Carlson School of Management
A call center is a primary channel for service encounters in a variety of industries, including airlines, hotels, and financial and telecom services. According to a recent industry report, calls to businesses are expected to exceed $169 billion per year by 2020. The quality of customer service provided by call centers is crucial in determining customer satisfaction (Jaiswal 2008). A poor service quality can lead to frustrated customers, bad word-of-mouth, and customer switching (Bitner 1990). Call centers usually use two approaches to manage service quality: sampling calls for manual inspection and customer call-backs. However, both approaches are costly and labor intensive (Jaiswal 2008). As a result, companies can only afford to check a small number of calls for potential quality problems, often long after the service encounters are over. Needless to say, cost-effective and scalable approaches for monitoring service quality are of great value to companies.
This study proposes and tests an automated approach of managing service quality based on real-time speech emotion recognition: acoustic and textual data is first extracted from customer calls, then several machine learning and deep learning technologies are applied to detect negative customer emotions. Finally, the recognized emotion sequences are used to detect service quality problems and provide real-time intelligence to customer service representatives (CSRs) and QA specialists in call centers. With this automated, machine-learning based approach, the researchers hope to bring forth a scalable, cost-effective solution for call service quality management.