A dynamic customer requirement mining method for continuous product improvement

Abstract

The key to successful product development is better understanding of customer requirements and efficiently identifying the product attributes. In recent years, a growing number of researchers have studied the mining of customer requirements and preferences from online reviews. However, since customer requirements often change dynamically on multi-generation products, most existing studies failed to discover the correlations between customer satisfaction and continuous product improvement. In this work, we propose a novel dynamic customer requirement mining method to analyze the dynamic changes of customer satisfaction of product attributes based on sentiment and attention expressed in online reviews, aiming to better meet customer requirements and provide the direction and content of future product improvement. Specifically, this method is divided into three parts. Firstly, text mining is adopted to collect online review data of multi-generation products and identify product attributes. Secondly, the attention and sentiment scores of product attributes are calculated with a natural language processing tool, and further integrated into the corresponding satisfaction scores. Finally, the improvement direction for next-generation products is determined based on the changing satisfaction scores of multi-generation product attributes. In addition, a case study on multi-generation phone products based on online reviews was conducted to illustrate the effectiveness and practicality of the proposed methodology. Our research completes the field of requirements analysis and provides a new dynamic approach to requirements analysis for continuous improvement of multi-generation products, which can help enterprises to accurately understand customer requirements and improve the effectiveness and efficiency of continuous product improvement.