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Requests classification in the customer service area for software companies using machine learning and natural

María Ximena Arias-Barahona1, Harold Brayan Arteaga-Arteaga1, Simón Orozco-Arias2,3

  • 1Department of Electronics and Automation, Universidad Autónoma de Manizales, Manizales, Caldas, Colombia.

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Summary

This study introduces an AI-powered incident classification model for software technical support. Utilizing machine learning (ML) and natural language processing (NLP), it automates request categorization, enhancing customer satisfaction and operational efficiency.

Keywords:
Consumer serviceMachine learningNatural language processingRequests classificationText classification

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • The business sector is increasingly adopting AI for transformative applications.
  • Manual resolution of customer requests in technical support is time-consuming and costly.
  • Automating incident classification can improve customer satisfaction and reduce operational expenses.

Purpose of the Study:

  • To propose and evaluate an incident classification model for a software development company's technical support.
  • To leverage machine learning (ML) and natural language processing (NLP) for automated request categorization.
  • To enhance customer satisfaction and reduce costs associated with manual support.

Main Methods:

  • Implementation of an incident classification model using ML and NLP techniques.
  • Analysis of historical company data to train and test the model.
  • Evaluation of various ML algorithms including Support Vector Machine (SVM), Extra Trees, and Random Forest.

Main Results:

  • The Support Vector Machine (SVM) algorithm achieved the highest accuracy at 98.97%.
  • Model performance was optimized through class balance, hyper-parameter tuning, and pre-processing techniques.
  • The proposed model effectively categorizes client requests, enabling data-driven analysis of customer behavior.

Conclusions:

  • AI-driven incident classification significantly improves technical support efficiency.
  • Automated categorization leads to better resource allocation and faster resolution times.
  • The developed ML/NLP model offers a scalable and accurate solution for managing customer incidents in software companies.