Mining Customer Requirement from Online Reviews: A Product Improvement Perspective

The wide availability of online reviews has provided academic researchers and industry practitioners with the opportunities to analyze the effects of online reviews on consumers’ choice and sales. From the view point of manufacturers, it becomes more important how to select helpful online reviews and what can be learned from the abundant online reviews for new product development. In this research, we first proposed an automatic filtering model to predict the helpfulness of online review from product designers’ perspective. Then the KANO method based on the classical conjoint analysis model is innovatively applied to analyze online reviews in order to develop appropriate product improvement strategies. Moreover, an empirical case study with the new method is conducted with the data we acquired from JD.com, one of the largest electronic marketplaces in China. The case study indicates the effectiveness and strong robustness of the proposed approach.