice-Hall. Maibach, E., & Flora, J. A. (1993). Symbolic modeling and cognitive rehearsal: Using video to promote AIDS prevention self-ef cacy. Communication Research, 20, 517–545. Maibach, E., Max eld, A. M., Radin, K., & Slater, M. D. (in press). Translating health psychology into effective health communication: The American Healthstyles audience segmentation project. Journal of Health Psychology. Mayr, E. (1964). Systematics and the origin of species. New York: Dover. McGuire, W. J. (1989). Theoretical foundations of campaigns. In R. E. Rice & C. K. Atkin (Eds.), Public communication campaigns (2nd ed., pp. 43–66). Newbury Park, CA: Sage. McKinney, J. C. (1966). Constructive typology and social theory. New York: Appleton, Century, Crofts. McQuail, D. (1983). Mass communication theory. Newbury Park, CA: Sage. Mitchell, A. (1983). The nine American lifestyles. New York: Warner Books. Morris, L. A., Tabak, E. R., & Olins, N. J. (1992). A segmentation analysis of prescription drug information-seeking motives among the elderly. Journal of Public Policy and Marketing, 11, 115–125. Panchen, A. L. (1992). Classication, evolution, and the nature of biology. New York: Cambridge University. Patterson, R. E., Haines, P. S., & Popkin, B. M. (1994). Health lifestyle patterns of U.S. adults. Preventive Medicine, 23, 453–460. Pellegrin, P. (1986). Aristotle’s classication of animals (A. Preus, Trans.). Berkeley: University of California Press. Rogers, E. M., & Storey, J. D. (1987). Communication campaigns. In C. R. Berger & S. H. Chaffee (Eds.), Handbook of communication science (pp. 817–846). Newbury Park, CA: Sage. Scott-Ram, N. R. (1990). Transformed cladistics, taxonomy, and evolution. New York: Cambridge University Press. Slater, M. D. (1995). Choosing audience segmentation strategies and methods for health communication. In E. Maibach & R. L. Parrott (Eds.), Designing health messages: Approaches from communication theory and public health practice (pp. 186–198). Newbury Park, CA: Sage. Slater, M. D., & Flora, J. A. (1991). Health lifestyles: Audience segmentation analysis for public health interventions. Health Education Quarterly, 18, 221–223. Slater, M. D., & Flora, J. A. (1994). Is health behavior consumer behavior? Health behavior determinants, audience segmentation, and designing media health campaigns. In E. Clark, D. Stewart, & T. Brock (Eds.), Attention, attitude and affect in response to advertising (pp. 273–285). Hillsdale, NJ: Erlbaum.
Health Audience Segmentation
283
Smith, W. R. (1956). Product differentiation and market segmentation as alternative marketing strategies. In C. G. Walters & D. P. Robin (Eds.), Classics in marketing (pp. 433–439). Santa Monica, CA: Goodyear. Sneath, P. H. A., & Sokal, R. R. (1973). Numerical taxonomy: The principles and practice of numerical classication. San Francisco, CA: Freeman. VanLeuven, J. K., & Slater, M. D. (1991). How publics, public relations, and the media shape the public opinion process. Public Relations Research Annual, 3, 165–178. Vincent, M. A., & Fazio, R. H. (1992). Attitude accessibility and its consequences for judgment and behavior. In M. Manfredo (Ed.), Inuencing human behavior: Applications for recreation and tourism (pp. 1–28). Champaign, IL: Sagamore. Weber, M. (1949). The methodology of the social sciences. Glencoe, IL: The Free Press. Weinstein, A. (1987). Market segmentation. Chicago: Probus. Wells, W. G. (1974). Lifestyle and psychographics: De nitions, uses, problems. In W. G. Wells (Ed.), Lifestyle and psychographics (pp. 325–363). Chicago: American Marketing Association. Wells, W. G., Burnett, J., & Moriarty, S. (1989). Advertising: Principles and practice. Englewood Cliffs, NJ: Prentice-Hall. Winkelman, M. (1987, August). Their aim is true. Public Relations Journal, 43, 18–19, 22–23, 39.
