We all understand that quality assurance (QA) is vital in maintaining and enhancing performance within contact centers.
Traditionally, human QA analysts have been responsible for assessing agent interactions to identify areas of improvement. However, with the advent of speech analytics and artificial intelligence (AI), the landscape of QA in contact centers is rapidly evolving.
Speech Analytics and QA
In recent years, the contact center industry has witnessed a significant shift in its approach to QA. According to a survey published in 2023 by Market Research Future, the speech analytics market is expected to reach $9.33 billion by 2030. This underscores the growing recognition of the importance of leveraging technology to gain actionable insights from customer interactions.
Speech analytics employs natural language processing (NLP) and machine learning (ML) algorithms to transcribe, categorize, and analyze recorded calls, enabling organizations to uncover valuable insights. It assists in identifying recurring customer issues, tracking agent adherence to scripts and compliance, and even detecting customer sentiment.
...AI may struggle to grasp the context of certain customer interactions.
According to a 2021 Aberdeen Group study, companies that employ speech analytics achieve a 16% higher first contact resolution rate and a 12% higher customer satisfaction rate than those that do not. These statistics underline the significant impact speech analytics can have on overall contact center performance. But there are limitations.
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.
...QA analysts need to identify trends and cherry-pick interactions that offer the greatest insights...
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.
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.
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.
- Problem-solving and adaptability. This new environment will present coaches and QA analysts with new challenges and complexities. This means they will need to be adaptable and possess strong problem-solving skills. It will no longer be all about compliance but instead evaluating the entire experience holistically.
- Continuous learning and upgrading skills. As new technologies evolve, everyone will need to consciously and methodically stay updated with the latest advancements and trends. This means actively seeking opportunities to upgrade their skills through training programs and certifications and staying connected with industry networks and communities (like the QATC).
This new environment will present coaches and QA analysts with new challenges and complexities.
It’s essential to recognize that AI is a tool to enhance human capabilities, and combining human expertise with AI insights will ultimately lead to better QA and customer experience outcomes.
The Future of QA in Contact Centers
There is no question that, as technology advances, tools supporting QA will evolve. Incorporating sentiment analysis, voice recognition, and emotional intelligence capabilities into AI tools holds the potential for more accurate and contextual coaching recommendations.
This will enable contact centers to provide agents with a deeper understanding of customer emotions, allowing them to tailor their interactions accordingly.
By leveraging the strengths of both human expertise and AI capabilities, contact centers can deliver enhanced customer experiences, improved agent performance, and increased operational efficiency.
As the market continues to embrace AI technology to engage directly with customers via self-serve options and indirectly via AI-supported QA, contact centers prioritizing these advancements will gain a competitive edge in the ever-evolving customer service landscape.