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We’ve all heard the message “This call may be monitored for quality purposes…” when calling into a contact center. The reality is that your call probably won’t be monitored – and even if it is, very little useful insight will come of it.
All companies and organizations are interested in optimizing agent performance to improve service quality. Unfortunately, most do an ineffective job of monitoring agent performance – because we use a process that’s inherently flawed.
Today, the standard approach to quality monitoring agent performance for Quality Management (QM) is as follows:
Aside from being costly and slow, this manual approach suffers from two big drawbacks that limit its usefulness.
Problem #1: The sample size is too small
Because you have to actually listen to and evaluate the calls, most companies sample only one to two percent of their calls. This small sample size has a lot of problems:
Problem #2: The measures are imprecise and subjective
It’s hard to get accurate and consistent results when different listeners interpret interactions in different ways. What does it mean to “be helpful” or “friendly”? What if the agent is friendly at first, then gets less friendly? How do you score that?
To really get to what’s happening, you need automated solutions that can evaluate all calls along concrete and objective measures. Today this is possible with Speech Analytics.
Speech Analytics breathes new life into Quality Management
At Genesys, we’ve seen our customers experience amazing results – including improving agent performance – when they’ve automated their contact center Quality Management processes with Speech Analytics. By automatically analyzing all calls for behaviors and applying objective measures, Speech Analytics offer new customer insight, at a scale never seen before.
To see the details of how Speech Analytics work in the real world in our webinar replay, “Revolutionizing Contact Center Quality Management with Speech Analytics.” We’ll cover a real case study and provide a live demonstration of Speech Analytics. In this particular case, hearing is believing.
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