Call Center Quality Management: From Addiction to Optimization

The following is a fictional (but relatively realistic) account of life as a team leader attempting to comply with traditional quality management techniques.

My name is John. I’m a team leader, and I’m an addict.

I have come to realize I am powerless over my addiction to evaluating my agents’ performance. This is my story.

I manage 15 agents. Each of them handles approximately 2,500 calls per month. My manger instructed me to evaluate my agents’ performance using our quality management system and to make sure that I’m as accurate as possible in my evaluations. I asked my manager: “How accurate would you like me to be?” She said that 90% accuracy sounded about right.

So I started digging around to find out how many calls to evaluate in order to make sure I’m as accurate as possible. I googled “Sample Size Calculators” and found numerous tools to help me arrive at the following calculations:

  • Number of calls per month: 2,500 per agent (our average call volume)
  • Confidence level (or accuracy) = 90% (as defined by my manager)
  • Confidence interval (margin of error) = ±5% (my accuracy can range from 85% to 95%)
  • Result = I must evaluate 246 calls for each of my 15 agents, or 3,690 calls, per month

Obviously this is not a realistic number!

A new approach

The story above parodies the pitfalls of traditional agent-centric quality management. As you can see, sample size and team leader bandwidth typically limit the accuracy of the results, often giving management a skewed view of agents’ performance.  

A new concept of quality management is emerging, shifting the focus from agent performance to business performance. Commonly known as Quality Optimization, this smart approach to improving quality overcomes old-school limitations by linking strategic business objectives and day-to-day agent evaluation within a single, unified call center quality management process.

Quality optimization starts with advanced interaction analytics, which categorizes calls by the:

  • Words and phrases customers use (e.g. words that indicate a First Call Resolution issue – ‘this is the second time I’m calling’; words that may signal a potential churn – ‘your competitor offer same service at a lower price’)
  • The sentiment and level of emotion they display
  • CTI data (e.g. holds, call transfers)
  • Customer account data (extracted from CRM or BI systems)

With this data, calls can be automatically selected for evaluation based on their impact on key performance indicators (KPIs), such as average handle time, service escalations or first call resolution; or strategic metrics, such as customer satisfaction and net promoter scores. No more sorting through thousands of irrelevant, random calls—a very inaccurate performance measurement method.

Using this business-centric approach, managers’ attention is drawn directly to those agents and interactions with greatest impact on business performance, enabling them to identify and resolve performance and process issues with the highest potential benefit.

As a by-product, quality optimization can dramatically reduce the incidence of evaluation addiction among team leaders. No need for counseling or rehabilitation. Just optimize your process!

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