Optimizing the Ticket Response Process in Customer Support Systems Using Data-Driven and Machine Learning Methods: A Case Study of IFDA
DOI:
https://doi.org/10.22105/opt.v1i2Keywords:
Machine learning, Tag cloud representation, Response time improvement, Practical applications in IT support, Automated IT support systems, NLPAbstract
Effective customer interaction through IT Support Ticketing (ITST) can enhance customer satisfaction, whether human-driven or automated, facilitating collaboration towards common goals. This principle is particularly critical in Information Technology (IT) Services, where clear communication ensures accurate interpretation of requests and efficient resolution of issues. This study employs Machine Learning (ML) algorithms, specifically Natural Language Processing (NLP) and Tag Cloud Representation, to prioritize issues in the support system. The research utilizes data collected from both individual and corporate entities over a one-month period, revealing that common problems predominantly involve password and username retrieval issues. The analysis conducted in this study emphasizes the importance of continuous planning and the integration of additional ML algorithms to enhance the support process further and advance the digitalization of IT systems. This research highlights the critical need for robust IT Service Management (ITSM) strategies to manage increasing ticket volumes and improve Response Times (RT).
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