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Overcoming the Blind Spots in a Quality Assurance Practice

December 20, 2022 | | Customer Service, Contact Center

Contact center quality assurance programs originally sprang from companies’ need to understand contact center agent performance. Due to technological limitations, the only way they could get any kind of pulse was to randomly sample a few calls each month. Over time, that workaround became the norm. Unfortunately, it still is the norm for a lot of brands, even in the face of new technologies enabling much better approaches.

When QA practices don’t evolve to include a deeper, more comprehensive analysis of all the calls that come into the contact center, blind spots are allowed to persist, which leads to missed coaching opportunities, inability to identify and address bad behaviors, and limited ability to recognize agents who are shining stars. To take a more comprehensive approach and eliminate those blind spots, there are a number of tools available to identify opportunities to improve the customer experience. 


Speech Analytics 

When quality analysts monitor calls randomly, they almost always need to select calls that are 1) long enough to be worth the effort, and 2) short enough that they can get through their workload in a given day/week/month. So what happens is that only calls over about 3 minutes in length that are no longer than 15 minutes ever get monitored for quality.

Today, there are a number of Speech Analytics solutions on the market that effectively and accurately transcribe customer interactions into searchable text. Many also have an analytics layer, allowing contact center leaders and marketing professionals the ability to see macro trends in call types, caller and agent behaviors, sentiment, and other factors.  These tools can also be used to analyze and evaluate 100% of individual agents’ calls no matter how long they are. This provides much deeper and richer insights into how well those agents are performing throughout their customer interactions - and that’s clearly much better than a few random calls per month.

When working with one client to make this very point, we compared calls of the length that they monitored (2.5 to 12 minutes) with calls over 15 minutes. With Speech Analytics, we focused on three areas: Agent Lack of Confidence, Lack of First Call Resolution, and Positive Caller Feedback. The differences were very telling:

  • Agent Lack of Confidence (using words/phrases like “I don’t know”, “let me check with someone”, etc) went from 13% across all interactions in the 2.5 - 12 minute calls to 63% in calls over 15 minutes
  • Lack of First Call Resolution (not fully resolving the issue and asking the customer to call back later with more information) went from only 2% in the regularly monitored calls to 13% on long calls
  • Positive Caller Feedback (callers expressing a lot of gratitude and praise for the agent) went from just 4% up to 10% on the longer calls

By only listening to 2.5 - 12 minute calls, this company was missing coaching or training opportunities in the longer calls with agent-displayed lack of confidence. They were missing potential bad behaviors by agents’ not fully resolving calls on which they knew no one would be listening. And, they missed recognition opportunities because it apparently took a longer call (and probably a stickier problem) for callers to express their gratitude and praise for the agent.

Post-Call Surveys

Asking customers for their feedback directly after the conclusion of their call is another way to remove blind spots. In this case, the blind spot is with unspoken sentiment, either positive or negative. While a caller may not tell a contact center agent directly that they are upset, disappointed, or offended (or the opposite), a direct set of questions via an impersonal survey can elicit those sentiments.

The other blind spot is with quality assurance team’s impressions of a call. Often, when Quality teams get together to “calibrate” and evaluate calls together, there are different opinions of how the call went. One might say it went fine while the next felt the agent should have been more engaging, for example. Asking the caller during the survey how engaging they felt the agent was can eliminate that blindspot and others like it because the answer comes straight from the source.

Desktop Analytics 

Another blind spot is particularly relevant in the work-from-home (WFH) era we find ourselves in, whether the new culture is fully WFH or some hybrid version. The blind spot in particular here is the inability to “walk the floor” to see what agents are up to, in busy times or when times are slow.  

In a call center environment, leaders can have a quick chat about an important topic or make sure agents are reading work emails, reviewing the knowledge base, cleaning up customer interaction notes, or whatever other productive and useful tasks they should take on when they can. In a distributed WFH environment, leaders cannot do that. They can instant message perhaps or look at their ready/not-ready status, but little else without better tools.

Desktop Analytics tools can monitor those kinds of behaviors as well as tell you who is playing a video game, perusing social media, or otherwise not using their time well.  But more to the point of ensuring quality interactions, they can also reveal agents who are or are not following processes.

Did they open the right page with a disclosure statement that needs to be read verbatim?  Did they click on and properly report a complaint or threat of litigation? How often are certain agents accessing the knowledge management system? Is the recommendations tool with targeted offers being regularly accessed?

Those are just a few examples of this particular blind spot.  Simply listening to calls cannot yield those insights. Even if screen recordings accompany the call audio, it is still random and an insufficient sample. Maybe the agent looks up product recommendations 90% of the time, but this month’s monitoring happened to find a call from the 10% where they didn’t. Or maybe they almost always wing it, but in the call being monitored, they actually opened and read the proper disclosure statement. This and the earlier blind spots discussed just leave too much to chance.

It’s Time to Remove Contact Center Blinders 

Contact center blind spots most often reside in:

  • Weak sample sets
  • Only reviewing calls of “manageable” length
  • Lack of insight into desktop/system usage

Not employing quality assurance processes that overcome these blind spots can leave an incomplete, and potentially inaccurate, picture of agent performance and interaction quality. Certainly, the tools and examples shared here can remove some of those blinders and lead to stronger agents, better interactions, and happier customers. To overcome contact center quality assurance blind spots, reach out to Andrew Reise today.

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