Revolutionizing QA with Speech Analytics
By Donna Fluss
Quality assurance (QA) is a mission-critical business function that identifies contact center and enterprise trends, and provides insights into how well each agent is performing. For the past 30 years QA has been performed in contact centers around the world in basically the same way. Managers search through volumes of recordings to find the 10% to 20% of calls that require attention, either because they are really bad or really good. If a call is bad, the QA specialist or supervisor must document the situation by completing a QA evaluation form and then arrange a coaching session with the agent. If the call is really good, the agent should be commended and, ideally, recognized.
QA is a labor-intensive function, based on an often random selection of calls, supported by technology but never truly automated. QA evaluations are frequently not performed at all, or left until the last minute and then rushed through, delivering little benefit for the department, agent or customer.
Contact center leaders are aware of the weaknesses of their current QA programs, but all agree that even if the process is imperfect, QA adds considerable value. Some information about trends, systems issues, bad policies and poorly performing agents, is better than none. However, the challenge has always been convincing front-line supervisors, who are already pulled in many directions, to perform evaluations and actually coach their agents. Exit studies at outsourcers often find that the number-one reason why agents resign is because they do not receive timely or effective coaching. This issue can be addressed with an automated QA process, which can monitor 100% of calls, categorize them, and identify emerging trends and patterns. An automated call identification process provides a closed-loop mechanism to deliver appropriate coaching to agents on a consistent and timely basis. The concept of automated quality assurance is called precision monitoring, and it represents the future of QA.
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