Call centers are evolving from reactive customer support operations to strategic customer experience accelerators, and data is leading the transformation. With each customer interaction representing an opportunity to capture and analyze data, organizations are now able to identify training gaps, increase coaching effectiveness, and drive agent training and performance improvements in measurable ways.

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Unleashing Your Automated QA Program

Empowered by data and analytics, contact centers can upgrade from “gut feeling” to agile, informed decisions that are smarter, faster, and more consistent. In this article, we’ll discuss how analytics-driven insights — and tools such as CallMiner — are helping leading organizations deliver better agent training, improved agent performance, and exceptional CX at scale.

In this article:

• The role of data and analytics in call center performance
• Leveraging data and analytics to improve call center agent training
• Elevating agent performance with CallMiner
• Frequently asked questions

The role of data and analytics in call center performance

In a data-driven contact center, decisions about agents, training, performance management, and customer experience are driven by measurable, objective data that’s captured and analyzed from customer interactions. This approach helps leaders understand what is happening, as well as why it’s happening and how to improve it.

Call centers generate large volumes of data every day. The most common data sources are:

• Call recordings, which capture the entire context of agent-customer interactions
• Speech and text analytics, which transcribe and interpret these conversations at scale
• CRM systems that reveal customer history, preferences, and purchase patterns
• Quality assurance (QA) forms, which capture evaluator notes on an agent’s tone, compliance, and call handling
• Post-call surveys and CSAT scores that provide the customer’s perspective on each interaction

When combined, these inputs build a foundation for deeper understanding about operations and agent performance.

Analytics in call centers typically operate on three levels:

• Descriptive analytics answers what happened (e.g., average handle times or call resolution rates)
• Diagnostic analytics answer the question of why it happened (e.g., how different agent behaviors contributed to an outcome)
• Predictive analytics forecast what’s likely to happen next (e.g., which agents are at risk of burnout, which customers are likely to churn)

Artificial intelligence (AI) and machine learning can now scale these insights, quickly scanning thousands of interactions across vast datasets that would be impossible to review and analyze manually. These systems automatically detect emotional and other cues, signals compliance risk and flags performance patterns, all in real time.

For example, CallMiner Eureka applies these analytics layers across every conversation. By applying AI to speech, pauses, and sentiment and emotion patterns, supervisors can immediately identify agents who are not performing with empathy or who are slipping on compliance.

The platform automatically surfaces example calls to review during coaching sessions. Over time, the system also learns from outcomes, continuously enhancing its models and turning call data into insights that improve training, drive consistency, and help every agent succeed.

Leveraging data and analytics to improve call center agent training

With a clear understanding of how data and analytics shape performance insights, the next step is applying those insights to elevate training and coaching. When call centers move from simply tracking metrics to using analytics as a continuous feedback loop, agent development becomes more targeted, efficient, and impactful.

Modern tools like CallMiner Eureka make this possible by turning every customer interaction into a learning opportunity, whether it’s real-time in the moment or post-call through performance analysis. The following strategies show how leading organizations are using data and analytics to enhance agent performance, streamline training, and build more effective teams.

1. Leverage tools that offer in-call guidance. “Eureka offers real-time call monitoring that can provide agents with insights during conversations with customers. Using artificial intelligence and machine learning, Eureka recognizes specific language and emotion in conversations and provides real-time, next-best-action guidance for agents that can help improve outcomes on every call. Eureka can help agents pinpoint and act on upsell opportunities or proactively escalate a call to improve the customer experience. Eureka can also alert agents and supervisors to customers who are at risk of churn, providing in-call suggestions that can turn around a potentially negative situation.”

- Conversation analytics drives agent and call center performance, CallMiner; X: @CallMiner

2. Make coaching and training more efficient. “Investigating the underlying causes of poor coaching effectiveness reveals a number of factors at play. First, there’s often less coaching taking place than companies expect. Agents often have regular coaching sessions built into their schedules, but supervisors and team leaders may struggle to keep to those slots, as they juggle meetings, administrative duties, and special projects.

“Measuring the amount of time supervisors actually spend coaching their agents can be illuminating. One company found that the percentage of time its managers spent on the floor coaching their teams varied by a factor of six. Among teams led by coaches who spent more than 60 percent of their time on the contact-center floor, staff-retention rates were twice the average. That’s a significant benefit given call centers’ perennially high attrition rates. Moreover, one retail bank found a direct correlation between the fraction of their time their service-to-sales team leaders spend on coaching and the conversion rates achieved by their teams.”

- Jeff Berg, Avinash Chandra Das, Vinay Gupta, and Paul Kline, Smarter call-center coaching for the digital world, McKinsey; X: @McKinsey

3. Analytics can uncover hidden skills and competencies in candidates. “AI algorithms can analyze vast datasets, including candidate resumes, online profiles and work history, to identify hidden skills and competencies. Unlike traditional applicant tracking systems, AI can go beyond buzzwords to review dynamic data about the applicant—including how long it took to complete an application or fill in a questionnaire response. In my experience working with dozens of contact center partners, these often-missed data points can reveal much about an agent's responsiveness, critical thinking skills and problem-solving ability.”

- Trevor Clark, How Contact Centers Can Find Better Talent With AI, Forbes; X: @Forbes

4. Analyze your training needs. “The company should analyze its training needs to match the desired outcome and assess its current state of contact center performance to determine where knowledge, skills and abilities are deficient. Performance gaps should be identified and resolved. It's also important to evaluate the performance of the underlying technologies, such as contact center software, to determine whether agents can use the available tools more effectively to achieve better customer outcomes.”

- Stephen J. Bigelow, 15 best practices for contact center agent training programs, TechTarget; X: @TechTargetNews

5. Create an ideal candidate profile to hire the right people. “The first step to getting the right people in the door is to understand what knowledge, skills, attributes, and experiences employees will need to thrive both in the entry-level role and in the company at large.

“By harnessing advanced analytics capabilities to assess data and identify characteristics of successful employees, organizations can build profiles of ideal candidates. Résumés can then be assessed against these profiles to identify which applicants are most likely to succeed. Data on demographics, previous job types and experiences, and skills obtained can be combined with results from online assessments to build a better understanding of candidates’ skills and mindset. These attributes are not always easy to assess in an in-person interview, which is why analytics can play a significant role in revamping recruiting.

“Analytics can help organizations predict both performance propensity and which employees are more likely to stay with the company for a longer period of time.”

- Eric Buesing, Vinay Gupta, Sarah Higgins, and Raelyn Jacobson, Customer care: The future talent factory; X: @McKinsey

6. Personalize training. “People have different skill sets, knowledge gaps, and learning styles—a blanket approach to training is a wasteful use of time and resources. To get the best results from your training process, you need to tailor your approach for each individual agent.

“Using tools like scorecards and analytics, you can identify weak spots and personalize training accordingly. An agent struggling with empathy, for example, may learn better from an in-person lesson, whereas an LMS module would suffice to learn new product specs.”

- Shane Croghan, 10 Pitfalls to Avoid to Improve Your Call Center Training, Scorebuddy; X: @score_buddy

7. Develop KSAC profiles. “Businesses should create role-based knowledge, skills, abilities and culture profiles to establish the KSAC requirements agents need for each role. A company can use this profile for recruitment and training program development. Following are the components of a KSAC profile and how they apply to contact center agents:

• Knowledge. Certain roles may require practical knowledge in a particular area. Examples include industry-specific knowledge, such as healthcare, retail and government. Agents can gain knowledge from experience, education and certifications.
• Skills. Agents can learn technical, social and CX proficiencies through training. Examples include customer service skills, technical skills and proficiency in specific software tools. Hiring teams can measure, observe and validate skills through tests and assessments.
• Abilities. Agents should demonstrate empathy and task switching capabilities.
• Culture. Agents should align with the company's culture, including its beliefs, values and behaviors.

“When creating KSAC profiles, keep these questions in mind: What level of skill, certification, proficiency or knowledge must agents meet? Do agents need certifications or other documents to meet education requirements? How many years of experience does the company require? When businesses understand what each role requires for success at a KSAC level, they can hire, promote and train more effectively.”

- Stephen J. Bigelow, 15 best practices for contact center agent training programs, TechTarget; X: @TechTargetNews

8. Create a team dashboard to gamify performance. “A team dashboard is an effective way to address common issues like agent motivation and inconsistent service levels. Displaying key call center metrics — handle time, first-call resolution, customer satisfaction, and sales — in real time creates transparency and accountability. Incorporating gamification elements, like points and leaderboards, adds a layer of healthy competition and encourages agents to strive for better results.

“Plus, publicly recognizing top performers is a great way to boost morale and foster a positive team environment. It’s a straightforward strategy that can make a real difference in overall call center efficiency and agent morale.

“Pro tip: Don’t just gamify outcomes. Include metrics that reflect effort and consistency, like calls handled or average talk time within target range. This prevents agents from focusing only on closing deals or cherry-picking easy calls and rewards consistent performance even during slow periods. It also encourages newer agents who might not be top performers yet.”

- 25 call center best practices for management, CX, and more, Zoom; X: @Zoom

9. Collect and analyze customer feedback. “Without collecting customer feedback at every stage of the customer journey, you won’t be able to find pain points and improve on what your team is doing right. Tracking this feedback against metrics – and making sure you’re acting on what you learn – will ensure your call center thrives. Your customer surveys should be delivered on a regular or ongoing basis to ensure that you’re getting the most current insights. And supplement this with always on omni-channel listening, which gives you even deeper insights to get the complete picture.

“You should try:

• Using call center software that can collect and collate feedback, turning it into useful insights.
• Designing survey questions that not only ask about general customer experience, but specific agent interactions as well. Asking how knowledgeable, helpful and clear the agent was during an interaction can help with agent training in future.”

- The top 20 call center best practices, Qualtrics XM; X: @Qualtrics

10. Leverage AI for automated quality montioring. “AI evaluates interactions against quality benchmarks at scale, eliminating the need for supervisors to manually review every call. This allows QA teams to focus on coaching and improvement, not just scoring.”

- Enhancing agent effectiveness with AI tools, CallMiner; X: @CallMiner

11. Invest in technology to empower call center agents. “All retailers know there are high-demand periods such as Black Friday and Cyber Monday when contact centers are flooded with calls. Agents are often overwhelmed by the spikes in traffic as they try to help as many customers as they can. Meanwhile, wait times for agents get increasingly longer. This is a key moment when chatbots and interactive voice response can help.

“But for it to work, humans must remain part of the equation. Most agents will tell you AI helps them solve customer queries faster. AI not only takes more inbound calls off their plates, but it also provides agents with the customer background they need to resolve more challenging issues.

“So, the potential benefits of chatbots aren’t limited to what they can provide customers. They also strengthen the human agent’s ability to serve by cutting down high call volumes and the resulting stress. Many companies provide chatbots as a means of empowering agents to access the information they need to help a customer.”

- Alok Kulkarni, When (And How) To Introduce AI Into Your Contact Center, Forbes; X: @Forbes

12. Place data in the flow of work to enhance performance. “Place data in the flow of work to give workers a view on their real-time performance, customer insights, and risk areas, to enable data-driven experiential learning, improved decision-making, and overall agility. For example, implement a pop-up window at the corner of the screen of customer care representatives with performance feedback and targeted learning resources.”

- Jen Stempel, Neha Yadav, Lindsey West, and Josh Rovner, Leveraging learning analytics to drive business impact, Deloitte; X: @Deloitte

13. Utilize a single source of truth. “Speech analytics and text analytics are frequently used in isolation across various parts of a business, but to be effective, a single source of truth is needed. Rather than using contact center analytics in silos across your contact center, sales teams, digital teams and more, all data should be analyzed in a singular platform to ensure that you can coordinate strategically for the best business results. For example, call data and the data from text-based digital interactions (such as those with self-service chatbots) are often analyzed separately, but this means that accurate performance metric analysis and the sharing of insights is lost.”

- Using contact center analytics to improve performance, Qualtrics XM; X: @Qualtrics

14. Leverage data and analytics to inform training and development strategy. “A reimagined approach to measuring training effectiveness and learning impact involves generating insights on priority skill building, informing adaptions to tailor for learner needs, and enabling data-driven investment decisions for optimal utilization of budget and resources. Mature learning and development organizations actively gather data from multiple sources at frequent intervals and empower leaders and learners at all levels to utilize that data to improve development and work outcomes.”

- Jen Stempel, Neha Yadav, Lindsey West, and Josh Rovner, Leveraging learning analytics to drive business impact, Deloitte; X: @Deloitte

15. Implement post-training monitoring and assessment. “Training isn’t a set-and-forget situation. You need to monitor its impact and determine whether or not a change of strategy is required. If you don’t track progress, how can you know what’s working and what isn’t?

“Post-training monitoring is also important for agents on an individual basis. Follow-up tasks and assessments help to reinforce the benefits of training and improve knowledge retention. You can use an LMS to create learning paths and assign these follow-up materials.”

- Shane Croghan, 10 Pitfalls to Avoid to Improve Your Call Center Training, Scorebuddy; X: @score_buddy

16. Build a demand management function. “For customer service operations, the shape of demand changes from week to week, sometimes dramatically. Installing a centralized hub of demand intelligence can reduce the time required to change policies, product design and agent training, in response to the latest demand trends. In most cases and with minimal added expense, banks can redeploy resources that currently spend time on low-value activities for call-quality checking.”

- Jen Stempel, Neha Yadav, Lindsey West, and Josh Rovner, Leveraging learning analytics to drive business impact, Deloitte; X: @Deloitte

17. Leverage AI conversation simulation to mimic real-world scenarios. “Modern contact centers use AI conversation simulation to create hyper-realistic voice and chat scenarios that deliver impactful learning experiences. The platforms use AI in NLP (Natural Language Processing) and NLU (Natural Language Understanding) to generate real-time customer responses simulating any customer topic or persona. Hyper-realistic voice and chat simulations help agents learn through practicing, solving problems, making mistakes and building confidence. It provides a low-risk training environment before engaging with a live customer.”

- Brian Tuite, How To Improve Contact Center Training With AI Conversation Simulation, Forbes; X: @Forbes

18. Implement microlearning. “Microlearning is a type of learning that breaks down learning content into small, bite-sized information modules. It is an effective training technique as learners get to apply the skills immediately after learning, which boosts knowledge retention.

“Curating short, self-contained modules that contain specific skills and topics is an efficient call center training strategy. For example, a short video on customer interaction followed by a quiz on the same subject can help agents learn and retain better.

“Microlearning is easily accessible and available on demand. Agents can learn conveniently according to their work schedules, complete modules at their own pace, skip or re-learn a topic, and create a learning schedule that does not hamper their productivity.”

- Disha Gupta, The Ultimate Call Center Training Guide (2025), Whatfix; X: @whatfix

19. Leverage best practices from your top-performing agents. “Call center training for new recruits often lasts for about six weeks where new agents spend time on theory as well as practicing calls with their managers. Training, however, should not be viewed as a one-time thing. Instead, you should incorporate regular coaching sessions into your processes to help underperforming agents. Leverage best practices from your top performing agents to get other team members up to their level. Conversation analytics software that tracks 100% of agent calls can help you identify the phraseology used during successful calls. Use this vital intelligence to help under-performers.”

- 12 call center best practices, CallMiner; X: @CallMiner

20. Implement interactive training methods. “Instead of traditional lecture-based training, design your training modules with interactive elements like role-playing, simulations, and gamification.

“Role-playing allows agents to practice and navigate through real-life customer scenarios, enhancing their problem-solving and communication skills.

“Simulations provide a safe environment to handle challenging customer interactions, while gamification introduces elements of competition and reward, making the learning process enjoyable and motivating.

“Interactive approaches make training more interesting and help agents retain information better and apply it effectively in their day-to-day customer interactions.

“Additionally, using software for training monitoring enables managers to track progress, identify skill gaps, and tailor support to individual learning needs.”

- Yatharth Jain, 8 Ways to Improve Call Center Training, Knoxmax; X: @Knoxmaxai

21. Design a comprehensive training curriculum. “Based on the knowledge and skills gaps you’ve identified in your call center, decide how you’re going to deliver training to your agents. Choose what kind of training method you’re going to use, such as classroom-based or e-learning modules, and leverage tools to curate training resources.

“For instance, learning management systems can integrate with your existing business databases and tools, allowing you to create comprehensive classes covering everything from how to use call center software to how to address a range of customer issues.

“Remember to think about how you’ll track the progress of your employees through different training modules and evaluate their development, such as with assessments or quizzes.”

- Kent Mao, Everything You Need to Know About Call Center Training, ComputerTalk; X: @iceComputerTalk

22. Provide refresher training sessions. “When your agents know and understand your company’s products and services, they can help customers and answer their questions with confidence. Since you’ll be delivering the training, start by getting familiar with every little detail of what your company offers. Make sure the information you’re sharing with your team is accurate and up-to-date.

“It’s also extremely helpful to schedule regular refresher training sessions to keep your agents updated on any new features or software updates. It’ll arm them with the right information to help their customers.

“To determine how often you should have these sessions, consider the complexity of your products or services, your call center’s performance data, and your agents’ level of experience.”

- Ella Mar, Call center training tips to boost performance, SC Training: X: @SafetyCultureHQ

23. Leverage tools that assess real-time data. “To truly improve performance, advanced analytics tools should not only increase efficiency and reduce costs but also proactively unlock new revenue. A virtual sales coach can accomplish that goal by assessing factors about a customer—not only existing data such as demographic and behavioral profiles and purchase history but also real-time data from a current service call—to predict the next product the customer is most likely to buy. It can then pull up a script to give the sales agent specific language designed to improve conversion rates for that customer.”

- Guy Benjamin, Jeff Berg, Avinash Chandra Das, and Vinay Gupta, How advanced analytics can help contact centers put the customer first, McKinsey & Company; X: @McKinsey

24. Diagnose underlying performance issues. “Contact center managers should analyze the data gathered from an agent's evaluation in quality assurance forms to determine where performance opportunities exist and identify any trends. These forms will show whether an agent consistently receives low or failing scores in certain metrics or from data collected in a call monitoring program.

“Managers should also determine if there are multiple agent performance issues, such as problems with communication skills, adhering to processes, accessing customer data and processing transactions.”

- Sarah Amsler, 10 strategies to improve contact center agent performance, TechTarget; X: @TechTargetNews

25. Monitor the agent effort score (AES). “Agent effort score reflects your agents’ level of effort to meet customer needs, resolve issues, and conduct their work. It measures how easy or difficult agents find their day-to-day tasks and handle customer interactions.

“A low AES means that agents have the necessary tools, resources, and support to be efficient in their roles, while a high AES means they face challenges that affect their performance or ability to help customers through their queries.

“AES helps you spot areas where agents need more support or training. Keep in mind that high effort often leads to burnout, which in turn impacts productivity and retention.

“Tip to improve AES: Regularly gather agent feedback to pinpoint workflow obstacles and dig into common hurdles.”

- 31 must-know call center metrics and KPIs for 2025, Zoom; X: @Zoom

Elevating agent performance with CallMiner

Traditional training programs are based on assumptions, instincts, and a handful of QA samples from agents. But advances in data and analytics are empowering a new model where leaders can access continuous, real-time feedback to inform coaching and training.

Every interaction with a customer becomes an opportunity to understand how agents are performing, what they’re doing well, where they could use help, and which actions will have the most impact on performance and the customer experience.

CallMiner Eureka makes this transformation practical and scalable. By analyzing 100% of interactions across voice and digital channels, CallMiner provides a complete view of agent behavior and customer sentiment.

Supervisors gain real-time guidance to coach in the moment, while automated quality monitoring ensures consistent performance measurement at scale. Predictive analytics surface early warning signs (e.g., burnout or compliance risk) so leaders can intervene proactively before issues affect KPIs or customer satisfaction.

See how CallMiner can help your agents perform at their best. Request a demo to explore how AI-powered analytics can turn every interaction into actionable insight.

Frequently asked questions

We already track metrics like average handle time (AHT) and CSAT. How is a data-driven approach different?

A traditional approach often looks at metrics in isolation, after the fact. A data-driven approach uses analytics to understand the why behind the metrics.

For example, instead of just seeing a high average handle time (AHT), conversation analytics can reveal that agents are struggling with a specific product issue or a complex script, allowing you to target training precisely. It connects descriptive data ("what happened") with diagnostic data ("why it happened") to create a continuous feedback loop for improvement.

With hundreds of calls per day, how can we possibly analyze them all effectively?

Manually reviewing every interaction is impossible. This is where AI-powered platforms like CallMiner Eureka excel. They use speech and text analytics to automatically process and analyze 100% of customer interactions across voice and digital channels.

The AI detects patterns, emotions, compliance risks, and performance trends at a scale no human team could match, surfacing the most critical insights and example calls for you.

How do we ensure that data from different sources (calls, CRM, surveys) provides a unified view?

A siloed approach is a common pitfall. The key is to use a single, integrated platform like CallMiner Eureka that acts as a "single source of truth." By bringing together data from call recordings, CRM systems, QA scores, and text-based chats, you can correlate information to get a complete picture. For instance, you can see how specific agent phrases (from conversation analytics) directly impact the CSAT score a customer provides (from surveys).

Our training is one-size-fits-all. How can data help us personalize it?

Data allows you to move from blanket training to personalized learning paths. By using scorecards and interaction analytics, you can identify individual agent weak spots, such as a lack of empathy, difficulty with upselling, or confusion about a specific process. You can then deliver targeted training modules, micro-lessons, or simulations that address those specific gaps, making training more efficient and impactful.