How Agent Coaching with Big Data Analytics Can Improve Your Customer Effort Score

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Brian Spraetz's picture

A contact center’s Customer Effort Score (CES) is the one of latest metrics for customer service. Some industry experts argue that Customer Effort Score is a better predictor of customer loyalty than Net Promoter Score or Customer Satisfaction Score. Although there are no “magic metrics” that guarantee success, CES may indeed be the best metric to focus on. One way to improve your Customer Effort Score is to eliminate repeat calls and restarts in your contact center.
 
On average, 40-50% of customer calls require a customer to call back or retell their story to another agent, making it the #1 source of customer dissatisfaction and customer effort. Approximately 50% of those repeat calls and unnecessary transfers are caused by agent execution failures, meaning they could have been completely avoided. The rest are caused due to a combination of process, policy or unique customer situation issues. 
 
While no one can deny the well-documented benefits to reducing repeat calls and increasing your FCR rate, it’s another thing to see the data in black and white (and dollar signs). For example, did you know that:

  • 30-50% of the 5.5 million global call center representative positions ($100B in wages) could be saved by avoiding repeat calls and transfers, saving $30-50 billion/year
  • The sales and marketing cost to replace churned customers due to repeat calls and transfers is estimated to cost companies another $150 billion/year
  • Companies that eliminate repeat calls and unnecessary transfers will lower their service costs (by up to 50%) and increase customer loyalty.

While contact centers have spent millions upon millions of dollars on initiatives to lower their FCR rate, many of these initiatives do not directly address the root cause of at least 50% of the repeat calls and transfers – agent skill gaps.  As a result, FCR and transfer rates (and the resulting customer effort) have not significantly improved over the last five years. 
 
The key to successfully reducing repeat calls and unnecessary transfers is as simple as it is hard – identify behaviors that drive repeats and transfers, the specific agents exhibiting these behaviors, and work with them to improve.  The big problem here is that it takes a lot of time to do this manually by listening to randomly recorded calls.
 
With big data analytics it is possible to predict which repeat calls and transfers are caused by agent behaviors (remember, that’s 50% of the time) versus processor policy issues, and accurately identify specific repeat and transferred calls that were due to an agent error. Automatic delivery of these “coachable opportunities” to supervisors provides opportunities for micro-targeted coaching focused on reducing repeats and transfers, and customer effort. By reducing typical coaching prep time from 30 to 5 minutes, big data analytics enables companies for the first time to deliver highly effective, micro-targeted coaching within a supervisor’s normal day.

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