Generative AI that auto-scores near 100% of your organization’s custom scorecard on every channel (call, chat, or email) at near 100% accuracy for 100% of your conversations.
Take your contact center quality assurance automation to the next level today.
Breaking the frontier of what’s possible with AI, QA-GPT automates the scoring of even the most difficult and subjective criteria of your scorecard with near human accuracy. Automate near 100% of your QA efforts with QA-GPT
⭐ 100% Coverage
⭐ 100% Automation
⭐ 100% Trusted
QA-GPT delivers transparent scores with supporting evidence and reasoning for every conversation.
Raise the bar of customer service and resolution by automatically detecting points of frustration, zeroing-in on meaningful customer feedback and creating a positive improvement loop.
"Our newly acquired ability to review 100% of our customer's calls has been nothing less than game-changing. It opened our eyes to new areas for improvement across the board, whether operational, product, documentation, or coaching opportunities."
Quality Assurance (QA) in a call center or contact center is used to ensure your product or service is performing according to company and consumer standards.
Quality assurance is key to running a thriving call center. QA Managers and Quality Assurance Call Center Specialists are responsible for monitoring operations and ensuring every customer receives a consistently positive experience.
On top of maintaining quality assurance in a call center and consistency in the customer experience, the quality assurance department can help minimize company costs and increase CLTV (customer lifetime value) by managing contact center agents’ efficiency.
Improving QA (quality assurance) in a modern contact center requires industry-leading technology in order to assist your contact center agents’ and QA specialists’ ability to handle consumer conversations efficiently.
Here are 7 of the most effective ways to improve quality assurance in a call center:
In a call center or contact center, QA (quality assurance) is often used to highlight common customer issues and improve communication for a more consistent customer experience.
Consistency and continuous improvement in responding to customer inquiries is important because happy customers are always going to be your most profitable and loyal customers.
Additionally, quality assurance is one of the main functions in a company that’s capable of reducing costs, spotting inefficiencies, and increasing the average CLTV (customer lifetime value) of consumers.
AI (Artificial Intelligence) plays a crucial role in a contact center QA (Quality Assurance) solution for several reasons:
In summary, AI matters for a contact center QA solution because it enhances efficiency, consistency, and scalability. It empowers supervisors with real-time insights and comprehensive analytics, enabling data-driven decision-making and continuous improvement efforts. By leveraging AI capabilities, contact centers can deliver a higher level of service, improve agent performance, and ultimately enhance the overall customer experience.
Being ready to handle nearly any scenario your contact center encounters is a complex challenge. Your contact center should strive to keep quality consistent across all customer touchpoints. Start by determining which QA framework suits your needs best.
There have been three common quality assurance frameworks till now, each of which applies to specific goals:
However, since every organization is unique and has its own quality assurance processes, complex QA scenarios require more flexibility.
To effectively respond to customers with tough problems, top QA teams use CCaaS (contact center as a service) platforms that can accurately monitor 100% of customer interactions.
Legacy QA systems are only able to accurately monitor 1-2% of customer interactions, on average.
QA rubrics, such as Level AI’s Rubric Builder, allow QA teams to create their own unique grading systems. For example, Rubric Builder makes it possible to customize QA grading questions, categories, quality scores, weightage, and more.
Advanced AI and NLU platforms, such as Level AI, will quickly learn how to best handle nearly any scenario your customer support agents encounter in real-time.
Contact centers measure quality assurance through several common QA metrics. Quality assurance, or QA, metrics refer to the data a company uses to measure the performance of different interactions between support agents and consumers.
Interestingly, QA metrics can vary significantly by industry, since they reflect performance as defined by that industry.
For contact centers, common quality assurance metrics include:
How do you incorporate AI and machine learning into quality assurance (QA)? AI now plays a major role in the most advanced QA teams. The key areas where AI technology plays a role in a contact center include:
The most advanced contact center software platforms for QA and agent training are using NLU (natural language understanding), as opposed to NLP (natural language processing) alone.
For example, Level AI is using NLU to uncover customer intent and to provide support agents the information they need to communicate accurate information to customers in real-time.
In fact, in 2022, the best AI-powered contact center software outperforms legacy systems, which rely entirely on keyword matching (text mining), with an average 2X (+100%) lift in accuracy.
The most advanced AI and NLU powered platforms for contact center QA teams will quickly learn how to best handle nearly any scenario your customer support agents encounter.
Call center automation software is a call center technology that automates elements of what call center agents and QA (quality assurance) teams would otherwise have to do manually on a day-to-day basis.
Ultimately, call center automation software helps in improving call center productivity and compliance.
Some the main ways call center automation is utilized today include:
