Construction of Objective Segmentation Model of Automobile Users Based on Unsupervised Learning

Abstract

With the rapid development of new energy vehicles and intelligent connected vehicles, today's automotive consumption has shifted from product centric to user centric. Grouping automobile users and conducting in-depth research on their characteristics and needs can provide certain support and guidance for manufacturers in planning and developing vehicle models, market validation, etc. However, current research on car user segmentation models is relatively lagging behind, and existing models use subjective factors such as values/automotive views/consumption views as modeling indicators. In the actual business development process of car companies, there are limitations of inconsistent user subjective cognition in various business processes, resulting in unclear vehicle development direction and inaccurate user positioning. In order to solve the above problems, this research innovatively constructs a set of automobile user segmentation model which is easy to understand and use by using unsupervised clustering algorithm based on objective indicators, and provides certain references for the development of new models and user positioning of automobile enterprises.