Customer complaints analysis for new product development using textual data mining and the outcome d

This paper proposes customer complaints analysis to develop new products; the analysis utilizes textual data mining and the outcome-driven innovation (ODI) method. This study includes observation research based on natural language text analysis to identify customers’ requirements. Customer complaints are collected from customer service centers, then nouns and verbs from the complaints are extracted by a Korean language parser. The extracted nouns and verbs are mutually compared to construct the similarity matrix, which is used for clustering analysis, which identifies customer requirements. Then high-priority customer requirements are discovered by the opportunity formula in the ODI method. This information will raise the efficiency of research and development to develop innovative technologies and products that meet customer requirements. In this paper, we present a case study of wall-mounted air conditioners to explain how the proposed method can identify high-priority customer requirements.