Open AccessKKMA - A Calculation Method for KANO Classification Based on User Reviews
Author(s) -
Alan Lu,
Yanpeng Sun,
Lei Zhao,
G. Li,
Jun Jing,
W. Liu,
Changjian Hu
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1043/2/022062
Subject(s) - kano model , product (mathematics) , computer science , reliability (semiconductor) , quality (philosophy) , customer satisfaction , focus (optics) , data mining , mathematics , marketing , service quality , service (business) , business , power (physics) , philosophy , physics , geometry , epistemology , quantum mechanics , optics
KANO model classification is helpful for us to recognize customer needs and to improve their satisfaction. The traditional method uses standard questionnaires to conduct surveys, classifies product attributes according to the survey results. However with the increase of product complexity and the speed of product iteration, the method of survey is more and more unable to meet our analysis needs; coupled with the increasing number of customers who do not want to give feedback for questionnaires, low responds ratio rate leads poor feedback quality which affects the reliability of the research results. Although many studies are about KANO model classification, few of them focus on how to improve responds ratio rate. This article creates a new method for KANO model classification. By collecting customer reviews and rating score, we build up regression model between the score and the degree to which product attributes meet user needs according to their text expression. Based on the curve shape of the model coefficients and the value of the coefficient we can identify which KANO classification will a product attribute belongs to. The experiment study for gaming notebook has proved that this method is efficient and can be widely used in other products. We call this method as KKMA (Kano, K-means, MDS, Ad boost).