A Data-Driven Customer Quality of Experience System for a Cellular Network

Improving customer-perceived service quality is a critical mission of telecommunication service providers. Using 35 billion call records, we develop a call quality score model to predict customer complaint calls. The score model consists of two components: service quality score and connectivity score models. It also incorporates human psychological impacts such as the peak and end effects. We implement a large-sized data processing system that manages real-time service logs to generate quality scores at the customer level using big data processing technology and analysis techniques. The experimental results confirm the validity of the developed model in distinguishing probable complaint callers. With the adoption of the system, the first call resolution rate of the call center increased from 45% to 73%, and the field engineer dispatch rate from 46% to 25%.