How to Build Effective Automated Agent Scorecards Using Speech Analytics
HOW TO BUILD EFFECTIVE AUTOMATED AGENT SCORECARDS
Automated scorecards are an advanced feature made possible through speech analytics—a technology that uses AI and machine learning to turn call recordings into actionable data.
Powered by machine learning, every conversation can be automatically scored based upon the rules defined by the company.
Automated scorecards are an advanced feature made possible through speech analytics—a technology that uses AI and machine learning to turn call recordings into actionable data.
Powered by machine learning, every conversation can be automatically scored based upon the rules defined by the company.
THE CHALLENGE
One of the main differences between automated and manual scorecards is that manually scoring calls requires a supervisor to listen through every call. This process is tedious and time-consuming.
In fact, manual scoring has traditionally been done overnight using offshore services. Outsourcing this process is costly and oftentimes untrustworthy.
THE SOLUTION
Automated scorecards allow technology to do what’s traditionally been done manually by supervisors one call recording at a time.
Using keywords and keyword lists to tag, categorize, and auto-answer crucial questions about every call, automated scorecards increase productivity and reduce compliance risk.
THE RESULTS
Adding speech analytics driven by machine learning, contact centers can reduce the time companies spend manually listening to and evaluating call recordings by 80%.
Automated scorecards help supervisors pinpoint specific areas of concern around script adherance, agent performance and training, and compliance.
In addition, speech analytics drastically reduces the time and effort of auditing recordings due to consumer complaints or pending FDCPA-related lawsuits. Searching for these types of calls will typically take an average of 14 minutes. However, speech analytics reduces this time to less than a minute.
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