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

• Published in: IEEE access Vol. 9; p. 1
• Main Authors: Nguyen, An, Foerstel, Stefan, Kittler, Thomas, Kurzyukov, Andrey, Schwinn, Leo, Zanca, Dario, Hipp, Tobias, Da Jun, Sun, Schrapp, Michael, Rothgang, Eva, Eskofier, Bjoern
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-01-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: big data; Case studies; Customer relationship management; Customer satisfaction; Customer service; Customer services; Data models; decision support system; Decision support systems; explainable AI; Feature extraction; industrial AI; IoT data; Machine learning; Medical devices; Medical electronics; Monitoring; Predictive models; Systems design; Troubleshooting; Workflow
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3106791

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.