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Marketing innovations to old-age consumers: A dynamic Bass model for different life stages

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  • Pannhorst, Matthias
  • Dost, Florian

Abstract

To identify context-dependent opportunities to market innovations to the elderly, this study empirically analyzes the most prevalent pathways through advanced age, demonstrating which circumstances in the old-age life course provide the strongest potential for specific targeting strategies. First, using a latent Markov model and longitudinal survey data spanning 15 years, we produce a dynamic life course model with transitions over time. Second, we link a modified Bass diffusion model — using both static and dynamic parameters — to our model, augmenting it with a second cross-sectional consumer behavior data set. The results show comparatively strong consumption spending, high media interaction, but diminishing social inclusion in old age, though all factors exhibit heterogeneity among old-age clusters. Employing dynamic diffusion models, we find that a static view of the elderly market that ignores life course transitions generally overestimates their spending power. Forecasts of cluster-specific adoption dynamics draw a differentiated picture of individual clusters' attractiveness. Our analysis underscores the influence of life events on individual behavior and shows that a dynamic view of elderly markets yields a more nuanced and accurate assessment of their potential and attractiveness. It also confirms that social status and income strongly affect consumer behavior and spending, though we identify several exceptions.

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  • Pannhorst, Matthias & Dost, Florian, 2019. "Marketing innovations to old-age consumers: A dynamic Bass model for different life stages," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 315-327.
  • Handle: RePEc:eee:tefoso:v:140:y:2019:i:c:p:315-327
    DOI: 10.1016/j.techfore.2018.12.022
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    3. Matthias Pannhorst & Florian Dost, 2022. "A Life-Course View on Ageing Consumers: Old-Age Trajectories and Gender Differences," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 17(2), pages 1157-1180, April.
    4. Zhukov, Dmitry & Khvatova, Tatiana & Millar, Carla & Zaltcman, Anastasia, 2020. "Modelling the stochastic dynamics of transitions between states in social systems incorporating self-organization and memory," Technological Forecasting and Social Change, Elsevier, vol. 158(C).

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