Call center quality management can present numerous challenges for your business.

More often than not, you’ll find yourself dealing with one of the following two issues: a lack of data for accurate quality management, or an abundance of data with limited ability to transform it into useful insights. 

Artificial intelligence (AI) has introduced a wide range of solutions that enable faster, more accurate, and more efficient call center quality management. 

Today, we’ll explore the different opportunities that integrating AI into your call center’s quality management efforts can open up for your business.

Agents being coached in a call center

Introduction to Call Center Quality Management

Defining Quality Management in Call Centers

Call center quality management comprises the processes and practices that involve monitoring and evaluating call center agent performance for the purpose of improving it to ultimately enhance the overall customer experience.

Quality management typically requires the use of quality monitoring tools, as well as measuring relevant metrics and collecting customer feedback.

Once the root causes behind low scores are identified, decision-makers within a contact center can start implementing personalized coaching sessions for agents to overcome their weaknesses and improve their performance in different customer interactions.

Investing in quality management and assurance enables you to deliver seamless customer service, as well as keep customer churn rates at a minimum. It also helps you maximize your agents’ potential, leading to more satisfied employees and lower turnover rates.

The Importance of Quality Assurance

Call center quality assurance (QA) ensures consistent service delivery that exceeds customer expectations, making them happier and more willing to remain loyal customers to your brand. 

When a customer reaches out to you, they expect to get a fast and accurate response to their query. If they find themselves having to wait forever in a virtual queue or talking to a clueless agent who’s unable to understand their issue or provide a suitable resolution for it, they become more likely to switch to one of your competitors.

Even if you’ve built a robust system, there’s no guarantee that things will always work out as expected all the time. That’s where a QA scorecard becomes useful — it serves as a tool for both agents and managers to evaluate customer interactions and touch on the pitfalls that lead to missed opportunities and frustrated customers. 

A typical scorecard includes the following points:

  • Authentication: Did the agent make sure that the customer is who they claim to be?
  • Call flow: how well the agent is directing the call flow and whether they’re sticking to the script.
  • Understanding the customer’s pain points: Was the agent able to comprehend the customer’s issues?
  • Behavioral cues: How does your agent’s behavior reflect their professionalism and eagerness to help the customer?
  • Resolution: Did the call end up with the customer’s problem resolved? Has the agent documented the call properly?

Understanding Quality Assurance in Call Centers

Metrics and Standards in Quality Measurement

If you really want to dig into what’s causing your customers to be dissatisfied, you need to calculate the key metrics that help you measure different aspects of your customer interactions.  Some of these metrics include:

  • First Call Resolution (FCR) Rate: Quantifies the percentage of inquiries that are fully resolved from the initial interaction, without requiring call rerouting or follow-up calls.
  • Average Handling Time (AHT): AHT is used to measure the average time it takes an agent to resolve the customer’s problem, from the moment they pick up the phone all the way to finishing their post-call work and note-taking.
  • Customer Satisfaction (CSAT) Score: CSAT indicates how happy your customers are, and it can be measured by asking your customers to share their opinions on the service level with a simple post-call survey. 
  • Net Promoter Score (NPS): The NPS measures customer retention and loyalty with a survey question that often focuses on the likelihood of the customer recommending your brand to others in their circles.
  • Call Abandonment Rate: This metric gives the percentage of callers who decide to leave the virtual queue before an agent is able to assist them.
  • Call Transfer Rate: The Call Transfer Rate measures the number of instances where an agent resorts to transferring the call to another agent, either due to inaccurate routing or their inability to assist the customers.
  • Compliance Metrics: Compliance-related metrics give you an overview of your agents’ adherence to compliance guidelines

The Role of Quality Assurance in Customer Satisfaction

Investing in a QA program in your contact center directly correlates with customer satisfaction.

By understanding what’s working and what’s not, you’ll be able to focus your efforts on the areas that actually need improvement, resulting in a more consistent customer experience and a lower customer churn rate. 

Monitoring how your agents interact with your customers, for example, can help you figure out who your top performers are and who could use some more guidance. 

Not to mention, capturing the areas where your agents fall short helps you create personalized coaching sessions for them. This qualifies your agents to be able to support customers and resolve their queries more efficiently every time they call you. 

How AI Enhances Customer Interaction and Satisfaction

Personalizing Customer Interactions with AI

AI conversation intelligence platforms make it easier for you to offer more personalized customer interactions. AI algorithms analyze past customer interactions, predict customer needs before they even explain them, and automatically create detailed customer profiles that go well beyond names and demographics.

When a customer calls you and finds out that you know exactly what they need, they become more inclined to remain loyal to your company.

AI in Handling Complex Customer Queries

Sometimes, your agents may not be able to assist customers because they don’t fully understand the problems they’re facing. 

Real-time AI assistance can capture customer sentiment and provide agents with response prompts that enable them to assist customers more effectively, especially when their queries are complex and uncommon.

Role of AI in Call Monitoring and Analysis

AI-driven Call Monitoring

AI call monitoring can help you identify problems by spotting and analyzing relevant keywords and phrases. For example, if customer card information is mentioned in a conversation, the AI program flags this as a non-compliance risk.

Further, AI can help you direct your effort to calls that require your attention, ensuring that as many calls as possible result in a satisfied customer or a closed deal. 

Analyzing Call Data for Quality Insights

AI is capable of analyzing 100% of calls in your contact center, ensuring that the quality insights you get are as accurate as possible. What’s more, it benchmarks the support quality based on preset performance guidelines to help you determine whether agents fully comprehend them. 

These insights are also made accessible to agents in real-time during calls, enabling them to tweak their performance and provide better support for customers. This results in higher FCR rates and quicker, more accurate resolutions. 

Improving Agent Performance with AI Tools

AI Tools for Agent Training and Development

AI is capable of pulling insights from all your contact center channels, and speech analytics, so you get a comprehensive view of how each agent handles customer queries. 

This enables you to identify your top performers and create personalized training programs for agents who need guidance and improvement. Further, AI encourages coaching with context — it enables you to specific moments in customer conversations, ensuring that your agents are fully engaged. 

Additionally, AI helps you identify how agent actions relate to customer sentiment. For instance, if an agent keeps on repeating the same words, this could make the customer angry. 

Enhancing Agent-Customer Interactions with AI

The number one reason behind customer dissatisfaction is the customer having to clarify their query over and over again. But with AI Agent Assist, agents will be able to promptly help agents with AI-powered responses, ensuring quicker resolutions and more personalized interactions.  

AI in Training and Coaching Call Center Agents

Innovative Training Techniques Using AI

Since AI analyzes 100% of your customer interactions, training your agents doesn’t have to be restricted to isolated cases. Instead, you can easily identify recurring issues and prioritize them in your training sessions, simultaneously earning agents’ trust and increasing their confidence. 

On top of that, with sentiment analysis, you’ll be able to assess your team’s soft skills and work on improving them to provide the best possible service quality for your customers. 

Continuous Learning and Skill Development

AI relates your relevant quality metrics and Key Performance Indicators (KPIs) to agent performance. By combining AI with quality scorecards, you can identify the root causes of customer nuances and resolve them in training sessions that encourage continuous learning and skill development.

For example, a high AHT score, supported by AI-enabled insights that signal a lack of technical knowledge from the agent’s side, means that additional technical training can lower the AHT for that agent.

Data-Driven Decision-Making in Quality Management

Harnessing Data for Strategic Decisions

Strategic decision-making should be built on a solid basis, and in the context of contact centers, this basis should ideally be data pulled from real-world agent-customer interactions. 

AI helps you capture this data and put it into good use to make strategic decisions like expanding or downsizing your team, implementing new technologies, investing in agent coaching, and others.

AI in Analyzing Customer Feedback

Lots of contact centers have a hard time getting any useful feedback from customers. A customer saying they’re “happy” in a post-call survey doesn’t tell much about how the call went or whether there’s anything that could’ve been done better. Not to mention, customer surveys tend to be skewed.

While these surveys can give you an idea about your customer satisfaction levels, AI takes customer feedback analysis one step further by capturing customer emotion and sentiment, identifying patterns, and transforming the data it pulled into comprehensive reports that drive decision-making. 

You can then combine the insights from these reports with the survey scores to get a more in-depth analysis of how your customers feel about their interactions with your contact center.

Enhancing Quality with Real-Time Feedback Systems

The Role of Instant Feedback in Quality Management

Providing agents with instant feedback enables them to improve their performance and prevent mistakes that lead to more lost sales or dissatisfied customers from becoming habits. It allows agents to continuously improve their skills and understand what makes them frustrated, which can gradually improve interaction quality for customers. 

Implementing Real-Time Feedback Mechanisms

Implementing real-time feedback alongside regular check-ins can boost your agents’ performance and encourage a culture of constructive feedback. 

With AI conversation intelligence, you can automatically monitor agent performance, ensure script adherence, and fix tonal and behavioral patterns by providing agents with automated feedback reports and suggestions. 

Managers can also utilize AI to provide more specific and constructive feedback that emphasizes empathy and fosters growth. Without AI, agents are more likely to receive generic or negative feedback that doesn’t result in actionable insights for improvement.

Impact on Agent Performance and Customer Satisfaction

Real-time feedback can significantly improve agent performance, resulting in higher customer satisfaction rates. By providing agents with targeted and detailed feedback, they’ll be able to overcome their weak points and track their progress. 

This ensures that customers get a great support experience every single time, boosting customer retention and reducing churn rates.

The Role of Natural Language Processing (NLP) in Quality Assurance

Basics of Natural Language Processing (NLP)

Natural Language Processing (NLP) is a rule-based modeling methodology of human speech analysis. It facilitates human-computer interactions by transforming what the person is saying into something that computers can understand. 

Use cases for NLP in the contact center include customer sentiment analysis, speech-to-text applications, and AI agent assistance.

Understanding Customer Sentiments Using NLP

NLP processes speech in two different ways: semantic analysis and syntactic analysis.

Semantic analysis attempts to uncover the meaning of each word the customer says. The syntactic analysis then derives the meaning of entire sentences. Both methods are combined to give a near-accurate picture of what the customer is trying to say by transforming speech into a computer-readable language. 

NLP also analyzes customer emotions and relates these emotions to what they’re saying, giving an even more comprehensive analysis of the customer’s speech. Further, NLP provides clues on how customers feel about their interactions with your call center, influencing your CSAT scores with more meaningful insights than those provided by post-call surveys.

However, NLP may still fail in some instances. For example, research and examples have proven that NLP may not be able to detect sarcasm and some forms of slang. 

Improving Interaction Quality with NLP

NLP automatically analyzes customer speech for cues and patterns, and based on that analysis, recommends the best course of action for the agent. This results in a better interaction quality for your customers.

Additionally, contact centers implement NLP in intelligent routing technologies. It interprets customer speech to decide whether the query can be resolved with automated responses or a live agent is required. 

If the call is routed to an agent, NLP will pick the best matching agent based on its interpretation of the customer’s query, saving customers time and ensuring that their queries are resolved from the initial interaction. 

Cost-Benefit Analysis of AI in Call Center Management

Assessing the Financial Implications of AI

Incorporating AI into your contact center operations offers a major perk: significant cost savings. Typically, call centers rely on a sizable workforce to manage customer inquiries and support. But with AI, you can automate repetitive tasks, cutting down the need for human involvement.

AI-powered chatbots and virtual assistants are capable of handling various customer queries, and delivering fast and precise responses, even outside business hours. By automating these interactions, you can cut down on costs linked to staffing, training, and overseeing large teams. 

Long-Term ROI of AI in Quality Management

Most companies who’ve implemented AI in quality management have witnessed a positive long-term ROI. Since AI eliminates manual processes and facilitates faster and more accurate agent performance monitoring and call scoring, you can expect significant efficiency improvements in your contact center that yield better results and boost revenue.

According to a recent research report by 8×8, 94% of contact centers have witnessed increased agent productivity, and 92% are able to handle customer queries faster. 

Best Practices for Implementing AI in Quality Management

Strategic Planning for AI Implementation

Successfully integrating AI into your contact center should be built around empowering your agents and maximizing their efficiency, without them feeling like they’re no longer in control. 

Due to the disruptive nature of AI, a well-crafted implementation plan is necessary to reap its benefits. 

A good starting point is to assemble your team and key decision makers to identify which areas of your contact center’s operations and customer experience touchpoints could use automation or some improvement in accuracy efficiency. You can then test how AI will work alongside your agents incrementally, collect feedback from both agents and customers and improve your approach whenever possible.

Keys to Successful Deployment

To formulate an AI integration strategy for your contact center, follow these steps:

  • Assess your operations: How can AI fit into your operations? Where would it be most useful? 
  • Set clear goals: What do you intend to achieve with AI? Is it better customer satisfaction, lower turnover rate, or higher call center QA scores?
  • Test the new system: start implementing AI with a pilot phase to identify issues and gather feedback
  • Employee training: Slowly introduce your agents to AI and how it can help them maximize their potential. Conduct workshops that teach your agents how AI encourages cross-functional collaboration and data democratization.
  • Maximize your ROI: Regularly track metrics and KPIs to measure the impact of AI on your contact center and look for ways to maximize its ROI.

Continuous Improvement and Adaptation

Once you’ve put a system in place, you need to continuously look for ways to leverage the capabilities of AI more extensively. Either through employee training, adapting new technologies, or improving existing solutions with machine learning. 

The AI industry is growing rapidly, and what you’re doing today could be outdated by next year, so it’s a good practice to keep up with this fast-paced evolution to meet changing customer expectations, keep your employees happy and productive, and stay competitive.

Transforming Your Contact Center Quality Management with Balto

Balto can redefine your contact center’s quality management with advanced AI technologies that go beyond the basic techniques.

With Balto, you can automatically score 100% of your calls instead of relying on random sampling, ensuring accurate call center performance insights and forecasts, as well as more targeted agent coaching programs.

Identify the root causes behind low scores with sentiment analysis. Understand why customer churn rates are getting higher. Empower your agents with AI-enabled agent assistance and get alerted when you need to intervene in a call that requires your attention.

We’re happy to offer you a free demo of the Balto platform so you can explore how it revolutionizes your contact center quality assurance program in action. Get started today!