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What are QA Metrics? Examples of QA Metrics for Call Centers, Aligning Metrics with Your Goals, and Best Practices


The Team at CallMiner

June 23, 2020

puzzle pieces Speed and Quality fit together

Definition of QA Metrics

Quality assurance, or QA, metrics refer to the data and facts your company uses to measure performance. Interestingly, QA metrics can vary significantly by industry, since they reflect performance as defined by that industry.

For example, QA metrics for software development could measure production cycles and average costs, while QA metrics for a carpentry business could track costs based on wood type and warranty requests (product failures). Every industry has its own benchmarks for customer satisfaction and performance.

For contact centers, common quality assurance metrics include: Average Speed of Answering (ASA), First-Call Resolution (FCR), Average Handle Time (AHT), Customer Satisfaction Score (CSAT), Net Promoter ScoreSM (NPS), and Customer Effort Score (CES).

How to Improve QA Metrics

The first step in improving your QA metrics, is to define “quality” in the first place: What is the goal or purpose of your quality assurance program? For example, are you working to improve the customer experience, increase customer satisfaction, and boost performance management [for your agents]?

Download our guide on how speech analytics can help improve CX!

From there, you must establish which QA metrics best align with your goals. In other words, what data and analytics will best measure your quality of service? Then, establish methods to record, track, and analyze these metrics.

Your call center performance metrics will depend largely on your goals. To get started, let’s look at those common contact center QA metrics we mentioned above:

  • Average Speed of Answering (ASA): Rolled into service level, ASA (also called average delay of calls) looks at QA from the customer’s POV, referring to the average time you take to answer an incoming call.
  • First-Call Resolution (FCR): This metric is also customer-centric, and tracks whether your reps are able to resolve customer problems on the first call, or whether follow-up is necessary.
  • Average Handle Time (AHT): Again, viewed from the customer’s perspective – there’s a theme here – this metric tracks the average time to successfully complete a transaction. Importantly, mere low AHT is not the goal: low AHT paired with high customer satisfaction is the objective.
  • Customer Satisfaction Score (CSAT): CSAT measures the degree to which a customer is satisfied with a contact center interaction – for example, on a scale of 1 to 10, 1 being not at all satisfied and 10 being thrilled.
  • Net Promoter Score (NPS): Your NPS is a tool to gauge customer loyalty and customer relationships, for example by tracking how likely a customer is to recommend your company to a friend.
  • Customer Effort Score (CES): This one’s important, as it tracks the customer experience in a different way. Instead of asking how satisfied a customer is at the end of the transaction, you ask how much effort the customer felt s/he had to make along the way. The ultimate goal is to reduce customer effort, instead of keeping them happy as they expend a huge effort to reach their own goal.

Examples of QA Metrics

Let’s work backwards for a moment. Most contact centers would agree that, given the same successful resolution, customers would prefer to spend less time on the phone. So, let’s take a look at two examples that work toward this goal:

Talk Time: With the help of CallMiner Eureka, one company was able to identify deficiencies in call routing – essentially, getting customers connected to the right rep – and as a result, were able to make changes that netted a reduced talk time of 82 seconds per call. That goes straight to the heart of your CES – reducing the effort your customer must put in, to achieve a desired result. What’s more, these changes also enabled the organization to handle a 12% increase in calls, without having to hire more agents.

Average Handle Time: Another company used CallMiner Eureka to identify long silence blocks during calls – they happened as agents waited for the system to respond to voice prompts – that were causing longer-than-necessary calls, and thus increasing customer frustrations. Once identified, the company handed the problem off the IT; one quick fix, and overnight the company reduced AHT by 11 seconds.

Benefits of QA Metrics

The major goal of QA metrics is to continually refine and improve customer satisfaction. Along the way, however, you’ll find that you naturally boost efficiency, call center productivity, and reduce costs:

  • Improve the Customer Experience: Stop wondering if you could improve the customer experience, and focus on how you can do it right now – through more efficient call handling, better call routing, and other contact center refinements.
  • Identify Agent Training Needs: As coaches work with your agents, you’ll slowly refine your training procedures and build stronger framework, to improve customer experience and efficiency from the very start.
  • Improve Operational Performance: Inefficiencies don’t just frustrate customers; they also affect performance. As you establish areas for improvement and brainstorm better methods, your operational performance will improve.
  • Reduce Costs: A well-oiled machine is an efficient machine. As you improve your customer interactions, you’ll find that efficiency goes up and costs go down.

Challenges of QA Metrics

QA metrics can be a challenging beast to tackle, especially when it comes to tracking them. Luckily, technology is working to make this challenge easier, with robust conversation analytics software.

This kind of software is designed specifically for contact centers, and allows you to establish parameters for data collectionsource from any types of data (ex. emails, chat, audio calls, surveys, CMS messages, etc.), auto-analyze these interactions for established metrics, and then provide multichannel analysis with immediate insight into your data.

So, you have that handled. But there are other challenges: How do you coach to better performance, without consequences for poor performance? How do you coordinate your entire team around the same quality goals? How do you establish attainable goals?

As QA metrics become clearer and easier to understand, and as deficiencies are revealed, these are challenges your company must tackle.

Best Practices for QA Metrics

QA metrics best practices are all about consistency – doing the same things, over and over, forever, while constantly refining your methods to achieve better results.

  • Track, Track, Track. Once you begin tracking QA metrics, don’t stop. Tweak, as you get better at understanding the data and your goals, but never stop. Do not rest on your laurels.
  • Diversify. Make sure that you have more than one QA manager/expert to score calls, in order to get different perspectives on the overall quality picture. Better yet, use interaction analytics to provide automated scoring and focus QA managers on coaching and training.
  • Rotate. Rotate quality coaches regularly. Remember, no two people are guaranteed to “vibe” off each other, and every coach has different strengths and points of view. It’s beneficial to your company and your agents, to rotate coaches regularly.
  • Go omnichannel. A good call center makes for a solid piece of the QA puzzle, but there is more to it than just audio. To be a true omnichannel contact center, be sure you’re tracking email, chat, text, social media, and other metrics, and holding them to the same quality standards as you do phone calls.

Always keep your customer in mind. In the big picture, what would make for a better omnichannel customer experience? When you know that, backtrack to the data that would prove that better experience. Then, backtrack again to how you can work toward the data that will mark that better experience.

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