Modeling Personalized Individual Semantics of New Energy Vehicle Consumers from User-Generated Content Considering Group Heterogeneity and Individual Consistency

PurposeThe purpose of the study is to propose a normative approach for market segmentation, profile and monitoring using computing and information technology to analyze User-Generated Content (UGC).Design/methodology/approachThe specific steps include performing a structural analysis of the UGC and extracting the base variables and values from it, generating a consumer characteristics matrix for segmenting process, and finally describing the segments' preferences, regional and dynamic characteristics. The authors verify the feasibility of the method with publicly available data. The external validity of the method is also tested through questionnaires and product regional sales data.FindingsThe authors apply the proposed methodology to analyze 53,526 UGCs in the New Energy Vehicle (NEV) market and classify consumers into four segments: Brand-Value Suitors (32%), Rational Consumers (21%), High-Quality Fanciers (26%) and Utility-driven Consumers (21%). The authors describe four segments' preferences, dynamic changes over the past six years and regional characteristics among China's top five sales cities. Then, the authors verify the external validity of the methodology through a questionnaire survey and actual NEV sales in China.Practical implicationsThe proposed method enables companies to utilize computing and information technology to understand the market structure and grasp the dynamic trends of market segments, which assists them in developing R&D and marketing plans.Originality/valueThis study contributes to the research on UGC-based universal market segmentation methods. In addition, the proposed UGC structural analysis algorithm implements a more fine-grained data analysis.

Under the background of the rapid development of China’s new energy vehicle market, the increasingly fierce competition and the increasing influence of social media, based on the analysis of the matrix construction and operation practice of N automobile, this paper discusses the digital marketing of new energy vehicle enterprises through social media to get the market volume. Through data analysis and case analysis, this paper analyzes the construction method, content planning and interactive strategy of N automobile Jinhua company’s human matrix on the red book. A social media communication method based on key opinion selling (KOS) content and user-generated content (UGC) is proposed. This method can help improve the brand’s exposure and user participation on social media and then enhance the brand influence. It provides a useful reference for the digital marketing of new energy automobile enterprises on social media and also provides reference and inspiration for other industries to use social media platforms to enhance brand influence.

How far is reality from vision: An online data-driven method for brand image assessment and maintenance

Online reviews offer a valuable source of information for identifying product defects and understanding customer requirements. However, previous research often focused solely on explicit quality issues within a single platform, while overlooking the distinct characteristics of various platforms. In practice, each platform features unique communication styles, and content types, leading to diverse but valuable insights for manufacturers. This study proposes a novel online review-driven modelling framework that utilizes multi-platform with data integration and fully leverages rich user-generated content (UGC) to capture the customer insights. The methodology consists of two stages: first, the analysis of after-sales product complaints to identify specific product defects and construct a Failure Modes and Effects Analysis (FMEA) database through heterogeneous information fusion; and second, the development of a strength-frequency Kano model to classify customer requirements extracted from online forums, with the goal of optimizing customer satisfaction. Case studies on new energy vehicles validate the effectiveness of the proposed method and offer valuable business insights for stakeholders.

From user-generated content to quality improvement: A multi-granularity analysis of customer satisfaction and attention in new energy vehicles using deep learning

User Satisfaction in the New Energy Vehicles: An Analysis Harnessing User-Generated Content and Sentiment Analytics