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Predicting product life cycle patterns

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  • Yair Orbach
  • Gila Fruchter

Abstract

In this paper, we propose a new model of adoption and repurchase due to upgrades driven by the utility of technology products that keep improving. The model is able to predict product life cycle patterns that could not be explained previously. Such patterns were used to challenge diffusion theory validity. Mathematically, the model is described as a nonlinear discrete system that depends on a small set of parameters. We investigate the dynamic properties of the nonlinear system using numerical stability analysis. We find domains in the parameters space in which the equilibrium point and the periodical orbits are stable. The domains correspond to population heterogeneity, tendency to upgrade, and the influence of industry response on market dynamics. We also implement our model to fit actual data of two real-world product life cycles with many irregularities and benchmark the results of our model vs. well-known models. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Yair Orbach & Gila Fruchter, 2014. "Predicting product life cycle patterns," Marketing Letters, Springer, vol. 25(1), pages 37-52, March.
    • Handle: RePEc:kap:mktlet:v:25:y:2014:i:1:p:37-52
    DOI: 10.1007/s11002-013-9239-0
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    References listed on IDEAS

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    Cited by:

    1. Yaniv Shani & Gil Appel & Shai Danziger & Ron Shachar, 2020. "When and Why Consumers “Accidentally” Endanger Their Products," Management Science, INFORMS, vol. 66(12), pages 5757-5782, December.
    2. Mehmet N. Aydin & N. Ziya Perdahci, 0. "Dynamic network analysis of online interactive platform," Information Systems Frontiers, Springer, vol. 0, pages 1-12.
    3. Mehmet N. Aydin & N. Ziya Perdahci, 2019. "Dynamic network analysis of online interactive platform," Information Systems Frontiers, Springer, vol. 21(2), pages 229-240, April.
    4. Goksel Yalcinkaya & Tevfik Aktekin & Sengun Yeniyurt & Setiadi Umar, 2017. "How often should a firm modify its products? A Bayesian analysis of automobile modification cycles," Marketing Letters, Springer, vol. 28(1), pages 85-97, March.

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