International Journal of New Developments in Engineering and Society, 2024, 8(5); doi: 10.25236/IJNDES.2024.080513.
Minlian Fan1, Shuchang Wang1, Ying Zhang2, Fuquan Qiu1, Guorui Zhao1
1School of Computer Science and Engineering, Guangdong Ocean University, Yangjiang, 529500, China
2School of Materials Science and Engineering, Guangdong Ocean University, Yangjiang, Guangdong, 529599, China
The research on constructing customer satisfaction index systems and prediction models of communication operators has theoretical significance and application value. With Beijing Mobile Communication as the research case, this paper creates a screening and prediction model for communication carriers' customer satisfaction indicators through the introduction of the gradient descent LASSO algorithm and machine learning model. In this case, the total sample and sub-sample regression models were mainly built using regularized LASSO to compress the high-latitude and complex indicator system. The findings show that 7 first-level indicators and 28 second-level indicators, including the sense of use and voice call stability, have a considerable impact on customer satisfaction in voice call services and Internet access services. On this basis, machine learning algorithms such as the Bayesian network model, multi-layer perceptron, and GBDT are introduced to construct a customer satisfaction prediction model for communication carriers.
Communications Operator, Customer Satisfaction, GBDT, MLP Multi-Layer Perceptron, LASSO model
Minlian Fan, Shuchang Wang, Ying Zhang, Fuquan Qiu, Guorui Zhao. Construction of Customer Satisfaction Indicator System and Analysis of Influential Factors of Communication Carriers Based on Gradient Descent LASSO. International Journal of New Developments in Engineering and Society (2024) Vol.8, Issue 5: 83-91. https://doi.org/10.25236/IJNDES.2024.080513.
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