Ceridian is a leading global business services company that helps organizations control costs, save time, optimize their workforce, grow revenue, and minimize financial risk. The company had contact center quality monitoring and analysis systems and processes that could show when First Call Resolution (FCR) rate and other KPIs needed improvement. But, these systems and processes couldn’t analyze enough calls in enough detail to discover macro-level issues, so they couldn’t identify how to improve performance.

Genesys Speech Analytics, with patented speech-to-phrase recognition, automatically categorizes all calls by call type and sub-topics discussed during the call, while also classifying customer service skills and processes used by agents. For Ceridian, it recognized phrases that indicated the customer had called before about an issue, and quickly identified which call types were generating the most callbacks. It identified the commonalities within those calls, revealing the root cause of repeat calls, which agents had the lowest FCR rates, and the drivers of low FCR rates.

Ceridian discovered that lack of a standardized process for callbacks and follow-up was causing increased customer care calls lowering FCR rates. As a result, Ceridian defined a new callback and follow-up process to ensure that their agents all used the same method to:  recognize that a callback is necessary; gather the necessary callback information; set the proper customer expectations regarding the callback; and keep their commitments.

Benefits

  • Improved FCR rates
  • Implemented processes to improve connection between callers & agents
  • Developed guidelines for callbacks
  • Improved percentage of customers  saying they were not likely to call back by 18%
  • Discovered the root cause of repeat calls

"Speech Analytics and analytics-driven coaching helped us significantly improve our First Call Resolution rate."

Pamela Cook, Quality Coach

Challenges

  • Needed insight into what is driving the FCR rate
  • Unable to analyze enough calls in enough detail to discover macro-level issues
  • Couldn’t discover how to improve performance with existing systems and processes