An explainable machine learning-based approach for analyzing customers' online data to identify the importance of product attributes

Online customer data provides valuable information for product design and marketing research, as it can reveal the preferences of customers. However, analyzing these data using artificial intelligence (AI) for data-driven design is a challenging task due to potential concealed patterns. Moreover, in these research areas, most studies are only limited to finding customers' needs. In this study, we propose a game theory machine learning (ML) method that extracts comprehensive design implications for product development. The method first uses a genetic …

artificial artificial intelligence cs.gt cs.lg customer customer data customers data data-driven design explainable machine learning identify importance information intelligence machine machine learning marketing patterns product product design research