Using supplementary reviews to improve customer requirement identification and product design development

Abstract

A clear understanding of customer needs is key to the success of new product development and customer-centric product design. Online reviews, particularly initial reviews, are commonly used as effective sources for mining customer requirements. Although customers may post truer and more objective supplementary reviews after a period of product use, such reviews are often overlooked when identifying customer requirements for product design improvement. In this study, we proposed a framework for identifying customer requirements by combining initial and supplementary reviews based on text mining. We categorized the identified customer needs into five product attribute categories using the Kano model. Two case studies were conducted in the laptop and cell phone industries to demonstrate the effectiveness of our method. Thus, dynamic customer requirements and satisfaction can be accurately mined and captured when considering supplementary reviews. In practice, the appropriate use of supplementary reviews may provide valuable guidance for product design and development strategies.

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