“This call may be monitored for quality purposes.” How many times have you heard this when you called a call center for technical support or a problem? Probably so many times that you have begun to tune it out. However, with over 56 million hours worth of customer conversations a day emitted from the industry, it is highly unlikely that a professional is sifting through each call to make sure that quality assurance (QA) is maintained – which is where speech analytics enters the picture.
Speech analytic technologies have completely revolutionized the way in which organizations analyze their performance, as it has the ability to allow supervisors and quality analysts to immediately review occurrences of events and infractions such as escalation attempts, the use of profanity, or the absence of compliance scripts automatically. Being able to react to these occurrences in real time rather than after the fact could mean the difference between saving a customer and losing them. It can also be used during sales calls as a coaching tool to help agents improve up-sell/cross sell performance.
Check out the video below to see how CallMiner’s (News - Alert) speech analytic program works:
CallMiner, a provider of speech analytic solutions, recently worked with a large financial services client who had been scoring calls in a traditional fashion, which was to outsource them to manual agent quality monitoring with an outside vendor. CallMiner found the top four reasons why the traditional method of manually reviewing calls proved ineffective against speech analytic technology. According to CallMiner, they are:
1. The number of calls monitored each month was statistically insignificant, which resulted in a lack of confidence in the scores.
Traditional sampling only covers about 5 percent of all calls that come into a call center, making it virtually impossible for managers to get a true sense of why customers are calling or how each individual agent is actually performing.
2. Manual reviews of calls were inconsistent and prone to error, causing additional oversight.
With human review, there are always bound to be errors. Perhaps the QA analyst evaluates the call too quickly, misunderstands sarcasm on the part of the caller, or overlooks a critical insight.
3. Manual review of calls was expensive and not scalable.
For a contact center that has hundreds or thousands of agents, manual call reviews are simply not a cost-effective or scalable QA methodology.
4. Monthly reporting was time consuming.
It’s always interesting to see how companies aggregate reports with a manual QA system. Most of the time, it consists of a difficult spreadsheets that require days of effort to update.
After the financial company switched from traditional methods to speech analytics, they were able to automatically monitor and score 100 percent of their agent interactions, which resulted in an expanded monitoring system to cover all agent teams at no extra cost, automatic categorization of calls, improved call selection, immediate analysis of results from coach and customized reporting that significantly reduced monthly report generation efforts.
With this success story at hand it is easy to see why call centers are updating to 21st century technology for their QA initiatives. For more information on how an organization can leverage speech analytics, please visit CallMiner.
Edited by Blaise McNamee