Elicitation of design factors through big data analysis of online customer reviews for washing machines

The volume of online consumer-generated content, such as opinions, personal feelings, and design requirements continually increases. However, the analysis of the large quantity of data available is not systematic, and customers' opinions and requirements are not properly utilized in product design. In this study, big data on customers' experience with front loading washers, represented by reviews and ratings on the BestBuy website, were collected and used to analyze the relationship between the customers' experience and the associated satisfaction by using text analytics. Words related to customer satisfaction that occurred frequently in the reviews were extracted, and the most significant words among them were selected as inputs for finding the major factors relevant to washer design by performing factor analysis. The influence of each factor was quantitatively estimated through linear regression analysis. This shows that the quantitatively elicited customer information from the big data can provide insights for new washing machine design.