Affiliations: [a]
Business School, University of Shanghai for Science and Technology, Shanghai, China
| [b]
School of Intelligent Emergency Management, University of Shanghai for Science and Technology, Shanghai, China
| [c]
Metrology & Test Center Office, COMAC Shanghai Aircraft Manufacturing Co., Ltd. Shanghai, PR, China
Correspondence:
[*]
Corresponding author. Xiuli Geng, University of Shanghai for Science and Technology, Shanghai, China. E-mail: xiuliforever@163.com.
Abstract: Against the backdrop of increasing global demand for reducing greenhouse gas emissions, promoting the use of energy-saving and environmentally friendly products has become a crucial aspect of low-carbon economic development. Customer satisfaction plays a vital role in the promotion of these products. To address the challenges of dealing with big data in the conventional customer satisfaction analysis tool, Importance Performance Analysis (IPA), a machine learning-based method is proposed to improve IPA. Firstly, the Latent Dirichlet Allocation (LDA) model is used to capture users’ opinions on different product topics. Then, the Support Vector Machine (SVM) and Random Forest (RF) algorithms are employed respectively to assess the satisfaction and importance of product attributes, enabling an objective measurement of customer satisfaction and adapting to the current trend of big data. The proposed method is applied to the analysis of water heater satisfaction on the JD platform, obtaining satisfaction levels for 10 topics. The research findings demonstrate that the improved IPA method based on SVM-RF effectively explores customer satisfaction and can provide some improvement strategies for platform managers and manufacturers.
Keywords: Low-carbon, customer satisfaction, importance performance analysis, latent dirichlet allocation, support vector machine, random forest
