A cohort analysis of household vehicle expenditure in the U.S. and Japan: A possibility of generational marketing
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A cohort analysis of household vehicle expenditure in the U.S. and Japan: A possibility of generational marketing Kosei Fukuda
Published online: 30 April 2009 # Springer Science + Business Media, LLC 2009
Abstract This paper shows the usefulness of cohort analysis for generational marketing. Aggregate data classified by age and period are decomposed into age, period, and generational cohort effects. We compare two cohort-analysis models, the constrained multiple regression model and the Bayesian cohort model. The empirical results that are common to the household vehicle expenditure ratio in the U.S. and Japan are as follows: (1) among a total of three effects, the period effect is the smallest; (2) with the exception of the latest birth cohort, the cohort effect shows a clear upward trend; (3) the age effect decreases in the 20s and 30s, and next increases with a peak detected in the late 50s, and finally decreases. We provide marketing implications for cohort segmentation and forecasting. Keywords Age–period–cohort decomposition . Bayesian cohort model . Constrained multiple regression model . Generational marketing . Household vehicle expenditure ratio
1 Introduction Since the seminal work of Reynolds and Rentz (1981), cohort analysis has gained recognition in the field of marketing literature. A cohort can be defined as an aggregate of individuals who experienced the same events within the same time interval (Ryder 1965), and who are usually connected by birth timing. Cohort analysis is a method designed to separate age, period, and cohort effects in order to examine consumer behaviors (Rentz et al. 1983). In the present study, cohort analysis and age– period–cohort decomposition are used interchangeably. These three effects have different causes (Reynolds and Rentz 1981): biological, psychological, and social aging for the age effect; environmental changes for the period effect; and historical
K. Fukuda (*) College of Economics, Nihon University, 1-3-2 Misakicho, Chiyoda-ku, Tokyo 101-8360, Japan e-mail: [email protected]
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Market Lett (2010) 21:53–64
differences in socialization, and genetic change (transmission from parents to children) for the cohort effect. In recent marketing literature, Wyner (2008) has noted that research on the different aspects of change can be enhanced by introducing some basic research design elements that focus on the influence of age, period, and cohort. The objectives of this paper are twofold. The first objective is to newly apply a Bayesian age–period–cohort decomposition to marketing research. In the marketing literature, the constrained multiple regression (henceforth, CMR) model has been applied by Rentz et al. (1983) and Rentz and Reynolds (1991). On the other hand, the Bayesian cohort (henceforth, BC) model developed by Nakamura (1982, 1986) has been applied in various fields except marketing. Age–period–cohort decomposition includes an identification problem. Since age, period, and cohort share a linear relationship, that is, age equals period minus cohort,
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