Research on the Mining of Failure Causes of Intelligent Customer Service Based on LDA Topic Model

DOI: https://doi.org/10.62517/jmsd.202512532

Author(s)

Shanshan Fang1,*, Jiwei Yang1, Zimeng Gui2, Hongyu Mo1

Affiliation(s)

1School of Business, Guilin University of Electronic Technology, Guilin, Guangxi, China 2School of Mechatronic Engineering, Guilin University of Electronic Technology, Guilin, Guangxi, China *Corresponding Author

Abstract

While the widespread adoption of intelligent customer service systems has significantly boosted service efficiency, the persistent surge in complaints has revealed notable service failure challenges. This study addresses the shortcomings of current analytical approaches, which suffer from inefficiency and high subjectivity. To surmount these obstacles, we utilized the Latent Dirichlet Allocation (LDA) topic model to perform a thorough analysis of 1,247 genuine complaint texts. By means of dual assessment using the perplexity metric and coherence score, we determined that the optimal number of topics is three. We pinpointed three primary issues: functional failure, procedural failure, and emotional failure, which account for 42%, 35%, and 23% of the total, respectively. Our research uncovers that the root causes of intelligent customer service failures lie in the discrepancy between technological capabilities and user expectations, the clash between cost-cutting and efficiency-enhancement measures versus service quality, as well as a bias in system design that favors efficiency over a user-centric approach. Drawing on these insights, this paper puts forward tailored improvement strategies, including enriching the knowledge base, streamlining the process for escalating to human support, and integrating sentiment analysis capabilities. These strategies aim to furnish enterprises with a theoretical foundation and practical guidance for refining their intelligent customer service systems.

Keywords

Service Failure; Text Analysis; Intelligent Customer Service; LDA Topic Model; Topic Mining

References

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