Hierarchical service analytics for improving productivity in an enterprise service center

ABSTRACT

Modern day service centers are the building blocks for highly efficient and productive business systems in a knowledge economy. In these service systems, accurate and timely delivery of pertinent information to service representatives becomes the cornerstone for delivering efficient customer service. There are two main steps in achieving this objective. The first step concerns efficient text mining to extract critical and pertinent information from the very long service request (SR) documents in the historical database. The second step concerns matching new service requests with previously stored service requests. Both lead to efficiencies by minimizing time spent by service personnel in extracting Intellectual Capital (IC). In this paper we present our text analytics system, the Service Request Analyzer and Recommender (SRAR), which is designed to improve the productivity in an enterprise service center for computer network diagnostics and support. SRAR unifies a text preprocessor, a hierarchical classifier, and a service request recommender, to deliver critical, pertinent, and categorized knowledge for improved service efficiency. The novel feature we report here is identifying the components of the diagnostic process underlying the creation of the original text documents. This identification is crucial in the successful design and prototyping of SRAR and its hierarchical classifier element. Equally, the use of domain knowledge and human expertise to generate features are indispensable synergistic elements in improving the accuracy of the text analysis toward identifying the components of the diagnostic process. The evaluation and comparison of SRAR with other benchmark approaches in the literature demonstrate the effectiveness of our framework and algorithms. This framework can be generalized to be applicable in many service industries and business functions that mine textual data to achieve increased efficiency in their service delivery. We observe significant service time responsiveness improvements during the first step of IC extraction in network service center context at Cisco.

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Index Terms

• Applied computing

  • Document management and text processing

• Information systems

  • Information systems applications