Remember the episode of Friends when Chandler sits on Joey’s recliner, it falls apart, and he immediately assumes he has broken it?
During the episode – “The One Where Rosita Dies” – Chandler panics and rushes to replace Joey’s chair with his own.
However, Chandler fails to consider that the chair was already broken – as it indeed was. Instead, he jumps to a conclusion.
As a result, he loses a prized possession in replacing the chair with his own, experiences lots of unnecessary stress, and perplexes Joey in the process.
Indeed, Joey thinks Rosita magically healed by her/itself.
Yet, if only Chandler had stopped, considered why the chair fell apart with such little force, and gathered a bit more info – he could have saved all that time, frustration, and his beloved chair.
Within that story lies a lesson for every contact center quality assurance (QA) team.
A Lesson for Contact Center QA Leaders
Like Chandler, contact center teams can sometimes be guilty of jumping to assumptions, quality assurance teams especially.
Still, many take a small snapshot of an agent’s performance – based on just one or two percent of their customer conversations – and use this as the basis of their evaluations.
As Chandler’s predicament proves, this can lead to analysts drawing the wrong conclusions.
After all, such a wafer-thin percentage of contacts cannot offer a fair representation of agent performance. Yet, analysts will use this to monitor agents – and sometimes even reward them.
The result is that QA becomes a game of potluck, agents fail to buy into the initiative, and broader performance trends go amiss.
From there, agent, business, and customer outcomes suffer.
The lesson: contact centers need a more thorough, consistent, and organized way to monitor their agents’ performance.
The One Where a Contact Center Automated Its Quality Assurance
Nowadays, many customer service teams leverage conversational intelligence solutions to score contacts automatically.
Explaining how these work, John Matthew Ortiz, Technology Sales Manager at MiaRec, said:
“The “Auto-QA” module pulls insights from every conversation, auto-fills scorecards, and even attaches metadata for deeper analysis.”
As such, the contact center can assess agent performance at a much more granular level and open the floodgates for vast streams of new insight.
Yet, the solution is no magic wand. For it to drive real change, quality analysts, team leaders, and coaches must all share a connected learning strategy.
For instance, an analyst may identify performance gaps at an individual or team level – which is now much easier with Auto-QA – but something must happen next.
Ideally, a coach will then address the gap with tailored training, a team leader will run post-training reinforcement, and the analysts will use Auto-QA to measure the impact.
With everyone working in lockstep, contact center teams can ensure the insights that Auto-QA surfaces drive real value.
Dig a Little Deeper (Like a Palaeontologist)
While Auto-QA can autonomously serve up new performance insights, Ortiz stresses: “It’s not a replacement to manual QA.
“Instead, it drives up the efficiency of manual QA, gives teams more time to dig deeper into trends, and – in doing so – helps analysts find the root cause of contact center problems.”
An excellent example of how it drives up efficiency is by informing the contacts that quality analysts choose to review. No more does it need to be a lucky-dip process.
Instead, analysts can listen to those where the Auto-QA score proved particularly low or high. These are most likely to withhold the best opportunities for agent learning and praise.
QA can also spot longer-term trends in agent performance – both good and bad – as Ortiz highlights. That insight paves the way for more personalized recognition and coaching.
Moreover, as all these insights are data-driven, the contact center removes the sense of subjectivity.
Yet, let’s go back to Ortiz’s point from earlier: contact centers can also attach metadata to auto-QA results. That may include topic analysis and sentiment data.
Consider the impact of topic analysis data first. Quality analysts may see which queries agents struggle with most, get to the root cause, and notify service leaders.
Next, with sentiment data, a quality analyst may see which agents typically achieve the highest scores, dig into why, and use that to update scorecard criteria.
All this, combined with Auto-QA, allows contact centers to achieve their version of unagi – the state of total awareness (not only a freshwater eel).
Such awareness is critical to driving up agent, business, and customer outcomes.
MiaRec: Always There for Your QA Team
MiaRec works with many brands, automating call scoring, extracting insights, and serving up a more holistic view of contact center performance.
Yet, many other vendors offer similar solutions, each with their own strengths and weaknesses. There is no single solution that is best for every business.
MiaRec is upfront about this, and – although it may well provide the ideal product for your business – if it does not, it’ll be upfront about it.
Indeed, its goal is to help your business find its Auto-QA lobster.
Eager to learn more about MiaRecs AutoQA capabilities? If so, visit: www.miarec.com
