Research on customer escalated complaints prediction based on LLM-RFE-XGBoost approach

Qiao Yaqian; Wang Qian; Liu Fang; Xu Yu; Jiang Jun

doi:10.11959/j.issn.1000-0801.2026033

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Research on customer escalated complaints prediction based on LLM-RFE-XGBoost approach

Engineering and Application | 更新时间：2026-04-08

- Research on customer escalated complaints prediction based on LLM-RFE-XGBoost approach
- Telecommunications Science Vol. 42, Issue 3, Pages: 200-207(2026)
- 作者机构：
  
  1.中移在线服务有限公司山东分公司，山东济南 250022
  2.中移在线服务有限公司AI能力中心，河南郑州 450001
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2026033
  CLC： TP391.4;TP181
- Received：16 July 2025，
  
  Revised：2025-10-31，
  
  Accepted：31 October 2025，
  
  Published：20 March 2026
- 稿件说明：
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乔雅倩,王倩,刘芳等.基于LLM-RFE-XGBoost方法的客户升级投诉预警研究[J].电信科学,2026,42(03):200-207.

Qiao Yaqian,Wang Qian,Liu Fang,et al.Research on customer escalated complaints prediction based on LLM-RFE-XGBoost approach[J].Telecommunications Science,2026,42(03):200-207.
乔雅倩,王倩,刘芳等.基于LLM-RFE-XGBoost方法的客户升级投诉预警研究[J].电信科学,2026,42(03):200-207. DOI： 10.11959/j.issn.1000-0801.2026033.

Qiao Yaqian,Wang Qian,Liu Fang,et al.Research on customer escalated complaints prediction based on LLM-RFE-XGBoost approach[J].Telecommunications Science,2026,42(03):200-207. DOI： 10.11959/j.issn.1000-0801.2026033.

摘要

客户升级投诉作为衡量服务质量的关键指标，其精准预测对服务质量改善与客户问题解决具有重要意义。基于此提出了一种基于大语言模型（LLM）、递归特征消除（RFE）以及XGBoost的混合学习方法对潜在升级投诉客户进行预警，首先利用大语言模型从客户通话文本中提取语义特征，再融合结构化数据后利用RFE进行最优特征选择，最后基于有效特征采用XGBoost进行预警。为了检验该模型的有效性，以某省运营商生产数据为研究对象进行了预测，实证结果表明提出的LLM-RFE-XGBoost混合方法具有最优的预测性能。实际上线应用后，升级投诉量下降了6.7%，成效显著，对提高服务质量及客户满意度具有重要意义。

Abstract

Customer escalated complaints serve as a critical indicator for measuring service quality and are vital for improving service quality and resolving customer issues. A hybrid learning approach LLM-RFE-XGBoost was proposed for early warning of potential escalated complaints. Firstly

large language models (LLM) was utilized to extract semantic features from customer call text. Then these features were integrated with original structured data

after which recursive feature elimination (RFE) was applied to select the optimal feature set. Finally

XGBoost was employed for prediction using all selected features. To validate the effectiveness of the model

predictive analysis was conducted using production data from a provincial telecom operator as the research subject. Empirical results demonstrate that the proposed LLM-RFE-XGBoost hybrid approach delivers optimal predictive performance. After practical application in a provincial telecom operator

escalated complaints decreased by 6.7%

which is of great significance for improvement of the service quality and customer satisfaction.

关键词

Keywords

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