A machine learning and clustering-based methodology for the identification of lead users and their needs from online communities

Xinghua Fang, Jian Zhou, Athanasios A. Pantelous, Wei Lu

Research output: Contribution to journalArticleResearchpeer-review


Nowadays, online community platforms provide firms with an important source of information for conducting dynamic marketing research. High-technology companies, in particular, rely heavily on lead users for the development of very novel products or easily adjustable services. In this paper, we present a three-phase methodology that integrates a machine-learning-based algorithm with a sophisticated clustering technique. The purpose of this methodology is to systematically identify lead users and their needs from a complex online community network. We also aim to identify important features, perceptions, and preferences for different groups of lead users. To validate the effectiveness of our approach, we conduct a real-world case study.

Original languageEnglish
Article number123381
Number of pages15
JournalExpert Systems with Applications
Publication statusPublished - 15 Aug 2024


  • Clustering technique
  • Lead users
  • New product development
  • Online community
  • Random forest-based algorithm

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