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

by   An Nguyen, et al.

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.


Customer Churn Prediction Model using Explainable Machine Learning

It becomes a significant challenge to predict customer behavior and reta...

Predictive analytics for appointment bookings

One of the service providers in the financial service sector, who provid...

The Impact of IoT In Field Service Management

A machine that you have been using smoothly, suddenly gets out of order!...

Customer Support Ticket Escalation Prediction using Feature Engineering

Understanding and keeping the customer happy is a central tenet of requi...

Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation

Microsoft Azure is dedicated to guarantee high quality of service to its...

Escalation Prediction using Feature Engineering: Addressing Support Ticket Escalations within IBM's Ecosystem

Large software organizations handle many customer support issues every d...

Please sign up or login with your details

Forgot password? Click here to reset