Demand forecasting in the early stage of the technology's life cycle using a Bayesian update

Abstract

The forecasting demand for new technology for which few historical data observations are available is difficult but essential to sustainable development. The current study suggests an alternative forecasting methodology based on a hazard rate model using stated and revealed preferences of consumers. In estimating the hazard rate, information is initially derived through conjoint analysis based on a consumer survey and then updated using Bayes' theorem with available market data. To compare the proposed models' performance with benchmark models, the Bass model, the logistic growth model, and a Bayesian approach based on analogy are adopted. The results show that the proposed model outperforms the benchmark models in terms of pre-launch and post-launch forecasting performances.