title: Advances in segmentation modeling for health communication and social marketing campaigns.
abstract: Large-scale communication campaigns for health promotion and disease prevention involve analysis of audience demographic and psychographic factors for effective message targeting. A variety of segmentation modeling techniques, including tree-based methods such as Chi-squared Automatic Interaction Detection and logistic regression, are used to identify meaningful target groups within a large sample or population (N = 750-1,000+). Such groups are based on statistically significant combinations of factors (e.g., gender, marital status, and personality predispositions). The identification of groups or clusters facilitates message design in order to address the particular needs, attention patterns, and concerns of audience members within each group. We review current segmentation techniques, their contributions to conceptual development, and cost-effective decision making. Examples from a major study in which these strategies were used are provided from the Texas Women, Infants and Children Program's Comprehensive Social Marketing Program.
authors.affiliation: Department of Community and Family Health, University of South Florida, Tampa 33612, USA. albrecht@cophdep2.coph.usf.edu
authors.forename: T L
authors.initials: TL
authors.lastname: Albrecht
authors.forename: C
authors.initials: C
authors.lastname: Bryant
mesh_terms: D000328:Adult; D000465:Algorithms; D001294:Attitude to Health; D016009:Chi-Square Distribution; D016000:Cluster Analysis; D003142:Communication; D003710:Demography; D005260:Female; D006293:Health Promotion / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D008297:Male; D008389:Marketing of Health Services / Q000379:methods*; D012947:Social Work; D013781:Texas