Integrating correspondence analysis with Grey relational model to implement a user-driven STP product strategy for smart glasses

To enhance customer retention and customer acquisition, product differentiation, product configuration and product recommendation are of importance to help firms implement segmentation–targeting–positioning strategies. in reality, however, user perceptions of product features are usually vague and diverse by individuals. consequently, for a manufacturer, learning an efficient way to balance the trade-offs between satisfying customer needs and optimizing product varieties has become much more challenging than before. in order to overcome the aforementioned difficulty, this paper presents a novel framework to assist firms in determining the optimal product varieties of smart glasses with consideration of diverse requirements of three distinct segments (i.e. home entertainment, medical healthcare, and industry service). in particular, correspondence analysis is employed to indicate which product attributes best characterize a specific segment for achieving product differentiation. then, by means of grey relational model, the top three priorities with regard to three segments are systematically identified for conducting product configuration. lastly, bayes theorem is utilized to assign a potential buyer to his/her most similar segment for accomplishing unsupervised product recommendation.