How AI Identifies Skill Gaps for Effective Call Center Training

According to new research from the MIT Center for Information Systems Research, business leaders estimate that 38 percent of employees will need to be retrained over the next three years due to skills gaps*. But how do you identify these skills gaps to know where to upskill talent? 

To address this, businesses are turning to AI to identify and close skill gaps. Through a process called skills inference, AI accurately gauges agent skill levels in a scalable way. 

In this article, we explore how AI can help you identify and resolve skills gaps in your call center agents.

• AI-powered tools like CloudTalk provide real-time insights to identify training gaps and help improve agent performance.
  
• Speech analytics and AI-powered coaching allow managers to offer personalized feedback, boosting agent skills and customer satisfaction.
  
• Making training an ongoing process helps call center agents stay sharp and prepared for future challenges. 

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What Are Training Gaps and Why Are They Hard to Identify? 

A training gap is the space between the skills agents currently possess and the skills they need to meet customer expectations and further business goals.

Identifying gaps in your agents’ training requires you to understand where their skills are today and what your business will require of them in the future. As Nick van der Meulen, Research Scientist at MIT puts it, “Skills are dynamic. What makes you successful today won’t make you successful three years, five years, or ten years from now.”

Traditionally, training gaps have been identified through manual evaluations, employee surveys, and performance reviews. While these methods can provide some insights, they’re often limited by biases in feedback, incomplete data, and the impracticality of assessing a large workforce across diverse skills.

Training gaps aren’t limited to service training. Frontline workers like call center agents need to be able to master various skill sets, including soft skills and problem-solving on demand. These qualities can be difficult to gauge by reviewing performance metrics alone.

How AI Can Help Identify Training Needs

Call center training is an opportunity to make sure your team is up to speed on product knowledge, the business vision, and industry regulations. Knowledge is power, and AI-powered tools provide the data you need to facilitate continuous learning.

Identifying skills gaps is the first step to more confident team members, better call handling, and an enhanced customer experience. Knowing what needs to be worked on helps you help your agents, giving them the tools they need to tackle future challenges. 

Call Data Analysis

Call Recording automatically records all your calls and stores them so you can review them later. When combined with Analytics, you get valuable insights into the customer journey. For example, the ability to identify common issues, measure agent performance, and understand customer sentiment.

AI call analysis tools can handle much larger amounts of call data than would be possible to analyze manually. This keeps you from getting bogged down in the data and allows you to spot patterns and trends as they’re emerging. By identifying customer pain points and listening to feedback, you can create training programs that are both targeted and effective.

Natural Language Processing

Natural Language Processing (NLP) helps identify language patterns that reveal gaps in agent training. With Call Transcription, calls are automatically transcribed. NLP tools can then analyze these transcripts to uncover recurring issues, such as unclear explanations or missed opportunities to upsell. This shows you where agents might need additional support.

Say you’re running a call center for a subscription-based streaming service and using NLP to analyze customer complaints about account cancellations. The tool identifies a recurring pattern: Agents often struggle to explain the differences between subscription tiers effectively, leading to confusion and dissatisfaction.

Armed with this insight, you go on to develop targeted training modules focused on improving agents’ knowledge of subscription plans and communication techniques, reducing cancellations, and boosting customer satisfaction.

Sentiment Analysis

AI tools equipped with Sentiment Analysis go beyond understanding what customers are saying—they pick up on how they’re saying it. By analyzing vocal tones, patterns, keywords, and phrases, these tools can gauge a customer’s emotional state to determine if they’re frustrated, satisfied, or confused.

This insight allows agents to adjust their responses in real time, matching their tone and approach to the customer’s mood. For example, if the tool detects rising frustration in a caller’s tone, it can prompt the agent to adopt a more empathetic and reassuring approach.

Imagine you’re a telecommunications company that’s just implemented sentiment analysis in its call center. During a call about a billing error, the AI flagged increasing frustration in the customer’s tone. The system prompted the agent with suggestions to acknowledge the frustration and offer an immediate resolution. 

By following the AI’s guidance, the agent quickly diffuses the situation, leaving the customer feeling heard and satisfied. This not only improves customer interactions but also provides valuable feedback for training agents to handle similar scenarios.

Real-Time Feedback and Suggestions

AI can analyze call data in real time, providing immediate insights that enable timely data-driven decisions. This is crucial in dynamic environments such as contact centers.

For example, imagine you’re running a call center for a global shipping and logistics company. ​​By analyzing call durations and resolution times, you notice that certain issues, like package delivery delays, consistently take longer to resolve. You realize agents were spending too much time manually checking delivery statuses and communicating with logistics teams.

You use AI to streamline workflows, integrating real-time delivery tracking tools into your support system. Agents can instantly access package information, cutting down call times significantly. The AI system also detects that customers are frustrated. It then provides actionable feedback to agents to help them handle delivery-related complaints more efficiently.

AI Tools and Technologies for Call Center Training

AI-driven performance management tools like CloudTalk are changing call center agent training. Features like speech analysis and AI-powered coaching make it easier for center managers to spot areas for improvement. With a clearer idea of where agents may be struggling, you can deliver meaningful feedback that actively improves agent performance. 

AI tools like chatbots also play a big role in training. They let agents practice handling different customer scenarios in a low-pressure environment, helping them build confidence before taking real calls.

Automated agent evaluations assess performance, looking at metrics like response time, call content, and even emotional tone. This helps ensure training is focused, personalized, and effective, boosting both agent skills and customer satisfaction.

Best Practices for Implementing AI to Identify Training Gaps

Follow these best practices to prepare your agents for future challenges and foster continuous growth:

• Define what skills will be most important in the future: Define the skills your organization will need to thrive in the next 5–10 years. For example, Johnson & Johnson identified 41 “future-ready” skills aligned with long-term business goals, such as mastering data management and robotic process automation.

• Understand your team’s current skills: Start by evaluating your team’s skills to spot areas for improvement. Combine insights from various data sources—like your HR system, recruiting database, learning platform, and project management tools—with the power of a large language model. This approach gives you a clear picture of where your agents stand and what they need to grow.
  
• Integrate AI with existing tools: Agents already have to juggle multiple tools on a daily basis. When adopting AI for skills analysis and training, keep it simple. Look for a tool that integrates with the call center software your agents already know how to use. 

• Focus on agent development, not just monitoring: When agents feel like they’re being put under a microscope, they rarely perform at their best. Make sure to acknowledge their efforts and provide constructive feedback along the way. 

• Encourage continuous learning and adaptation: A successful call center training program delivers crucial onboarding and ongoing training opportunities to new and existing agents. Agents need refresher training to keep their service skills sharp and to learn about important business changes and updates.

• Make it fun: Try incorporating training methods like role-playing, competitive rankings, quizzes, and incentives for mastering certain training topics. Approaching training sessions in this way helps to empower new agents and does wonders for employee engagement and retention.

Most Business Leaders Struggle to Forecast Future Needs

69% of CX leaders say forecasting future labor requirements poses a significant challenge—but it doesn’t have to! 

AI offers you a glimpse into the future, helping you identify gaps and improve performance before something goes wrong. Tools like speech analytics, chatbots, and automated evaluations provide insights to guide your training approach, boost agent skills, and prepare your team for future challenges. 

Plus, when you partner with CloudTalk, you’ll significantly speed up all your processes. Reduce your team’s after-call work through manual task automation, like automatically logging data in your CRM. Improve your business intelligence along with your customer experience, all while saving time and money.

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Sources

MIT. Resolving Workforce Skills Gaps With AI-Powered Insights.