Call Center Quality Assurance — Comprehensive Guide

Call Center Quality Assurance (QA) main purpose is to ensure your contact center delivers great customer service. Learn about how mySQM™ Auto QA, the #1 Ranked Software on G2 for Customer Satisfaction for the Americas Market, can help you deliver great customer service on every call.

QA Metrics and KPIs are perhaps the most crucial step in your call center quality assurance program to identify the specific metrics and KPIs you want to measure and benchmark. It is vital to select metrics and KPIs that are both measurable and aligned with your call center's objectives, such as Customer Satisfaction, First Call Resolution, CX Sentiment, Total QA Score, Compliance, and Average Handle Time (AHT).

Given the numerous metrics and KPIs available, it is important to track those that best reflect your call center's goals and objectives. For example, suppose your priority is delivering efficient, high-quality customer service with effective resolution. In that case, you should focus on KPIs like Customer Satisfaction, CX Sentiment, Average Handle Time, and First Call Resolution.

Customer experience agent behavior standards are a set of guidelines and expectations designed to ensure that agents consistently deliver a positive and effective experience for customers during every interaction. These standards define the specific behaviors, communication styles, and service practices that agents should follow to meet or exceed customer expectations and align with the company's customer service goals.

Key components of these behavior standards typically include Friendliness and Courtesy, Active Listening, Empathy and Emotional Intelligence, Clear and Effective Communication, Problem-Solving and Resolution Skills, Positive Attitude, and Adherence to Procedures and Compliance.

These standards are designed to create a consistent, high-quality customer experience across all interactions, helping to build customer satisfaction, loyalty, and trust. Organizations measure adherence to these standards through quality assurance programs, customer feedback, and performance metrics, using the results to guide coaching, training, and development efforts.

Manual call center QA involves human evaluators, often quality assurance evaluators or supervisors, who listen to recorded calls or monitor live interactions to assess agent performance against predefined criteria. This approach allows for nuanced judgment and qualitative feedback, such as evaluating tone, empathy, and adherence to soft skills. However, it can be time-consuming, subject to human error or bias, and limited in scope due to the time and effort required to review each interaction individually.

Automated call center QA uses software and artificial intelligence (AI) to automatically analyze and evaluate up to 100% of customer interactions. This approach can process large volumes of data quickly, identify trends, and provide real-time insights by measuring performance against metrics such as call duration, CX sentiment analysis, compliance, and the ability to predict customer satisfaction. While automated QA is faster and more scalable, it may not fully capture the subtleties of human communication and might require fine-tuning to align with specific business objectives.

In summary, manual QA offers depth and a human touch in evaluating complex interactions, while automated QA provides speed, scalability, and data-driven insights. Many call centers use a combination of human and auto QA methods to achieve the positive aspects of each approach for their quality assurance program.

Agent Self-Coaching uses AI, which is a process where agents use AI-driven tools to guide their CSat performance improvement. AI analyzes customer interactions (e.g., calls, chats) for sentiment, tone, keywords, and adherence to scripts or behavior standards, providing instant feedback to agents on their performance.

Unlike traditional coaching, where supervisors or QA specialists provide feedback and training, self-coaching encourages agents to review their own calls or interactions independently, reflect on their performance, and identify areas for improvement based on AI insights.

Agent Self-Coaching typically involves providing agents with access to recorded calls, performance metrics, and quality assurance scores. Agents use this data to evaluate their interactions against key performance indicators (KPIs) such as Customer Satisfaction, First Call Resolution, and Average Handle Time. They can also identify strengths and weaknesses in areas like communication style, problem-solving skills, resolution, and adherence to company guidelines.

This approach helps agents build self-awareness, fosters a sense of ownership over their performance, and encourages continuous learning and development. It also reduces the burden on supervisors by allowing agents to address performance issues proactively before they become significant problems.

Agent Self-Training uses AI, which is a method where agents use AI-powered tools to assess and improve their CSat performance independently. AI provides insights, personalized feedback, and tailored learning resources, enabling agents to take control of their development and continuously enhance service quality.

Agent Self-Training for customer experience soft skills involves developing and enhancing the interpersonal soft skills needed to deliver exceptional customer service. These soft skills are crucial for creating positive interactions, building rapport, resolving issues effectively, and providing great customer satisfaction. Here are some key aspects of agent self-training for CX and FCR soft skills:

• Active Listening
• Caring and Empathy
• Communication
• Call Resolution
• Problem-Solving

Role-playing uses call reason simulation to resolve calls and deliver great customer satisfaction. Agents take quizzes and tests to gauge CX and FCR soft skills understanding and identify areas for improvement. Agent Self-Training is delivered through QA software.

Agent recognition and rewards program is a structured initiative designed to acknowledge and reward agents for achieving high performance in near real-time, meeting quality standards, and delivering great customer satisfaction. This program aims to motivate agents, boost morale, and encourage continuous improvement by celebrating their successes and contributions to the organization.

Agents are recognized and rewarded based on their QA scores, which measure their adherence to CX behavior standards and other key performance indicators such as CSat, FCR, CX Sentiment score, total QA score, and AHT. The following are examples of monetary and non-monetary rewards.

Monetary Rewards: Cash bonuses, gift cards, or commission-based incentives are provided to achieve high performance or meet specific targets.

Non-Monetary Rewards: Awards can include certificates, plaques, trophies, additional time off, or special privileges (like flexible schedules or preferred shifts).

Gamification techniques can be used where agents earn points, badges, or levels based on their performance, which can be redeemed for rewards or more cash bonuses.

A well-structured recognition and rewards program not only enhances agent motivation and engagement but also contributes to improved service quality, customer satisfaction, and overall organizational success.

QA calibration using post-call CX surveys involves aligning quality assurance evaluations with direct customer feedback collected immediately after a call or interaction. This process ensures that the internal assessment of an agent's performance aligns with the customer's perspective on service quality, satisfaction, and overall experience.

After a customer interaction, a post-call survey was conducted to gather immediate feedback on their experience. These surveys typically measure factors such as customer satisfaction and specific aspects of the service, like agent professionalism, empathy, and problem resolution.

The customer feedback from post-call surveys is compared with the internal QA evaluations of the same interaction. This comparison helps identify any discrepancies between how the QA team or AI assessed the interaction and how the customer perceived it.

Analyzing these gaps highlights differences in perspectives, such as instances where an agent received a high QA score or predicted CSat score, but the customer reported dissatisfaction, or vice versa.

During calibration sessions, the QA team reviews both the internal scores and customer feedback to align their evaluation criteria more closely with what customers value most. This helps ensure that QA metrics reflect actual customer priorities and satisfaction drivers. QA calibration based on the CX surveys process can be used in the manual and automated QA methods.

QA calibration using post-call CX surveys ensures that QA evaluations are aligned with the aspects of service that customers care most about, leading to improvements in customer satisfaction. In addition, it reduces subjectivity by incorporating objective customer feedback into the QA process, reducing the potential for bias or misalignment in internal evaluations.

QA root cause analysis is a systematic process used to identify the underlying reasons for recurring quality issues or performance gaps in call center operations. By understanding the root causes of these problems, call centers can implement targeted corrective actions to improve service quality, enhance customer satisfaction, and optimize agent performance.

Begin by pinpointing the specific issues or performance gaps revealed through QA evaluations. These could include high rates of customer dissatisfaction, low FCR, high AHT, frequent compliance violations, or any other recurring problems.

Collect data related to the identified problem from various sources, such as QA scores, customer feedback, post-call surveys, call recordings, agent performance metrics, and interaction logs.

Analyze the data to look for patterns, trends, or correlations that might indicate why the issue is occurring. For example, suppose low customer satisfaction scores are consistently associated with a specific type of call, agent behavior, or time of day. In that case, these patterns can provide clues to the underlying causes.

Based on the validated root causes, develop specific, actionable strategies to address the issues. These might include revising agent behavior standards, providing targeted agent training, adjusting processes, improving technology tools, or refining QA evaluation criteria. Implement these corrective actions systematically and monitor their effectiveness over time. To enhance your QA root cause analysis, it is helpful to use a voice of the customer closed-loop process where you identify, develop, check, and then act.