Enterprise Call Scoring: Humans vs. Machines [Infographic]

Posted by Emily Blazensky on Sep 7, 2016 1:30:00 AM

Most Call Centers Humanly Score less than 5% of all Calls...

Human scoring was the only option for enterprise call centers to score, monitor, and manage their agents and call interactions for a long time. At that volume of agents and calls, many call center managers quickly discovered they could not afford to re-listen to every call, but they also couldn't afford not to. So they compromised.They said "We'll score just a few calls from every agent, and then use that sample as a representation of our entire operation, it'll be great." Unfortunately, it's not great, not at all. 

Human scoring as it turns out, is not perfect, it is not fast, and it is not consistent. But hey, it's all you had...until recently. The last 10 years have paved the way for machine learning and automation to take over many monotonous human tasks, (and many not-so-monotonous ones) and provide businesses with richer data, quicker results, and more cost effective solutions.

This infographic will analyze the major differences (and similarities) between Human Call Scoring and Machine Call Scoring.

Not sure how Auto Call Scoring works? Here are some references on that: 

humcan call scoring vs machine call scoring


Topics: big voice, speech analytics, transcription, call scoring

Written by Emily Blazensky

As Director of Marketing for VoiceBase, an API-based and enterprise grade speech analytics platform, Emily has developed unique and successful marketing strategies to meet the needs of an emerging industry leader, with a rapidly growing customer base. Emily has a keen grasp of the strategies to elevate awareness of the multiple ways speech technology can be applied to improve business process automation.
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VoiceBase is defining the future of deep learning and communications by providing unparalleled access to spoken information for businesses to make better decisions. With flexible APIs developers and enterprises build scalable solutions with VoiceBase by embedding speech-to-text, speech analytics, and predictive analytics capabilities into any big voice application. 



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