Mobile Customer Satisfaction Scoring Research Based on Quadratic Dimension Reduction and Machine Learning Integration
Source abstract
Abstract
Customer satisfaction is a measure of the degree of satisfaction of customer experience. Among the three major operators in China, China Mobile plays an important role in the communication field. A study of customer satisfaction with China Mobile will have a significant positive impact on the sustainable development of the entire communication industry. In order to respond to customer needs accurately, a mobile customer satisfaction research method based on quadratic dimensionality reduction and machine learning integration is proposed. Firstly, the core evaluation system of impact satisfaction is established, through the integration of systematic clustering and exploratory factor analysis for quadratic dimensionality reduction. Then, unreasonable data in the core influencing factors are eliminated. Finally, the gradient-boosted decision tree (GBDT) machine learning algorithm is applied to predict satisfaction, with a prediction accuracy of up to 99%, and the highly accurate satisfaction prediction can quickly respond to customer needs and feedback to improve customer experience and satisfaction.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Technology Adoption and User Behaviour · Organizational and Employee Performance · Digital Marketing and Social Media
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
Funding
the National Natural Science Foundation of China · the Ministry of Transport and applied basic research project of China · the National Defense Pre-Research Project of Wuhan University of Science and Technology
References (41)
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