Marketing Letters

, Volume 21, Issue 1, pp 53–64 | Cite as

A cohort analysis of household vehicle expenditure in the U.S. and Japan: A possibility of generational marketing

  • Kosei Fukuda


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.


Age–period–cohort decomposition Bayesian cohort model Constrained multiple regression model Generational marketing Household vehicle expenditure ratio 



The author is grateful to the co-editor, Joe Urbany, and three anonymous reviewers for their very useful comments and suggestions. Needless to say, any remaining errors are the author’s.


  1. Akaike, H. (1980). Likelihood and the Bayes procedure. In J. M. Bernardo, M. H. DeGroot, D. V. Lindley & A. F. M. Smith (Eds.), Bayesian statistics, pp. 143–166. Valencia: University Press.Google Scholar
  2. Braun-LaTour, K. A., LaTour, M. S., & Zinkhan, G. M. (2007). Using childhood memories to gain insight into brand meaning. Journal of Marketing, 71, 45–60. doi: 10.1509/jmkg.71.2.45.CrossRefGoogle Scholar
  3. Deaton, A., & Paxson, P. (1994). Saving, growth, and aging in Taiwan. In D. A. Wise (Ed.), Studies in the economics of aging, pp. 331–357. Chicago: Chicago University Press for NBER.Google Scholar
  4. Du, R. Y., & Kamakura, W. A. (2006). Household life cycles and lifestyles in the United States. JMR, Journal of Marketing Research, 43, 121–132. doi: 10.1509/jmkr.43.1.121.CrossRefGoogle Scholar
  5. Fukuda, K. (2006). Age–period–cohort decomposition of aggregate data: an application to U.S. and Japanese household saving rates. Journal of Applied Econometrics, 21, 981–998. doi: 10.1002/jae.906.CrossRefGoogle Scholar
  6. Fukuda, K. (2007). An empirical analysis of U.S. and Japanese health insurance using age–period–cohort decomposition. Health Economics, 16, 475–489. doi: 10.1002/hec.1179.CrossRefGoogle Scholar
  7. Fukuda, K. (2008). Age–period–cohort decomposition of U.S. and Japanese birth rates. Population Research and Policy Review, 27, 385–402. doi: 10.1007/s11113-008-9074-9.CrossRefGoogle Scholar
  8. Higgins, K. T. (1998). Generational marketing. Marketing Management, 7, 6–9.Google Scholar
  9. Holbrook, M. B., & Schindler, R. M. (1994). Age, sex, and attitude toward the past as predictors of consumers’ aesthetic tastes for cultural products. JMR, Journal of Marketing Research, 31, 412–422. doi: 10.2307/3152228.CrossRefGoogle Scholar
  10. Japanese government. (2001). Monthly Economic Report, November Issue.Google Scholar
  11. Kotler, K. (2000). Marketing management. New Jersey: Prentice-Hall.Google Scholar
  12. Kritz, G. H., & Arsenault, P. M. (2006). Teaching cohort analysis: an important marketing management tool. Marketing Education Review, 16, 37–43.Google Scholar
  13. Lambert-Pandraud, R., Laurent, G., & Lapersonne, E. (2005). Repeat purchasing of new automobiles by older consumers: empirical evidence and interpretations. Journal of Marketing, 69, 97–113. doi: 10.1509/jmkg. Scholar
  14. Meredith, G., & Schewe, C. D. (1994). The power of cohorts. American Demographics, 12, 22–31.Google Scholar
  15. Nakamura, T. (1982). A Bayesian cohort model for standard cohort table analysis. Proceedings of the Institute of Statistical Mathematics, 29, 77–97. In Japanese.Google Scholar
  16. Nakamura, T. (1986). Bayesian cohort models for general cohort table analyses. Annals of the Institute of Statistical Mathematics, 38B, 353–370. doi: 10.1007/BF02482523.CrossRefGoogle Scholar
  17. Punj, G., & Brookes, R. (2002). The influence of pre-decisional constraints on information search and consideration set formation in new automobile purchases. International Journal of Research in Marketing, 19, 383–400. doi: 10.1016/S0167-8116(02)00100-3.CrossRefGoogle Scholar
  18. Rentz, J. O., & Reynolds, F. D. (1991). Forecasting the effects of an aging population on product consumption: an age–period–cohort framework. JMR, Journal of Marketing Research, 28, 355–360. doi: 10.2307/3172871.CrossRefGoogle Scholar
  19. Rentz, J. O., Reynolds, F. D., & Stout, R. G. (1983). Analyzing changing consumption patterns with cohort analysis. JMR, Journal of Marketing Research, 20, 12–20. doi: 10.2307/3151407.CrossRefGoogle Scholar
  20. Reynolds, F. D., & Rentz, J. O. (1981). Cohort analysis: an aid to strategic planning. Journal of Marketing, 45, 62–70. doi: 10.2307/1251542.CrossRefGoogle Scholar
  21. Ryder, N. B. (1965). The cohort as a concept in the study of social change. American Sociological Review, 30, 843–861. doi: 10.2307/2090964.CrossRefGoogle Scholar
  22. Schewe, C. D., & Noble, S. M. (2000). Market segmentation by cohorts: the value and validity of cohorts in America and abroad. Journal of Marketing Management, 16, 129–142. doi: 10.1362/026725700785100479.CrossRefGoogle Scholar
  23. Schewe, C. D., & Meredith, G. (2004). Segmenting global markets by generational cohorts: determining motivations by age. Journal of Consumer Behaviour, 4, 51–63. doi: 10.1002/cb.157.CrossRefGoogle Scholar
  24. Schuman, H., & Scott, J. (1989). Generations and collective memories. American Sociological Review, 54, 359–381. doi: 10.2307/2095611.CrossRefGoogle Scholar
  25. Smith, J. W. (2003). Marketing in an anxious age. Marketing Management, 12, 56–56.Google Scholar
  26. Smith, J. W., & Clurman, A. (1997). Rocking the ages. New York: Harper Business.Google Scholar
  27. Wyner, G. A. (2008). Measuring change through cohort analysis. Marketing Research, 20, 6–7.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.College of EconomicsNihon UniversityTokyoJapan

Personalised recommendations