  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.
