Can a Machine Learn Through Customer Sentiment?: A Cost-Aware Approach to Predict Support Ticket Escalations

Werner, Colin; Li, Ze Shi; Damian, Daniela

doi:10.1109/ms.2019.2923408

Cited by 11 publications

(15 citation statements)

References 11 publications

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“…The second place (7 papers) is taken by (versions of) the Random Forrest (RF) approach that performed best in 3 papers. Here, we found maximal accuracies of 78% [30], 90% [6] and 92 % [36].…”

Section: Machine Learning Algorithms Usedsupporting

confidence: 52%

“…Werner, Li [30] x x x Gajananan, Loyola [31] (x) x Gupta, Asadullah [1] x x Han, Goh [32] x Koehler, Fux [33] x x Lyubinets, Boiko [34] (x) x Meng, Xu [35])…”

Section: Sent Pred Othermentioning

confidence: 99%

“…According to Revina, Buza [23], the earliest approaches of support ticket classification were ruledbased approaches that were outperformed by RF and SVM. This probably is the reason that mostly in the years 2018 and 2019, RF and SVM were the approaches most implemented and evaluated and also best performing [2,6,8,9,30,36]. Since 2019, some authors also propose artificial-neuronal-networkbased solutions, because of the good performance of neuronal network approaches in other fields of ML [19,21,23,29].…”

Section: <60% Accuracymentioning

confidence: 99%

“…An escalation of a customer request mostly occurs when the customer creating the request or a support manager is dissatisfied with the way a support ticket is processed and requests its escalation. This request escalation leads to a concentration of extensive human resources for solving the customer request [30] within the support providing company. This concentration of human resources means more stress for the involved support agents and support managers and additionally, these escalations are mostly expensive for the company and significantly lower customer satisfaction [30,43].…”

Section: Request Escalation Predictionmentioning

confidence: 99%

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Improving Support Ticket Systems Using Machine Learning: A Literature Review

Fuchs¹,

Drieschner²,

Wittges³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Processing customer support requests via a support ticket system is a key-element for companies to provide support to their customers in an organized and professional way. However, distributing and processing such tickets is much work, increasing the cost for the support providing company and stretching the resolution time. The advancing potential of Machine Learning has led to the goal of automating those support ticket systems. Against this background, we conducted a Literature Review aiming at determining the present state-of-the-art technology in the field of automated support ticket systems. We provide an overview about present trends and topics discussed in this field. During the Literature Review, we found creating an automated incident management tool being the majority topic in the field followed by request escalation and customer sentiment prediction and identified Random Forrest and Support Vector Machine as best performing algorithms for classification in the field.

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Section: Machine Learning Algorithms Usedsupporting

confidence: 52%

“…Werner, Li [30] x x x Gajananan, Loyola [31] (x) x Gupta, Asadullah [1] x x Han, Goh [32] x Koehler, Fux [33] x x Lyubinets, Boiko [34] (x) x Meng, Xu [35])…”

Section: Sent Pred Othermentioning

confidence: 99%

Section: <60% Accuracymentioning

confidence: 99%

Section: Request Escalation Predictionmentioning

confidence: 99%

See 2 more Smart Citations

Improving Support Ticket Systems Using Machine Learning: A Literature Review

Fuchs¹,

Drieschner²,

Wittges³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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show abstract

“…CRM is a broad discipline, including strategies and processes for organizations to handle customer interactions and keep track of customer-related information [12], [13]. Customer escalations are mostly predicted solely based on customer support ticket data only [14], [15].…”

Section: Introductionmentioning

confidence: 99%

System Design for a Data-Driven and Explainable Customer Sentiment Monitor Using IoT and Enterprise Data

et al. 2021

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The most important goal of customer service is to keep the customer satisfied. However, service resources are always limited and must prioritize specific customers. Therefore, it is essential to identify customers who potentially become unsatisfied and might lead to escalations. Data science on IoT data (especially log data) for machine health monitoring and analytics on enterprise data for customer relationship management (CRM) have mainly been researched and applied independently. This paper presents a data-driven decision support system framework that combines IoT and enterprise data to model customer sentiment and predicts escalations. The proposed framework includes a fully automated and interpretable machine learning pipeline using state-of-the-art methods. The framework is applied in a real-world case study with a major medical device manufacturer providing data from a fleet of thousands of high-end medical devices. An anonymized version of this industrial benchmark is released for the research community based on the presented case study, which has interesting and challenging properties. In our extensive experiments, we achieve a Recall@50 of 50.0% for the task of predicting customer escalations. In addition, we show that combining IoT and enterprise data can improve prediction results and ease troubleshooting. Additionally, we propose a practical workflow for end-users when applying the proposed framework.INDEX TERMS customer service, decision support system, IoT data, explainable AI, machine learning, big data, industrial AI.

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