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Call Center Analytics: How to Use Them

Optimize your customer service with 4 key types of call center analytics.

Call Center Analytics — The Complete Guide

Call center analytics are a critical tool to help you monitor and improve contact center performance and the customer experience. Analytics help you make informed decisions about how to enhance the service experience and drive business success. Since 88% of people say good customer service experiences make them more likely to make another purchase, you want those experiences to be top-notch.

But how do you use call center analytics to improve customer service? The first step is to choose the right type of analytics for the metrics you want to monitor. In this article, we’ll explore what call center analytics are, what you need to consider when choosing the right analytics for your situation, how to use four different types of analytics, and how to get started.

What are call center analytics?

Call center analytics refer to the collection, measurement, and analysis of key performance indicators (KPIs) and metrics within a contact center to optimize operations. These analytics provide valuable insights into various aspects of call center performance, including agent efficiency, customer satisfaction, and overall service quality. By leveraging call center analytics, organizations can make informed decisions to enhance the customer experience and drive business success.

Call center analytics solutions encompass various data-driven insights organizations can leverage to enhance their operations and customer service. These include omnichannel analytics, which focuses on interactions across multiple channels (such as phone calls, emails, chat, and social media), predictive analytics for forecasting call volumes and resource allocation, voice analytics to extract valuable information from recorded calls, and self-service analytics to optimize tools like IVR systems and chatbots, ultimately improving overall efficiency.

Simply put, call center analytics means collecting and analyzing data to uncover valuable insights about your service organization’s performance. For example, you might use analytics to uncover information about customer satisfaction (CSAT), revenue, customer retention, customer effort score, Net Promoter Score (NPS), or service-level agreement (SLA) performance. You can then use that information to identify areas of strength and areas for improvement.

How to approach call center analytics

Even with contact center software, many contact centers struggle to have a real-time, holistic view of call center performance across all channels. This is not surprising, considering what happens daily — service outages, customer complaints, and underperforming agents. It’s hard to keep up, but this is how call center analytics can help.

Successful contact centers use advanced call center analytics software to monitor and review agent performance, not only from a customer lens but also from the perspective of both employees and management.

Each type offers advantages and combines to provide a comprehensive understanding of call center performance. To choose the best call center analytics system, determine what area your business needs to improve and see how analytics can help.

Before you start, it’s important to identify exactly what you want to know. That way, you can choose the right metrics to focus on. Here are some important questions to consider:

  • What do I want to learn about my call center?
  • What metrics will help me get the insights I need?
  • Which metrics will be easy to measure? Which will be difficult?
  • Who will be responsible for collecting measurements? How will they do it?
  • Can I have too many metrics?
  • What are my targets for these key performance indicators (KPIs)?
  • What are my goals to improve these metrics? What is the timeline for achieving those goals?

Types of call center analytics and how to use them

These types of call center analytics may be helpful: omni-channel analytics, predictive analytics, voice analytics, and self-service analytics.

1. Omni-channel analytics

Customers today often reach out through multiple channels: 71% say they prefer different channels depending on context. To deliver an excellent omni-channel service experience, your agents must see all of a customer’s interactions with your business. And at a broader level, service managers and leaders need visibility into what’s happening across all the customer engagement channels.

71% of customers prefer different channels depending on context.

Source: "State of Connected Customer," Salesforce Research, August 2023

This is where omni-channel analytics comes in. Omni-channel analytics crunches all the data about how your customers interact — and where your team needs to improve its response — and serves it up as useful facts. For instance, you can review CSAT, handle time, or case volume by channel. You can look at agent work volume, average speed to answer, and other helpful metrics. This information helps you react to real-time situations and also plan for the future.

2. Predictive analytics

Predictive analytics are exactly what they sound: they use data to predict the future. In customer service, predictive analytics uses artificial intelligence (AI) to analyze call center data and apply logic from past solutions to solve current or future problems. Predictive analytics builds on the data in your customer relationship management (CRM) system, producing relevant and actionable insights. These data points help agents manage individual customer service cases, and also help managers direct the contact center as a whole.

For example, you might use predictive analytics to determine the category or severity of a case as it is logged, for more effective case routing. Once a case is in process, predictive analytics can surface the likely CSAT for this customer based on the issue — then provide suggested next steps. Predictive analytics could be used to determine the right amount of agents to staff over the holiday shopping season. Or plan for high call volumes when a new product rolls out. Or, you could use it to predict churn risk and identify any potential product or customer issues before they happen.

3. Voice analytics

Advances in customer service software have made it possible for voice to become a digital channel — with the same potential for insights as any writing-based channel such as email or chat. When voice is a digital channel, AI monitors conversations real-time and turns up insights that improve the customer experience.

Voice analytics shows you how many calls your contact center is getting, how quickly your agents are responding, and how long customers are waiting in a queue or being put on hold. It reports review average call handle time, as well as average handle time per agent. Transcripts of individual calls drafted by AI help you see if a particular agent needs more training on how to handle a particular issue, like an exception with a return. Reviewing multiple AI-powered transcripts for calls might reveal that the entire team needs training on a particular issue.

4. Customer self-service analytics

Self-service channels like your help center, customer portal, or customer community empower customers to resolve simple issues on their own while deflecting more cases for your company.

You can use self-service analytics to see how well these channels are working for both your customers and employees. Review case deflection scores and see if there are any slowdowns or problems in the experience. Use self-service analytics to review common searches and identify any new trends in customer requests. Then, use this information to improve the customer experience, perhaps by creating new knowledge articles to address commonly asked concerns.

Call Center Analytics Software Solutions

Call center analytics software solutions are technological platforms designed to analyze and optimize call center operations' performance. These solutions leverage advanced analytics techniques to process large volumes of data generated from customer interactions, agent activities, and various other sources within the call center environment.

By extracting valuable insights from this data, call center analytics software empowers organizations to enhance efficiency, improve customer service, and drive strategic decision-making.

Here are some key components and features typically found in call center analytics software solutions:

  1. Data collection and integration: Call center analytics software aggregates data from multiple sources, including call logs, CRM systems, IVR systems, chat transcripts, emails, and social media platforms. Integration capabilities ensure that all relevant data is collected and consolidated for analysis.
  2. Real-time monitoring: These solutions offer real-time monitoring capabilities to track key performance indicators (KPIs) such as call volume, wait times, agent availability, and service levels. Supervisors can monitor ongoing activities and address issues promptly to maintain service quality.
  3. Performance metrics and reporting: Call center analytics software provides comprehensive reporting tools to measure various performance metrics, including average handling time (AHT), first call resolution (FCR), customer satisfaction, and Net Promoter Score. Customizable dashboards and reports give stakeholders actionable insights into call center operations and agent performance.
  4. Predictive analytics: Advanced analytics techniques like predictive modeling and machine learning enable call center analytics software to forecast future trends and outcomes. Predictive analytics can anticipate call volumes, identify emerging issues, and recommend proactive resource allocation and service improvement strategies.
  5. Speech and text analytics: Many call center analytics solutions incorporate speech and text analytics capabilities to analyze customer interactions. Natural language processing (NLP) algorithms extract insights from recorded calls, chat transcripts, and emails, identifying customer sentiments, common issues, and opportunities for process optimization.
  6. Agent performance management: Call center analytics software helps organizations evaluate agent performance based on various metrics, such as call quality, script adherence, and compliance with regulatory requirements. Performance scores and feedback mechanisms facilitate coaching and training initiatives to improve agent effectiveness and customer satisfaction.
  7. Customer journey analysis: Understanding the customer journey is essential for delivering personalized and seamless experiences. Call center analytics software enables organizations to map out customer interactions across multiple touchpoints, identifying pain points, bottlenecks, and opportunities for service enhancement.
  8. Root cause analysis: When issues arise within the call center, root cause analysis capabilities help organizations identify the underlying factors contributing to problems. By pinpointing the root causes of issues such as high call abandonment rates or low customer satisfaction scores, organizations can implement targeted solutions to address the systemic problems.

Overall, call center analytics software solutions are crucial in optimizing the performance and efficiency of call center operations. By harnessing the power of data analytics, organizations can improve service quality, increase customer satisfaction, and drive business growth in today's competitive market landscape.

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