Customer Segmentation in Social Media Marketing: Analyzing Hidden Customer Heterogeneity through Finite Mixture Partial Least Squares (FIMIX-PLS) and Importance-Performance Map Analysis (IPMA)

Objectives: In recent years, there has been a growing importance of consumer-business interactions on social media platforms. Utilizing the finite mixture partial least squares (FIMIX-PLS) method, this study aims to uncover how social media marketing activities (SMMA) influence brand equity and user behavioral intentions. Furthermore, it seeks to achieve a deep understanding of customer segmentation through the identification and categorization of social media user characteristics. Methods: In this research, the sample consisted of individuals who are at least 20 years old with previous exposure to social media marketing (SMM). The survey was administered through an online platform and the analysis of 521 data sets using Smart-PLS. By evaluating path coefficients and Importance-performance map analysis (IPMA) outcomes, the participants were categorized into three distinct groups, and their respective attributes were delineated. Results: The study findings confirm that SMMA significantly impact brand equity, brand attitudes, and behavioral intentions. The interactive aspect of SMM has a significant impact on brand awareness and brand image, emphasizing the importance of consumer-brand interaction. Moreover, unobserved heterogeneity was discovered using FIMIX-PLS, resulting in an appropriate number of three segments based on the information criterion, confirming the presence of unobserved heterogeneity throughout the data. Conclusions: The findings hold importance as they demonstrate the suitability of path estimation following the consideration of unobserved variations within a singular uniform population. Furthermore, they indicate that the effective implementation of a SMM strategy can reliably forecast consumer actions and cultivate a steadfast customer following.