System Design for a Data-driven and Explainable Customer Sentiment Monitor

TL;DR

This paper presents a comprehensive, data-driven framework combining IoT and enterprise data to model and monitor customer sentiment, enabling proactive customer service prioritization and resource allocation in a real-world medical device context.

Contribution

It introduces a novel, interpretable machine learning pipeline integrating IoT and enterprise data for customer sentiment analysis, deployed in an industrial setting.

Findings

Successfully deployed in a medical device company

Learns from terabytes of data for real-time sentiment monitoring

Provides an anonymized benchmark dataset for research

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.