Mining Novel Customer Needs from Online Product Review
Shaoqin Huang, Yue Wang, Daniel Y. W. Mo, Hai Liu · 2024
Identifying new customer needs is essential for companies to take advantage of evolving technology and social trends. However, the traditional methods used to discover emerging needs require much time, expense, and intensive labor. They often leading to delays in product development. Recent years have witnessed the emergence of online product reviews as a promising alternative for uncovering fresh customer requirements. In this study, we suggest utilizing online reviews to identify new customer needs by treating it as a text classification problem. We exploit the BERT language model, which is pre-trained using general text corpus, to create a classifier that can detect reviews containing innovative content. Our experiments validate the effectiveness of this structured approach, even when dealing with reviews of varying lengths. By implementing this methodology, companies can automate the process of identifying new customer needs quickly and efficiently, reducing the need for extensive expert resources. This advancement in research could have implications for product development and other related fields.