FULLY AUTOMATED QA THAT YOU CAN TRUST POWERED BY QA-GPT

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:

1. Advanced AI and NLU: Upgrade your speech analytics software with modern AI and NLU (natural language understanding) that recognizes customer intent and doesn’t rely on outdated keyword matching.
2. Automated Assignment: Keep your QA managers efficient with automated case assignment that identifies key interactions and assigns them to QA managers directly.
3. Omnichannel Analytics: Take an omnichannel approach to your call center’s data with a platform such as Level AI.
4. Contact Center Buy-In: QA should become part of every facet of the contact center. Encourage company wide buy-in starting at the executive level.
5. Quality Assurance Trends: Keep up on the latest QA monitoring trends for call centers, such as adopting machine learning platforms able to provide 100% coverage of support conversations.
6. Smart Data: Use conversational intelligence data to identify your best scripts and incorporate them into your QA strategy.
7. Accurately Monitor and Score Communications: Use CCaaS (contact center as a service) platforms that can accurately monitor 100% of interactions based on real scenarios and QA rubrics. Legacy QA software is only able to accurately monitor 1-2% of customer interactions on average. 
8. Augmented Quality Assurance: Take advantage of augmented QA to save QA managers time. Augmented QA allows agents to review their own performance and to learn autonomously.

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:

1. Efficiency: AI-powered QA solutions can automatically analyze and evaluate a large volume of customer interactions much faster than human evaluators. This efficiency saves time and resources, allowing supervisors to focus on other critical tasks.
2. Scalability: As contact centers handle an increasing number of interactions across multiple channels, AI can scale effortlessly to handle the growing QA demands without the need for additional human resources.
3. Consistency: AI ensures a consistent evaluation process by applying predefined criteria to interactions uniformly. This reduces subjective biases and ensures that agents are evaluated fairly based on the same set of standards.
4. Speech and Text Analytics: AI-powered speech and text analytics can identify keywords, sentiment, and trends within interactions. These insights can help detect emerging issues, assess customer satisfaction, and provide valuable data for continuous improvement.
5. Natural Language Processing (NLP): NLP capabilities enable AI to understand and interpret unstructured data, such as voice recordings and chat transcripts. This allows AI to extract meaningful information and identify the context and intent of customer interactions accurately.
6. Predictive Analytics: AI can analyze historical data to identify patterns and trends that may impact future interactions. Predictive analytics can help contact centers proactively address potential issues and optimize agent training and resource allocation.
7. Continuous Improvement: With AI, contact center QA solutions can continuously learn and adapt based on new data and feedback. This adaptability ensures that QA processes evolve with changing customer needs and expectations.

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.

GPT can enhance call center automation by assisting in various text-based interactions. It can be integrated into chatbot systems to provide instant and accurate responses to customer inquiries, reducing agent workload. GPT’s natural language understanding enables it to categorize and route calls more efficiently, improving call routing and customer satisfaction. Additionally to aid in quality assurance tasks, GPT can analyze call transcripts for sentiment analysis, helping supervisors gauge customer emotions and agent performance. By automating text-based interactions and offering valuable insights, GPT streamlines call center operations, increases productivity, and delivers a more personalized and seamless 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:

• Tactical QA: A tactical approach to QA centers around self-assessment.
• Operational QA: Operational QA deals mostly with efficiency and consistency.
• Strategic QA: The strategic QA framework combines both tactical and operational QA approaches and refocuses your call center’s efforts on understanding the impact of procedural changes and emerging trends in performance.

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:

• ASA = Average Speed of Answering
• FCR = First-Call Resolution
• AHT = Average Handle Time
• CSAT = Customer Satisfaction Score
• NPS = Net Promoter ScoreSM
• CES = Customer Effort Score

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:

• Quality assurance
• Agent monitoring and training
• Contact center analytics

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:

• Customer interactions are automatically reviewed based on real scenarios and QA rubrics.
• Contact center agents can review their own performance and learn autonomously, which allows for continuous improvement and shorter onboarding times.
• Agents and QA managers can easily search topics, scenarios, actions, flags, or FAQs which are tagged for QA teams.
• Automatically score or grade agent interactions with customers based on predefined rubrics.
• Quickly identify key moments in any interaction with your contact center.