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Demand Forecasting in the Early Stage of the Technology's Life Cycle Using Bayesian update

Author

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  • Chul-Yong Lee
  • Jongsu Lee

    (Technology Management, Economics and Policy Program(TEMEP), Seoul National University)

Abstract

Forecasting demand for new technology for which few historical data observations are available is difficult but essential to successful marketing. The current study suggests an alternative forecasting methodology based on a hazard rate model using stated and revealed preferences. In estimating the hazard rate, information is derived initially through conjoint analysis based on a consumer survey and then updated using Bayes¡¯ theorem with available market data. Based on the results of the empirical analysis, the model described here can significantly improve demand forecasting for newly introduced technologies.

Suggested Citation

  • Chul-Yong Lee & Jongsu Lee, 2009. "Demand Forecasting in the Early Stage of the Technology's Life Cycle Using Bayesian update," TEMEP Discussion Papers 200903, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Apr 2009.
    • Handle: RePEc:snv:dp2009:200903
    as

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    File URL: http://temep-repec.my-groups.de/DP-03.pdf
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    Keywords

    demand forecasting; conjoint analysis; Bayesian update; telematics service;
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