Testing Times for the Contact Centre and Semantics

author: Christopher Baker, University of New Brunswick
published: Dec. 23, 2010,   recorded: December 2010,   views: 3733


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Funded by the Atlantic Canada Opportunities Agency and the Atlantic Innovation Foundation, Innovatia Inc. have pioneered the design and testing of semantic technologies for use by Contact Centre agents who provide Technical Support to customers in the Telecommunications sector. This is in response to increasing contact center costs for companies whose products and information support services must rapidly evolve. Numerous opportunities exist for increasing the productivity of knowledge workers involved in searching separate and disconnected product-specific knowledge bases, case resolution databases, training manuals and technical documentation. Our technical solution comprises of OWL-DL knowledge base populated from a wide variety of document formats with sentence-triples generated by a telecommunications-specific text mining pipeline that leverages document segmentation techniques to instantiate with state of the art precision and recall. This talk will outline critical features of the platform and the performance of agents using the prototype with various user interface tools from commercial vendors with customized features to test this search paradigm. Pilot studies required agents to troubleshoot customer queries from fault symptoms to root causes and to identify procedures to resolve the causes of faults. During testing agents used visual queries, advanced dynamic form-based search, and SPARQL queries saved as frequently asked questions. In particular we have validated that Tier 1 agents can navigate a sequence of workflows resulting in an increase in findability from 75 to 100% under real time conditions and overall search time has been reduced by 50%. Moreover, time savings can be made at the critical junction of case handoff from Tier 1 to Tier 2 agents, such that Tier 1 agents can achieve better performance. We review contact center performance metrics and comment the on the suitability of Semantic Technologies for this business process as well as issues related to rollout and adoption of this platform across the enterprise.

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