2nd September 2024

In this blog, we summarize the key points from a recent article from David McGeough at Scorebuddy where he explored how automation and AI are transforming quality management in contact centres to meet modern demands.
Quality management is crucial for upholding service standards in contact centers, ensuring each interaction aligns with your expectations and that agents consistently perform well.
However, as communication channels expand beyond just phone calls and interaction volumes surge, maintaining quality has become increasingly complex.
To meet this growing demand and sustain high service levels, automating your quality management process is now essential. Fortunately, the rise in interaction volume has been matched by advancements in contact centre technology, particularly in automated quality management solutions.
But do we need artificial intelligence, automation, and all these fancy other tools to keep up with the modern demands of a contact centre? Let’s dive in and see what’s really going on.
While the traditional approach to quality management is widely known, it’s far from perfect, particularly in today’s tech-driven world.
Several issues hinder its efficiency, accuracy, and overall performance in call centres. Understanding these challenges highlights the need for change in call centre operations.
Traditional call centre quality assurance is time-consuming. Evaluators spend hours listening to call recordings, reviewing transcripts, taking notes, and compiling data, with a single evaluation often taking 20 minutes or more.
This manual process demands significant time and labor that could be better allocated elsewhere, like targeted coaching, QA data analysis, or strategic initiatives.
Besides being time-intensive, traditional QA also struggles to provide comprehensive insights. Typically, only 2 to 3% of interactions are captured and analyzed, leaving a vast amount of data untapped.
This limited dataset makes it hard to get a true sense of performance, customer experience, or key metrics. Important trends may go unnoticed, making it tough to make informed decisions or track progress effectively.
Human evaluators bring inherent subjectivity to the QA process, leading to inconsistent results. Different team members may interpret the same call differently, which can create bias and reduce the standardization of quality across interactions.
Consistency is key for an effective quality management system, but it’s hard to achieve when evaluations depend on individual perspectives.
As call centres grow, traditional QA methods struggle to keep up. Customer expectations are higher than ever, and tracking multiple channels manually becomes nearly impossible.
Manual reviews can become overwhelming as interaction volumes increase, forcing companies to either hire more evaluators (which is costly) or cut corners, risking the quality of the customer experience.
Human error is inevitable in manual processes, leading to blind spots in QA. Even minor mistakes can have significant consequences, such as compliance breaches. With such a low sample size in manual QA, these risks increase as call volumes grow, making it harder to meet regulatory standards.
Automated quality management leverages modern technology to monitor, evaluate, and improve call centre interactions. Unlike manual methods, AQM uses AI and machine learning to analyze every call, chat, email, and other communication in real-time.
This automation ensures complete coverage of all customer interactions, providing consistent and transparent evaluations while saving time. The benefits? Reduced human bias, more accurate evaluations, improved agent morale, higher service quality, real-time feedback, and stronger compliance.
Automated quality management transforms call centre operations through technologies like natural language processing (NLP) and machine learning (ML).
NLP enables computers to understand and analyze the language used in customer interactions, breaking down conversations to detect tone, sentiment, and key phrases. This helps assess communication quality and monitor compliance, making it easier to identify customer concerns and measure agent performance.
ML focuses on pattern recognition and predictive analytics. By learning from the vast amount of data generated by call centres, ML algorithms improve over time, identifying trends and areas for improvement.
This allows for the refinement of training programs, optimization of call scripts, and prediction of future outcomes, creating a more efficient and personalized call centre.
Implementing AQM in your contact centre can vastly improve operations and customer experience. Here are seven reasons why it’s a smart investment:
Manual quality management is no longer the best option for evaluating your contact centre’s performance. With modern technology, automated quality management offers superior efficiency, comprehensive coverage, and significant cost savings, making it the clear choice for a better ROI.