Predicting Self-Reported Customer Satisfaction of Interactions with a Corporate Call Center

Abstract

Timely identification of dissatisfied customers would provide corporations and other customer serving enterprises the opportunity to take meaningful interventions. This work describes a fully operational system we have developed at a large US insurance company for predicting customer satisfaction following all incoming phone calls at our call center. To capture call relevant information, we integrate signals from multiple heterogeneous data sources including: speech-to-text transcriptions of calls, call metadata (duration, waiting time, etc.), customer profiles and insurance policy information. Because of its ordinal, subjective, and often highly-skewed nature, self-reported survey scores presents several modeling challenges. To deal with these issues we introduce a novel modeling workflow: First, a ranking model is trained on the customer call data fusion. Then, a convolutional fitting function is optimized to map the ranking scores to actual survey satisfaction scores. This approach produces more accurate predictions than standard regression and classification approaches that directly fit the survey scores with call data, and can be easily generalized to other customer satisfaction prediction problems. Source code and data are available at https://github.com/cyberyu/ecml2017 .