Method for analyzing product quality parameters and measuring consumer satisfaction for high-contact and low-contact products

A computer-implemented method and system for analyzing product quality parameters and measuring consumer satisfaction
across high-contact and low-contact products is disclosed. The invention collects multi-source structured and unstructured data
including service interaction metrics, manufacturing performance data, transactional records, survey responses, and digital
feedback. Products are automatically classified based on interaction density, customization level, and consumer touchpoint
frequency. Heterogeneous quality parameters are normalized into standardized indices and dynamically weighted using adaptive
machine learning models to compute a composite satisfaction score. The system correlates objective quality metrics with
behavioral and perceptual satisfaction indicators such as repeat purchase rate, complaint frequency, and retention metrics.
Predictive analytics identify dissatisfaction trends and generate category-specific quality improvement recommendations. The
invention enables unified benchmarking, proactive quality management, and secure deployment across cloud, on-premise, and
hybrid environments