Abstract
The most important goal of customer services is to keep the customer satisfied. However, service resources are always limited and must be prioritized. Therefore, it is important to identify customers who potentially become unsatisfied and might lead to escalations. Today this prioritization of customers is often done manually. Data science on IoT data (esp. log data) for machine health monitoring, as well as analytics on enterprise data for customer relationship management (CRM) have mainly been researched and applied independently. In this paper, we present a framework for a data-driven decision support system which combines IoT and enterprise data to model customer sentiment. Such decision support systems can help to prioritize customers and service resources to effectively troubleshoot problems or even avoid them. The framework is applied in a real-world case study with a major medical device manufacturer. This includes a fully automated and interpretable machine learning pipeline designed to meet the requirements defined with domain experts and end users. The overall framework is currently deployed, learns and evaluates predictive models from terabytes of IoT and enterprise data to actively monitor the customer sentiment for a fleet of thousands of high-end medical devices. Furthermore, we provide an anonymized industrial benchmark dataset for the research community.
Highlights
Manufacturing companies are interested in monitoring and improving their installed systems’ performance to keep their customers satisfied
We demonstrate that customer sentiment can be better estimated when looking at the system performance based on machine log data and enterprise data
We observed during our experiments that XGB and deep learning-based models were more prone to overfit on the training data than Random Forest (RF)
Summary
Manufacturing companies are interested in monitoring and improving their installed systems’ performance to keep their customers satisfied. The performance of a system depends on the health status of a machine and customer perception [1] To this end, they can rely on a wealth of data collected on an ongoing basis to monitor their installed systems effectively. The first group comprises IoT data (i.e., machine logs) generated on the system, allowing one to study the problem from a machine health perspective. This is often considered in disciplines like predictive maintenance. The second group comprises data derived from complementary enterprise systems, which enables studies from a customer perspective
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