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This study used a secondary data set to identify older consumer segments that share similar patterns of lifestyle and to test for differences among these segments in monthly clothing purchases and demographic characteristics. Latent class analysis, a model-based clustering technique, was conducted. Based on model fit indices, five older consumer lifestyle groups were identified from individuals’ participation in diverse activities. Comparing the five groups in ANOVA, there were significant differences. Social activities and outdoor activities were found to be the most distinctive attributes for the groups with higher monthly clothing purchases. The results indicate that older consumers across the age spectrum engage in heterogeneous lifestyle activities, which may affect their consumption behavior. We expect the findings will assistfashion brands and older consumers in developing and growing a market that needs further exploration.

Lifestyle Segmentation for Older Fashion Consumers Using Latent Class Analysis