Call Center Analytics: All That a Manager Should Know

Learn about call center analytics, including different types, data analysis and examples of analytics dashboards.

Bhavya AggarwalJayadeep Subhashis
March 24, 2024
11 min read

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What is call center analytics? 

Call center analytics systematically analyzes data collected from various customer touchpoints within the call center ecosystem, offering invaluable insights into customer interactions, agent performance and operational efficiency. Essentially, it provides a roadmap for optimizing customer service delivery and enhancing overall performance.

Call center analytics data is obtained from multiple customer service channels, including inbound and outbound calls, emails, live chats, social media interactions and more, providing a comprehensive view of customer engagements.  

Moreover, call center analytics empowers decision-makers with actionable intelligence to optimize workforce management, streamline processes and drive continuous improvement across the call center environment. 

From frontline supervisors to C-suite executives, call center analytics caters to diverse stakeholders. Operations managers rely on it to optimize staffing levels, improve agent productivity and reduce average hold times, while customer experience leaders leverage it to identify pain points, measure service quality and prioritize service enhancements. Additionally, senior executives utilize call center analytics to align operational initiatives with overarching business goals, drive revenue growth and foster a culture of data-driven decision-making.

Why is it important to analyze call center data?

Analyzing call center data is crucial for organizations aiming to excel in customer service. Here are several key reasons why call center data analysis is indispensable: 

Improved customer experience: Call center analytics enables you to identify trends, patterns and areas for improvement in the customer journey. By addressing issues promptly and proactively, you can enhance customer satisfaction and build loyalty.

Increased operational efficiency: Call center data analysis empowers organizations to optimize operational efficiency and resource allocation. By leveraging call forecasting techniques, you can optimize staffing levels to match fluctuating demand, ensuring that customer service levels remain consistently high while minimizing operational costs. 

Empowered agents: Data from call center analytics equips agents with the necessary tools, training and support to excel at their jobs. Insights gained from data such as customer satisfaction scores and interaction histories can indicate call center coaching opportunities and recognize top performers based on resolution times and customer feedback. This empowerment leads to agents delivering personalized and proactive customer service, which is critical to a positive CX.  

Predictive capabilities: By leveraging advanced call center analytics techniques, you can use data to predict future trends and customer behaviors. This predictive capability allows you to anticipate customer needs, proactively address potential issues and stay ahead of the competition. 

Compliance and risk management: Analyzing call center data can help you ensure compliance with regulations and industry standards. By monitoring calls for compliance-related issues and identifying potential risks, you can promptly mitigate legal and reputational risks.

What type of data should you analyze for call center performance 

It can be challenging for call center managers to decide what types of data they should analyze to gauge their call center's actual performance. Here's a helpful table to get you started. 

Type of data 


How to find it 

Customer interaction data 

Records of customer interactions (calls, chats, emails etc.), including timestamps, duration and content 

- Call recordings
- Contact center CRM systems 
- Chat and email platforms 

Agent performance metrics 

Metrics assessing agent productivity, effectiveness and quality of services, such as first call resolution, average handle time, customer satisfaction and schedule adherence   

- Agent performance dashboard 
 - Workforce management dasboard 
 - Quality management dashboard  

Operational KPIs 

Key performance indicators (KPIs) tracking call center efficiency and effectiveness, such as call center service level, response time, abandonment rate and occupancy rate  

- Call center reporting dashboard  
 - Workforce management system  
 - CRM systems  

Customer feedback 

Direct feedback from customers regarding their experiences with the call center, including customer surveys, ratings and comments 

- Customer surveys 
 - CSAT and NPS scores 
 - Social media monitoring dashboard 

Market trends 

External data sources provide insights into industry trends, competitor analysis and customer preferences 

- Market research  
 - Social listening dashboard 

10 types of call center analytics 

Call center analytics encompasses various types of analysis that help you extract valuable insights from your customer interactions and operational data. Here are some key types of call center analytics: 

Descriptive analytics 

Descriptive analytics examines historical data to understand past events and trends. This type of analysis focuses on answering the question, "What happened?" and provides insights into your call center operations over a specified period.  

Descriptive analytics analyzes call volumes, average handle time, first-call resolution rates and customer satisfaction scores. By examining these call center key performance indicators (KPIs) over time, you can identify trends, patterns and areas of improvement.  

For example, descriptive analytics might reveal that call volumes tend to spike during certain times of the day or week, indicating peak demand periods. Understanding these patterns allows you to adjust staffing levels and resources to ensure optimal service levels and minimize customer wait times.

Diagnostic analytics 

Diagnostic analytics builds upon the insights gained from descriptive analytics by delving deeper into the underlying causes of observed trends or issues. This type of analysis focuses on answering the question, "Why did it happen?" and involves identifying the root causes of performance gaps or customer dissatisfaction. 

Diagnostic analytics involves examining call recordings, call center agent performance metrics, customer feedback and operational processes. 

For example, diagnostic analytics might reveal that a sudden increase in average handle time is due to a newly implemented software system causing delays in call resolution. By identifying this root cause, you can take corrective actions such as providing additional call center agent training or fine-tuning the software to improve efficiency. 

Speech analytics 

Speech analytics is a technology that utilizes algorithms and natural language processing to transcribe and analyze call center recordings. It can detect important phrases, sentiments and speech patterns, including positive and negative feedback.  

For instance, when speech analytics software identifies frequent negative sentiments regarding product defects or billing discrepancies, it alerts supervisors to train agents to focus on these specific issues and how to communicate empathetically with customers.  

Moreover, speech analytics software can identify voice interactions that reveal new customer concerns or specific service complaints, allowing for proactive response and refinement of service strategies. 

Good to know: Thanks to advanced speech analytics software, you can quickly and easily measure your call center's performance against its key performance indicators (KPIs), service levels and customer satisfaction scores (CSATs) in real time without sifting through reports. By identifying the top contact drivers, you can gain insight into the specific people, processes or customer service workflows that can be improved to enhance your customers' experience. 

Speech Analytics Software Powered by Sprinklr Service

Text analytics 

Text analytics involves analyzing unstructured data from sources such as call transcripts, emails and chat logs to extract insights and sentiment. It extracts information like key customer queries or common complaint themes, enhancing customer service by tracking response times and analyzing sentiments.  

For example, analyzing chat transcripts can reveal issues like website navigation difficulties or product inquiries, help improve website FAQs and sometimes even point to emerging market trends.

Insight extraction and sentiment analysis from chat logs with Sprinklr Service

Interaction analytics 

Customer interaction analytics is a process that combines speech and text analytics to provide a comprehensive view of customer interaction across multiple customer service channels, such as phone calls, email, chat and social media. This method tracks the customer journey, identifying patterns such as the path to purchase or frequent drop-off points.

Additionally, it analyzes each interaction in detail, providing insights on the level of personalization in conversations, customer feedback on products or services and an assessment of the overall quality of service delivered. 

Self-service analytics 

Customer self-service analytics focuses on interactions with automated tools such as IVR, knowledge bases and chatbots. Self-service analytics tools analyze data like user navigation paths and common drop-off points in self-service menus, enhancing customer self-service efficiency.

For instance, IVR menus might be restructured based on usage patterns to simplify customer navigation and reduce call volumes.

Predictive analytics 

Predictive analytics uses historical data like past call volumes, customer feedback and service request trends to anticipate future patterns such as increased support needs for new product launches or seasonal call volume fluctuations. It applies statistical algorithms for call center forecasting, like projecting call volumes during promotional periods or identifying potential service bottlenecks. By doing so, it enables you to prepare for future demands and optimize operations.

Omnichannel analytics 

Omnichannel analytics helps understand customer interactions across all platforms and customer service channels, including phone, email, chat, social media and more. This type of analytics provides a detailed view of the customer experience, ensuring consistency and seamlessness across all channels.

For example, omnichannel analytics might reveal that customers who initially contact your call center via social media have higher satisfaction levels than those who use traditional phone channels. With this insight, you can prioritize social media engagement and allocate resources to enhance customer satisfaction and loyalty.

Desktop analytics  

Desktop analytics analyzes agent desktop activities and workflows to optimize the call center's productivity, efficiency and compliance. This analysis provides insights into how agents interact with software applications, navigate systems and handle customer inquiries.  

For example, it can highlight if your agents are spending too much time navigating between different systems, which suggests that you need to invest in more integrated solutions for your call center. 

Do you know: Modern AI-powered unified agent console software helps to merge multiple channels into a single screen and reduces the average handling time (AHT) by up to 30%. By displaying all the necessary data and resources on a single tab, your agents can easily manage multiple interactions simultaneously and quickly access the history of conversations and customer information, regardless of the channel used.

Unified Agent Console Powered by Sprinklr Service

Mobile analytics 

Mobile analytics helps analyze customer interactions and behaviors within mobile applications or mobile-friendly websites. It provides insights into how customers engage with your brand through mobile devices, including smartphones and tablets. 

For example, mobile analytics might reveal that a significant portion of customer inquiries originate from your mobile app, highlighting the importance of providing robust customer self-service options and intuitive navigation within the app. Based on this insight, you can prioritize app enhancements and allocate resources to improve the mobile customer experience.

While the types of call center analytics described above focus on different elements of customer interactions and call center performance, the results themselves are presented across a variety of dashboards.  

Call center analytics dashboard examples 

Call center analytics dashboards streamline the data consumption process, allowing managers to quickly identify trends, pinpoint issues and make informed decisions without getting bogged down in data analysis. For decision-makers, dashboards can significantly reduce the time taken to interpret vast amounts of information. 

Operational dashboard 

Operational dashboards display vital metrics such as call volume, average hold time, call abandonment rate, average speed of answer, escalations, service level adherence and current queue status.  

An operational dashboard facilitates on-the-fly decision-making. It allows managers and team leaders to quickly respond to changes in call patterns, manage agent workload efficiently and ensure that service levels are consistently met. 

Operational Dashboard-Call Center Analytics

Agent performance dashboard 

An agent performance dashboard provides real-time metrics on an agent's performance. It allows supervisors to monitor and assess key performance indicators such as the average speed of answering a call, CSAT scores and adherence to service level agreements (SLAs).  

For instance, it can show if an agent's CSAT has improved significantly from a previous period, indicating the effectiveness of new strategies or training programs. These dashboards empower supervisors and managers to analyze performance trends and provide targeted call center coaching for continuous improvement. 

Agent Performance Dashboard Powered by Sprinklr Service

Agent productivity dashboard 

Agent productivity dashboards are designed to focus on call center efficiency and workload metrics, providing supervisors and managers with valuable insights to optimize agent performance and resource allocation within the call center.  

Key metrics such as occupancy rate, average login hours and task completion are prominently displayed to facilitate informed decision-making and proactive management. By monitoring these metrics in real time, supervisors can identify opportunities to streamline processes, improve efficiency and ensure agents meet their targets effectively.

Agent productivity dashboard with Sprinklr Service

Good Read: Improving Agent Productivity with Unified Customer Experience Management 

Impact of call center analytics on customer experience 

Let's break down exactly how call center analytics impacts customer experience.   

👥 Enhanced personalization: By analyzing customer data and interactions, call center analytics enables you to personalize customer interactions based on individual preferences, history and behavior. This personalization enhances engagement, builds rapport and fosters customer loyalty

🎧 Proactive issue resolution: Call center analytics allows you to identify emerging issues and trends in real time, enabling proactive issue resolution before they escalate. By addressing customer concerns promptly and effectively, you can minimize customer frustration and enhance satisfaction levels. 

⏳Reduced wait times: Through predictive analytics and real-time monitoring, call center analytics help you anticipate peak demand periods and optimize staffing levels to minimize customer wait times.  

🕓Real-time feedback and insights: Call center analytics provides real-time feedback and insights into customer interactions, enabling you to adapt and respond quickly to changing customer needs and preferences. By leveraging this actionable intelligence, you can continuously refine your service offerings and stay ahead of the competition. 

Leveraging call center analytics is essential for driving informed strategies, but relying on substandard tools or misinterpreting data can lead to costly mistakes. With Sprinklr's suite of next-gen, AI-powered call center tools and integrated analytics platform, you can ensure that accurate insights and actionable intelligence guide your strategies.   

From detailed agent performance analysis to comprehensive customer satisfaction evaluation, the Sprinklr reporting and analytics platform provides everything you need to manage and continuously improve your call center operations for lasting success. 

Wondering how Sprinklr seamlessly integrates with your existing tech stack? 

Talk to our experts today 


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