Identification of key customer requirements based on online reviews

Abstract

Customer requirements are the essential driving force for successful product development. They can be grouped into several categories, including basic requirements, indifferent requirements, reverse requirements, expected requirements, and attractive requirements. Among these, the latter two are crucial for improving customer satisfaction and can be classified as key requirements. However, the literature on identifying key requirements suffers from issues related to subjective interference and the lack of a specific quantitative calculation process. Thus, this study proposes a model for identifying critical customer requirements. First, use Python to run the web crawler for extracting online customer reviews. Second, extract product engineering characteristics using the relevant text mining technology and latent Dirichlet allocation topic clustering algorithm. Third, we combine sentiment analysis and other factors that influence customer satisfaction with the product engineering characteristics to conduct the conjoint analysis and calculate utility values for the product engineering characteristics. Finally, integrate Kano model to formulate the requirements hierarchy rules, determine the final key requirements index, and identify the key customer requirements. And a case study implemented the key customer requirements identification problem for a smartphone to demonstrate the feasibility and effectiveness of the proposed methodology.