 Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approachHakyeon Lee, Sang Gook Kim, Hyun-woo Park and Pilsung Kang Technological Forecasting and Social Change, 2014, vol. 86, issue C, 49-64 Abstract: This study proposes a novel approach to the pre-launch forecasting of new product demand based on the Bass model and statistical and machine learning algorithms. The Bass model is used to explain the diffusion process of products while statistical and machine learning algorithms are employed to predict two Bass model parameters prior to launch. Initially, two types of databases (DBs) are constructed: a product attribute DB and a product diffusion DB. Taking the former as inputs and the latter as outputs, single prediction models are developed using six regression algorithms, on the basis of which an ensemble prediction model is constructed in order to enhance predictive power. The experimental validation shows that most single prediction models outperform the conventional analogical method and that the ensemble model improves prediction accuracy further. Based on the developed models, an illustrative example of 3D TV is provided. Keywords: Pre-launch forecasting; Bass model; Multivariate linear regression; Machine learning; Ensemble (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:86:y:2014:i:c:p:49-64 DOI: 10.1016/j.techfore.2013.08.020 Access Statistics for this article Technological Forecasting and Social Change is currently edited by Fred Phillips More articles in Technological Forecasting and Social Change from Elsevier |
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