Demand Forecasting in the Early Stage of the Technology's Life Cycle Using Bayesian update
AbstractForecasting 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.
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Bibliographic InfoPaper provided by Seoul National University; Technology Management, Economics, and Policy Program (TEMEP) in its series TEMEP Discussion Papers with number 200903.
Length: 20 pages
Date of creation: Apr 2009
Date of revision: Apr 2009
demand forecasting; conjoint analysis; Bayesian update; telematics service;
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TEMEP Discussion Papers
200902, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Jan 2009.
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