Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach

Hakyeon Lee, Sang Gook Kim, Hyun woo Park, Pilsung Kang

Research output: Contribution to journalArticle

23 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)49-64
Number of pages16
JournalTechnological Forecasting and Social Change
Publication statusPublished - 2014 Jul



  • Bass model
  • Ensemble
  • Machine learning
  • Multivariate linear regression
  • Pre-launch forecasting

ASJC Scopus subject areas

  • Business and International Management
  • Applied Psychology
  • Management of Technology and Innovation

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