Research on the Method of Generating Customer Satisfaction Analysis Reports

Xu Peicai

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

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Research on the Method of Generating Customer Satisfaction Analysis Reports

更新时间：2026-05-11

- Research on the Method of Generating Customer Satisfaction Analysis Reports
- Telecommunications Science (2026)
- 作者机构：
  
  China Mobile Group Design Institute Co., Ltd. Fujian Branch, Fuzhou 350000, China
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.DXKX250631
  CLC： TP393
- Received：28 October 2025，
  
  Revised：2025-12-04，
  
  Accepted：09 April 2026，
- 稿件说明：
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Xu Peicai. Research on the Method of Generating Customer Satisfaction Analysis Reports[J/OL]. Telecommunications Science, 2026.
DOI：

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

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