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

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.