Dynamic Monitoring of Product Opportunities with Online Product Review Data
Jaemin Chung, Byeongki Jeong, Janghyeok Yoon · Journal of Korean Institute of Industrial Engineers · 2019
Improvements of a product can be carried out in accordance with customer requirements for the product, and thus it is necessary for product-based firms to identify product topics customers currently discuss and transform the topics into product opportunities. Despite social media mining studies for product opportunities, they have limitations in monitoring product topics and prioritizing them from dynamic aspects. Therefore, this paper suggests a social media mining-based dynamic product opportunity identification approach, which consists of 1) collecting large-scale online product review data of a product and extracting product-related keywords from them, 2) recognizing common product topics customers interested in through topic modeling, 3) calculating indicators, such as importance, satisfaction and growth, over time for the identified topics, and 4) employing TOPSIS to monitor and prioritize changing product topics. To demonstrate the applicability of the approach, this study carries out a case study with iPad product review data. This study tracks dynamically evolving product opportunities to help set future improvement directions of a product and accordingly enables product-based firms to quickly cope with changing customer needs for their product.