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Optimizing the Ticket Response Process in Customer Support Systems Using Data-Driven and Machine Learning Methods: A Case Study of IFDA
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Abstract
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).
Keywords
Machine learning Tag cloud representation Response time improvement Practical applications in IT support Automated IT support systems NLPReferences (40)
- [1] Hertvik, J. (2020). Service desk TIPS explained: Ticket, incident, problem, service request. https://www.bmc.com/blogs/ticket-vs-incident-vs-problem-vs-service-request/
- [2] Mani, S., Sankaran, A., & Aralikatte, R. (2019). Deeptriage: exploring the effectiveness of deep learning for bug triaging. Proceedings of the ACM India joint international conference on data science and management of data (pp. 171–179). Association for Computing Machinery. https://doi.org/10.1145/3297001.3297023
- [3] Kurnaedi, D., Oktora, E., Dharmawan, E., Nasrullah, I., & Drajat, M. (2022). Web-based IT helpdesk ticketing system at PT. Dayacipta Kemasindo. Bit-tech: binary digital-technology, 5(2), 121–127. https://doi.org/10.32877/bt.v5i2.617
- [4] Mani, S., Sankaranarayanan, K., Sinha, V. S., & Devanbu, P. (2014). Panning requirement nuggets in stream of software maintenance tickets. Proceedings of the 22nd ACM sigsoft international symposium on foundations of software engineering (pp. 678–688). Association for Computing Machinery. https://doi.org/10.1145/2635868.2635897
- [5] Orta, E., Ruiz, M., Hurtado, N., & Gawn, D. (2014). Decision-making in IT service management: a simulation based approach. Decision support systems, 66, 36–51. http://dx.doi.org/10.1145/2635868.2635897
- [6] Fontes, M., Nascimento, W., Carvalho, K., Moreira, E., Cardoso, M., & Montoni, M. (2014). Improving the software factory contracting process in the public area: the rio de janeiro city hall’s experience. 2014 9th international conference on the quality of information and communications technology (pp. 152–155). IEEE.
- [7] Newcomer, C., Withrow, J., Sturgill, R. E., & Dadi, G. B. (2019). Towards an automated asphalt paving construction inspection operation. Advances in informatics and computing in civil and construction engineering: proceedings of the 35th CIB w78 2018 conference: it in design, construction, and management (pp. 593–600). Springer. https://doi.org/10.1007/978-3-030-00220-6_71
- [8] Paramesh, S., & Shreedhara, K. S. (2019). It help desk incident classification using classifier ensembles. ICTACT journal on soft computing, 9(04), 331–346. DOI:10.21917/ijsc.2019.0276
- [9] Agarwal, S., Aggarwal, V., Akula, A. R., Dasgupta, G. B., & Sridhara, G. (2017). Automatic problem extraction and analysis from unstructured text in IT tickets. IBM journal of research and development, 61(1), 4–41. https://doi.org/10.1147/JRD.2016.2629318
- [10] Rizun, N., Meister, V., & Revina, A. (2020). Discovery of stylistic patterns in business process textual descriptions: it ticket case. 33rd international business information management association conference (pp. 2103–2113). IBIMA. https://ibima.org/accepted-paper/discovery-of-stylistic-patterns-in-business-process-textual-data-it-ticket-case/
- [11] Hassan-Montero, Y., & Herrero-Solana, V. (2024). Improving tag-clouds as visual information retrieval interfaces. https://doi.org/10.48550/arXiv.2401.04947
- [12] Rizun, N., Revina, A., & Meister, V. (2019). Method of decision-making logic discovery in the business process textual data. International conference on business information systems (pp. 70–84). Springer.
- [13] Harun, N. A., Huspi, S. H., & Iahad, N. A. (2021). Question classification framework for helpdesk ticketing support system using machine learning. 2021 7th international conference on research and innovation in information systems (ICRIIS) (pp. 1–7). IEEE. https://doi.org/10.1109/ICRIIS53035.2021.9617077
- [14] Chagnon, C. J., Trapp, A. C., & Djamasbi, S. (2017). Creating a decision support system for service classification and assignment through optimization [presentation]. Twenty-third americas conference on information systems (pp. 1–5). https://scholar.archive.org/work/qhgkenuucff4lp4brdv3ox3kcm/access/wayback/http://aisel.aisnet.org:80/cgi/viewcontent.cgi?article=1393&context=amcis2017
- [15] Revina, A., & Rizun, N. (2019). Multi-criteria knowledge-based recommender system for decision support in complex business processes. https://mostwiedzy.pl/pl/publication/multi-criteria-knowledge-based-recommender-system-for-decision-support-in-complex-business-processes,150018-1
- [16] Dekhtyar, A., & Fong, V. (2017). Re data challenge: requirements identification with word2vec and tensorflow. 2017 IEEE 25th international requirements engineering conference (RE) (pp. 484–489). IEEE.
- [17] Revina, A., Buza, K., & Meister, V. G. (2020). It ticket classification: The simpler, the better. IEEE access, 8, 193380–193395. https://doi.org/10.1109/ACCESS.2020.3032840
- [18] Diao, Y., & Bhattacharya, K. (2008). Estimating business value of it services through process complexity analysis. NOMS 2008-2008 IEEE network operations and management symposium (pp. 208–215). IEEE.
- [19] Pingclasai, N., Hata, H., & Matsumoto, K. (2013). Classifying bug reports to bugs and other requests using topic modeling. 2013 20th asia-pacific software engineering conference (APSEC) (Vol. 2, pp. 13–18). IEEE.
- [20] Al-Hawari, F., & Barham, H. (2021). A machine learning based help desk system for IT service management. Journal of king saud university-computer and information sciences, 33(6), 702–718. https://doi.org/10.1016/j.jksuci.2019.04.001
- [21] Prasetio, R. T., Ramdhani, Y., & Alamsyah, D. P. (2021). Scrum method in help-desk ticketing and project management system. 2021 3rd international conference on cybernetics and intelligent system (ICORIS) (pp. 1–6). IEEE. https://doi.org/10.1109/ICRIIS53035.2021.9617077
- [22] Rodríguez-Robayo, K. J., Méndez-López, M. E., Molina-Villegas, A., & Juárez, L. (2020). What do we talk about when we talk about milpa? A conceptual approach to the significance, topics of research and impact of the mayan milpa system. Journal of rural studies, 77, 47–54. https://doi.org/10.1016/j.jrurstud.2020.04.029
- [23] Bottou, L., Curtis, F. E., & Nocedal, J. (2018). Optimization methods for large-scale machine learning. SIAM review, 60(2), 223–311. https://doi.org/10.1137/16M1080173
- [24] Houssein, E. H., Mohamed, R. E., & Ali, A. A. (2021). Machine learning techniques for biomedical natural language processing: a comprehensive review. IEEE access, 9, 140628–140653. https://doi.org/10.1109/ACCESS.2021.3119621
- [25] Cortes, E., Woloszyn, V., Binder, A., Himmelsbach, T., Barone, D., & Möller, S. (2020). An empirical comparison of question classification methods for question answering systems. Proceedings of the twelfth language resources and evaluation conference (pp. 5408–5416). European Language Resources Association (ELRA).
- [26] Basha, S. M., & Rajput, D. S. (2019). Survey on evaluating the performance of machine learning algorithms: Past contributions and future roadmap. In Deep learning and parallel computing environment for bioengineering systems (pp. 153–164). Elsevier. https://doi.org/10.1016/B978-0-12-816718-2.00016-6
- [27] Carpinteiro, O. A. S., Lima, I., Assis, J. M. C., de Souza, A. C. Z., Moreira, E. M., & Pinheiro, C. A. M. (2006). A neural model in anti-spam systems. Artificial neural networks--icann 2006: 16th international conference, athens, greece, part II 16 (pp. 847–855). Springer. https://doi.org/10.1007/11840930_88
- [28] Ye, Y., Lee, B., Flynn, R., Murray, N., & Qiao, Y. (2017). HLAF: heterogeneous-latency adaptive forwarding strategy for peer-assisted video streaming in ndn. 2017 IEEE symposium on computers and communications (ISCC) (pp. 657–662). IEEE. https://doi.org/10.1109/ISCC.2017.8024603
- [29] Awad, W. A., & ELseuofi, S. M. (2011). Machine learning methods for spam e-mail classification. AIRCC’s international journal of computer science and information technology, 3(1), 173–184. DOI : 10.5121/ijcsit.2011.3112
- [30] Yayah, F. C., Ghauth, K. I., & Ting, C.-Y. (2022). The Performance Of Classification Method In Telco Customer Trouble Ticket Dataset. IAENG international journal of computer science, 49(2). http://www.iaeng.org/IJCS/issues_v49/issue_2/IJCS_49_2_05.pdf
- [31] Yang, N., Wu, G., MacEachren, A. M., Pang, X., & Fang, H. (2023). Comparison of font size and background color strategies for tag weights on tag maps. Cartography and geographic information science, 50(2), 162–177. https://doi.org/10.1080/15230406.2022.2152098
- [32] Denton, E. L., Zaremba, W., Bruna, J., LeCun, Y., & Fergus, R. (2014). Exploiting linear structure within convolutional networks for efficient evaluation. https://doi.org/10.48550/arXiv.1404.0736
- [33] Gong, Y., Liu, L., Yang, M., & Bourdev, L. (2014). Compressing deep convolutional networks using vector quantization. https://doi.org/10.48550/arXiv.1412.6115
- [34] Jaderberg, M., Vedaldi, A., & Zisserman, A. (2014). Speeding up convolutional neural networks with low rank expansions. https://doi.org/10.48550/arXiv.1405.3866
- [35] Sujata, S., & Ramya, M. (2020). Real-time IT help desk ticket classification using deep learning approach. International journal of scientific and technology research, 9(4), 239–244.
- [36] Paramesh, S. P., & Shreedhara, K. S. (2019). Automated it service desk systems using machine learning techniques. Data analytics and learning: proceedings of dal 2018 (pp. 331–346). Springer.
- [37] Abakouy, R., En-Naimi, E. M., El Haddadi, A., & Lotfi, E. (2019). Data-driven marketing: how machine learning will improve decision-making for marketers. ACM international conference proceeding series (pp. 1–5). Association for Computing Machinery. DOI: 10.1145/3368756.3369024
- [38] Aouiche, K., Lemire, D., & Godin, R. (2009). Web 2.0 olap: from data cubes to tag clouds. Web information systems and technologies: 4th international conference (pp. 51–64). Springer. https://doi.org/10.1007/978-3-642-01344-7_5
- [39] Sinclair, J., & Cardew-Hall, M. (2008). The folksonomy tag cloud: when is it useful? Journal of information science, 34(1), 15–29. https://doi.org/10.1177/0165551506078083
- [40] Mandal, A., Malhotra, N., Agarwal, S., Ray, A., & Sridhara, G. (2019). Automated dispatch of helpdesk email tickets: pushing the limits with AI. Proceedings of the AAAI conference on artificial intelligence (Vol. 33, pp. 9381–9388). IAAI. https://doi.org/10.1609/aaai.v33i01.33019381
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- Published 2024-10-12
- Issue Vol. 1 No. 2 (2024): OPT
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Saadati, H. ., & Hakimi, A. . (2024). Optimizing the Ticket Response Process in Customer Support Systems Using Data-Driven and Machine Learning Methods: A Case Study of IFDA. Optimality, 1(2), 188-204. https://doi.org/10.22105/opt.v1i2.57