Resumen
This study focuses on improving multifunctional product development. Instead of face-to-face or other survey methods, we used text mining of online reviews to confirm which characteristics consumers prefer. The reference probability (importance), and the difference between positive and negative opinions (satisfaction) were indexed. By linking ?importance? and ?satisfaction? with a product?s quantitative performance, the correlation between satisfaction and quantitative performance was confirmed, and the method of setting a product?s design requirements was presented. To verify the validity of the method, we used the subcompact SUV (Sports Utility Vehicle) market in South Korea as a case study. The average importance and satisfaction with each performance aspect of the cars in the market was extracted, and the successful entry of new products in the market, which reflects these market characteristics, was confirmed. The proposed methodology is meaningful in that it reduces the risk (biased, inefficient) of existing consumer survey methods by utilizing big data to identify consumer preferences. Companies can use these findings during the product development process to improve customer satisfaction. This study improves product development methods by combining them with the latest advances in big data-related technologies.