In the life of a present-day contact center, one of the most important aspects to not only maintaining but also improving operations is Quality Management; and in some countries and sectors, regular agent evaluations are a regulatory must.
“Quality Management (QM) plays a significant role in cost reduction by identifying causes that lead to repeat calls, escalations, or complaints and addressing them proactively,” notes Britta Chiaia, Product Manager at ASC Technologies.
“It also increases customer satisfaction by ensuring high-quality, accurate, and empathetic interactions, and helps in building positive brand reputation.”
That said, Quality Management, in its manual form, is one of the least favorite tasks for agents and supervisors alike; and that’s where AI comes in.
“AI-infused QM can quite extraordinarily take the downsides of this ‘necessary evil’ and turn them into advantages, both benefiting the contact center and relieving agent stress,” Chiaia notes.
To truly understand how AI can transform the QM process for contact centers, we must understand the related challenges they are currently facing.
The Challenges of Manual Quality Management
Manually performed QM gives rise to multiple different challenges for agents, supervisors, and the contact center as a whole. Here are the most common ones.
Subjectivity: Manual quality evaluations done by humans are subjective in nature, as different evaluators may have different interpretations of quality criteria. This can lead to inconsistencies in scoring and feedback, as well as pose a risk of discrimination.
“The extreme scenario would be of a team lead using agent evaluation as a bullying method,” Chiaia suggests. “But even when it doesn’t come to that, human evaluations can never be 100% neutral.”
Limited Resources: Implementing and maintaining Quality Management processes requires significant time, effort, and resources.
“Contact centers are required to train staff, monitor interactions, analyze data, and implement improvements, all of which can be quite challenging for smaller contact centers with limited resources,” Chiaia explains.
Costs vs. Benefits: While Quality Management aims to improve customer satisfaction and operational efficiency, the return on investment (ROI) may not always be immediately apparent.
“It can be rather challenging to quantify the direct impact of quality management efforts on key performance indicators and financial outcomes, making it difficult to judge whether it’s ‘worth it’ or not.”
Customer Disruption and Agent Pressure: QM processes like call monitoring or customer surveys can disrupt the natural flow of interactions, leading both to customer dissatisfaction and increased pressure on agents.
“Intrusive monitoring or excessive survey requests can annoy customers and damage relationships,” Chiaia says. “Also, when an agent knows that they’re being listened to, stress levels rise, and the natural flow of the conversation can be compromised.”
AI-Powered Quality Management: Quick, Neutral, Economical
“These are all challenges that our customers experience, and they can be reduced (and sometimes eliminated) with the help of AI,” Chiaia says.
Resource intensity, for example, is reduced significantly when contact centers don’t have to listen to conversations anymore; and the benefits outweigh the costs.
“Some of our (potential) customers are still doing that – sitting next to the agent and evaluating them,” she shares.
While a certain cost is still there – having to pay for the AI solution doing the work – it’s more cost-effective and provides notable relief to humans previously doing manual QM.
In many cases, AI can actually do the entire evaluation itself, with supervisors merely checking the answers to see if they fit the call summary.
“LLMs can answer the questions in the evaluation scorecard, saving precious time for supervisors and agents and boosting efficiency,” Chiaia explains.
This also touches upon the subjectivity issue mentioned earlier.
“If a neutral LLM is answering the questions, determining an agent’s performance and allocating a certain number of points to it, the evaluation is infinity more neutral than before,” Chiaia explains.
Automated Quality Management: The ASC Way
Ultimately, contact center Quality Management is about creating and filling out a scorecard. A scorecard is effectively a questionnaire that can include several sections, and within each section, certain evaluation criteria. For each criterion, evaluators assign points based on an agent’s calls.
“With ASC’s automated QM as part of our Recording Insights solution, you have both possibilities – answering manually or having an AI answering – and you can also mix and match between the two,” Chiaia explains.
In any case, once the evaluation is finished, the scorecard summary can be evaluated manually even if it was done by the AI solution. If an agent or supervisor has doubts regarding an LLM’s choice, they can listen to the call recording to check or even ask specific questions about the transcript.
“And it’s really simple – there’s no complicated setup involved,” Chiaia shares.
“Since contact centers typically have existing evaluation questionnaires as Excel sheets, they just need to put them into a scorecard to use them within the Recording Insights interface.”
In the end, AI has the power to facilitate contact center Quality Management in a way that turns it from a task no one wants to do into a manageable process with clear benefits.
״Whether used as automated self-assessment for agents or traditional QM for supervisors and department heads, AI-powered QM not only saves time but also ensures professional customer communication, reducing stress for employees,” Chiaia concludes.
To learn more about ASC’s Recording Insights and automated Quality Management, visit their website.
