While speech analytics provides valuable insight into “ what ” is happening in customer interactions , it isn ’ t quite there yet when it comes to “ why ” things are happening or “ how ” trends can be reversed .
THE SHIFTING LANDSCAPE OF QUALITY ASSURANCE
There is no question that speech analytics has supercharged our ability to analyze vast quantities of customer interactions , but there are limitations .
While speech analytics can analyze customer sentiment without being influenced by biases or emotions , AI may struggle to grasp the context of certain customer interactions .
For example , sarcasm or irony can be challenging for AI algorithms to identify accurately . AI may not fully capture cultural nuances and differences in expressions of sentiment . And while AI can detect basic emotions like happiness , anger , or sadness , it may struggle with more nuanced emotions or subtle changes in tone , making it less reliable .
Why is understanding customer emotions important ? As more and more customers leverage the ever-expanding array of self-serve options , when they do interact with a human customer service representative , the interaction will be complex and laden with customer feelings , reactions , and beliefs . Recognizing customer emotions and responding appropriately , accurately , and empathetically will be critical going forward . This is where great customer service organizations will excel .
THE IMPACTS OF VIRTUAL ASSISTANTS AND CHATBOTS
A key area of activity within most contact centers lies with virtual assistants or chatbots . But these are not the bots of 10 years ago that worked with a limited , pre-determined set of responses .
Today ’ s virtual assistants leverage NLP and ML algorithms to understand customer queries , provide relevant responses , and perform tasks or transactions without human intervention . They can answer frequently asked questions , provide product information , guide users through processes , and even initiate transactions or service requests .
The underlying ML algorithms enable virtual assistants to continuously learn from interactions and improve their responses over time , enhancing their effectiveness and accuracy .
[ Check out these great articles for more insight on the future of AI in Customer Service : “ The Human-Touched CX Magic of Conversational AI ” and “ Revolutionizing Customer Service ”]
In fact , research conducted by Tidio and published in 2023 found that the majority of customers would use an online chatbot to see if it can help them instead of waiting for a customer service rep to take their call .
Chatbots are expected to save companies huge amounts of money annually . Over time , these tools will significantly reduce the number of human-assisted interactions required ( voice , live chat , email ) and , in turn , the necessary type and volume of QA .
... QA ANALYSTS NEED TO IDENTIFY TRENDS AND CHERRY-PICK INTERACTIONS THAT OFFER THE GREATEST INSIGHTS ...
THE ROLE OF QA ANALYSTS AND AI-ENABLED COACHING
While speech analytics provides a wealth of information , it falls short in addressing the crucial question of how to improve performance . At times , the sheer amount of data can overwhelm many coaches . We all know that QA without coaching is just data and does nothing to raise the bar .
This is where human QA analysts must step in to bridge the gap . While AI tools may do the grunt work in analyzing large volumes of interactions , QA analysts need to identify trends and cherry-pick interactions that offer the greatest insights and opportunities for coaching . Thereby enabling targeted feedback , training recommendations , and actionable performance improvement plans for individual agents .
QUALITY ASSURANCE
NEW COACHING AND QA ANALYST SKILLS IN AN AI WORLD
With the increasing integration of AI in QA ( and that the interactions customer service representatives will be handling will be more complex and emotion-laden ), coaches and QA analysts alike will need to hone their skills to stay ahead of the curve . The days of “ ticking boxes ” and single-dimension coaching are behind us . Some of these skills include :
• Data analysis and interpretation . Coaches and QA analysts will need to be proficient in analyzing and interpreting data generated by AI tools . They must have a strong understanding of data analytics techniques and be able to draw meaningful insights from AI-generated reports and performance metrics .
• Human judgment and empathy . While AI provides valuable insights , coaches and QA analysts must retain their human judgment and empathetic approach in assessing agent performance . They will need to balance AI-generated recommendations with their expertise and contextual understanding to ensure fair and accurate evaluations .
• Coaching and feedback delivery . As AI tools provide insights and recommendations for performance improvement , QA analysts need to excel in translating those recommendations into actionable coaching plans .
How coaching and feedback are delivered to frontline agents will be critical to lifting performance : particularly as what we expect of agents will increase exponentially .
Often it will require a number of skills used together to raise the bar , and coaches / QA analysts need to understand how to convey this effectively . Everyone will need to hone their coaching skills to more sophisticated levels .
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