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

Brand image assessment and maintenance are essential for branding. This paper constructs an online data-driven quantitative brand image assessment method using the classic brand image theory as a conceptual model. The method is organized as follows. First, with domain expert knowledge and deep learning, this paper constructs a task ontology to clearly describe the brand image constituent content, constituent relationship, properties, and property values. Then, using the task ontology as a priori knowledge, we identify the content of brand associations from User-generated Content (UGC) and Firm-generated content (FGC), respectively, and calculate the associations' favorability, strength and uniqueness; classify brand associations into three categories: functional, experiential, and symbolic to achieve a dual-perspectives (consumer perceptions & corporate claims) brand image assessment. Finally, this study compares the dual-perspective brand images from the components and benefits to construct a brand image communication and maintenance strategy. The development and validation of the methodology take the Chinese New Energy Vehicle (NEV) market as the analysis object. The proposed dual-perspective brand image quantitative assessment model is a new development of brand image evaluation and maintenance theoretical method in the digital era. It is also a practical tool for brand management in enterprises.