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

Chul-Yong Lee and Jongsu Lee (jongsu.lee@snu.ac.kr)

No 200903, TEMEP Discussion Papers from Seoul National University; Technology Management, Economics, and Policy Program (TEMEP)

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.

Keywords: demand forecasting; conjoint analysis; Bayesian update; telematics service (search for similar items in EconPapers)
Pages: 20 pages
Date: 2009-04, Revised 2009-04
New Economics Papers: this item is included in nep-for and nep-mkt
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Persistent link: https://EconPapers.repec.org/RePEc:snv:dp2009:200903

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