Research on a complaint prediction model utilizing joint neural networks
By conducting in-depth exploration on the key factors affecting repeat complaints of telecom operators, this study aimed to improve service quality and construct a risk prediction model.Based on the operator’s customer service data, the study employed Logistic regression, BP neural network, and thei...
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2024-01-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024006/ |
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| _version_ | 1841533682456723456 |
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| author | Xiaoliang MA Ying LIU Jie GAO |
| author_facet | Xiaoliang MA Ying LIU Jie GAO |
| author_sort | Xiaoliang MA |
| collection | DOAJ |
| description | By conducting in-depth exploration on the key factors affecting repeat complaints of telecom operators, this study aimed to improve service quality and construct a risk prediction model.Based on the operator’s customer service data, the study employed Logistic regression, BP neural network, and their combined modeling methods.The Logistic regression model identified five major influencing factors, predicting the probability of repeat complaints with an accuracy of 80.0%.The BP neural network selected 81 influencing factors, achieving a prediction accuracy of 90.6%.On this basis, a combined model was constructed with an accuracy rate of up to 92.8%.After practical application in a provincial telecom operator, the repeat complaint rate decreased by 3.2%, demonstrating a significant impact.Strong support is provided for improving the service quality of telecom operators and reducing repeat complaints, which is of great significance for the development of the telecom industry in China. |
| format | Article |
| id | doaj-art-f2fe88a09eea47f88fdf4ab97e1e7b72 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2024-01-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-f2fe88a09eea47f88fdf4ab97e1e7b722025-01-15T02:57:30ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012024-01-0140485859556869Research on a complaint prediction model utilizing joint neural networksXiaoliang MAYing LIUJie GAOBy conducting in-depth exploration on the key factors affecting repeat complaints of telecom operators, this study aimed to improve service quality and construct a risk prediction model.Based on the operator’s customer service data, the study employed Logistic regression, BP neural network, and their combined modeling methods.The Logistic regression model identified five major influencing factors, predicting the probability of repeat complaints with an accuracy of 80.0%.The BP neural network selected 81 influencing factors, achieving a prediction accuracy of 90.6%.On this basis, a combined model was constructed with an accuracy rate of up to 92.8%.After practical application in a provincial telecom operator, the repeat complaint rate decreased by 3.2%, demonstrating a significant impact.Strong support is provided for improving the service quality of telecom operators and reducing repeat complaints, which is of great significance for the development of the telecom industry in China.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024006/AI customer servicejoint modelingrepeated complaintLogistic regressiondeep learning model |
| spellingShingle | Xiaoliang MA Ying LIU Jie GAO Research on a complaint prediction model utilizing joint neural networks Dianxin kexue AI customer service joint modeling repeated complaint Logistic regression deep learning model |
| title | Research on a complaint prediction model utilizing joint neural networks |
| title_full | Research on a complaint prediction model utilizing joint neural networks |
| title_fullStr | Research on a complaint prediction model utilizing joint neural networks |
| title_full_unstemmed | Research on a complaint prediction model utilizing joint neural networks |
| title_short | Research on a complaint prediction model utilizing joint neural networks |
| title_sort | research on a complaint prediction model utilizing joint neural networks |
| topic | AI customer service joint modeling repeated complaint Logistic regression deep learning model |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024006/ |
| work_keys_str_mv | AT xiaoliangma researchonacomplaintpredictionmodelutilizingjointneuralnetworks AT yingliu researchonacomplaintpredictionmodelutilizingjointneuralnetworks AT jiegao researchonacomplaintpredictionmodelutilizingjointneuralnetworks |