Improving Quality of B2B Customer Satisfaction Using Innovative Predictive Model

Purpose: This paper presents a predictive model for strategic management of B2B customer satisfaction, designed for Quality 5.0 challenges and situations with limited data. Methodology/Approach: A two-phase methodology was used. In the first phase, the methodology identifies key quality factors using focus groups and online surveys. In the second phase, a trend model is derived from a numerical correlation matrix that describes all past and possible future states of the system. Findings: The results show two main strategies (dynamic and conservative) for transitioning from declining satisfaction to growth. It was found that this goal can be achieved by managing fully controllable factors such as website navigation, information complexity, and graphic design quality. Research Limitations/Implications: By focusing on qualitative scenarios, the model is effective in environments with insufficient data and opens up possibilities for future expansion with fuzzy logic. Originality/Value of Paper: The presented model is an innovative formal tool that complements existing quantitative methods in areas where data is lacking. Its value lies in its function as a strategic management simulator that converts qualitative descriptions of trends into structured action scenarios, thereby strengthening managerial decision-making in the context of Quality 5.0.