Customer Support: A Machine Learning Approach to Smarter Task Allocation and Employee Performance Management

The Support Ticket System is designed to enhance customer service by automating task allocation and evaluating employee performance using machine learning. It addresses the inefficiencies of manual ticket assignment and tracking by implementing the Random Forest Algorithm to assign tasks based on employee experience, workload, and expertise. The system also streamlines complaint management, product sales, and employee assessments. Key performance indicators such as resolution rate, experience, and satisfaction scores are used to train the model, ensuring accurate task distribution. The model's evaluation provides an Employee Performance Score (EPS) to assess productivity and optimize workload distribution. Results demonstrate that the system significantly improves task assignment efficiency, reduces manual intervention, and enhances customer satisfaction through faster issue resolution. By integrating machine learning, this project contributes to better workflow automation, optimized resource allocation, and an improved support experience, making it a valuable solution for modern customer service management.

Authors

Publication Details

Journal
International Journal of Science and Research (IJSR)
Published
2025-04-19
DOI
https://doi.org/10.21275/sr25416165528
Primary Topic
AI and HR Technologies
Type
article
Field-Weighted Citation Impact
0.00
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article

Customer Support: A Machine Learning Approach to Smarter Task Allocation and Employee Performance Management

L. Anju, Philip S. Thomas
International Journal of Science and Research (IJSR)
AI and HR Technologies
article

Customer Support: A Machine Learning Approach to Smarter Task Allocation and Employee Performance Management

L. Anju, Philip S. Thomas
article en

Abstract

The Support Ticket System is designed to enhance customer service by automating task allocation and evaluating employee performance using machine learning. It addresses the inefficiencies of manual ticket assignment and tracking by implementing the Random Forest Algorithm to assign tasks based on employee experience, workload, and expertise. The system also streamlines complaint management, product sales, and employee assessments. Key performance indicators such as resolution rate, experience, and satisfaction scores are used to train the model, ensuring accurate task distribution. The model's evaluation provides an Employee Performance Score (EPS) to assess productivity and optimize workload distribution. Results demonstrate that the system significantly improves task assignment efficiency, reduces manual intervention, and enhances customer satisfaction through faster issue resolution. By integrating machine learning, this project contributes to better workflow automation, optimized resource allocation, and an improved support experience, making it a valuable solution for modern customer service management.

International Journal of Science and Research (IJSR)Vol. 14(4)
Decent work and economic growth
Openalex Percentile: Top 91%
AI and HR Technologies
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