Ensuring high quality service is essential, especially as customer needs become even greater. 93% of customer support teams agree that expectations are higher than ever—and call center quality assurance automation plays a crucial role in meeting these expectations.
It’s an essential part of any contact center, ensuring not just that agents follow scripts but that they’re trained effectively to deliver excellent customer service. But it comes with its own disadvantages, too—it’s a lengthy, time-consuming process that’s limited by the number of evaluators employed and how fast they can work.
With new technology like quality assurance automation, many of these drawbacks are quickly becoming nonexistent. It can speed up processes, eliminate tedious tasks, reduce costs, and much more.
Incorporating AI-driven QA automation into your call center can transform your quality management processes, enhancing efficiency and allowing your team to focus on delivering outstanding customer experiences.
In this blog post, we’ll go over the challenges of traditional QA processes and how QA automation can make an impact. Plus, a look at the hurdles associated with automating QA—and how to overcome them.
Call center QA is the backbone of maintaining high service standards and ensuring customer satisfaction. It covers call monitoring and evaluating agent performance to identify areas for improvement, ensuring compliance with company protocols and government/industry regulations, and enhancing the contact center's overall service quality.
Unfortunately, with the massive increase in call center activity within the last decade and the explosion of new technologies, the manual method of handling QA is becoming a sluggish process that simply can’t keep up with modern demands.
One of the biggest issues with manual QA is its limited scope. Typically, only about 2-3% of interactions get reviewed by evaluators, leaving the vast majority of customer interactions a total mystery.
This (incredibly small) sample size means that critical insights can easily be missed, leading to a skewed understanding of your call center’s performance. Additionally, the process of manually scoring these interactions is incredibly slow and time-consuming, requiring significant investments in time and resources that could be used elsewhere.
Bias and inconsistency are also prevalent in manual QA processes. Different evaluators might have varying standards and interpretations, leading to inconsistent call scoring and feedback. This lack of uniformity can result in unfair—or incorrect—evaluations and missed opportunities for agent development.
Plus, the element of human bias can impact the objectivity of these reviews, further affecting the QA process.
And the consequences of these shortcomings can reach far and wide. Limited insights from such a small sample size means you’ll struggle to find widespread issues and trends within your contact center. That poor accuracy can lead to ineffective agent training processes, lack of compliance with regulations, and poor performance.
Ultimately, the insignificant impact on the overall customer experience can result in unsatisfied customers and lost business. Relying solely on traditional manual QA methods is no longer an option.
If manual QA processes are no longer an option, how can we expect call centers to keep up with these rising demands?
Well, with the explosion of advancements in automation and artificial intelligence, quality assurance processes can now be automated with an incredible amount of efficiency and accuracy.
Instead of struggling to capture 3% of your interactions, imagine using QA automation to analyze and score every single interaction in your call center. That’s the power of these new tools.
AI-powered QA tools can dramatically transform how you manage and assess your contact center’s performance. These advanced systems are designed to automate the scoring of interactions, transcribe and analyze calls, deliver real-time feedback, identify trends, and integrate seamlessly with your existing tech stack. Let's look at a few real-world use cases:
The impact of these capabilities extends past just quality assurance automation. By implementing AI-powered QA solutions, you can unlock a plethora of benefits that significantly boost your call center’s efficiency, performance, and customer satisfaction.
The shift to quality assurance automation isn’t just a new tech upgrade for your call center. It represents a significant leap forward in how you can—and likely will—operate, as well as how your competitors operate, too.
We’ve covered the drawbacks of manual QA, and we’ve sung the praises of what QA automation can do for call centers. Here’s a more detailed comparison of these two approaches and how they match up in categories like speed, accuracy, cost, and engagement.
As you can see, there’s a clear winner: QA automation.
Speed, by far, has the biggest impact on your contact center. Evaluators are human, and they can only work at a human’s pace. The average call center receives about 4,400 calls in a month, meaning only about 132 of those calls will be evaluated and scored. With QA automation, this becomes every interaction in just a fraction of the time.
Along with the full coverage of interactions are the insights you can glean from them. With such a small sample size, companies can struggle to find trends and patterns within their products and customer experience. But with the addition of automation, you’re able to get a complete picture of your call center’s performance—and how your customers are feeling.
Implementing call center QA automation can significantly improve your key performance indicators (KPIs), driving improvements all across your contact center. Here’s how it can impact some of the most important KPIs:
CSAT measures how happy your customers are with the service they receive.
QA automation helps you consistently evaluate and improve agent performance, leading to higher service quality. By providing accurate and timely feedback, automated QA ensures customer service agents are better equipped to handle interactions, boosting CSAT scores overall.
AHT is the average duration of a customer interaction, including hold time, follow-up, and after-call work.
With QA automation, your teams can identify inefficiencies and patterns in agent interactions, enabling them to take corrective action and streamline processes. And with automated insights, you can implement targeted training programs to improve agent efficiency, lowering AHT without losing service quality.
As the name implies, FCR is a percentage of your calls that are resolved within the first interaction. High FCR rates are crucial for CSAT and operational efficiency.
QA automation provides comprehensive insights into common issues and agent performance, allowing you to address root causes and improve training. This leads to better trained agents who can resolve issues more effectively, boosting your FCR rate.
Compliance ensures your call center adheres to industry and governmental regulations, alongside company policies.
Automated QA tools continuously monitor interactions for compliance breaches, flagging any deviations in real-time for your teams to correct. This proactive approach not only reduces the risk of non-compliance penalties, but helps maintain a high standard of service integrity and trust with your customers.
A call center’s cost to serve is the total cost involved with serving a customer (including labor, technology, and operational costs).
QA automation reduces the need for extensive manual evaluations, cutting labor costs. Additionally, by improving efficiency and effectiveness in handling interactions, automation helps lower operational costs overall. This means you can maximize the impact you have on customers without increasing the cost to serve them.
While the benefits of QA automation are massive, that doesn’t mean they’re perfect. It’s important to address some of the concerns you and your company may have when considering these tools.
Understanding these challenges, and their solutions, can help you make an informed decision and get the most out of your call center.
The problem: With the increasing use of AI and automated systems, ensuring the privacy and security of customer data is paramount. There have always been—and always will be—concerns over data breaches and compliance with regulations.
The solution: Choosing a QA automation tool that adheres to strict security standards and data protection regulations like GDPR and CCPA helps alleviate many of these concerns. Look for providers that offer end-to-end encryption, regular security audits, and robust data governance policies to safeguard sensitive data.
The problem: Integrating any new technology into your existing infrastructure can be a hassle. It often ends up costly and time-consuming, potentially disrupting operations.
The solution: Opt for QA solutions that are designed to easily integrate into call center tech (like CRMs, contact center platforms, etc). Speak to your providers and see if they offer comprehensive support and guides to help integrate and transition to these new tools.
The problem: Employees may resist the adoption of new automation tools due to the fear of job displacement and changes within their roles.
The solution: Be human, talk to your agents, and show them how AI is not intended to replace them but to increase their efficiency and make their jobs easier. Offer training sessions and development programs to help employees adapt to new technologies and use automation to improve their performance, not take their jobs.
The problem: Adopting new tech can be expensive, not just with the product itself but the costs of getting it integrated and training too. Many C-suites and execs can view this as an unnecessary expense, especially if the return on investment (ROI) isn’t clear.
The solution: Illustrate the long-term benefits and cost savings that come with the addition of QA automation. Show them case studies of QA automation in action, and the difference in KPIs that can be gained from these tools.
The problem: The biggest elephant in the room is the reliability of AI tools, including generative AI solutions like large language models (LLMs).
The solution: Look for automation tools with a proven track record and positive user testimonials. You can also create pilot programs to test these tools on a smaller scale before widespread adoption to help ensure accuracy.
Automation is an incredibly powerful addition to any workplace. With the addition of call center QA automation, you can get a full picture of service quality, agent performance, customer satisfaction, and lots more.
AI is here to stay, and now it’s up to companies to learn how to best take advantage of these tools to stay ahead of the game.
With Scorebuddy’s new GenAI Auto Scoring tool, call centers gain access to powerful ‘virtual agents’ that can scale up to 100% interaction coverage by automatically scoring interactions and analyzing them, giving you deep insights into your business.
Ready to take the next step with your contact center? Sign up for a demo today and see the power of QA automation and Scorebuddy in action.
