Mobile Customer Satisfaction Scoring Research Based on Quadratic Dimension Reduction and Machine Learning Integration

• Published in: Applied sciences 2023-09, Vol.13 (17), p.9681
• Main Authors: Zeng, Fei, He, Yuqing, Yang, Chengqin, Hu, Xinkai, Yuan, Yining
• Format: Article
• Language: English
• Subjects: Accuracy; algorithmic integration; Algorithms; Brand loyalty; Communication; Competitive advantage; Customer feedback; Customer satisfaction; Customers; Data mining; Decision making; Decision trees; Machine learning; mobile customer satisfaction scoring research; quadratic dimension reduction; Statistical analysis; User experience; User needs; User satisfaction
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13179681

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.