{"id":325835,"date":"2016-11-22T12:27:01","date_gmt":"2016-11-22T20:27:01","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=325835"},"modified":"2018-10-16T21:04:52","modified_gmt":"2018-10-17T04:04:52","slug":"optimizing-automated-call-routing-integrating-spoken-dialog-models-queuing-models","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/optimizing-automated-call-routing-integrating-spoken-dialog-models-queuing-models\/","title":{"rendered":"Optimizing Automated Call Routing by Integrating Spoken Dialog Models with Queuing Models"},"content":{"rendered":"<p>Organizations are increasingly turning to spoken dialog systems for automated call routing to reduce call center costs.\u00a0 To maintain quality service even in cases of failure, these systems often resort to ad-hoc rules for dispatching calls to a human operator.\u00a0 We present a principled procedure for determining when callers should be transferred to operators based on a cost-benefit analysis.\u00a0 The procedure integrates models that predict when a call is likely to fail using spoken dialog features with queuing models of call center volume and service time.\u00a0 We evaluate how the procedure would have performed on cases drawn from logs of interactions with a legacy spoken dialog system.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Organizations are increasingly turning to spoken dialog systems for automated call routing to reduce call center costs.\u00a0 To maintain quality service even in cases of failure, these systems often resort to ad-hoc rules for dispatching calls to a human operator.\u00a0 We present a principled procedure for determining when callers should be transferred to operators based 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