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