客户满意度分析报告生成方法研究——徐**<sup>1</sup>

Xu Peicai

doi:10.11959/j.issn.1000-0801.DXKX250631

您当前的位置：

首页 >

文章列表页 >

客户满意度分析报告生成方法研究——徐**¹

更新时间：2026-05-11

- 客户满意度分析报告生成方法研究——徐**¹
- Research on the Method of Generating Customer Satisfaction Analysis Reports
- 电信科学 2026年
- 作者机构：
  
  China Mobile Group Design Institute Co., Ltd. Fujian Branch, Fuzhou 350000, China
- 作者简介：
  
  徐***，
- 基金信息：
  
  国家自然科学基金资助项目
- DOI：10.11959/j.issn.1000-0801.DXKX250631
  中图分类号： TP393
- 收稿：2025-10-28，
  
  修回：2025-12-04，
  
  录用：2026-04-09，
- 稿件说明：
移动端阅览
Xu Peicai. 客户满意度分析报告生成方法研究——徐**¹[J/OL]. 电信科学, 2026.

Xu Peicai. Research on the Method of Generating Customer Satisfaction Analysis Reports[J/OL]. Telecommunications Science, 2026.
Xu Peicai. 客户满意度分析报告生成方法研究——徐**¹[J/OL]. 电信科学, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX250631.

Xu Peicai. Research on the Method of Generating Customer Satisfaction Analysis Reports[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX250631.

摘要

在通信行业，保证高水平的客户满意度是保持竞争优势的关键，客户满意度预测因受样本稀缺或数据不完整情况，导致预测准确率低，同时，模型预测结果的可解释性差，无法为不满客户进行差异性关怀提供有效的数据支撑。提出一种结合零样本学习的客户满意度预测方法，通过ETS和t-SNE提取的辅助特征为零样本学习提供了必要的支持，使得模型能够在没有大量标注数据的情况下，依然保持较高的预测准确度，同时，采用SHAP清晰地展示每个特征对预测结果的贡献，帮助企业识别并定位客户不满意的关键因素。采集东南某省电调客户满意度数据对模型进行验证，结果表明，该方法准确率提升近9%左右，基于不满客户的解释性分析结果进行客户关怀的成功率达81%以上。

Abstract

In the communication industry

ensuring a high level of customer satisfaction is the key to maintaining competitive advantage. Customer satisfaction prediction has low accuracy due to the scarcity of samples or incomplete data. At the same time

the interpretability of model prediction results is poor

which cannot provide effective data support for differentiated care for dissatisfied customers. This article proposes a customer satisfaction prediction method that combines zero sample learning. The auxiliary features extracted by ETS and t-SNE provide necessary support for zero sample learning

enabling the model to maintain high prediction accuracy without a large amount of labeled data. At the same time

SHAP is used to clearly demonstrate the contribution of each feature to the prediction results

helping enterprises identify and locate key factors of customer dissatisfaction. Collecting customer satisfaction data from a certain province in Southeast China to validate the model

the results showed that the accuracy of customer satisfaction prediction in this paper improved by nearly 9%

and the success rate of customer care based on explanatory analysis of dissatisfied customers reached over 81% .

关键词

Keywords

references

Verbeke W. , Dejaeger K. , Martens D. , et al . New Insights into Churn Prediction in the Telecommunication Sector: A Profit Driven Data Mining Approach [J ] . European Journal of Operational Research , 2012 , 218 ( 1 ): 211 - 229 .

Ascarza E. , Neslin S. A. , Netzer O. , et al . In pursuit of enhanced customer retention management: Review, key issues, and future directions [J ] . Customer Needs and Solutions , 2018 , 5 : 65 - 81 .

Xie Y. , Wang Y. , & Lin Y. Telecom Customer Churn Prediction in Big Data: A Cognition Computing Approach [J ] . IEEE Access , 2016 , 4 : 3773 - 3782 .

Coussement K. , Lessmann S. , & Verstraeten G. A comparative analysis of data preparation algorithms for customer churn prediction: A case study in the telecommunication industry [J ] . Decision Support Systems , 2017 , 95 : 27 - 36 .

Liu Y. , Sun L. , & Wu J. Customer churn prediction in telecommunications industry using data balance processing and ensemble learning [J ] . Computers, Materials & Continua , 2018 , 56 ( 1 ): 1 - 19 .

M. R. Mohaimin , B. C. Das, R. Akter et al. Predictive Analytics for Telecom Customer Churn: Enhancing Retention Strategies in the US Market [J ] .Journal of Computer Science and Technology Studies, 2025 , 7 ( 1 ): 30 - 45 .

Somya , S. Kaushik , S. Saini , S. et al . Telecom Churn Prediction Using Data Science [J ] . Educational Administration: Theory and Practice , 2024 , 30 ( 5 ): 11026 - 11034 .

S. Saha , C. Saha , M. M. Haque , et al . ChurnNet: Deep Learning Enhanced Customer Churn Prediction in Telecommunication Industry [J ] . IEEE Access , 2024 , 12 : 4471 - 4484 .

B. Prabadevi , R. Shalini , and B. R. Kavitha . Customer churning analysis using machine learning algorithms [J ] . International Journal of Intelligent Networks , 2023 , 4 : 145 - 154 .

S. Höppner , E. Stripling , B. Baesens , et al . Profit driven decision trees for churn prediction [J ] . European Journal of Operational Research , 2020 , 284 ( 3 ): 920 – 933 .

G. Sam , P. Asuquo , and B. Stephen , Customer Churn Prediction using Machine Learning Models [J ] . Journal of Engineering Research and Reports , 2024 , 26 ( 2 ): 181 - 193 .

Yingying Wu , Yiqun Liu , Yen-His Richard Tsai , et al . Investigating the Role of Eye Movements and Physiological Signals in Search Satisfication Prediction using Geometric Analysis [J ] . Journal of Neurosurgery Spine , 2019 , 8 : 981 - 999 .

吕品 , 钟珞 , 唐琨皓 . 在线产品评论客户满意度综合评价研究 [J ] . 电子学报 , 2014 , 42 ( 4 ): 740 - 746 .

马艳 . 模糊理论与信息熵在电力设计行业顾客满意度测评中的应用 [J ] . 计算机系统应用 , 2005 , 14 ( 5 ): 39 - 42 .

Y. Rong , T. Leemann , Thai-Trang N . et al . Towards Human-Centered Explainable AI: A Survey of User Studies for Model Explanations [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024 , 46 ( 4 ): 2104 - 2122 .

C. Özkurt, Transparency in Decision-Making: The Role of Explainable AI (XAI) in Customer Churn Analysis [J ] . Information Technology in Economics and Business , 2025 : 1 - 15 .

C. Qin , A. Zhang , Z. Zhang , et al . Is ChatGPT a General-Purpose Natural Language Processing Task Solver? [J/OL ] . ArXiv , . 2023 [ 2025-03-01 ] . http://arxiv.org/abs/2302.06476 http://arxiv.org/abs/2302.06476 .

H. Naveed , A. U. Khan , Shi Qiu et al . , A Comprehensive Overview of Large Language Models [J/OL ] . ArXiv , 2023 [ 2025-03-01 ] . http://arxiv.org/abs/2307.06435 http://arxiv.org/abs/2307.06435 .

任世超 , 黄子良 . 基于二维信息增益加权的朴素贝叶斯分类算法 [J ] . 计算机系统应用 , 2019 , 28 ( 6 ): 135 - 140 .

韩存鸽 , 叶球孙 . 决策树分类算法中C4.5算法的研究与改进 [J ] . 计算机系统应用 , 2019 , 28 ( 6 ): 198 - 202 .

江勋林 . 多目标支持向量机及其在少样本故障诊断中的应用 [J ] . 计算机系统应用 , 2022 , 31 ( 9 ): 287 - 293 .

谢国荣 , 郑宏 , 林伟圻 , 等 . 基于改进随机森林算法的停电敏感客户分类 [J ] . 计算机系统应用 , 2019 , 28 ( 3 ): 104 - 110 .

A. Manzoor , M. Atif Qureshi , E. Kidney , et al . A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners [J ] . IEEE Access , 2024 , 12 : 70434 - 70463 .

杜睿山 , 黄玉朋 , 孟令东 , 等 . 基于BiLSTM-XGBoost混合模型的储层岩性识别 [J ] . 计算机系统应用 , 2024 , 33 ( 6 ): 108 - 116 .

刘中强 , 邹维维 . 基于采样技术和LightGBM的客户用电异常检测模型 [J ] . 计算机系统应用 , 2021 , 30 ( 9 ): 232 - 236 .

白子诚 , 周艳玲 , 张龑 . GM-FastText多通道词向量短文本分类模型 [J ] . 计算机系统应用 , 2022 , 31 ( 9 ): 403 - 408 .

贠恺 , 贾荣浩 , 魏国辉 , 等 . 基于CNN与Transformer混合模型的肺炎辅助诊断 [J ] . 计算机系统应用 , 2025 , 34 ( 2 ): 216 - 224 .

M. D. Gabhane , A. Suriya , and S. B. Kishor . Churn Prediction in Telecommunication Business using CNN and ANN [J ] . Journal of Positive School Psychology , 2022 , 6 ( 4 ): 4672 - 4680 .

A. Rai . Explainable AI: from black box to glass box .[J ] . Journal of the Academy of Marketing Science , 2020 , 48 ( 1 ): 137 - 141 .

M. Fisher , M. Robb , D. M. Wolf , et al . Implementing a Standardized Discussion Forum Rubric Across 3 Online Nursing Programs [J ] . Nurse Educator , 2019 , 44 ( 6 ): 291 - 292 .

Jiang F , Li Z , Wang Y , et al . A Comprehensive Survey of Large AI Models for Future Communications: Foundations, Applications and Challenges [J/OL ] . ArXiv , 2025 [ 2025-03-01 ] . http://arxiv.org/abs/2505.03556 http://arxiv.org/abs/2505.03556 .

Wan H , Zhang L , Zhao Y , et al . Automating Modelica Module Generation Using Large Language Models: A Case Study on Building Control Description Language [J/OL ] . ArXiv , 2025 [ 2025-03-01 ] . http://arxiv.org/abs/2509.14623 http://arxiv.org/abs/2509.14623 .

靳东明 , 金芝 , 陈小红 , 等 . ChatModeler：基于大语言模型的人机协作迭代式需求获取和建模方法 [J ] . 计算机研究与发展 , 2024 , 61 ( 2 ): 338 - 350 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据

客户满意度分析报告生成方法研究——徐**1

Research on the Method of Generating Customer Satisfaction Analysis Reports

DOI：10.11959/j.issn.1000-0801.DXKX250631

摘要

Abstract

关键词

Keywords

references

客户满意度分析报告生成方法研究——徐**¹